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"abstract": "Summary form only given. Bitmap compression reduces storage space and transmission time for unstructured bit sequences like in inverted files, spatial objects, etc. On the down side, the compressed bitmaps loose their functional properties. For example, checking a given bit position, set intersection, union, and difference can be performed only after full decoding, thus causing a many-folded operational speed degradation. The proposed byte-aligned bitmap compression method (BBC) aims to support fast set operations on the compressed bitmap formats and, at the same time, to retain a competitive compression rate. To achieve this objective, BBC abandons the traditional approach of encoding run-lengths (distances between two ones separated by zeros). Instead, BBC deals only with byte aligned byte-size bitmap portions that are easy to fetch, store, AND, OR, and convert. The bitmap bytes are classified as gaps containing only zeros or only ones and maps containing a mixture of both. We also introduced a simple extension mechanism for existing methods to accommodate a dual-gap (zeros and ones) run-length encoding. With this extension, encoding of long \"one\" sequences becomes as efficient and better than arithmetic encoding.",
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"content": "Summary form only given. Bitmap compression reduces storage space and transmission time for unstructured bit sequences like in inverted files, spatial objects, etc. On the down side, the compressed bitmaps loose their functional properties. For example, checking a given bit position, set intersection, union, and difference can be performed only after full decoding, thus causing a many-folded operational speed degradation. The proposed byte-aligned bitmap compression method (BBC) aims to support fast set operations on the compressed bitmap formats and, at the same time, to retain a competitive compression rate. To achieve this objective, BBC abandons the traditional approach of encoding run-lengths (distances between two ones separated by zeros). Instead, BBC deals only with byte aligned byte-size bitmap portions that are easy to fetch, store, AND, OR, and convert. The bitmap bytes are classified as gaps containing only zeros or only ones and maps containing a mixture of both. We also introduced a simple extension mechanism for existing methods to accommodate a dual-gap (zeros and ones) run-length encoding. With this extension, encoding of long \"one\" sequences becomes as efficient and better than arithmetic encoding.",
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"normalizedAbstract": "Summary form only given. Bitmap compression reduces storage space and transmission time for unstructured bit sequences like in inverted files, spatial objects, etc. On the down side, the compressed bitmaps loose their functional properties. For example, checking a given bit position, set intersection, union, and difference can be performed only after full decoding, thus causing a many-folded operational speed degradation. The proposed byte-aligned bitmap compression method (BBC) aims to support fast set operations on the compressed bitmap formats and, at the same time, to retain a competitive compression rate. To achieve this objective, BBC abandons the traditional approach of encoding run-lengths (distances between two ones separated by zeros). Instead, BBC deals only with byte aligned byte-size bitmap portions that are easy to fetch, store, AND, OR, and convert. The bitmap bytes are classified as gaps containing only zeros or only ones and maps containing a mixture of both. We also introduced a simple extension mechanism for existing methods to accommodate a dual-gap (zeros and ones) run-length encoding. With this extension, encoding of long \"one\" sequences becomes as efficient and better than arithmetic encoding.",
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"title": "2016 8th International Conference on Information Technology in Medicine and Education (ITME)",
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"title": "A Method of Fire and Smoke Detection Based on Surendra Background and Gray Bitmap Plane Algorithm",
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"abstract": "Fire and smoke detection is an important technology in the fire monitoring system. This paper presents a method of fire and smoke detection based on Surendra background and gray bitmap plane algorithm. In our method, the dynamic background based on Otsu adaptive threshold and Surendra background first is modeled, and then the moving target is extracted by the continuous cumulative average of moving target area and the mathematical morphology processing method, the smoke area is extracted by color feature, and finally the fire area is extracted by OHTA color space and gray bitmap plane algorithm. The experimental results show that our method can effectively detect the fire area in the moving vehicle scene, the red plastic bag waving scene, the red balloon fluttering scene, or the moving vehicle similar to fire color.",
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"content": "Fire and smoke detection is an important technology in the fire monitoring system. This paper presents a method of fire and smoke detection based on Surendra background and gray bitmap plane algorithm. In our method, the dynamic background based on Otsu adaptive threshold and Surendra background first is modeled, and then the moving target is extracted by the continuous cumulative average of moving target area and the mathematical morphology processing method, the smoke area is extracted by color feature, and finally the fire area is extracted by OHTA color space and gray bitmap plane algorithm. The experimental results show that our method can effectively detect the fire area in the moving vehicle scene, the red plastic bag waving scene, the red balloon fluttering scene, or the moving vehicle similar to fire color.",
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"fno": "3906a370",
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"Fires",
"Image Color Analysis",
"Feature Extraction",
"Morphology",
"Video Sequences",
"Vehicle Dynamics",
"Adaptation Models",
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"Gray Bitmap Plane",
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"fullName": "Li Jinlan",
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"fullName": "Wang Lin",
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"fullName": "Zhang Ruliang",
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"fullName": "Huang Chengquan",
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"doi": "10.1109/GCIS.2009.302",
"title": "Manifold-Based Combination of Visual Features and Keyword Features for Image Retrieval",
"normalizedTitle": "Manifold-Based Combination of Visual Features and Keyword Features for Image Retrieval",
"abstract": "To bridge the semantic gap between low-level visual features and high-level semantic concepts, this paper puts forward a novel semi-supervised learning framework of combining visual and keyword features. We assume all of images in the database have been annotated. In this framework, the visual space graph (VSG) and keyword space graph (KSG) are first constructed by means of manifold-based algorithm to explore the submanifold of Visual space and keyword space. Then, semantic space graph (SSG) is constructed by using neighbor information of KSG and VSF. Afterwards, the VSG and KSG are modified and unified by propagating semantic matrix. Finally, we propose a flexible ranking formula and introduce different relevance feedback methods of different query styles. Experimental results on COREL 5000 images show our method improves image retrieval performance from all aspects.",
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"abstractType": "Regular",
"content": "To bridge the semantic gap between low-level visual features and high-level semantic concepts, this paper puts forward a novel semi-supervised learning framework of combining visual and keyword features. We assume all of images in the database have been annotated. In this framework, the visual space graph (VSG) and keyword space graph (KSG) are first constructed by means of manifold-based algorithm to explore the submanifold of Visual space and keyword space. Then, semantic space graph (SSG) is constructed by using neighbor information of KSG and VSF. Afterwards, the VSG and KSG are modified and unified by propagating semantic matrix. Finally, we propose a flexible ranking formula and introduce different relevance feedback methods of different query styles. Experimental results on COREL 5000 images show our method improves image retrieval performance from all aspects.",
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"fno": "3571c554",
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"Image Retrieval",
"Manifold Learning",
"Semi Supervised",
"Visual Space Graph VSG",
"Keyword Space Graph KSG",
"Semantic Space Graph SSG"
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"fullName": "Jing Li",
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"fullName": "Fuqiang Liu",
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"fullName": "Zhipeng Li",
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"fullName": "Jianzhu Cui",
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"abstract": "This paper presents a methodology for designing bipartite tables for accurate function approximation. Bipartite tables use two parallel table lookups to obtain a carry-save (borrow-save) function approximation. A carry propagate adder can then convert this approximation to a two's complement number or the approximation can be directly Booth encoded. Our method for designing bipartite tables, called the Symmetric Bipartite Table Method, utilizes symmetry in the table entries to reduce the overall memory requirements. It has several advantages over previous bipartite table methods in that it (1) provides a closed form solution for the table entries, (2) has tight bounds on the maximum absolute error, (3) requires smaller table lookups to achieve a given accuracy, and (4) can be applied to a wide range of functions. Compared to conventional table lookups, the symmetric bipartite tables presented in this paper are 15.0 to 41.7 times smaller when the operand size is 16 bits and 99.1 to 273.9 times smaller when the operand size is 24 bits.",
