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"abstract": "The current standard for diagnosing liver tumors is contrast-enhanced multiphase computed tomography. On this basis, several software tools have been developed by different research groups worldwide to support physicians for example in measuring remnant liver volume, analyzing tumors, and planning resections. Several algorithms have been developed to perform these tasks. Most of the time, the segmentation of the liver is at the beginning of the processing chain. Therefore, a vast amount of CT-based liver segmentation algorithms have been developed. However, clinics slowly move from CT as the current gold standard for diagnosing liver diseases towards magnetic resonance imaging. In this work, we utilize a Probabilistic Active Shape Model with an MR specific preprocessing and appearance model to segment the liver in contrast enhanced MR images. Evaluation is based on 8 clinical datasets.",
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"content": "The current standard for diagnosing liver tumors is contrast-enhanced multiphase computed tomography. On this basis, several software tools have been developed by different research groups worldwide to support physicians for example in measuring remnant liver volume, analyzing tumors, and planning resections. Several algorithms have been developed to perform these tasks. Most of the time, the segmentation of the liver is at the beginning of the processing chain. Therefore, a vast amount of CT-based liver segmentation algorithms have been developed. However, clinics slowly move from CT as the current gold standard for diagnosing liver diseases towards magnetic resonance imaging. In this work, we utilize a Probabilistic Active Shape Model with an MR specific preprocessing and appearance model to segment the liver in contrast enhanced MR images. Evaluation is based on 8 clinical datasets.",
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"proceeding": {
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"title": "2012 21st International Conference on Pattern Recognition (ICPR 2012)",
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"title": "Graph cut energy minimization in a probabilistic learning framework for 3D prostate segmentation in MRI",
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"abstract": "Variations in inter-patient prostate shape, and size and imaging artifacts in magnetic resonance images (MRI) hinders automatic accurate prostate segmentation. In this paper we propose a graph cut based energy minimization of the posterior probabilities obtained in a supervised learning schema for automatic 3D segmentation of the prostate in MRI. A probabilistic classification of the prostate voxels is achieved with a probabilistic atlas and a random forest based learning framework. The posterior probabilities are combined to obtain the likelihood of a voxel being prostate. Finally, 3D graph cut based energy minimization in the stochastic space provides segmentation of the prostate. The proposed method achieves a mean Dice similarity coefficient (DSC) value of 0.91±0.04 and 95% mean Haus-dorff distance (HD) of 4.69±2.62 voxels when validated with 15 prostate volumes of a public dataset in a leave-one-patient-out validation framework. The model achieves statistically significant t-test p-value<;0.0001 in mean DSC and mean HD values compared to some of the works in literature.",
"abstracts": [
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"abstractType": "Regular",
"content": "Variations in inter-patient prostate shape, and size and imaging artifacts in magnetic resonance images (MRI) hinders automatic accurate prostate segmentation. In this paper we propose a graph cut based energy minimization of the posterior probabilities obtained in a supervised learning schema for automatic 3D segmentation of the prostate in MRI. A probabilistic classification of the prostate voxels is achieved with a probabilistic atlas and a random forest based learning framework. The posterior probabilities are combined to obtain the likelihood of a voxel being prostate. Finally, 3D graph cut based energy minimization in the stochastic space provides segmentation of the prostate. The proposed method achieves a mean Dice similarity coefficient (DSC) value of 0.91±0.04 and 95% mean Haus-dorff distance (HD) of 4.69±2.62 voxels when validated with 15 prostate volumes of a public dataset in a leave-one-patient-out validation framework. The model achieves statistically significant t-test p-value<;0.0001 in mean DSC and mean HD values compared to some of the works in literature.",
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"normalizedAbstract": "Variations in inter-patient prostate shape, and size and imaging artifacts in magnetic resonance images (MRI) hinders automatic accurate prostate segmentation. In this paper we propose a graph cut based energy minimization of the posterior probabilities obtained in a supervised learning schema for automatic 3D segmentation of the prostate in MRI. A probabilistic classification of the prostate voxels is achieved with a probabilistic atlas and a random forest based learning framework. The posterior probabilities are combined to obtain the likelihood of a voxel being prostate. Finally, 3D graph cut based energy minimization in the stochastic space provides segmentation of the prostate. The proposed method achieves a mean Dice similarity coefficient (DSC) value of 0.91±0.04 and 95% mean Haus-dorff distance (HD) of 4.69±2.62 voxels when validated with 15 prostate volumes of a public dataset in a leave-one-patient-out validation framework. The model achieves statistically significant t-test p-value<;0.0001 in mean DSC and mean HD values compared to some of the works in literature.",
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"affiliation": "Univ. de Bourgogne, France",
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"affiliation": "Univ. de Bourgogne, France",
"fullName": "J. Mitra",
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"affiliation": "Univ. de Girona, Spain",
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"affiliation": "Univ. de Girona, Spain",
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"affiliation": "Univ. de Girona, Spain",
"fullName": "X. Lladó",
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"affiliation": "Univ. de Girona, Spain",
"fullName": "J. Freixenet",
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"affiliation": "Girona Magnetic Resonance Center, Spain",
"fullName": "J. C. Vilanova",
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"affiliation": "Univ. de Bourgogne, France",
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"doi": "10.1109/WACVW54805.2022.00083",
"title": "Generalizing Imaging Through Scattering Media With Uncertainty Estimates",
"normalizedTitle": "Generalizing Imaging Through Scattering Media With Uncertainty Estimates",
"abstract": "Imaging through scattering media is challenging: object features are hidden under highly-scattered photons. Conventional methods that characterize scattering properties, such as the media input-output transmission matrix, are susceptible to environmental disturbance that is not ideal for many imaging scenarios, especially in biomedical imaging. Learning from examples is ideal for imaging in highly scattered regimes because it is adaptable and accurate even when the microstructures of the scattering media change. In current approaches, network output on unseen scattering media contain artifacts that inhibit meaningful object recognition. We present a network architecture that is able to generate high quality images over a range of different scattering media and image sizes with minimal artifacts. Our network learns the statistical information within highly scattered speckle intensity patterns. This allows us to compute an accurate mapping from different speckle patterns to their corresponding objects given scattering media with varying microstructures. Our network demonstrates superior performance compared to similar models, especially when trained on a single scattering medium and then tested on unseen scattering media. We estimate the uncertainty of our approach and use the available data efficiently, increasing the generalizability of predicting objects from unseen scattering media with multiple different diffusers.",
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"content": "Imaging through scattering media is challenging: object features are hidden under highly-scattered photons. Conventional methods that characterize scattering properties, such as the media input-output transmission matrix, are susceptible to environmental disturbance that is not ideal for many imaging scenarios, especially in biomedical imaging. Learning from examples is ideal for imaging in highly scattered regimes because it is adaptable and accurate even when the microstructures of the scattering media change. In current approaches, network output on unseen scattering media contain artifacts that inhibit meaningful object recognition. We present a network architecture that is able to generate high quality images over a range of different scattering media and image sizes with minimal artifacts. Our network learns the statistical information within highly scattered speckle intensity patterns. This allows us to compute an accurate mapping from different speckle patterns to their corresponding objects given scattering media with varying microstructures. Our network demonstrates superior performance compared to similar models, especially when trained on a single scattering medium and then tested on unseen scattering media. We estimate the uncertainty of our approach and use the available data efficiently, increasing the generalizability of predicting objects from unseen scattering media with multiple different diffusers.",
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"affiliation": "Massachusetts Institute of Technology 77 Massachusetts Ave, Cambridge,Department of Electrical Engineering and Computer Science,MA,02139",
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"affiliation": "Massachusetts Institute of Technology 77 Massachusetts Ave, Cambridge,Department of Electrical Engineering and Computer Science,MA,02139",
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"affiliation": "Massachusetts Institute of Technology 77 Massachusetts Ave, Cambridge,Department of Electrical Engineering and Computer Science,MA,02139",
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"abstract": "Bone scintigraphy is a popular method for the diagnosis of bone metastasis that typically occurs when cancer cells from the primary tumor relocate to the bone. In bone scintigraphy, the whole patient’s body is scanned and the generated bone scan visualization provides a valuable source of information for the evaluation of various bone-related pathologies, including bone inflammation and fractures, nonmalignant bone lesions, bone infections, or even the spread of cancer to the bone. ?n particular, bone cancer is among the most frequently appeared diseases to patients suffering from metastatic cancer such as breast cancer patients. However, hot spots in bone scans indicating inflammations or cancer metastasis can be misleading. Accurate detection of pathological hot spots can be a very challenging procedure, with the experience of clinicians playing a critical role in the interpretation of the images. Artificial intelligence has emerged as a key enabler in the interpretation of medical imaging being able to model the aforementioned uncertainties and providing a reliable automated solution. So far, a number of convolutional neural networks (CNN)-based techniques have been proposed in the recent literature coping with the problem of bone metastasis classification. To the best of our knowledge, localization of pathological and degenerative hot spots in scintigraphy images is a scientific area that has not been explored. This paper contributes to the first ever deployment of advanced deep learning networks for bone metastasis localization in nuclear imaging data of breast cancer patients. The methodology relies on the latest advances of object detection via the use of two powerful and recent models (scaled YOLO v4 and Detectron2). The efficacy of the proposed methodology was demonstrated utilizing an extensive experimentation setup. The proposed methodology demonstrates unique potential in bone metastasis localization therefore facilitating the clinical interpretation of bone scintigraphy scans.",
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"abstract": "It is desirable for the non-and-mini traumatic laser surgical procedures to remove tissue accurately with the minimal thermal damage to the adjacent non-irradiated tissues. Photo-thermal interactions between laser rays and biological tissues have various thermal effects such as coagulation, vaporization and carbonization. The energy of laser continuously accumulates and diffuses in biological tissues, so that different extent thermal effects and thermal damage of biological tissues have been induced. In this paper, the thermal conduction model and optical parameters of biological tissues are discussed, the relevant thermal effects calculation equations are deduced, the instantaneous temperature distribution of biological tissue irradiated by high power laser is simulated, the thermal damage depth is numerically predicted and the relationship curve between time and the thermal penetraction depth is described. The study is of significance in revealing the mechanisms of laser thermal interaction with biological tissue, as well as developing clinical application of laser medicine.",
"abstracts": [
{
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"content": "It is desirable for the non-and-mini traumatic laser surgical procedures to remove tissue accurately with the minimal thermal damage to the adjacent non-irradiated tissues. Photo-thermal interactions between laser rays and biological tissues have various thermal effects such as coagulation, vaporization and carbonization. The energy of laser continuously accumulates and diffuses in biological tissues, so that different extent thermal effects and thermal damage of biological tissues have been induced. In this paper, the thermal conduction model and optical parameters of biological tissues are discussed, the relevant thermal effects calculation equations are deduced, the instantaneous temperature distribution of biological tissue irradiated by high power laser is simulated, the thermal damage depth is numerically predicted and the relationship curve between time and the thermal penetraction depth is described. The study is of significance in revealing the mechanisms of laser thermal interaction with biological tissue, as well as developing clinical application of laser medicine.",
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"normalizedAbstract": "It is desirable for the non-and-mini traumatic laser surgical procedures to remove tissue accurately with the minimal thermal damage to the adjacent non-irradiated tissues. Photo-thermal interactions between laser rays and biological tissues have various thermal effects such as coagulation, vaporization and carbonization. The energy of laser continuously accumulates and diffuses in biological tissues, so that different extent thermal effects and thermal damage of biological tissues have been induced. In this paper, the thermal conduction model and optical parameters of biological tissues are discussed, the relevant thermal effects calculation equations are deduced, the instantaneous temperature distribution of biological tissue irradiated by high power laser is simulated, the thermal damage depth is numerically predicted and the relationship curve between time and the thermal penetraction depth is described. The study is of significance in revealing the mechanisms of laser thermal interaction with biological tissue, as well as developing clinical application of laser medicine.",
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"abstract": "For traditional laser processing methods, processing defects such as recast layers, micro cracks, and high concentration of heat-affected zones are inevitably generated, which reduces the quality of the micro-structured surface and service life. This paper proposes an electrolyte assisted laser mic micro fabrication technique that uses a laser beam and a corrosive jet of electrolyte fluid Coaxial on the artifacts. The corrosive electrolyte jet continuously scours, cools and micro-etches the processing area while the laser is working. The effect can reduce the thickness of the recast layer, reduce the number of micro-cracks, and eliminate the high concentration of heat affected zone. In this paper, the mechanism of laser-assisted laser micro fabrication is studied; mathematical model of fluid field and temperature field in laser-assisted drilling of electrolyte jet is established; experiment of electrolyte-assisted laser drilling and analysis of result are carried out. Electrolyte-jet-assisted laser micro fabrication, the basic processing technology, lays the foundation for further research on the electrolyte assisted laser micro fabrication technology .",
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"content": "In the total reflection prism laser gyro, the frequency stabilization accuracy of the ring laser directly affects the working accuracy of the gyro. Based on the control system theory, the transfer function of the prismatic laser gyro frequency stabilization control system is established. The equivalent mathematical model of the ring laser in the frequency stabilization control system is established by the method of experimental test. Through theoretical analysis and MATLAB simulation, the performance of the frequency stabilization control system of the laser gyro can be accurately evaluated, which provides a theoretical basis for the system design and improves the performance of the frequency stabilization control system. improved the gyro performance and debugging efficiency.",
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"abstract": "Three-dimensional brain imaging through cutting-edge MRI technology allows assessment of physical and chemical tissue properties at sub-millimeter resolution. In order to improve brain understanding as part of diagnostic tasks using MRI images, other imaging modalities to obtain deep cerebral structures and cytoarchitectural boundaries have been investigated. Under availability of postmortem samples, the fusion of MRI to brain histology supports more accurate description of neuroanatomical structures since it preserves microscopic entities and reveal fine anatomical details, unavailable otherwise. Nonetheless, histological processing causes severe tissue deformation and loss of the brain original 3D conformation, preventing direct comparisons between MRI and histology. This paper proposes an interactive computational pipeline designed to register multimodal brain data and enable direct histology-MRI correlation. Our main contribution is to develop schemes for brain data fusion, distortion corrections, using appropriate diffeomorphic mappings to align the 3D histological and MRI volumes. We describe our pipeline and preliminary developments of scalable processing schemes for highresolution images. Tests consider a postmortem human brain, and include qualitatively and quantitatively results, such as 3D visualizations and the Dice coefficient (DC) between brain structures. Preliminary results show promising DC values when comparing our scheme results to manually labeled neuroanatomical regions defined by a neurosurgeon on MRI and histology data sets. DC was computed for the left caudade gyrus (LC), right hippocampus (RH) and lateral ventricles (LV).",
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"content": "Three-dimensional brain imaging through cutting-edge MRI technology allows assessment of physical and chemical tissue properties at sub-millimeter resolution. In order to improve brain understanding as part of diagnostic tasks using MRI images, other imaging modalities to obtain deep cerebral structures and cytoarchitectural boundaries have been investigated. Under availability of postmortem samples, the fusion of MRI to brain histology supports more accurate description of neuroanatomical structures since it preserves microscopic entities and reveal fine anatomical details, unavailable otherwise. Nonetheless, histological processing causes severe tissue deformation and loss of the brain original 3D conformation, preventing direct comparisons between MRI and histology. This paper proposes an interactive computational pipeline designed to register multimodal brain data and enable direct histology-MRI correlation. Our main contribution is to develop schemes for brain data fusion, distortion corrections, using appropriate diffeomorphic mappings to align the 3D histological and MRI volumes. We describe our pipeline and preliminary developments of scalable processing schemes for highresolution images. Tests consider a postmortem human brain, and include qualitatively and quantitatively results, such as 3D visualizations and the Dice coefficient (DC) between brain structures. Preliminary results show promising DC values when comparing our scheme results to manually labeled neuroanatomical regions defined by a neurosurgeon on MRI and histology data sets. DC was computed for the left caudade gyrus (LC), right hippocampus (RH) and lateral ventricles (LV).",
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"normalizedAbstract": "Three-dimensional brain imaging through cutting-edge MRI technology allows assessment of physical and chemical tissue properties at sub-millimeter resolution. In order to improve brain understanding as part of diagnostic tasks using MRI images, other imaging modalities to obtain deep cerebral structures and cytoarchitectural boundaries have been investigated. Under availability of postmortem samples, the fusion of MRI to brain histology supports more accurate description of neuroanatomical structures since it preserves microscopic entities and reveal fine anatomical details, unavailable otherwise. Nonetheless, histological processing causes severe tissue deformation and loss of the brain original 3D conformation, preventing direct comparisons between MRI and histology. This paper proposes an interactive computational pipeline designed to register multimodal brain data and enable direct histology-MRI correlation. Our main contribution is to develop schemes for brain data fusion, distortion corrections, using appropriate diffeomorphic mappings to align the 3D histological and MRI volumes. We describe our pipeline and preliminary developments of scalable processing schemes for highresolution images. Tests consider a postmortem human brain, and include qualitatively and quantitatively results, such as 3D visualizations and the Dice coefficient (DC) between brain structures. Preliminary results show promising DC values when comparing our scheme results to manually labeled neuroanatomical regions defined by a neurosurgeon on MRI and histology data sets. DC was computed for the left caudade gyrus (LC), right hippocampus (RH) and lateral ventricles (LV).",
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"Submillimeter Resolution",
"Diagnostic Tasks",
"Deep Cerebral Structures",
"Cytoarchitectural Boundaries",
"Postmortem Samples",
"Neuroanatomical Structures",
"Histological Processing",
"Brain Original 3 D Conformation",
"Interactive Computational Pipeline",
"Multimodal Brain Data Registration",
"Direct Histology MRI Correlation",
"Brain Data Fusion",
"Distortion Corrections",
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"Postmortem Human Brain",
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"Right Hippocampus",
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"affiliation": "Univ. of Wυerzburg, Wϋerzburg, Germany",
"fullName": "Helmut Heinsen",
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"fullName": "Eduardo Alho",
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"fullName": "Lilla Zöllei",
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"affiliation": "Lawrence Berkeley Nat. Lab., Univ. of California Berkeley, Berkeley, CA, USA",
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"title": "Part-to-Whole Registration of Histology and MRI Using Shape Elements",
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"abstract": "Image registration between histology and magnetic resonance imaging (MRI) is a challenging task due to differences in structural content and contrast. Too thick and wide specimens cannot be processed all at once and must be cut into smaller pieces. This dramatically increases the complexity of the problem, since each piece should be individually and manually pre-aligned. To the best of our knowledge, no automatic method can reliably locate such piece of tissue within its respective whole in the MRI slice, and align it without any prior information. We propose here a novel automatic approach to the joint problem of multi-modal registration between histology and MRI, when only a fraction of tissue is available from histology. The approach relies on the representation of images using their level lines so as to reach contrast invariance. Shape elements obtained via the extraction of bitangents are encoded in a projective-invariant manner, which permits the identification of common pieces of curves between two images. We evaluated the approach on human brain histology and compared resulting alignments against manually annotated ground truths. Considering the complexity of the brain folding patterns, preliminary results are promising and suggest the use of characteristic and meaningful shape elements for improved robustness and efficiency.",
"abstracts": [
{
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"content": "Image registration between histology and magnetic resonance imaging (MRI) is a challenging task due to differences in structural content and contrast. Too thick and wide specimens cannot be processed all at once and must be cut into smaller pieces. This dramatically increases the complexity of the problem, since each piece should be individually and manually pre-aligned. To the best of our knowledge, no automatic method can reliably locate such piece of tissue within its respective whole in the MRI slice, and align it without any prior information. We propose here a novel automatic approach to the joint problem of multi-modal registration between histology and MRI, when only a fraction of tissue is available from histology. The approach relies on the representation of images using their level lines so as to reach contrast invariance. Shape elements obtained via the extraction of bitangents are encoded in a projective-invariant manner, which permits the identification of common pieces of curves between two images. We evaluated the approach on human brain histology and compared resulting alignments against manually annotated ground truths. Considering the complexity of the brain folding patterns, preliminary results are promising and suggest the use of characteristic and meaningful shape elements for improved robustness and efficiency.",
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"normalizedAbstract": "Image registration between histology and magnetic resonance imaging (MRI) is a challenging task due to differences in structural content and contrast. Too thick and wide specimens cannot be processed all at once and must be cut into smaller pieces. This dramatically increases the complexity of the problem, since each piece should be individually and manually pre-aligned. To the best of our knowledge, no automatic method can reliably locate such piece of tissue within its respective whole in the MRI slice, and align it without any prior information. We propose here a novel automatic approach to the joint problem of multi-modal registration between histology and MRI, when only a fraction of tissue is available from histology. The approach relies on the representation of images using their level lines so as to reach contrast invariance. Shape elements obtained via the extraction of bitangents are encoded in a projective-invariant manner, which permits the identification of common pieces of curves between two images. We evaluated the approach on human brain histology and compared resulting alignments against manually annotated ground truths. Considering the complexity of the brain folding patterns, preliminary results are promising and suggest the use of characteristic and meaningful shape elements for improved robustness and efficiency.",
"fno": "1034a107",
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"fullName": "Jonas Pichat",
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"affiliation": "Dept. of Brain Repair & Rehabilitation, UCL Inst. of Neurology, London, UK",
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"title": "Dual Structured Convolutional Neural Network with Feature Augmentation for Quantitative Characterization of Tissue Histology",