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"content": "In Named Data Networking (NDN), forwarding lookup is based on tokenized variable-length names instead of fixed-length host addresses, and therefore it requires a new approach for designing a fast packet forwarding lookup engine. In this paper, we propose a design of an NDN software forwarding lookup engine based on hash tables and evaluate its performance with different design options. With a good hash function and table design combined with Bloom filters and data prefetching, we demonstrate that our design reaches about 1.5MPPS with a single thread on an Intel 2.0GHz Xeon processor.",
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"abstract": "Abstract: Construction site management mainly involves rational layout of site facilities, optimum usage of space and dynamic management of plants. It directly influences security, machine running, material supply, power distribution as well as construction progress and cost. Applying computers to make visual site layout has become a hot spot attracting many researchers. This paper presents an interactive 4D Construction Site Management System (CSMS). In CSMS, a 4D site management model (4DSMM) has been generated, which links a 3D model of construction project with a project activity schedule, each activity suitably annotated with its resource requirements including material, plant and work space. The system provides a 4D visualization capability for construction facilities layout, the use of site space,, and intelligent decisions based on a knowledge base. The application of 4D intelligent site management is seen to have considerable potential.",
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"content": "In the last decade, virtual reality (VR) systems have been used to enhance the visualization of CAD projects. The immersive VR techniques allow to the designer interacting and modeling in a more intuitive and efficient way. Current 4D and animated simulation CAD tools are a new challenge for immersive visualization. In this paper we propose a general purpose cave-like system that enables interactive visualization of 4D and animated CAD models. In an automated way, the system is able to treat static and dynamic 3D environments, allowing to share the experience of navigation in the scene among the users, even geographically distributed. The system proposed is validated through a case-study using dynamic 3D models created on digital manufacturing softwares of Shipbuilding and Offshore Industries.",
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"abstract": "Isolating and analyzing a single cell remains a challenging task in both biological and medical researches since the analysis at the single-cell level offers great potential in gaining a unique insight in cellular activity. The aim of this work is to develop a lab-on-chip device that consists in a fully integrated droplet-based microfluidics platform devoted to real-time and single-cell analysis and capable of performing a sequence of several steps such as single cell encapsulation, droplet characterization, droplet sorting, droplet trapping and the analysis of droplets of interest. Only the results of the first part of this project is presented in this paper, in which we present a simple and fast standard microelectronic technique to fabricate the microfluidic platform. In addition, we show the possibility to use the electrochemical impedance technique as an alternative to those based on optical methods to detect and characterize droplets flowing in a microfluidic channel.",
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"title": "2022 IEEE 10th International Conference on Healthcare Informatics (ICHI)",
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"title": "Efficient Matching of Single Cells and Cell Types",
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"abstract": "Next-generation RNA sequencing enables investigation of cellular diversity within tissues at the single-cell level, allowing unprecedented precision in discovering new cellular functions or monitoring disease progression. There is a need for interpretable, scalable, and interactive methods to explain cell types in a single-cell RNA -seq dataset. Here, we design a rank statistic-based annotation method, which simultaneously computes a visual projection and assigns cell types unbiasedly and independent of dataset size. We implement an interactive exploration tool, scAnnotator, as a part of the Orange data mining suite. We elucidate and benchmark annotation of diverse cells in human pancreas tissues blood, achieving up to 84.5 % accuracy compared to manually annotated cells. Easy-to-use data exploration with scAnnotator within Orange has the potential to aid discovery and diagnostics, as well as bring scRNA-seq data closer to decision-makers.",
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"content": "Next-generation RNA sequencing enables investigation of cellular diversity within tissues at the single-cell level, allowing unprecedented precision in discovering new cellular functions or monitoring disease progression. There is a need for interpretable, scalable, and interactive methods to explain cell types in a single-cell RNA -seq dataset. Here, we design a rank statistic-based annotation method, which simultaneously computes a visual projection and assigns cell types unbiasedly and independent of dataset size. We implement an interactive exploration tool, scAnnotator, as a part of the Orange data mining suite. We elucidate and benchmark annotation of diverse cells in human pancreas tissues blood, achieving up to 84.5 % accuracy compared to manually annotated cells. Easy-to-use data exploration with scAnnotator within Orange has the potential to aid discovery and diagnostics, as well as bring scRNA-seq data closer to decision-makers.",
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"affiliation": "University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia",
"fullName": "Primož Godec",
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"affiliation": "University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia",
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"affiliation": "University of Ljubljana,Faculty of Computer and Information Science,Ljubljana,Slovenia",
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"affiliation": "Broad Institute of MIT and Harvard,Cambridge,MA,USA",
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"title": "Machine and human interpretable patient visualizations",
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"abstract": "A growing amount of data is stored in electronic health records, which are crucial for the clinical decision-making process. A large part of these data has a tabular form, consisting of numerical and categorical values originating from various laboratory examinations and sensors. Unlike medical images and clinical notes, tabular data lack higher semantics and, combined with the high dimensionality and heterogeneity, their interpretation by a human is challenging. On the other hand, we have witnessed superior performance of deep convolutional neural network (DCNN) models in the visual medical domain. In this paper, we propose visual representations of complex tabular medical data readable simultaneously by humans and machines. To show that these representations can encode the patient’s data semantics effectively, we use them to fine-tune a DCNN to predict the disability level of patients suffering from multiple sclerosis. Our experiments show that the visual models could match the performance of non-visual models. Moreover, the visual representations add the benefit o f s ummarizing complex information about the patient’s state to a human.",
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"content": "A growing amount of data is stored in electronic health records, which are crucial for the clinical decision-making process. A large part of these data has a tabular form, consisting of numerical and categorical values originating from various laboratory examinations and sensors. Unlike medical images and clinical notes, tabular data lack higher semantics and, combined with the high dimensionality and heterogeneity, their interpretation by a human is challenging. On the other hand, we have witnessed superior performance of deep convolutional neural network (DCNN) models in the visual medical domain. In this paper, we propose visual representations of complex tabular medical data readable simultaneously by humans and machines. To show that these representations can encode the patient’s data semantics effectively, we use them to fine-tune a DCNN to predict the disability level of patients suffering from multiple sclerosis. Our experiments show that the visual models could match the performance of non-visual models. Moreover, the visual representations add the benefit o f s ummarizing complex information about the patient’s state to a human.",
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"affiliation": "Charles University and General University Hospital,MS center, First Faculty of Medicine,Prague,Czechia",
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"abstract": "Augmented Reality (AR) has shown significant potential in supporting in-situ decision-making in various application areas. Many prior works have shown how AR can visualize the decision support data in various contexts. However, prior research about AR-based decision support systems rarely explored how the explanations were visualized. Providing context-aware explanations within AR-based recommendation systems may help users instantly understand the recommendations they have been given. Therefore, this paper presents the world-first user study exploring AR explanation visualization designs. Three feature importance analysis visualizations that apply different color-coding and size-scaling strategies were designed to explain the recommendations provided by a context-aware AR shopping assistant system. Twenty-four participants were recruited to evaluate these three explanations in a shopping scenario. The results revealed novel findings that could help guide the appropriate utilization of descriptive parameters when designing AR explanation artifacts. The results also show the potential of providing intuitive visualization to explain recommendations in AR.",