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"abstract": "We present a dual convolutional neural network (dCNN) architecture for extracting multi-scale features from histological tissue images for the purpose of automated characterization of tissue in digital pathology. The dual structure consists of two identical convolutional neural networks applied to input images with different scales, that are merged together and stacked with two fully connected layers. It has been acknowledged that deep networks can be used to extract higher-order features, and therefore, the network output at final fully connected layer was used as a deep dCNN feature vector. Further, engineered features, shown in previous studies to capture important characteristics of tissue structure and morphology, were integrated to the feature extractor module. The acquired quantitative feature representation can be further utilized to train a discriminative model for classifying tissue types. Machine learning based methods for detection of regions of interest, or tissue type classification will advance the transition to decision support systems and computer aided diagnosis in digital pathology. Here we apply the proposed feature-augmented dCNN method with supervised learning in detecting cancerous tissue from whole slide images. The extracted quantitative representation of tissue histology was used to train a logistic regression model with elastic net regularization. The model was able to accurately discriminate cancerous tissue from normal tissue, resulting in blockwise AUC=0.97, where the total number of analyzed tissue blocks was approximately 8.3 million that constitute the test set of 75 whole slide images.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present a dual convolutional neural network (dCNN) architecture for extracting multi-scale features from histological tissue images for the purpose of automated characterization of tissue in digital pathology. The dual structure consists of two identical convolutional neural networks applied to input images with different scales, that are merged together and stacked with two fully connected layers. It has been acknowledged that deep networks can be used to extract higher-order features, and therefore, the network output at final fully connected layer was used as a deep dCNN feature vector. Further, engineered features, shown in previous studies to capture important characteristics of tissue structure and morphology, were integrated to the feature extractor module. The acquired quantitative feature representation can be further utilized to train a discriminative model for classifying tissue types. Machine learning based methods for detection of regions of interest, or tissue type classification will advance the transition to decision support systems and computer aided diagnosis in digital pathology. Here we apply the proposed feature-augmented dCNN method with supervised learning in detecting cancerous tissue from whole slide images. The extracted quantitative representation of tissue histology was used to train a logistic regression model with elastic net regularization. The model was able to accurately discriminate cancerous tissue from normal tissue, resulting in blockwise AUC=0.97, where the total number of analyzed tissue blocks was approximately 8.3 million that constitute the test set of 75 whole slide images.",
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"normalizedAbstract": "We present a dual convolutional neural network (dCNN) architecture for extracting multi-scale features from histological tissue images for the purpose of automated characterization of tissue in digital pathology. The dual structure consists of two identical convolutional neural networks applied to input images with different scales, that are merged together and stacked with two fully connected layers. It has been acknowledged that deep networks can be used to extract higher-order features, and therefore, the network output at final fully connected layer was used as a deep dCNN feature vector. Further, engineered features, shown in previous studies to capture important characteristics of tissue structure and morphology, were integrated to the feature extractor module. The acquired quantitative feature representation can be further utilized to train a discriminative model for classifying tissue types. Machine learning based methods for detection of regions of interest, or tissue type classification will advance the transition to decision support systems and computer aided diagnosis in digital pathology. Here we apply the proposed feature-augmented dCNN method with supervised learning in detecting cancerous tissue from whole slide images. The extracted quantitative representation of tissue histology was used to train a logistic regression model with elastic net regularization. The model was able to accurately discriminate cancerous tissue from normal tissue, resulting in blockwise AUC=0.97, where the total number of analyzed tissue blocks was approximately 8.3 million that constitute the test set of 75 whole slide images.",
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"doi": "10.1109/ICCVW.2019.00045",
"title": "Deep Multiresolution Cellular Communities for Semantic Segmentation of Multi-Gigapixel Histology Images",
"normalizedTitle": "Deep Multiresolution Cellular Communities for Semantic Segmentation of Multi-Gigapixel Histology Images",
"abstract": "Tissue phenotyping in cancer histology images is a fundamental step in computational pathology. Automatic tools for tissue phenotyping assist pathologists for digital profiling of the tumor microenvironment. Recently, deep learning and classical machine learning methods have been proposed for tissue phenotyping. However, these methods do not integrate the cellular community interaction features which present biological significance in tissue phenotyping context. In this paper, we propose to exploit deep multiresolution cellular communities for tissue phenotyping from multi-level cell graphs and show that such communities offer better performance compared to the deep learning and texture-based methods. We propose to use deep features extracted from two distinct layers of a deep neural network at the cell-level, in order to construct cellular graphs encoding cellular interactions at multiple scales. From these graphs, we extract cellular interaction-based features, which are then employed to construct patch-level graphs. Multiresolution communities are detected by considering the patch-level graphs as layers of multi-level graphs, and also by proposing novel objective function based on non-negative matrix factorization. We report results of our experiments on two datasets for colon cancer tissue phenotyping and demonstrate excellent performance of the proposed algorithm as compared to current state-of-the-art methods.",
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"content": "Tissue phenotyping in cancer histology images is a fundamental step in computational pathology. Automatic tools for tissue phenotyping assist pathologists for digital profiling of the tumor microenvironment. Recently, deep learning and classical machine learning methods have been proposed for tissue phenotyping. However, these methods do not integrate the cellular community interaction features which present biological significance in tissue phenotyping context. In this paper, we propose to exploit deep multiresolution cellular communities for tissue phenotyping from multi-level cell graphs and show that such communities offer better performance compared to the deep learning and texture-based methods. We propose to use deep features extracted from two distinct layers of a deep neural network at the cell-level, in order to construct cellular graphs encoding cellular interactions at multiple scales. From these graphs, we extract cellular interaction-based features, which are then employed to construct patch-level graphs. Multiresolution communities are detected by considering the patch-level graphs as layers of multi-level graphs, and also by proposing novel objective function based on non-negative matrix factorization. We report results of our experiments on two datasets for colon cancer tissue phenotyping and demonstrate excellent performance of the proposed algorithm as compared to current state-of-the-art methods.",
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"normalizedAbstract": "Tissue phenotyping in cancer histology images is a fundamental step in computational pathology. Automatic tools for tissue phenotyping assist pathologists for digital profiling of the tumor microenvironment. Recently, deep learning and classical machine learning methods have been proposed for tissue phenotyping. However, these methods do not integrate the cellular community interaction features which present biological significance in tissue phenotyping context. In this paper, we propose to exploit deep multiresolution cellular communities for tissue phenotyping from multi-level cell graphs and show that such communities offer better performance compared to the deep learning and texture-based methods. We propose to use deep features extracted from two distinct layers of a deep neural network at the cell-level, in order to construct cellular graphs encoding cellular interactions at multiple scales. From these graphs, we extract cellular interaction-based features, which are then employed to construct patch-level graphs. Multiresolution communities are detected by considering the patch-level graphs as layers of multi-level graphs, and also by proposing novel objective function based on non-negative matrix factorization. We report results of our experiments on two datasets for colon cancer tissue phenotyping and demonstrate excellent performance of the proposed algorithm as compared to current state-of-the-art methods.",
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"Biomedical Optical Imaging",
"Cancer",
"Cellular Biophysics",
"Feature Extraction",
"Image Segmentation",
"Image Texture",
"Matrix Decomposition",
"Medical Image Processing",
"Neural Nets",
"Tumours",
"Deep Neural Network",
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"Colon Cancer Tissue Phenotyping",
"Deep Multiresolution Cellular Communities",
"Multigigapixel Histology",
"Cancer Histology Images",
"Deep Learning",
"Classical Machine Learning",
"Cellular Community Interaction Features",
"Texture Based Methods",
"Semantic Segmentation",
"Feature Extraction",
"Tumors",
"Image Resolution",
"Cancer",
"Pathology",
"Neural Networks",
"Support Vector Machines",
"CANCER",
"HISTOLOGY",
"TISSUE PHENOTYPING",
"Cancer Histology Images",
"Microenvironment"
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"affiliation": "Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates",
"fullName": "Sajid Javed",
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"affiliation": "Information Technology University, Lahore, Pakistan",
"fullName": "Arif Mahmood",
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{
"affiliation": "Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates",
"fullName": "Naoufel Werghi",
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{
"affiliation": "Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK",
"fullName": "Nasir Rajpoot",
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"proceeding": {
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"title": "CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images",
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"abstract": "Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tissue micro-environment by assessing the cell-level information along with the morphology of the gland. However, current automated methods for CRC grading typically utilise small image patches and therefore fail to incorporate the entire tissue micro-architecture for grading purposes. To overcome the challenges of CRC grading, we present a novel cell-graph convolutional neural network (CGC-Net) that converts each large histology image into a graph, where each node is represented by a nucleus within the original image and cellular interactions are denoted as edges between these nodes according to node similarity. The CGC-Net utilises nuclear appearance features in addition to the spatial location of nodes to further boost the performance of the algorithm. To enable nodes to fuse multi-scale information, we introduce Adaptive GraphSage, which is a graph convolution technique that combines multi-level features in a data-driven way. Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity. We show that modeling the image as a graph enables us to effectively consider a much larger image (around 16× larger) than traditional patch-based approaches and model the complex structure of the tissue micro-environment. We construct cell graphs with an average of over 3,000 nodes on a large CRC histology image dataset and report state-of-the-art results as compared to recent patch-based as well as contextual patch-based techniques, demonstrating the effectiveness of our method.",
"abstracts": [
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"content": "Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tissue micro-environment by assessing the cell-level information along with the morphology of the gland. However, current automated methods for CRC grading typically utilise small image patches and therefore fail to incorporate the entire tissue micro-architecture for grading purposes. To overcome the challenges of CRC grading, we present a novel cell-graph convolutional neural network (CGC-Net) that converts each large histology image into a graph, where each node is represented by a nucleus within the original image and cellular interactions are denoted as edges between these nodes according to node similarity. The CGC-Net utilises nuclear appearance features in addition to the spatial location of nodes to further boost the performance of the algorithm. To enable nodes to fuse multi-scale information, we introduce Adaptive GraphSage, which is a graph convolution technique that combines multi-level features in a data-driven way. Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity. We show that modeling the image as a graph enables us to effectively consider a much larger image (around 16× larger) than traditional patch-based approaches and model the complex structure of the tissue micro-environment. We construct cell graphs with an average of over 3,000 nodes on a large CRC histology image dataset and report state-of-the-art results as compared to recent patch-based as well as contextual patch-based techniques, demonstrating the effectiveness of our method.",
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"normalizedAbstract": "Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tissue micro-environment by assessing the cell-level information along with the morphology of the gland. However, current automated methods for CRC grading typically utilise small image patches and therefore fail to incorporate the entire tissue micro-architecture for grading purposes. To overcome the challenges of CRC grading, we present a novel cell-graph convolutional neural network (CGC-Net) that converts each large histology image into a graph, where each node is represented by a nucleus within the original image and cellular interactions are denoted as edges between these nodes according to node similarity. The CGC-Net utilises nuclear appearance features in addition to the spatial location of nodes to further boost the performance of the algorithm. To enable nodes to fuse multi-scale information, we introduce Adaptive GraphSage, which is a graph convolution technique that combines multi-level features in a data-driven way. Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity. We show that modeling the image as a graph enables us to effectively consider a much larger image (around 16× larger) than traditional patch-based approaches and model the complex structure of the tissue micro-environment. We construct cell graphs with an average of over 3,000 nodes on a large CRC histology image dataset and report state-of-the-art results as compared to recent patch-based as well as contextual patch-based techniques, demonstrating the effectiveness of our method.",
"fno": "502300a388",
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"authors": [
{
"affiliation": "The Chinese University of Hong Kong, China",
"fullName": "Yanning Zhou",
"givenName": "Yanning",
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{
"affiliation": "University of Warwick, UK",
"fullName": "Simon Graham",
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"affiliation": "University of Warwick, UK",
"fullName": "Navid Alemi Koohbanani",
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"affiliation": "University of Warwick, UK",
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"title": "Visualizing field-measured seismic data",
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"abstract": "This paper presents visualization of field-measured, time-varying multidimensional earthquake accelerograph readings. Direct volume rendering is used to depict the space-time relationships of seismic readings collected from sensor stations in an intuitive way such that the progress of seismic wave propagation of an earthquake event can be directly observed. The resulting visualization reveals the sequence of seismic wave initiation, propagation, attenuation over time, and energy releasing events. We provide a case study on the magnitude scale Mw 7.6 Chi-Chi earthquake in Taiwan, which is the most thoroughly recorded earthquake event ever in the history. More than 400 stations recorded this event, and the readings from this event increased global strong-motion records five folds. Each station measured east-west, north-south, and vertical component of acceleration for approximately 90 seconds. The sensor network released the initial raw data within minutes after the Chi-Chi mainshock. It is essential to have a visualization system for fast data exploring and analyzing, offering crucial visual analytical information for scientists to make quick judgments. Raw data requires preprocessing before it can be rendered. We generated a sequence of ground-motion wave-field maps of 350 × 200 regular grid covers the entire Taiwan island from the sensor network readings. The result is a total of 1000 ground-motion wave-field maps with 0.1 second interval, forming a 1000 × 350 × 200 volume data set. We show that visualizing the time-varying component of the data spatially uncovers the changing features hidden in the data.",
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"content": "This paper presents visualization of field-measured, time-varying multidimensional earthquake accelerograph readings. Direct volume rendering is used to depict the space-time relationships of seismic readings collected from sensor stations in an intuitive way such that the progress of seismic wave propagation of an earthquake event can be directly observed. The resulting visualization reveals the sequence of seismic wave initiation, propagation, attenuation over time, and energy releasing events. We provide a case study on the magnitude scale Mw 7.6 Chi-Chi earthquake in Taiwan, which is the most thoroughly recorded earthquake event ever in the history. More than 400 stations recorded this event, and the readings from this event increased global strong-motion records five folds. Each station measured east-west, north-south, and vertical component of acceleration for approximately 90 seconds. The sensor network released the initial raw data within minutes after the Chi-Chi mainshock. It is essential to have a visualization system for fast data exploring and analyzing, offering crucial visual analytical information for scientists to make quick judgments. Raw data requires preprocessing before it can be rendered. We generated a sequence of ground-motion wave-field maps of 350 × 200 regular grid covers the entire Taiwan island from the sensor network readings. The result is a total of 1000 ground-motion wave-field maps with 0.1 second interval, forming a 1000 × 350 × 200 volume data set. We show that visualizing the time-varying component of the data spatially uncovers the changing features hidden in the data.",
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"Sensor Stations",
"Seismic Wave Propagation",
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"Seismic Wave Attenuation",
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"Seismic Waves",
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"I 3 8 Computer Graphics Applications",
"I 3 3 Computer Graphics Picture Image Generation"
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"affiliation": "National Taipei University of Technology, Taiwan",
"fullName": "Tung-Ju Hsieh",
"givenName": "Tung-Ju",
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"affiliation": "University of California, Davis, USA",
"fullName": "Cheng-Kai Chen",
"givenName": "Cheng-Kai",
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"affiliation": "University of California, Davis, USA",
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"abstract": "Seismic exploration plays an important role in petroleum industry. It is widely admitted that there are a lot of limitations of conventional data analysis ways in oil and gas industry. Traditional methods in petroleum engineering are knowledge-driven and often neglect some underlying factors. On the contrary, data mining is to deal with mass of data and never overlook any important phenomena. Due to large volumes of seismic data, we apply data mining to seismic exploration in this paper. K-means based Cluster analysis is applied for the 3-D seismic and well log data. Comparing the clustering results with the well log data, it is easy to display the distribution of lithology in geo-space.",
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"abstract": "One of the main methods of determining the Earth structure is the analysis of the seismic data. Based on the seismic data, we produce a 3-D map describing the density at different locations on different depths. Due to incomplete coverage and measurement uncertainty, this map provides only an approximate description of the actual density distribution. For example, based on the seismic data, it is impossible to distinguish between the densities at two nearby points. In other words, what we actually reconstruct is not a function of 3 variables, but rather values determined on the appropriate spatial granules. Because of this granularity, it is necessary to find the spatial resolution at different locations and at different depths, i.e., in other words, it is necessary to determine the corresponding granules. In this paper, we show how Type-2 methods can help in determining these granules.",
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{
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"content": "One of the main methods of determining the Earth structure is the analysis of the seismic data. Based on the seismic data, we produce a 3-D map describing the density at different locations on different depths. Due to incomplete coverage and measurement uncertainty, this map provides only an approximate description of the actual density distribution. For example, based on the seismic data, it is impossible to distinguish between the densities at two nearby points. In other words, what we actually reconstruct is not a function of 3 variables, but rather values determined on the appropriate spatial granules. Because of this granularity, it is necessary to find the spatial resolution at different locations and at different depths, i.e., in other words, it is necessary to determine the corresponding granules. In this paper, we show how Type-2 methods can help in determining these granules.",
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"abstract": "A software system is described that uses artificial intelligence techniques to analyze seismic events. Seismic events are described in terms of scripts and matched to observed detections which are represented by frames. Heuristics constrain the matching process and rules are used to eliminate uninteresting detections. Since accurate interpretation is not possible, multiple interpretations are allowed. Each interpretation has a confidence associated with it based on probability theory. In addition, scripts are automatically generated to classify common seismic events. By overcoming the limitations of conventional techniques the system provides better analysis (although not exactly quantifiable) while still being computationally practical. In addition, the system is extensible, whereas previous systems were not.<>",
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"content": "A software system is described that uses artificial intelligence techniques to analyze seismic events. Seismic events are described in terms of scripts and matched to observed detections which are represented by frames. Heuristics constrain the matching process and rules are used to eliminate uninteresting detections. Since accurate interpretation is not possible, multiple interpretations are allowed. Each interpretation has a confidence associated with it based on probability theory. In addition, scripts are automatically generated to classify common seismic events. By overcoming the limitations of conventional techniques the system provides better analysis (although not exactly quantifiable) while still being computationally practical. In addition, the system is extensible, whereas previous systems were not.<>",
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"abstract": "Abstract: The problem considered is the discrimination between natural and artificial seismic events, based on their waveform recording. We build a classification environment consists of several ensembles of neural networks trained on bootstrap sample sets, using various data representations and architectures. The integration of the different ensembles is made in a non-constant signal adaptive manner, using a posterior confidence measure based on the agreement (variance) within the ensembles. The proposed integrated classification machine achieved 92.1% correct classification on the seismic test data. Cross validation tests and comparisons indicate that such integration of a collection of ANN's ensembles is a robust way for handling high dimensional problems with a complex non-stationary signal space as in the current seismic classification problem.",
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{
"abstractType": "Regular",
"content": "Abstract: The problem considered is the discrimination between natural and artificial seismic events, based on their waveform recording. We build a classification environment consists of several ensembles of neural networks trained on bootstrap sample sets, using various data representations and architectures. The integration of the different ensembles is made in a non-constant signal adaptive manner, using a posterior confidence measure based on the agreement (variance) within the ensembles. The proposed integrated classification machine achieved 92.1% correct classification on the seismic test data. Cross validation tests and comparisons indicate that such integration of a collection of ANN's ensembles is a robust way for handling high dimensional problems with a complex non-stationary signal space as in the current seismic classification problem.",
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"affiliation": "Sch. of Math. Sci., Tel Aviv Univ., Israel",
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"abstract": "Wavelet analysis is widely used in seismic data processing, but is relatively less used in the study of resolution. The article takes the corresponding analysis in the wavelet decomposition for the seismic data in volcanic rock region. Result shows that due to the earth filtering effects, the practically processed section planes are not the section planes with the best resolution, there lies great relativities among the neighboring traces, the section planes that being processed by wavelet decomposition with certain scales have the best resolution. Only improving the main dominant frequency of the seismic sections, the practically processed section planes may relatively approach the section planes with the best resolution.",
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"content": "Wavelet analysis is widely used in seismic data processing, but is relatively less used in the study of resolution. The article takes the corresponding analysis in the wavelet decomposition for the seismic data in volcanic rock region. Result shows that due to the earth filtering effects, the practically processed section planes are not the section planes with the best resolution, there lies great relativities among the neighboring traces, the section planes that being processed by wavelet decomposition with certain scales have the best resolution. Only improving the main dominant frequency of the seismic sections, the practically processed section planes may relatively approach the section planes with the best resolution.",
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"abstract": "The seismic signal components depicting the underlying geology are usually corrupted by the noise components in field data. To obtain the high quality seismic records, geophysicists have to attenuate noise in prestack data. But the corruption of seismic signal components by the noise attenuation processing is unavoidable. Thus attenuating the undesirable noise components with minimal damage to the geologic signal is very important in seismic data processing. The curvelet transform developed recently is greatly suitable for seismic data processing, because the curvelets are little plane waves with enough spatial and frequency localization, complete with optimal sparsity. In this paper, we explore an effective noise attenuation approach based on curvelet transform. Applying this method to a synthetic data set and to a field data set shows that the curvelet-based approach outperforms the traditional method in noise attenuation with minimal impact on the desirable signal components.",
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{