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"abstract": "Considers the problem of spotlight-mode synthetic aperture radar (SAR) imaging for an arbitrary radar path. A general imaging scenario involves a 3-D scattering surface with data collected along an arbitrary 3-D radar path. This approach is useful, for example, in military applications where the radar platform may undergo some maneuvers, and in radar astronomy where the relative motion is, at least in part, determined by the natural paths of celestial bodies. The authors show that nonideal platform motion can create phase variations in the data which lead to spatially-varying shifts and blurring. A correction procedure is proposed and demonstrated.",
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"content": "Considers the problem of spotlight-mode synthetic aperture radar (SAR) imaging for an arbitrary radar path. A general imaging scenario involves a 3-D scattering surface with data collected along an arbitrary 3-D radar path. This approach is useful, for example, in military applications where the radar platform may undergo some maneuvers, and in radar astronomy where the relative motion is, at least in part, determined by the natural paths of celestial bodies. The authors show that nonideal platform motion can create phase variations in the data which lead to spatially-varying shifts and blurring. A correction procedure is proposed and demonstrated.",
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"title": "Study on Terrain Heterogeneity and Its Influence in "Sanjiangyuan" Area",
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"abstract": "Based on the DEM grid image of \"sanjiangyuan\" region and the temperature data of 16 meteorological stations from 1951 to 2010, this paper calculated and processed the topographic fluctuation, elevation standard deviation and surface roughness indexes representing topographic heterogeneity, and analyzed the impact of topographic heterogeneity on local climate and other aspects. The results show that the overall terrain elevation of \"sanjiangyuan\" region is high, the surface fluctuation is large, and the south and southeast of the region are the places with the most drastic topographic changes, and the absolute value of topographic fluctuation, elevation standard deviation and surface roughness are all large. In the central and western regions, the terrain changes little, the relief degree is small, the elevation standard deviation and the surface roughness are also small. In the whole study area, the climate in the area with similar latitude was significantly affected by terrain heterogeneity. The climate in the area with large terrain heterogeneity was correspondingly characterized by warmer and wetter, more precipitation and higher temperature.",
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"content": "Based on the DEM grid image of \"sanjiangyuan\" region and the temperature data of 16 meteorological stations from 1951 to 2010, this paper calculated and processed the topographic fluctuation, elevation standard deviation and surface roughness indexes representing topographic heterogeneity, and analyzed the impact of topographic heterogeneity on local climate and other aspects. The results show that the overall terrain elevation of \"sanjiangyuan\" region is high, the surface fluctuation is large, and the south and southeast of the region are the places with the most drastic topographic changes, and the absolute value of topographic fluctuation, elevation standard deviation and surface roughness are all large. In the central and western regions, the terrain changes little, the relief degree is small, the elevation standard deviation and the surface roughness are also small. In the whole study area, the climate in the area with similar latitude was significantly affected by terrain heterogeneity. The climate in the area with large terrain heterogeneity was correspondingly characterized by warmer and wetter, more precipitation and higher temperature.",
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"Climatology",
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"abstract": "We report on using computed tomography (CT) as a model acquisition tool for complex objects in computer graphics. Unlike other modeling and scanning techniques the complexity of the object is irrelevant in CT, which naturally enables to model objects with, for example, concavities, holes, twists or fine surface details. Once the data is scanned, one can apply post-processing techniques for data enhancement, modification or presentation. For demonstration purposes we chose to scan a Christmas tree which exhibits high complexity which is difficult or even impossible to handle with other techniques. However, care has to be taken to achieve good scanning results with CT. Further, we illustrate post-processing by means of data segmentation and photorealistic as well as non-photorealistic surface and volume rendering techniques.",
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"abstract": "Scan-line optimization via cost accumulation has become very popular for stereo estimation in computer vision applications and is often combined with a semi-global cost integration strategy, known as SGM. This paper introduces this combination as a general and effective optimization technique. It is the first time that this concept is applied to 3D medical image registration. The presented algorithm, SGM-3D, employs a coarse-to-fine strategy and reduces the search space dimension for consecutive pyramid levels by a fixed linear rate. This allows it to handle large displacements to an extent that is required for clinical applications in high dimensional data. SGM-3D is evaluated in context of pulmonary motion analysis on the recently extended DIR-lab benchmark that provides ten 4D computed tomography (CT) image data sets, as well as ten challenging 3D CT scan pairs from the COPDgene study archive. Results show that both registration errors as well as run-time performance are very competitive with current state-of-the-art methods.",
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"abstract": "Automatic detection of anatomical landmarks has wide range of application in medical image analysis. In this short paper, we present a two-stage method to detect 181 landmarks simultaneously. In the first stage, each landmark is independently searched by a dedicated detector which outputs a list of candidate positions for the target landmark. Each detector is composed of an appearance-based initial detector and a classifier ensemble to estimate the probabilities of detected candidates and to eliminate false positives. Here, the appearance shape used in each detector is optimized by a cross-validation-based variable selection algorithm in advance. Then, in the following second stage, a single combination of all landmark positions is determined from all the candidate lists. The determination is performed by maximum a posteriori (MAP) estimation in which the posterior probability is calculated from both the likelihoods of detected candidates (estimated by the classifier ensemble) and a statistical spatial distribution model of the all landmarks. This MAP estimation process can also determine whether each landmark is within the given CT volume or out of the imaging range. The proposed system was trained for 181 landmarks with 60 human torso CT datasets and evaluated with another 60 datasets. The datasets include both plain CT and contrast enhanced CT volumes with various imaging ranges. In the result, 69.0% and 87.9% of the landmarks were successfully detected within 1 and 2 cm from the ground truth point, respectively. The average detection error was 9.58 mm. From these results, applicability of the proposed system to various CT datasets was verified.",
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"abstract": "In the current medical system, medical image segmentation technology has high clinical application value. Whether it is surgical resection or quantitative analysis of lesions, it is necessary to accurately understand important information such as the size, three-dimensional structure and specific location of organs and tumors, medical image segmentation is an important means. In this paper, a method for object segmentation based on MRI and CT data is proposed, and research is conducted around 3D modeling of objects in bladder MRI images and kidney CT images. In this paper, a data growth strategy combining Gaussian noise, gamma transformation and spatial geometric transformation is adopted to avoid the phenomenon of excessive adaptation due to the small dataset. Experiments verify the effectiveness of the method proposed in this paper for MRI and CT data. Segmenting the bladder wall and bladder volume into two-dimensional MRI images achieves high accuracy and small degree of difference. Therefore, research on automatic segmentation algorithms for bladder tumor images and kidney tumor images is a realistic demand for the treatment of this disease. At present, the combination of preoperative CT image information and intraoperative video images is an important solution for realizing surgical guidance. The final results of the study show that the MRI-CT segmentation results have the highest accuracy and are closer to manual annotation. Among them, the detection accuracy of the MRT-CT model on the bladder wall and bladder tumor is 94.31% and 92.70%, respectively. The MRT proposed in this paper. -CT method can achieve higher segmentation accuracy.",