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"content": "The seismic signal components depicting the underlying geology are usually corrupted by the noise components in field data. To obtain the high quality seismic records, geophysicists have to attenuate noise in prestack data. But the corruption of seismic signal components by the noise attenuation processing is unavoidable. Thus attenuating the undesirable noise components with minimal damage to the geologic signal is very important in seismic data processing. The curvelet transform developed recently is greatly suitable for seismic data processing, because the curvelets are little plane waves with enough spatial and frequency localization, complete with optimal sparsity. In this paper, we explore an effective noise attenuation approach based on curvelet transform. Applying this method to a synthetic data set and to a field data set shows that the curvelet-based approach outperforms the traditional method in noise attenuation with minimal impact on the desirable signal components.",
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"abstract": "Earth pressure acting on retaining wall will increase dramatically during earthquakes. Traditional seismic earth pressure calculation method is M-O method, but it can not consider the influence of cohesion force. Furthermore, it can only been used in a limited scope. In order to solve the limitation of M-O method, a new active seismic earth pressure formula is deduced in terms of energy conversation law. In addition, the method has been applied on an engineering case to check its applicability. The calculation results show that the method can consider the effect of cohesion force and the result is reliable.",
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"abstract": "Numerical weather prediction (NWP) supports our daily lives. Weather models require higher spatiotemporal resolutions to prepare for extreme weather disasters and reduce the uncertainty of predictions. The accuracy of the initial state of the weather simulation is also critical; thus, we need more advanced data assimilation (DA) technology. By combining resolution and ensemble size, we have achieved the world’s largest weather DA experiment using a global cloud-resolving model and an ensemble Kalman filter method. The number of grid points was Z_$\\sim$_Z4.4 trillion, and 1.3 PiB of data was passed from the model simulation part to the DA part. We adopted a data-centric application design and approximate computing to speed up the overall system of DA. Our DA system, named NICAM-LETKF, scales to 131,072 nodes (6,291,456 cores) of the supercomputer Fugaku with a sustained performance of 29 PFLOPS and 79 PFLOPS for the simulation and DA parts, respectively.",
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"Data Assimilation",
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"Geophysics Computing",
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"Parallel Machines",
"Weather Forecasting",
"1024 Member Ensemble Data Assimilation",
"Numerical Weather Prediction",
"Weather Models",
"Higher Spatiotemporal Resolutions",
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"Weather Simulation",
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"Ensemble Kalman Filter Method",
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"Numerical Models",
"Meteorology",
"Data Assimilation",
"Numerical Weather Prediction",
"Data Assimilation",
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"affiliation": "National Institute for Environmental Studies (NIES),Tsukuba,Japan",
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"affiliation": "RIKEN Center for Computational Science (R-CCS),Kobe,Japan",
"fullName": "Takemasa Miyoshi",
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"fullName": "Toshiyuki Imamura",
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"affiliation": "RIKEN Center for Computational Science (R-CCS),Kobe,Japan",
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"title": "Modeling of a Precise MOS Sensor Array System for 2D Detection of Magnetic Fields",
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"abstract": "A highly sensitive linear MOS sensor system for two-dimensional detection of magnetic fields is suggested and its characteristics is systematically investigated. First, the effects of device geometric parameters and biasing conditions on the sensor sensitivity are accurately determined using an efficient physical simulator which couples the magnetic field equation and the carrier transport equations. From the simulation results, an enhanced equivalent circuit model for MOS magnetic sensor is proposed and finally the performance of an integrated smart structure which is able to fully detect the magnetic field variations in two-directions is analyzed",
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"content": "A highly sensitive linear MOS sensor system for two-dimensional detection of magnetic fields is suggested and its characteristics is systematically investigated. First, the effects of device geometric parameters and biasing conditions on the sensor sensitivity are accurately determined using an efficient physical simulator which couples the magnetic field equation and the carrier transport equations. From the simulation results, an enhanced equivalent circuit model for MOS magnetic sensor is proposed and finally the performance of an integrated smart structure which is able to fully detect the magnetic field variations in two-directions is analyzed",
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"fullName": "A. Abou-Elnour",
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"title": "Axial Magnetic Transmission Mechanism and the Calculation of Magnetic Torque",
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"abstract": "Magnetic transmission has many advantages of no abrasion, no leakage, and energy saving etc. Magnetic force is transmitted torque by magnetic field. Magnetic circuit is influenced by permanent magnet (PM) distributed according to the theory of magnetic transmission mechanism. The mathematical model of its torque is constructed based on the equivalent magnetic charge principle. Magnetic torque is calculated on these parameters of axial height, circuit distribution of permanent magnet, air gap of active and passive magnetic pole and leakage coefficient. Magnetic force is varied with those parameters changed. The result shows axial magnetic torque can be increased by increasing the density of magnetic distribution and decreasing active and passive air gap.",
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{
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"content": "Magnetic transmission has many advantages of no abrasion, no leakage, and energy saving etc. Magnetic force is transmitted torque by magnetic field. Magnetic circuit is influenced by permanent magnet (PM) distributed according to the theory of magnetic transmission mechanism. The mathematical model of its torque is constructed based on the equivalent magnetic charge principle. Magnetic torque is calculated on these parameters of axial height, circuit distribution of permanent magnet, air gap of active and passive magnetic pole and leakage coefficient. Magnetic force is varied with those parameters changed. The result shows axial magnetic torque can be increased by increasing the density of magnetic distribution and decreasing active and passive air gap.",
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"normalizedAbstract": "Magnetic transmission has many advantages of no abrasion, no leakage, and energy saving etc. Magnetic force is transmitted torque by magnetic field. Magnetic circuit is influenced by permanent magnet (PM) distributed according to the theory of magnetic transmission mechanism. The mathematical model of its torque is constructed based on the equivalent magnetic charge principle. Magnetic torque is calculated on these parameters of axial height, circuit distribution of permanent magnet, air gap of active and passive magnetic pole and leakage coefficient. Magnetic force is varied with those parameters changed. The result shows axial magnetic torque can be increased by increasing the density of magnetic distribution and decreasing active and passive air gap.",
"fno": "3729a100",
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"Magnetic Torque",
"Equivalent Magnetic Charge",
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"affiliation": null,
"fullName": "Zhang Zhiming",
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"title": "Magnetic circuit of electromagnetic flow meter with capacitive electrodes",
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"abstract": "This paper deals with analysis of magnetic circuit of electromagnetic flow meter with capacitive electrodes. There are several possibilities how to design magnetic circuit for creating preferably homogenous magnetic field with constant magnetic flux density. In the case of electromagnetic flow meter with capacitive electrodes, there is strong requirement to high level and homogeneity of magnetic flux density, because of very low level signal on the capacitive electrodes. One of possibilities how to create strong homogenous magnetic field is to use driving coil with ferrite magnetic circuit. There is analyzed shape and value of magnetic field flux density inside the pipe of designed magnetic circuit and simulated in ANSYS software in this paper. There are also compared characteristics of this design with ferrite circuit with characteristics of magnetic field with classical coils placed directly on the measuring pipe. Evaluated results from simulation software are compared with experimental measurement in final discussion.",
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"content": "This paper deals with analysis of magnetic circuit of electromagnetic flow meter with capacitive electrodes. There are several possibilities how to design magnetic circuit for creating preferably homogenous magnetic field with constant magnetic flux density. In the case of electromagnetic flow meter with capacitive electrodes, there is strong requirement to high level and homogeneity of magnetic flux density, because of very low level signal on the capacitive electrodes. One of possibilities how to create strong homogenous magnetic field is to use driving coil with ferrite magnetic circuit. There is analyzed shape and value of magnetic field flux density inside the pipe of designed magnetic circuit and simulated in ANSYS software in this paper. There are also compared characteristics of this design with ferrite circuit with characteristics of magnetic field with classical coils placed directly on the measuring pipe. Evaluated results from simulation software are compared with experimental measurement in final discussion.",
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"doi": "10.1109/IMCCC.2011.102",
"title": "Research of Computation about Magnetic Fluid under Magnetic Fluid",
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"abstract": "The distribution and shape of magnetic fluid can be influenced by the adding magnetic field. The magnetism of magnetic fluid can be presented by the adding magnetic field. At the same time, the magnetism can influence the distribution of the adding magnetic field. There is the decoupled question between the magnetic field and force under the adding magnetic field. When the magnetic field is stronger, the influence can be neglected that the magnetism of magnetic fluid affects the distribution of the adding magnetic field. The smaller is the saturation magnetization of magnetic fluid, the smaller is the error of decoupled computation and linear computation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The distribution and shape of magnetic fluid can be influenced by the adding magnetic field. The magnetism of magnetic fluid can be presented by the adding magnetic field. At the same time, the magnetism can influence the distribution of the adding magnetic field. There is the decoupled question between the magnetic field and force under the adding magnetic field. When the magnetic field is stronger, the influence can be neglected that the magnetism of magnetic fluid affects the distribution of the adding magnetic field. The smaller is the saturation magnetization of magnetic fluid, the smaller is the error of decoupled computation and linear computation.",
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"normalizedAbstract": "The distribution and shape of magnetic fluid can be influenced by the adding magnetic field. The magnetism of magnetic fluid can be presented by the adding magnetic field. At the same time, the magnetism can influence the distribution of the adding magnetic field. There is the decoupled question between the magnetic field and force under the adding magnetic field. When the magnetic field is stronger, the influence can be neglected that the magnetism of magnetic fluid affects the distribution of the adding magnetic field. The smaller is the saturation magnetization of magnetic fluid, the smaller is the error of decoupled computation and linear computation.",
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"affiliation": null,
"fullName": "Meng Zhao",
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{
"affiliation": null,
"fullName": "Jibin Zou",
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"proceeding": {
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"title": "2017 IEEE 67th Electronic Components and Technology Conference (ECTC)",
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"id": "12OmNyRPgwG",
"doi": "10.1109/ECTC.2017.265",
"title": "Chip-Scale SERF Atomic Magnetometer without Magnetic Shield",
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"abstract": "Atomic magnetometer operating in spin-exchange-relaxation-free (SERF) regime has a wide range of applications due to its ultra-high sensitivity. However, the requirement of operating in near-zero magnetic field restricts some of the applications which need the atomic magnetometer to work in the high field environment such as monitoring the magnetic anomalies. A novel scheme of the chip-scale SERF atomic magnetometer operating in geomagnetic field without magnetic shield is demonstrated here. A 18 bits high-precise digital-to-analog converter (DAC) is applied to ensure the stability of the compensated magnetic field. The fluctuation of the compensated magnetic field is less than 8.3nT and a measurement sensitivity of 12.4pT/Hz1/2 is achieved in the laboratory environment without any magnetic shields.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Atomic magnetometer operating in spin-exchange-relaxation-free (SERF) regime has a wide range of applications due to its ultra-high sensitivity. However, the requirement of operating in near-zero magnetic field restricts some of the applications which need the atomic magnetometer to work in the high field environment such as monitoring the magnetic anomalies. A novel scheme of the chip-scale SERF atomic magnetometer operating in geomagnetic field without magnetic shield is demonstrated here. A 18 bits high-precise digital-to-analog converter (DAC) is applied to ensure the stability of the compensated magnetic field. The fluctuation of the compensated magnetic field is less than 8.3nT and a measurement sensitivity of 12.4pT/Hz1/2 is achieved in the laboratory environment without any magnetic shields.",
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"fno": "07999941",
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"Digital Analogue Conversion",
"Magnetometers",
"Spin Exchange Relaxation Free Regime",
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"Superconducting Magnets",
"Coils",
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"abstract": "According to researching the spin traveling wave pump, the relationship of the characteristics of magnetic fluid and the press is investigated under the spin magnetic field by the theory method. The relationship of moving, magnetic field and press is investigated by the decoupled computation between the magnetic field and force. The dynamic equations are analyzed under the traveling wave magnetic field. The velocity equation, the spin velocity equation and flux equation per unit time are also analyzed. The method is scientificity and rationality by the testing. The distributing shape of magnetic fluid in the pump is affected by the adding magnetic field under the spin magnetic field when the magnetic fluid is filled in the pump. At the same time, the adding magnetic field is affected by magnetic particles of magnetic fluid. The magnetic fluid can be moved by the effect of the adding magnetic field in the pump. The flux of magnetic fluid increases with the magnetic field.",
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"title": "A Novel Permanent Magnet System for Magnetic Refrigerator and its Magnetic Field Analysis",
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"abstract": "This paper presents a novel configuration of the permanent magnet system for rotating magnetic refrigerator, which consists of stator and rotor. The stator of this system includes two permanent magnets and iron yoke. The rotor includes shaft, iron core and magnetic refrigerant material. When the magnetic refrigerant material is located under the pole of the permanent magnet, it is magnetized by a strong magnetic field and has a high temperature. As the rotor rotates, the magnetic refrigerant material is continuously demagnetized and its temperature gets lower and lower. When the rotor has turned 90 °space angle, the magnetic refrigerant material lies in the smallest magnetic field and its temperature becomes minimal. With the rotation of the permanent magnet system rotor, the magnetic refrigerant material is periodically magnetized and demagnetized and produces the magnetic thermal power for magnetic refrigeration. The permanent magnet system can generate the magnetic field of up to 1.2 T. It is much stronger than that generated by the two permanent magnets placed parallel to each other. Compared with the existing ones, this permanent magnet system has the advantages of high magnetic field strength, small size, simple structure, compact, high operation reliability, no one-side magnetic pull on the rotor and small vibration and noise, etc..",
"abstracts": [
{
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"content": "This paper presents a novel configuration of the permanent magnet system for rotating magnetic refrigerator, which consists of stator and rotor. The stator of this system includes two permanent magnets and iron yoke. The rotor includes shaft, iron core and magnetic refrigerant material. When the magnetic refrigerant material is located under the pole of the permanent magnet, it is magnetized by a strong magnetic field and has a high temperature. As the rotor rotates, the magnetic refrigerant material is continuously demagnetized and its temperature gets lower and lower. When the rotor has turned 90 °space angle, the magnetic refrigerant material lies in the smallest magnetic field and its temperature becomes minimal. With the rotation of the permanent magnet system rotor, the magnetic refrigerant material is periodically magnetized and demagnetized and produces the magnetic thermal power for magnetic refrigeration. The permanent magnet system can generate the magnetic field of up to 1.2 T. It is much stronger than that generated by the two permanent magnets placed parallel to each other. Compared with the existing ones, this permanent magnet system has the advantages of high magnetic field strength, small size, simple structure, compact, high operation reliability, no one-side magnetic pull on the rotor and small vibration and noise, etc..",
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"normalizedAbstract": "This paper presents a novel configuration of the permanent magnet system for rotating magnetic refrigerator, which consists of stator and rotor. The stator of this system includes two permanent magnets and iron yoke. The rotor includes shaft, iron core and magnetic refrigerant material. When the magnetic refrigerant material is located under the pole of the permanent magnet, it is magnetized by a strong magnetic field and has a high temperature. As the rotor rotates, the magnetic refrigerant material is continuously demagnetized and its temperature gets lower and lower. When the rotor has turned 90 °space angle, the magnetic refrigerant material lies in the smallest magnetic field and its temperature becomes minimal. With the rotation of the permanent magnet system rotor, the magnetic refrigerant material is periodically magnetized and demagnetized and produces the magnetic thermal power for magnetic refrigeration. The permanent magnet system can generate the magnetic field of up to 1.2 T. It is much stronger than that generated by the two permanent magnets placed parallel to each other. Compared with the existing ones, this permanent magnet system has the advantages of high magnetic field strength, small size, simple structure, compact, high operation reliability, no one-side magnetic pull on the rotor and small vibration and noise, etc..",
"fno": "4031e928",
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"fullName": "Dong Xia",
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"abstract": "In this paper, the small magnetic field from a small magnet is analyzed. It describes the measurement and utilization of the magnetic field signals generated by the movement of small magnets. After analyzing the signal, we find clues to find out the location of the magnet. Small magnets can be attached to the end of the catheter, a medical instrument that is inserted into the body. Positional tracking allow the catheter to be directed to the desired point when inserted into the body.",
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"content": "Ink mismatch detection provides important clues to forensic document examiners by identifying whether a particular handwritten note was written with a specific pen, or to show that some part (e.g. signature) of a note is written with a different ink as compared to the rest of the note. In this paper, we show that a hyper spectral image (HSI) of handwritten notes can discriminate between inks that are visually similar in appearance. For this purpose, we develop the first ever hyper spectral image database of handwritten notes in various blue and black inks, comprising a total of 70 hyper spectral images each in 33 bands of the visible spectrum. In an unsupervised clustering scheme, the spectral responses of inks fall into separate clusters to allow segmentation of two different inks in a questioned document. The same method fails to segment inks correctly when applied to RGB scans of these documents, since the inks are very hard to distinguish in the visible spectral range. HSI overcomes the shortcomings of RGB and allows better discrimination between inks. We further evaluate which subset of bands from HSI is most useful for the purpose of ink mismatch detection. We hope that these findings will stimulate the use of HSI in document analysis research, especially for questioned document examination.",
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"abstract": "The convolutional neural network (CNN) has been widely used for hyperspectral image (HSI) classification in recent years. There are plentiful spectral features and spatial features in HSI, and we can use spectral-spatial features to obtain a better classification result. Therefore, this paper proposes an HSI classification method based on CNN, which uses pixel’s spectral features and spatial features around the current pixel (CNN-USS). Compared with some advanced classification methods, the result we conduct experiments based on three common hyperspectral datasets shows that CNN-USS has a competitive advantage.",
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"content": "The convolutional neural network (CNN) has been widely used for hyperspectral image (HSI) classification in recent years. There are plentiful spectral features and spatial features in HSI, and we can use spectral-spatial features to obtain a better classification result. Therefore, this paper proposes an HSI classification method based on CNN, which uses pixel’s spectral features and spatial features around the current pixel (CNN-USS). Compared with some advanced classification methods, the result we conduct experiments based on three common hyperspectral datasets shows that CNN-USS has a competitive advantage.",
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"Plentiful Spectral Features",
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"HSI Classification Method",
"Pixel",
"Advanced Classification Methods",
"Information Science",
"Fuses",
"Feature Extraction",
"Convolutional Neural Networks",
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"Artificial Intelligence",
"Hyperspectral Imaging",
"Convolutional Neural Network CNN",
"Hyperspectral Image HSI",
"Spectral Spatial Features"
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"affiliation": "Yunnan Normal University,School of Information Science and Technology,Kunming,China,650500",
"fullName": "Yangming Zhang",
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"affiliation": "Yunnan Normal University,Faculty of Geography,Kunming,China,650500",
"fullName": "Lei Yuan",
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"surname": "Yuan",
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{
"affiliation": "Yunnan Normal University,Faculty of Geography,Kunming,China,650500",
"fullName": "Kun Yang",
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"surname": "Yang",
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{
"affiliation": "Shanghai Maritime University,College of Transport and Communication,Shanghai,China,201306",
"fullName": "Panpan Liu",
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"abstract": "Analysis of hyperspectral images is of great importance in many scientific disciplines. Obtaining the spectral and spatial information simultaneously from time-varying hyperspectral images is a challenging task due to their high dimensionality. In this paper, we design an interface that allows users to study hyperspectral images interactively and obtain spectral features and enhanced images at the same time. The image fusion results change dynamically with the regions of interest selected by users and convey both the spatial and spectral information. We show the usefulness of our approach using time-varying hyperspectral plant images. We compare our method with existing hyperspectral image analysis techniques. Our evaluation indicates that our interface can help users determine important bands, identify regions of interest, and generate image fusion results for time-varying hyperspectral plant images.",
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"title": "Unsupervised Deep Hyperspectral Super-Resolution With Unregistered Images",
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"abstract": "Fusion based hyperspectral image (HSI) super-resolution has long been the research focus of hyperspectral image processing since it can generate a high-resolution (HR) HSI in both spatial and spectral domains. However, the success of the existing fusion based HSI super-resolution methods depends on the premise that the images utilized for fusion (i.e. the input low-spatial-resolution HSI and the low-spectral-resolution multispectral image) are exactly registered. Although such a premise is too idealistic to comply with in real cases, few efforts have considered this problem. To fill this gap, we propose to incorporate image registration into HSI super-resolution for joint unsupervised learning in this study. Specifically, a spatial transformer network (STN) is introduced to learn the parameters of the affine transformation between the input two images. In order to avoid over-fitting, we constrain the STN with a novel constraint during learning. By doing this, both the STN and super-resolution network can be cast into a weighted joint learning model without any supervision from the latent HR HSI. Experimental results demonstrate the effectiveness of the proposed method in coping with unregistered input images.",
"abstracts": [
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"content": "Fusion based hyperspectral image (HSI) super-resolution has long been the research focus of hyperspectral image processing since it can generate a high-resolution (HR) HSI in both spatial and spectral domains. However, the success of the existing fusion based HSI super-resolution methods depends on the premise that the images utilized for fusion (i.e. the input low-spatial-resolution HSI and the low-spectral-resolution multispectral image) are exactly registered. Although such a premise is too idealistic to comply with in real cases, few efforts have considered this problem. To fill this gap, we propose to incorporate image registration into HSI super-resolution for joint unsupervised learning in this study. Specifically, a spatial transformer network (STN) is introduced to learn the parameters of the affine transformation between the input two images. In order to avoid over-fitting, we constrain the STN with a novel constraint during learning. By doing this, both the STN and super-resolution network can be cast into a weighted joint learning model without any supervision from the latent HR HSI. Experimental results demonstrate the effectiveness of the proposed method in coping with unregistered input images.",
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"affiliation": "Northwestern Polytechnical University,School of Computer Science,Xi’an,Shaanxi,China,710072",
"fullName": "Jiangtao Nie",
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"affiliation": "Northwestern Polytechnical University,School of Computer Science,Xi’an,Shaanxi,China,710072",
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"affiliation": "Research & Development Institute of Northwestern Polytechnical University in Shenzhen,Shenzhen,China,518057",