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"content": "In the current medical system, medical image segmentation technology has high clinical application value. Whether it is surgical resection or quantitative analysis of lesions, it is necessary to accurately understand important information such as the size, three-dimensional structure and specific location of organs and tumors, medical image segmentation is an important means. In this paper, a method for object segmentation based on MRI and CT data is proposed, and research is conducted around 3D modeling of objects in bladder MRI images and kidney CT images. In this paper, a data growth strategy combining Gaussian noise, gamma transformation and spatial geometric transformation is adopted to avoid the phenomenon of excessive adaptation due to the small dataset. Experiments verify the effectiveness of the method proposed in this paper for MRI and CT data. Segmenting the bladder wall and bladder volume into two-dimensional MRI images achieves high accuracy and small degree of difference. Therefore, research on automatic segmentation algorithms for bladder tumor images and kidney tumor images is a realistic demand for the treatment of this disease. At present, the combination of preoperative CT image information and intraoperative video images is an important solution for realizing surgical guidance. The final results of the study show that the MRI-CT segmentation results have the highest accuracy and are closer to manual annotation. Among them, the detection accuracy of the MRT-CT model on the bladder wall and bladder tumor is 94.31% and 92.70%, respectively. The MRT proposed in this paper. -CT method can achieve higher segmentation accuracy.",
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"normalizedAbstract": "In the current medical system, medical image segmentation technology has high clinical application value. Whether it is surgical resection or quantitative analysis of lesions, it is necessary to accurately understand important information such as the size, three-dimensional structure and specific location of organs and tumors, medical image segmentation is an important means. In this paper, a method for object segmentation based on MRI and CT data is proposed, and research is conducted around 3D modeling of objects in bladder MRI images and kidney CT images. In this paper, a data growth strategy combining Gaussian noise, gamma transformation and spatial geometric transformation is adopted to avoid the phenomenon of excessive adaptation due to the small dataset. Experiments verify the effectiveness of the method proposed in this paper for MRI and CT data. Segmenting the bladder wall and bladder volume into two-dimensional MRI images achieves high accuracy and small degree of difference. Therefore, research on automatic segmentation algorithms for bladder tumor images and kidney tumor images is a realistic demand for the treatment of this disease. At present, the combination of preoperative CT image information and intraoperative video images is an important solution for realizing surgical guidance. The final results of the study show that the MRI-CT segmentation results have the highest accuracy and are closer to manual annotation. Among them, the detection accuracy of the MRT-CT model on the bladder wall and bladder tumor is 94.31% and 92.70%, respectively. The MRT proposed in this paper. -CT method can achieve higher segmentation accuracy.",
"fno": "341000a001",
"keywords": [
"Biological Organs",
"Biomedical MRI",
"Computerised Tomography",
"Diseases",
"Gaussian Noise",
"Image Segmentation",
"Kidney",
"Medical Image Processing",
"Surgery",
"Tumours",
"3 D Modeling",
"Automatic Segmentation Algorithms",
"Bladder MRI Images",
"Bladder Tumor Images",
"Bladder Volume",
"Bladder Wall",
"CT Data",
"CT Method",
"Current Medical System",
"Data Growth Strategy",
"High Clinical Application Value",
"Important Solution",
"Intraoperative Video Images",
"Kidney CT Images",
"Kidney Tumor Images",
"Medical Image Segmentation Technology",
"MRI CT Segmentation Results",
"MRT CT Model",
"Object Segmentation",
"Organs",
"Preoperative CT Image Information",
"Spatial Geometric Transformation",
"Surgical Resection",
"Three Dimensional Structure",
"Tumors",
"Two Dimensional MRI Images",
"Urinary System",
"Image Segmentation",
"Solid Modeling",
"Three Dimensional Displays",
"Magnetic Resonance Imaging",
"Computed Tomography",
"Surgery",
"Manuals",
"Mri And Ct Data",
"Urinary System",
"Data Enhancement",
"3 D Modeling"
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{
"affiliation": "The First Affiliated, Hospital of Hebei North University,Zhangjiakou,China,075000",
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"abstract": "For the traditional watershed algorithm has the problem of over-segmentation and under-segmentation, an improved watershed algorithm is used to segment brain CT images in this paper. By the segmentation, the segmentation results of the improved watershed algorithm show that noise and over-segmentation can be effectively suppressed, and the hemorrhagic areas on brain CT images can also be marked out.",
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{
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"Hemorrhagic Areas",
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"Image Reconstruction",
"Clustering Algorithms",
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"Filtering",
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{
"affiliation": "Hainan Normal University",
"fullName": "Jiang Wenjuan",
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"affiliation": "Hainan Normal University",
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{
"affiliation": "Hainan Normal University",
"fullName": "Li Fuyun",
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"title": "2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR)",
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"title": "A Coarse to Fine Framework for Multi-organ Segmentation in Head and Neck Images",
"normalizedTitle": "A Coarse to Fine Framework for Multi-organ Segmentation in Head and Neck Images",
"abstract": "Radiotherapy is widely used in the treatment of head and neck cancer. Due to the harmfulness of radiation, it is necessary to protect our healthy organs during the radiotherapy. Therefore, the accurate delineation of diseased region and surrounding healthy organs is the precondition for doctors to make the radiation plan. In real life, the delineation work is usually done manually. It is time-consuming and requires high professional skill. A fast and accurate organ segmentation method can greatly improve the efficiency of treatment. Most CT image datasets are 3D volumes and each volume can be divided into a series of 2D slice images. For multi-organ segmentation task, how to generate the stable organ features from CT images is still the plagued problem. For 2D framework, which processes the images slice by slice, the network cannot learn the correlation between continuous slices. It will lead to the loss of spatial information. For 3D framework, which processes the images volume by volume, the patch training is commonly used to against the massive increase of network parameters. The 3D patch will limit the maximum reception field of the network. For the organ, which is larger than the patch size, it is easy to lose global information. To solve these incompatible problems, we proposed a coarse to fine framework to take advantage of both 2D framework and 3D framework. The multi-view coarse network is designed to generate the organ probability maps and the coarse segmentation mask in 2D case. The organ volumes are extracted with the probability maps. These organ volumes are sent to the organ-based fine network to refine the mask of each organ in 3D case. Our proposed method is tested on the Head and Neck Automatic Segmentation Challenge datasets in 2015 and predict for 9 different organs. The result show that our framework performs the lowest error range for most organs and three of them achieve the top evaluation results in comparison with existing methods. Contribution-The main contribution of this paper is to propose a novel two-stage, Coarse to Fine, framework for multi-organ segmentation and verify its effectiveness in head and neck CT images.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Radiotherapy is widely used in the treatment of head and neck cancer. Due to the harmfulness of radiation, it is necessary to protect our healthy organs during the radiotherapy. Therefore, the accurate delineation of diseased region and surrounding healthy organs is the precondition for doctors to make the radiation plan. In real life, the delineation work is usually done manually. It is time-consuming and requires high professional skill. A fast and accurate organ segmentation method can greatly improve the efficiency of treatment. Most CT image datasets are 3D volumes and each volume can be divided into a series of 2D slice images. For multi-organ segmentation task, how to generate the stable organ features from CT images is still the plagued problem. For 2D framework, which processes the images slice by slice, the network cannot learn the correlation between continuous slices. It will lead to the loss of spatial information. For 3D framework, which processes the images volume by volume, the patch training is commonly used to against the massive increase of network parameters. The 3D patch will limit the maximum reception field of the network. For the organ, which is larger than the patch size, it is easy to lose global information. To solve these incompatible problems, we proposed a coarse to fine framework to take advantage of both 2D framework and 3D framework. The multi-view coarse network is designed to generate the organ probability maps and the coarse segmentation mask in 2D case. The organ volumes are extracted with the probability maps. These organ volumes are sent to the organ-based fine network to refine the mask of each organ in 3D case. Our proposed method is tested on the Head and Neck Automatic Segmentation Challenge datasets in 2015 and predict for 9 different organs. The result show that our framework performs the lowest error range for most organs and three of them achieve the top evaluation results in comparison with existing methods. Contribution-The main contribution of this paper is to propose a novel two-stage, Coarse to Fine, framework for multi-organ segmentation and verify its effectiveness in head and neck CT images.",