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"affiliation": "Xi’an University of Posts and Telecommunications,School of Computer Science & Technology,Xi’an,Shaanxi,China,710121",
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"affiliation": "Northwestern Polytechnical University,School of Computer Science,Xi’an,Shaanxi,China,710072",
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"abstract": "The results of using the mu -interaction measure as an analysis tool in systematically designing decentralized controllers for multivariable systems are given. The mu -interaction measure serves as the upper bound for the maximum allowable gains of a diagonal controller such that stability of the closed-loop diagonal system also implies stability of the closed loop full system. Several examples found in the literature are used to demonstrate this analysis technique. The diagonal controllers are designed using the internal model control method. An investigation has also been carried out to study the incorporation of model uncertainty into the mu -interaction measure analysis. Results indicate that, although the mu -interaction measure may render a somewhat conservative design, it does provide an effective means of assessing the stability of the full system when the controller design is based on the diagonalized plant system.<>",
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"content": "The results of using the mu -interaction measure as an analysis tool in systematically designing decentralized controllers for multivariable systems are given. The mu -interaction measure serves as the upper bound for the maximum allowable gains of a diagonal controller such that stability of the closed-loop diagonal system also implies stability of the closed loop full system. Several examples found in the literature are used to demonstrate this analysis technique. The diagonal controllers are designed using the internal model control method. An investigation has also been carried out to study the incorporation of model uncertainty into the mu -interaction measure analysis. Results indicate that, although the mu -interaction measure may render a somewhat conservative design, it does provide an effective means of assessing the stability of the full system when the controller design is based on the diagonalized plant system.<>",
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"abstract": "Proper placement of FACTS devices is very important for the rapid and successful operation because of high cost and circuit complexities. In this paper best location of UPFC (Unified Power Flow Controller) is obtained both for static and transient voltage stability enhancement of an IEEE 14 bus power system. The simulation is done on PSAT (Power System Analysis Tool-box) in MATLAB and optimal location is found out by Continuation Power Flow (CPF) and Line stability index. The bus having lowest voltage is the critical bus and the line having largest value of index for maximum permissible load with respect to a bus is the most critical line referred to that bus. It is found that by properly placing UPFC load ability margin of the system has been increased considerably leading to improvement of voltage stability and stability index value decreases at each reactive load with the insertion of the device at right place. Transient stability analysis is also done for an IEEE 14 bus system with a fault created at a bus. It is found from the time domain simulation that proper placement of UPFC increases the transient performance of the system by damping out the power oscillation under large disturbance conditions.",
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"abstract": "An important component of any spatial temporal gradient motion estimation algorithm is the accuracy by which spatial gradients are calculated. When an image sequence is corrupted by noise, the problem of determining these spatial gradients becomes extremely difficult. This is immediately apparent, since the magnitude response of the derivative operator is |/spl omega/|. In other words, the components of an image are amplified upon differentiation in proportion to their frequency value. Thus, high-frequency noise terms will dominate any low-frequency features in the differentiated image. If this corrupted differentiated image is then used within a spatio-temporal gradient motion estimator, the noise will erroneously influence the estimated motion vector. The problem of estimating the spatial gradient is treated as an inverse problem with noise. Formulating the problem in this manner results in a recursive gradient estimator that suppresses the effects of noise.",
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"content": "An important component of any spatial temporal gradient motion estimation algorithm is the accuracy by which spatial gradients are calculated. When an image sequence is corrupted by noise, the problem of determining these spatial gradients becomes extremely difficult. This is immediately apparent, since the magnitude response of the derivative operator is |/spl omega/|. In other words, the components of an image are amplified upon differentiation in proportion to their frequency value. Thus, high-frequency noise terms will dominate any low-frequency features in the differentiated image. If this corrupted differentiated image is then used within a spatio-temporal gradient motion estimator, the noise will erroneously influence the estimated motion vector. The problem of estimating the spatial gradient is treated as an inverse problem with noise. Formulating the problem in this manner results in a recursive gradient estimator that suppresses the effects of noise.",
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"abstract": "While scalar image segmentation has been studied extensively, diffusion tensor imaging (DTI) segmentation is a relatively new and challenging task. Either existent segmentation methods have to be adapted to deal with tensorial information or completely new segmentation methods have to be developed to accomplish this task. Alternatively, what this work proposes is the computation of a tensorial morphological gradient of DTI, and its segmentation by IFT-based watershed transform. The strength of the proposed segmentation method is its simplicity and robustness, consequences of the tensorial morphological gradient computation. It enables the use, not only of well known algorithms and tools from the mathematical morphology, but also of any other segmentation method to segment DTI, since the computation of the tensorial morphological gradient transforms tensorial images in scalar ones. In order to validate the proposed method, synthetic diffusion tensor fields were generated, and Gaussian noise was added to them. A set of real DTI was also used in the method validation. All segmentation results confirmed that the proposed method is capable to segment different diffusion tensor images, including noisy and real ones.",
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"abstract": "Based on fractional derivative gradient, tracking operator is proposed to extract three-dimensional boundary surface within biomedical image. Firstly, the gradient and zero-crossing are calculated via the integer order derivative to extract the main structure of three-dimensional boundary surface. Then the slice images are enhanced by fractional differential, based on coplanar principle, the detailed subtle structures are tracked in three-dimensional images. The experimental result indicates that the method can track more detailed subtle three-dimensional structures. The improved tracking algorithm illustrates a good prospect of application and extension to other 3D biomedical image analyses and researches by comparing with the traditional boundary surface tracking algorithm.",
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"content": "Based on fractional derivative gradient, tracking operator is proposed to extract three-dimensional boundary surface within biomedical image. Firstly, the gradient and zero-crossing are calculated via the integer order derivative to extract the main structure of three-dimensional boundary surface. Then the slice images are enhanced by fractional differential, based on coplanar principle, the detailed subtle structures are tracked in three-dimensional images. The experimental result indicates that the method can track more detailed subtle three-dimensional structures. The improved tracking algorithm illustrates a good prospect of application and extension to other 3D biomedical image analyses and researches by comparing with the traditional boundary surface tracking algorithm.",
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"abstract": "We present a new technique for visualizing surfaces from 3D ultrasound data. 3D ultrasound datasets are typically fuzzy, contain a substantial amount of noise and speckle, and suffer from several other problems that make extraction of continuous and smooth surfaces extremely difficult. We propose a novel opacity classification algorithm for 3D ultrasound datasets, based on the variational principle. More specifically, we compute a volumetric opacity function that optimally satisfies a set of simultaneous requirements. One requirement makes the function attain nonzero values only in the vicinity of a user-specified value, resulting in soft shells of finite, approximately constant thickness around isosurfaces in the volume. Other requirements are designed to make the function smoother and less sensitive to noise and speckle. The computed opacity function lends itself well to explicit geometric surface extraction, as well as to direct volume rendering at interactive rates. We also describe a new splatting algorithm that is particularly well suited for displaying soft opacity shells. Several examples and comparisons are included to illustrate our approach and demonstrate its effectiveness on real 3D ultrasound datasets.",
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"content": "We present a new technique for visualizing surfaces from 3D ultrasound data. 3D ultrasound datasets are typically fuzzy, contain a substantial amount of noise and speckle, and suffer from several other problems that make extraction of continuous and smooth surfaces extremely difficult. We propose a novel opacity classification algorithm for 3D ultrasound datasets, based on the variational principle. More specifically, we compute a volumetric opacity function that optimally satisfies a set of simultaneous requirements. One requirement makes the function attain nonzero values only in the vicinity of a user-specified value, resulting in soft shells of finite, approximately constant thickness around isosurfaces in the volume. Other requirements are designed to make the function smoother and less sensitive to noise and speckle. The computed opacity function lends itself well to explicit geometric surface extraction, as well as to direct volume rendering at interactive rates. We also describe a new splatting algorithm that is particularly well suited for displaying soft opacity shells. Several examples and comparisons are included to illustrate our approach and demonstrate its effectiveness on real 3D ultrasound datasets.",
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"affiliation": "The Hebrew University of Jerusalem",
"fullName": "Raanan Fattal",
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"affiliation": "The Hebrew University of Jerusalem",
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"abstract": "Tracking and visualizing local features from a time-varying volumetric data allows the user to focus on selected regions of interest, both in space and time, which can lead to a better understanding of the underlying dynamics. In this paper, we present an efficient algorithm to track time-varying isosurfaces and interval volumes using isosurfacing in higher dimensions. Instead of extracting the data features such as isosurfaces or interval volumes separately from multiple time steps and computing the spatial correspondence between those features, our algorithm extracts the correspondence directly from the higher dimensional geometry and thus can more efficiently follow the user selected local features in time. In addition, by analyzing the resulting higher dimensional geometry, it becomes easier to detect important topological events and the corresponding critical time steps for the selected features. With our algorithm, the user can interact with the underlying time-varying data more easily. The computation cost for performing time-varying volume tracking is also minimized.",
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"abstract": "The quality of volume visualization depends strongly on the quality of the underlying data. In virtual colonoscopy, CT data should be acquired at a low radiation dose that results in a low signal-to-noise ratio. Alternatively, MRI data is acquired without ionizing radiation, but suffers from noise and bias (global signal fluctuations). Current volume visualization techniques often do not produce good results with noisy or biased data. This paper describes methods for volume visualization that deal with these imperfections. The techniques are based on specially adapted edge detectors using first and second order derivative filters. The filtering is integrated into the the visualization process. The first order derivative method results in good quality images but suffers from localization bias. The second order method has better surface localization, especially in highly curved areas. It guarantees minimal detail smoothing resulting in a better visualization of polyps.",
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"content": "The quality of volume visualization depends strongly on the quality of the underlying data. In virtual colonoscopy, CT data should be acquired at a low radiation dose that results in a low signal-to-noise ratio. Alternatively, MRI data is acquired without ionizing radiation, but suffers from noise and bias (global signal fluctuations). Current volume visualization techniques often do not produce good results with noisy or biased data. This paper describes methods for volume visualization that deal with these imperfections. The techniques are based on specially adapted edge detectors using first and second order derivative filters. The filtering is integrated into the the visualization process. The first order derivative method results in good quality images but suffers from localization bias. The second order method has better surface localization, especially in highly curved areas. It guarantees minimal detail smoothing resulting in a better visualization of polyps.",