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"normalizedAbstract": "Radiotherapy is widely used in the treatment of head and neck cancer. Due to the harmfulness of radiation, it is necessary to protect our healthy organs during the radiotherapy. Therefore, the accurate delineation of diseased region and surrounding healthy organs is the precondition for doctors to make the radiation plan. In real life, the delineation work is usually done manually. It is time-consuming and requires high professional skill. A fast and accurate organ segmentation method can greatly improve the efficiency of treatment. Most CT image datasets are 3D volumes and each volume can be divided into a series of 2D slice images. For multi-organ segmentation task, how to generate the stable organ features from CT images is still the plagued problem. For 2D framework, which processes the images slice by slice, the network cannot learn the correlation between continuous slices. It will lead to the loss of spatial information. For 3D framework, which processes the images volume by volume, the patch training is commonly used to against the massive increase of network parameters. The 3D patch will limit the maximum reception field of the network. For the organ, which is larger than the patch size, it is easy to lose global information. To solve these incompatible problems, we proposed a coarse to fine framework to take advantage of both 2D framework and 3D framework. The multi-view coarse network is designed to generate the organ probability maps and the coarse segmentation mask in 2D case. The organ volumes are extracted with the probability maps. These organ volumes are sent to the organ-based fine network to refine the mask of each organ in 3D case. Our proposed method is tested on the Head and Neck Automatic Segmentation Challenge datasets in 2015 and predict for 9 different organs. The result show that our framework performs the lowest error range for most organs and three of them achieve the top evaluation results in comparison with existing methods. Contribution-The main contribution of this paper is to propose a novel two-stage, Coarse to Fine, framework for multi-organ segmentation and verify its effectiveness in head and neck CT images.",
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"Probability",
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"Radiotherapy",
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"Fast Organ Segmentation Method",
"Accurate Organ Segmentation Method",
"CT Image Datasets",
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"authors": [
{
"affiliation": "Graduate School of Information, Production and System, Waseda University,Kitakyushu,Japan",
"fullName": "Yan Pu",
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{
"affiliation": "Graduate School of Information, Production and System, Waseda University,Kitakyushu,Japan",
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{
"affiliation": "Graduate School of Information, Production and System, Waseda University,Kitakyushu,Japan",
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"abstract": "In this work we introduce a two-terminal memristive device using Mn doped HfO2. The devices can emulate synaptic behavior based on their transient characteristics. These properties can be exploited to show spike-timing based learning in a network of neurons and synapses. We use the device characteristics to simulate a 4×4 crossbar array of synapses and observe the evolution of the weights over time. The effect of device variability on the performance of synaptic weight update has been examined based on different test conditions of initial randomness and variation in percentage change of strength during spike-timing based updates. Some inferences have been drawn regarding the need of additional circuits for improving reliability of the cross-bar arrays. We believe this study is critical in assessing the design constraints and requirements necessary for integrating memristive devices in crossbars for spike based computations.",
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"content": "In this work we introduce a two-terminal memristive device using Mn doped HfO2. The devices can emulate synaptic behavior based on their transient characteristics. These properties can be exploited to show spike-timing based learning in a network of neurons and synapses. We use the device characteristics to simulate a 4×4 crossbar array of synapses and observe the evolution of the weights over time. The effect of device variability on the performance of synaptic weight update has been examined based on different test conditions of initial randomness and variation in percentage change of strength during spike-timing based updates. Some inferences have been drawn regarding the need of additional circuits for improving reliability of the cross-bar arrays. We believe this study is critical in assessing the design constraints and requirements necessary for integrating memristive devices in crossbars for spike based computations.",
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"abstract": "The advances in microscopic imaging technology have created great amount of unprecedentedly clear images of neurons. How to extract useful information from this massive data has created another challenge for neuroscientists. The connectivity relationships among these neurons provide critical clues for understanding how the information is transmitted in the brain. In this paper, we analyze the neurons in the Drosophila brain, find the possible connecting path, and furthermore, perform cluster analysis from the connectivity information of thousands of tangled neurons.",
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"affiliation": "National Center for High-performance Computing, Hsinchu, Taiwan",
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"abstract": "To understand and mimic the working mechanism of the brain, neuroscientists rely on brain simulations that operate in a time-driven manner. The simulation involves evaluating how the neurons change their states over time and transferring spikes to the connected neurons through synapses. It also simulates learning by evaluating how the synapses change their weights according to the spiking activity of the neurons. To explore various behaviors of the brain and thus make great advances, neuroscientists need a methodology to support large-scale simulations in both an accurate and efficient manner. For accurate simulations, existing simulators adopt a time-precise simulation methodology where the simulator computes all the neuronal and the synaptic state changes in time order. Unfortunately, they suffer from significant underutilization and energy inefficiency as the simulator scales.In this paper, we present NeuroSync, a fast, energy-efficient, and scalable hardware-based accelerator for accurate brain simulations. The key idea is to adopt a speculative simulation methodology at a minimum overhead along with architectural support. NeuroSync achieves high efficiency using an optimal dataflow for the speculative simulations. At the same time, it ensures simulation accuracy by carefully designing a rollback and recovery mechanism to handle mis-speculations. To implement the methodology at a low cost, NeuroSync further proposes a speculation-optimal learning simulation method. Our evaluations show that 64-chip NeuroSync achieves 3.37× speedup and 3.81× higher energy efficiency with only 10.96% area overhead. The evaluations also show that NeuroSync is extremely scalable with higher speedup as the system scales.",
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"content": "To understand and mimic the working mechanism of the brain, neuroscientists rely on brain simulations that operate in a time-driven manner. The simulation involves evaluating how the neurons change their states over time and transferring spikes to the connected neurons through synapses. It also simulates learning by evaluating how the synapses change their weights according to the spiking activity of the neurons. To explore various behaviors of the brain and thus make great advances, neuroscientists need a methodology to support large-scale simulations in both an accurate and efficient manner. For accurate simulations, existing simulators adopt a time-precise simulation methodology where the simulator computes all the neuronal and the synaptic state changes in time order. Unfortunately, they suffer from significant underutilization and energy inefficiency as the simulator scales.In this paper, we present NeuroSync, a fast, energy-efficient, and scalable hardware-based accelerator for accurate brain simulations. The key idea is to adopt a speculative simulation methodology at a minimum overhead along with architectural support. NeuroSync achieves high efficiency using an optimal dataflow for the speculative simulations. At the same time, it ensures simulation accuracy by carefully designing a rollback and recovery mechanism to handle mis-speculations. To implement the methodology at a low cost, NeuroSync further proposes a speculation-optimal learning simulation method. Our evaluations show that 64-chip NeuroSync achieves 3.37× speedup and 3.81× higher energy efficiency with only 10.96% area overhead. The evaluations also show that NeuroSync is extremely scalable with higher speedup as the system scales.",
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"Time Order",
"Significant Underutilization",
"Energy Inefficiency",
"Simulator Scales",
"Scalable Hardware Based Accelerator",
"Accurate Brain Simulations",
"Costs",
"Computational Modeling",
"Neurons",
"Computer Architecture",
"Brain Modeling",
"Energy Efficiency",
"Behavioral Sciences",
"Brain Simulation",
"Domain Specific Accelerator"
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"affiliation": "Seoul National University,Department of Electrical and Computer Engineering",
"fullName": "Hunjun Lee",
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"affiliation": "Seoul National University,Department of Electrical and Computer Engineering",
"fullName": "Chanmyeong Kim",