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"normalizedAbstract": "The quality of volume visualization depends strongly on the quality of the underlying data. In virtual colonoscopy, CT data should be acquired at a low radiation dose that results in a low signal-to-noise ratio. Alternatively, MRI data is acquired without ionizing radiation, but suffers from noise and bias (global signal fluctuations). Current volume visualization techniques often do not produce good results with noisy or biased data. This paper describes methods for volume visualization that deal with these imperfections. The techniques are based on specially adapted edge detectors using first and second order derivative filters. The filtering is integrated into the the visualization process. The first order derivative method results in good quality images but suffers from localization bias. The second order method has better surface localization, especially in highly curved areas. It guarantees minimal detail smoothing resulting in a better visualization of polyps.",
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"abstract": "In this paper, a new approach for haptic volume interaction with high resolution voxel-based anatomic models is presented. The haptic rendering is based on a multi-point collision detection approach which provides realistic tool interaction with the models. Both haptics and graphics are rendered at sub-voxel resolution, which leads to a high level of detail and enables the exploration of the models at any scale. Forces are calculated at an update rate of 6000 Hz and sent to a 3-Degree-of-Freedom (3-DOF) force-feedback device. Compared to point-based haptic rendering, the unique approach of the multi-point collision detection in combination with sub-voxel rendering provides more realistic and very detailed haptic sensations. As a main application, a simulator for petrous bone surgery was developed. With a simulated drill, bony structure can be removed and the access path to the middle ear can be studied.",
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"abstract": "The steady growth of the Internet has dramatically changed the way information is shared, and modern users expect near real-time delivery, high quality images together with in-depth navigation and exploration of 3D models. Multiresolution is a promising approach for fast distributed volume visualisation employing Levels-of-Detail. We review multiresolution algorithms and visualisation systems on the WWW. Our discussion is based on experience gained in the development of the IAEVA-II project funded by the European Commission. A new method for rapid data classification/rendering of multiresolution volumes, based on shear-warp factorisation, is described. We can change classification functions and data resolution during rendering without significant reduction in interactivity. A method for constructing multiresolution transfer functions for determining opacity is also investigated. Finally, future trends in developing www visualisation systems are discussed.",
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"content": "The steady growth of the Internet has dramatically changed the way information is shared, and modern users expect near real-time delivery, high quality images together with in-depth navigation and exploration of 3D models. Multiresolution is a promising approach for fast distributed volume visualisation employing Levels-of-Detail. We review multiresolution algorithms and visualisation systems on the WWW. Our discussion is based on experience gained in the development of the IAEVA-II project funded by the European Commission. A new method for rapid data classification/rendering of multiresolution volumes, based on shear-warp factorisation, is described. We can change classification functions and data resolution during rendering without significant reduction in interactivity. A method for constructing multiresolution transfer functions for determining opacity is also investigated. Finally, future trends in developing www visualisation systems are discussed.",
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"abstract": "This paper describes a vectorial representation that can be used to assess the symmetry of objects in 2D images. The method exploits a magneto-static analogy. Commencing from the gradient-field extracted from filtered grey-scale images we construct a vector-potential. Our magneto-static analogy is that tangential gradient vectors represent the elements of a current distribution on the image plane. By embedding the image plane in an augmented 3-dimensional space, we compute the vector potential by performing volume integration over the current distribution. The associated magnetic field is computed by taking the curl of the vector-potential. The auxiliary spatial dimension provides a natural scale-space sampling of the generating current-distribution; as the height above the image plane is increased, so the volume over which averaging is effected also increases. We extract edge and symmetry lines through a topographic analysis of the vector-field at various heights above the image plane. Symmetry axes are lines of where the curl of the vector-potential vanishes; at edges the divergence of the vector-potential vanishes.",
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"abstract": "This paper addresses the problem of the visualization of vector-valued images. It is attempted to synthesize a display matched to the capabilities of a human observer. The problem is reduced to the extraction of the best linear feature of the vector field. Several new nonparametric feature extraction criteria (projection indices) that make use of both the spatial and multivariate structures of the data have been recently proposed and demonstrated. A theoretical analysis of these projection indices and a Monte-Carlo study of their effectiveness is presented here. The study uses performance measures derived from a decision-theoretic model of the human observer.",
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"abstract": "A novel method for crowd flow modeling and anomaly detection is proposed for both coherent and incoherent scenes. The novelty is revealed in three aspects. First, it is a unique utilization of particle trajectories for modeling crowded scenes, in which we propose new and efficient representative trajectories for modeling arbitrarily complicated crowd flows. Second, chaotic dynamics are introduced into the crowd context to characterize complicated crowd motions by regulating a set of chaotic invariant features, which are reliably computed and used for detecting anomalies. Third, a probabilistic framework for anomaly detection and localization is formulated. The overall work-flow begins with particle advection based on optical flow. Then particle trajectories are clustered to obtain representative trajectories for a crowd flow. Next, the chaotic dynamics of all representative trajectories are extracted and quantified using chaotic invariants known as maximal Lyapunov exponent and correlation dimension. Probabilistic model is learned from these chaotic feature set, and finally, a maximum likelihood estimation criterion is adopted to identify a query video of a scene as normal or abnormal. Furthermore, an effective anomaly localization algorithm is designed to locate the position and size of an anomaly. Experiments are conducted on known crowd data set, and results show that our method achieves higher accuracy in anomaly detection and can effectively localize anomalies.",
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"content": "A novel method for crowd flow modeling and anomaly detection is proposed for both coherent and incoherent scenes. The novelty is revealed in three aspects. First, it is a unique utilization of particle trajectories for modeling crowded scenes, in which we propose new and efficient representative trajectories for modeling arbitrarily complicated crowd flows. Second, chaotic dynamics are introduced into the crowd context to characterize complicated crowd motions by regulating a set of chaotic invariant features, which are reliably computed and used for detecting anomalies. Third, a probabilistic framework for anomaly detection and localization is formulated. The overall work-flow begins with particle advection based on optical flow. Then particle trajectories are clustered to obtain representative trajectories for a crowd flow. Next, the chaotic dynamics of all representative trajectories are extracted and quantified using chaotic invariants known as maximal Lyapunov exponent and correlation dimension. Probabilistic model is learned from these chaotic feature set, and finally, a maximum likelihood estimation criterion is adopted to identify a query video of a scene as normal or abnormal. Furthermore, an effective anomaly localization algorithm is designed to locate the position and size of an anomaly. Experiments are conducted on known crowd data set, and results show that our method achieves higher accuracy in anomaly detection and can effectively localize anomalies.",
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"affiliation": "Computer Vision Lab, University of Central Florida",
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"affiliation": "Department of Mathematics University of Central Florida",
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"affiliation": "Computer Vision Lab, University of Central Florida",
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"abstract": "This paper presents a strategy for seeding streamlines in 3D flow fields. Its main goal is to capture the essential flow patterns and to provide sufficient coverage in the field while reducing clutter. First, critical points of the flow field are extracted to identify regions with important flow patterns that need to be presented. Different seeding templates are then used around the vicinity of the different critical points. Because there is significant variability in the flow pattern even for the same type of critical point, our template can change shape depending on how far the critical point is from transitioning into another type of critical point. To accomplish this, we introduce the /spl alpha/-/spl beta/ map of 3D critical points. Next, we use Poisson seeding to populate the empty regions. Finally, we filter the streamlines based on their geometric and spatial properties. Altogether, this multi-step strategy reduces clutter and yet captures the important 3D flow features.",
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"abstract": "Geospatial information services composition is a promising approach to construct complex Web GIS applications. BPEL, which is the language and the industry standard that expresses complex concurrent business processes for web services composition, is efficient at composing the geospatial information services chain. This paper concludes the BPEL control flows to four basic models which can be applied to orchestrate the geospatial information services, and realizes the data reduction process service based on BPEL in a watershed management system.",
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"abstract": "Existing web service technologies are only at the syntactic level, we still have semantic gaps in cross-domain resource discovery, heterogeneous resource query, resource translation from one domain to another at the semantic level. The Semantic web seeks to expose the vast information resource of the web as data that computers can automatically interpret. This paper presents how semantic web technologies add values in building semantic geospatial web service using REST (Representational State Transfer, R. T. Fielding, 2000) based design methodologies. We illustrate semantic web service application in GIS domain, describe the characteristics of semantic REST requests/response approach and map the HTTP REST operations onto semantic ways. Semantic URIs (Uniform Resource Identifiers) rules are used to identify geospatial resources, and using RDF(Resource Description Framework) to describe the relationships of the geospatial resource and its concepts which shared meaning with ontologies and finally querying the distributed geospatial knowledge using SPARQL(SPARQL Protocol and RDF Query Language)/Update.",
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"content": "Existing web service technologies are only at the syntactic level, we still have semantic gaps in cross-domain resource discovery, heterogeneous resource query, resource translation from one domain to another at the semantic level. The Semantic web seeks to expose the vast information resource of the web as data that computers can automatically interpret. This paper presents how semantic web technologies add values in building semantic geospatial web service using REST (Representational State Transfer, R. T. Fielding, 2000) based design methodologies. We illustrate semantic web service application in GIS domain, describe the characteristics of semantic REST requests/response approach and map the HTTP REST operations onto semantic ways. Semantic URIs (Uniform Resource Identifiers) rules are used to identify geospatial resources, and using RDF(Resource Description Framework) to describe the relationships of the geospatial resource and its concepts which shared meaning with ontologies and finally querying the distributed geospatial knowledge using SPARQL(SPARQL Protocol and RDF Query Language)/Update.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Existing web service technologies are only at the syntactic level, we still have semantic gaps in cross-domain resource discovery, heterogeneous resource query, resource translation from one domain to another at the semantic level. The Semantic web seeks to expose the vast information resource of the web as data that computers can automatically interpret. This paper presents how semantic web technologies add values in building semantic geospatial web service using REST (Representational State Transfer, R. T. Fielding, 2000) based design methodologies. We illustrate semantic web service application in GIS domain, describe the characteristics of semantic REST requests/response approach and map the HTTP REST operations onto semantic ways. Semantic URIs (Uniform Resource Identifiers) rules are used to identify geospatial resources, and using RDF(Resource Description Framework) to describe the relationships of the geospatial resource and its concepts which shared meaning with ontologies and finally querying the distributed geospatial knowledge using SPARQL(SPARQL Protocol and RDF Query Language)/Update.",
"fno": "3930a208",
"keywords": [
"Web Service",
"Semantic GWS Geospatial Web Service",
"REST",
"Resource"
],
"authors": [
{
"affiliation": null,
"fullName": "Yao Gang",
"givenName": "Yao",
"surname": "Gang",
"__typename": "ArticleAuthorType"
}
],
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"pubDate": "2009-12-01T00:00:00",
"pubType": "proceedings",
"pages": "208-210",
"year": "2009",
"issn": null,
"isbn": "978-0-7695-3930-0",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
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