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"surname": "Kim",
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{
"affiliation": "Seoul National University,Department of Electrical and Computer Engineering",
"fullName": "Minseop Kim",
"givenName": "Minseop",
"surname": "Kim",
"__typename": "ArticleAuthorType"
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{
"affiliation": "Seoul National University,Department of Electrical and Computer Engineering",
"fullName": "Yujin Chung",
"givenName": "Yujin",
"surname": "Chung",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Seoul National University,Department of Electrical and Computer Engineering",
"fullName": "Jangwoo Kim",
"givenName": "Jangwoo",
"surname": "Kim",
"__typename": "ArticleAuthorType"
}
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"abstract": "Physical models for biological molecules have been used since the 50s to represent their structures at a macroscopic scale. Nowadays, advanced modular non-rigid models (such as the Peppytide tangible interface) successfully approximate the bonding rotational energies and enable realistic folding of small proteins. Such tangible interfaces are also an interesting way to interact with 3D virtual objects but methods to make the 3D physical interfaces converse with virtual models of molecules are still lacking. We present a markerless tracking method to recover the 3D molecular structure of the Peppytide tangible interface from a simple smartphone video, allowing to reconstruct 3D secondary and tertiary structure. This method is based on an up-to-date blob detection to extract reliable 2D positions of atoms from the video and apply a Structure-from-Motion recovery method to get an approximation of the molecule 3D structure. We then estimate the peptide energy after several minimization steps, using classical simulation tools such as GROMACS coupled to a coarse-grained forcefield. The visualisation is done with UnityMol, a molecular editor with advanced rendering features based on the Unity professional game engine, making it easy to add information like the energy of peptide folding.",
"abstracts": [
{
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"content": "Physical models for biological molecules have been used since the 50s to represent their structures at a macroscopic scale. Nowadays, advanced modular non-rigid models (such as the Peppytide tangible interface) successfully approximate the bonding rotational energies and enable realistic folding of small proteins. Such tangible interfaces are also an interesting way to interact with 3D virtual objects but methods to make the 3D physical interfaces converse with virtual models of molecules are still lacking. We present a markerless tracking method to recover the 3D molecular structure of the Peppytide tangible interface from a simple smartphone video, allowing to reconstruct 3D secondary and tertiary structure. This method is based on an up-to-date blob detection to extract reliable 2D positions of atoms from the video and apply a Structure-from-Motion recovery method to get an approximation of the molecule 3D structure. We then estimate the peptide energy after several minimization steps, using classical simulation tools such as GROMACS coupled to a coarse-grained forcefield. The visualisation is done with UnityMol, a molecular editor with advanced rendering features based on the Unity professional game engine, making it easy to add information like the energy of peptide folding.",
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"normalizedAbstract": "Physical models for biological molecules have been used since the 50s to represent their structures at a macroscopic scale. Nowadays, advanced modular non-rigid models (such as the Peppytide tangible interface) successfully approximate the bonding rotational energies and enable realistic folding of small proteins. Such tangible interfaces are also an interesting way to interact with 3D virtual objects but methods to make the 3D physical interfaces converse with virtual models of molecules are still lacking. We present a markerless tracking method to recover the 3D molecular structure of the Peppytide tangible interface from a simple smartphone video, allowing to reconstruct 3D secondary and tertiary structure. This method is based on an up-to-date blob detection to extract reliable 2D positions of atoms from the video and apply a Structure-from-Motion recovery method to get an approximation of the molecule 3D structure. We then estimate the peptide energy after several minimization steps, using classical simulation tools such as GROMACS coupled to a coarse-grained forcefield. The visualisation is done with UnityMol, a molecular editor with advanced rendering features based on the Unity professional game engine, making it easy to add information like the energy of peptide folding.",
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"Proteins",
"Three Dimensional Displays",
"Biological System Modeling",
"Solid Modeling",
"Computational Modeling",
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"affiliation": "VENISE group, CNRS-LIMSI, Orsay (France)",
"fullName": "Xavier Martinez",
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"affiliation": "VENISE group, CNRS-LIMSI, Orsay (France)",
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"affiliation": "VENISE group, CNRS-LIMSI, Orsay (France)",
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"title": "2014 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)",
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"title": "A CUDA Implementation of the Spatial TAU-Leaping in Crowded Compartments (STAUCC) Simulator",
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"abstract": "The increasing awareness of the pivotal role of noise in biochemical systems has given rise to a strong need for suitable stochastic algorithms for the description and the simulation of biological phenomena. However, the high computational demand that characterizes stochastic simulation approaches coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviors makes the application of such kind of algorithms often unfeasible. So far, different parallelization approaches have been employed to reduce the computational time required for the analysis of biochemical systems modeled using stochastic algorithms. Most of the proposed solutions use an embarrassingly parallel approach to run in parallel several simulations using the cores of a workstation and/or the nodes of a cluster. In this work we present the Spatial TAU-leaping in Crowded Compartments (STAUCC) simulator, a software that relies on an efficient CUDA implementation of the Stau-DPP algorithm, a voxel-based method for the stochastic simulation of Reaction-Diffusion processes. We evaluate its application and performance for the modeling of diffusion processes simultaneously occurring within a space represented considering different levels of granularity.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The increasing awareness of the pivotal role of noise in biochemical systems has given rise to a strong need for suitable stochastic algorithms for the description and the simulation of biological phenomena. However, the high computational demand that characterizes stochastic simulation approaches coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviors makes the application of such kind of algorithms often unfeasible. So far, different parallelization approaches have been employed to reduce the computational time required for the analysis of biochemical systems modeled using stochastic algorithms. Most of the proposed solutions use an embarrassingly parallel approach to run in parallel several simulations using the cores of a workstation and/or the nodes of a cluster. In this work we present the Spatial TAU-leaping in Crowded Compartments (STAUCC) simulator, a software that relies on an efficient CUDA implementation of the Stau-DPP algorithm, a voxel-based method for the stochastic simulation of Reaction-Diffusion processes. We evaluate its application and performance for the modeling of diffusion processes simultaneously occurring within a space represented considering different levels of granularity.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "The increasing awareness of the pivotal role of noise in biochemical systems has given rise to a strong need for suitable stochastic algorithms for the description and the simulation of biological phenomena. However, the high computational demand that characterizes stochastic simulation approaches coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviors makes the application of such kind of algorithms often unfeasible. So far, different parallelization approaches have been employed to reduce the computational time required for the analysis of biochemical systems modeled using stochastic algorithms. Most of the proposed solutions use an embarrassingly parallel approach to run in parallel several simulations using the cores of a workstation and/or the nodes of a cluster. In this work we present the Spatial TAU-leaping in Crowded Compartments (STAUCC) simulator, a software that relies on an efficient CUDA implementation of the Stau-DPP algorithm, a voxel-based method for the stochastic simulation of Reaction-Diffusion processes. We evaluate its application and performance for the modeling of diffusion processes simultaneously occurring within a space represented considering different levels of granularity.",
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"affiliation": "Inst. for Appl. Math. & Inf. Technol., Genoa, Italy",
"fullName": "Giulia Pasquale",
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"affiliation": "Dept. of Comput. Sci., Syst. & Commun., Univ. of Milano-Bicocca, Milan, Italy",
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"doi": "10.1109/PDP.2011.81",
"title": "On Designing Multicore-Aware Simulators for Biological Systems",
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"abstract": "The stochastic simulation of biological systems is an increasingly popular technique in bioinformatics. It often is an enlightening technique, which may however result in being computational expensive. We discuss the main opportunities to speed it up on multi-core platforms, which pose new challenges for parallelisation techniques. These opportunities are developed in two general families of solutions involving both the single simulation and a bulk of independent simulations (either replicas of derived from parameter sweep). Proposed solutions are tested on the parallelisation of the CWC simulator (Calculus of Wrapped Compartments) that is carried out according to proposed solutions by way of the Fast Flow programming framework making possible fast development and efficient execution on multi-cores.",
"abstracts": [
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"content": "The stochastic simulation of biological systems is an increasingly popular technique in bioinformatics. It often is an enlightening technique, which may however result in being computational expensive. We discuss the main opportunities to speed it up on multi-core platforms, which pose new challenges for parallelisation techniques. These opportunities are developed in two general families of solutions involving both the single simulation and a bulk of independent simulations (either replicas of derived from parameter sweep). Proposed solutions are tested on the parallelisation of the CWC simulator (Calculus of Wrapped Compartments) that is carried out according to proposed solutions by way of the Fast Flow programming framework making possible fast development and efficient execution on multi-cores.",
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"abstract": "Children diagnosed with cerebral palsy (CP) appear to be at high risk for developing neurobehavioral and motor disorders. The most common disorders for these children are impaired visual-perception skills and motor planning. Besides, they often have impaired executive functions, which can contribute to problematic emotional adjustment such as depression. Additionally, literature suggests that the tendency to develop these cognitive impairments and emotional abnormalities in pediatric CP is influenced by age and IQ. Because there are many other medical co-morbidities that can occur with CP (e.g., seizures and shunt placement), prediction of what percentages of patients will incur cognitive impairment and emotional abnormality is a difficult task. The purpose of this study was to investigate the associations between possible factors mentioned above, and neurobehavioral and motor disorders from a clinical database of pediatric subjects diagnosed with CP. The study resulted in 22 rules that can predict negative outcomes. These rules reinforced the growing body of literature supporting a link between CP, executive dysfunction, and subsequent neurobehavioral problems. The antecedents and consequents of some association rules were single factors, while other statistical associations were interactions of factor combinations. Further research is needed to include children's comprehensive treatment and medication history in order to determine additional impacts on their neurobehavioral and motor disorders.",
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"content": "Children diagnosed with cerebral palsy (CP) appear to be at high risk for developing neurobehavioral and motor disorders. The most common disorders for these children are impaired visual-perception skills and motor planning. Besides, they often have impaired executive functions, which can contribute to problematic emotional adjustment such as depression. Additionally, literature suggests that the tendency to develop these cognitive impairments and emotional abnormalities in pediatric CP is influenced by age and IQ. Because there are many other medical co-morbidities that can occur with CP (e.g., seizures and shunt placement), prediction of what percentages of patients will incur cognitive impairment and emotional abnormality is a difficult task. The purpose of this study was to investigate the associations between possible factors mentioned above, and neurobehavioral and motor disorders from a clinical database of pediatric subjects diagnosed with CP. The study resulted in 22 rules that can predict negative outcomes. These rules reinforced the growing body of literature supporting a link between CP, executive dysfunction, and subsequent neurobehavioral problems. The antecedents and consequents of some association rules were single factors, while other statistical associations were interactions of factor combinations. Further research is needed to include children's comprehensive treatment and medication history in order to determine additional impacts on their neurobehavioral and motor disorders.",
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"normalizedAbstract": "Children diagnosed with cerebral palsy (CP) appear to be at high risk for developing neurobehavioral and motor disorders. The most common disorders for these children are impaired visual-perception skills and motor planning. Besides, they often have impaired executive functions, which can contribute to problematic emotional adjustment such as depression. Additionally, literature suggests that the tendency to develop these cognitive impairments and emotional abnormalities in pediatric CP is influenced by age and IQ. Because there are many other medical co-morbidities that can occur with CP (e.g., seizures and shunt placement), prediction of what percentages of patients will incur cognitive impairment and emotional abnormality is a difficult task. The purpose of this study was to investigate the associations between possible factors mentioned above, and neurobehavioral and motor disorders from a clinical database of pediatric subjects diagnosed with CP. The study resulted in 22 rules that can predict negative outcomes. These rules reinforced the growing body of literature supporting a link between CP, executive dysfunction, and subsequent neurobehavioral problems. The antecedents and consequents of some association rules were single factors, while other statistical associations were interactions of factor combinations. Further research is needed to include children's comprehensive treatment and medication history in order to determine additional impacts on their neurobehavioral and motor disorders.",
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"affiliation": "Dept. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA",
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"title": "2015 12th International Conference on Information Technology - New Generations (ITNG)",
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"abstract": "We propose a new method for automatically detecting early esophageal cancer from an endoscopic image. We decompose the original image into four components, namely, the RGB and luminance components, and apply the discrete wavelet transform (DWT) to these components twice. The fractal dimensions are computed at each small block using the box-counting method, and the abnormal regions are detected based on the fractal dimensions. In addition, to process the endoscopic image quickly, we clip the portion that does not contain the esophageal cancer from the original endoscopic image and resize the remaining image to 1024 x 1024 pixels by mirroring. Finally, we show the abnormal region by using the product of the computed fractal dimensions from the four components. Our method is not intended for providing accurate diagnosis of the detected abnormal regions as esophageal cancer, but is intended to provide additional information to help doctors in their diagnosis. Therefore, our method only needs to detect all of the regions suspected of being cancer even if the detect results are false positives. We describe the procedure used in our method in detail and present experimental results demonstrating that our method is able to detect abnormal regions suspected of being early esophageal cancer using practical endoscopic images.",
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{
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"content": "We propose a new method for automatically detecting early esophageal cancer from an endoscopic image. We decompose the original image into four components, namely, the RGB and luminance components, and apply the discrete wavelet transform (DWT) to these components twice. The fractal dimensions are computed at each small block using the box-counting method, and the abnormal regions are detected based on the fractal dimensions. In addition, to process the endoscopic image quickly, we clip the portion that does not contain the esophageal cancer from the original endoscopic image and resize the remaining image to 1024 x 1024 pixels by mirroring. Finally, we show the abnormal region by using the product of the computed fractal dimensions from the four components. Our method is not intended for providing accurate diagnosis of the detected abnormal regions as esophageal cancer, but is intended to provide additional information to help doctors in their diagnosis. Therefore, our method only needs to detect all of the regions suspected of being cancer even if the detect results are false positives. We describe the procedure used in our method in detail and present experimental results demonstrating that our method is able to detect abnormal regions suspected of being early esophageal cancer using practical endoscopic images.",
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"normalizedAbstract": "We propose a new method for automatically detecting early esophageal cancer from an endoscopic image. We decompose the original image into four components, namely, the RGB and luminance components, and apply the discrete wavelet transform (DWT) to these components twice. The fractal dimensions are computed at each small block using the box-counting method, and the abnormal regions are detected based on the fractal dimensions. In addition, to process the endoscopic image quickly, we clip the portion that does not contain the esophageal cancer from the original endoscopic image and resize the remaining image to 1024 x 1024 pixels by mirroring. Finally, we show the abnormal region by using the product of the computed fractal dimensions from the four components. Our method is not intended for providing accurate diagnosis of the detected abnormal regions as esophageal cancer, but is intended to provide additional information to help doctors in their diagnosis. Therefore, our method only needs to detect all of the regions suspected of being cancer even if the detect results are false positives. We describe the procedure used in our method in detail and present experimental results demonstrating that our method is able to detect abnormal regions suspected of being early esophageal cancer using practical endoscopic images.",
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"fullName": "Jumpei Yamaguchi",
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"proceeding": {
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"title": "2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)",
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"title": "Diagnosis of balance disorders using decision support systems based on data mining techniques",
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"abstract": "In this work we present the decision support of the EMBalance platform. EMBalance is a platform for the management of balance disorders in terms of diagnosis, treatment and evolution. The EMBalance platform aims to extend existing but generic and currently uncoupled balance modelling activities leading to a multi-scale and patient-specific balance model, which will be incorporated in a Decision Support System (DSS), towards the early diagnosis, prediction and the efficient treatment planning of balance disorders. The diagnosis part of the decision support system uses various data ranging from demographic characteristics to clinical examinations, auditory and vestibular tests. Currently we present some initial technical choices and indicative results of the decision support system for diagnosing balance disorders, based on data mining techniques and clinical guidelines.",
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"content": "In this work we present the decision support of the EMBalance platform. EMBalance is a platform for the management of balance disorders in terms of diagnosis, treatment and evolution. The EMBalance platform aims to extend existing but generic and currently uncoupled balance modelling activities leading to a multi-scale and patient-specific balance model, which will be incorporated in a Decision Support System (DSS), towards the early diagnosis, prediction and the efficient treatment planning of balance disorders. The diagnosis part of the decision support system uses various data ranging from demographic characteristics to clinical examinations, auditory and vestibular tests. Currently we present some initial technical choices and indicative results of the decision support system for diagnosing balance disorders, based on data mining techniques and clinical guidelines.",
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"Medical Diagnostic Imaging",
"Diseases",
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"affiliation": "Institute of Communication and Computer Systems, National Technical University of Athens, Greece",
"fullName": "Themis P. Exarchos",
"givenName": "Themis P.",
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"affiliation": "Institute of Communication and Computer Systems, National Technical University of Athens, Greece",
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"affiliation": "Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece",
"fullName": "George Rigas",
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"affiliation": "National and Kapodistrian University of Athens, Athens, Greece",
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"abstract": "Since pathology is supported by information technology new opportunities and questions have arisen. The digital age enables analyzing histopathological data with artificial intelligence methods to reveal further information and correlations. In this paper existing approaches to visualization of medical decision processes are presented as well as the relevance of explainability in decision making. The first step for implementing decision-paths in systems is to retrace an experienced pathologist's diagnosis finding process. Recording a route through a landscape composed of human tissue in terms of a roadbook is one possible approach to collect information on how diagnoses are found. Choosing the roadbook metaphor provides a simple schema, that holds basic directions enriched with metadata regarding landmarks on a rally - in the context of pathology such landmarks provide information on the decision finding process.",
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"abstract": "Assessment of the output power of wind generators is always associated with some uncertainties due to wind speed and other weather parameters alteration, and precise short-term forecasts are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we propose a double stage hierarchical particle swarm optimization based adaptive neuro-fuzzy inference system (double-stage hybrid PSO-ANFIS) for short-term wind power prediction of a microgrid wind farm in Beijing, China. The approach has two hierarchical stages. The first PSO-ANFIS stage employs numerical weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted next day's wind speed by the first stage is applied to the second stage to forecast next day's wind power. The influence of input data dependency on prediction accuracy has been analyzed by dividing the input data into five subsets. The proposed approach has attained significant prediction accuracy improvements. The performance of the proposed model is compared with five other prediction approaches and showed the best accuracy improvement of all.",
"abstracts": [
{
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"content": "Assessment of the output power of wind generators is always associated with some uncertainties due to wind speed and other weather parameters alteration, and precise short-term forecasts are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we propose a double stage hierarchical particle swarm optimization based adaptive neuro-fuzzy inference system (double-stage hybrid PSO-ANFIS) for short-term wind power prediction of a microgrid wind farm in Beijing, China. The approach has two hierarchical stages. The first PSO-ANFIS stage employs numerical weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted next day's wind speed by the first stage is applied to the second stage to forecast next day's wind power. The influence of input data dependency on prediction accuracy has been analyzed by dividing the input data into five subsets. The proposed approach has attained significant prediction accuracy improvements. The performance of the proposed model is compared with five other prediction approaches and showed the best accuracy improvement of all.",
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"normalizedAbstract": "Assessment of the output power of wind generators is always associated with some uncertainties due to wind speed and other weather parameters alteration, and precise short-term forecasts are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we propose a double stage hierarchical particle swarm optimization based adaptive neuro-fuzzy inference system (double-stage hybrid PSO-ANFIS) for short-term wind power prediction of a microgrid wind farm in Beijing, China. The approach has two hierarchical stages. The first PSO-ANFIS stage employs numerical weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted next day's wind speed by the first stage is applied to the second stage to forecast next day's wind power. The influence of input data dependency on prediction accuracy has been analyzed by dividing the input data into five subsets. The proposed approach has attained significant prediction accuracy improvements. The performance of the proposed model is compared with five other prediction approaches and showed the best accuracy improvement of all.",
"fno": "07923980",
"keywords": [
"Distributed Power Generation",
"Fuzzy Reasoning",
"Load Forecasting",
"Power Distribution Control",
"Power Generation Control",
"Power Generation Scheduling",
"Wind Power Plants",
"Short Term Wind Power Forecasting",
"Double Stage Hierarchical Hybrid PSO ANFIS Model",
"Wind Generators",
"Wind Speed",
"Weather Parameters Alteration",
"Transmission System Operators",
"Transmission System Schedulers",
"Distribution System Operators",
"Distribution System Schedulers",
"Power Network Control",
"Power Network Management",
"Particle Swarm Optimization",
"Adaptive Neuro Fuzzy Inference System",
"Microgrid Wind Farm",
"Beijing",
"China",
"Numerical Weather Prediction",
"Meteorological Parameters",
"Turbine Hub Height",
"Wind Power Generation",
"Wind Forecasting",
"Wind Speed",
"Predictive Models",
"Wind Farms",
"Forecasting",
"Fuzzy Logic",
"Neural Network",
"Numerical Weather Prediction",
"Particle Swarm Optimization",
"Wind Power"
],
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"fullName": "Han Li",
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"affiliation": null,
"fullName": "Jianhua Zhang",
"givenName": "Jianhua",
"surname": "Zhang",
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{
"affiliation": null,
"fullName": "Dehua Zheng",
"givenName": "Dehua",
"surname": "Zheng",
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"title": "2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)",
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"abstract": "Evaporation is a key element for irrigation system design, water resource management, and hydrological modelling. In this research work, monthly evaporation (Ep) was projected by utilising Support Vector Machine (SVM). Monthly meteorological statistics from a Malaysian weather station were utilised for training and testing the model by employing climatic aspects, such as mean temperature, minimum temperature, maximum temperature, wind speed, relative humidity, and solar radiation for the period 2000 to 2019. Various models were formulated by utilising diverse combination of input elements and other model parameters. The performance of the formulated model was assessed by utilising standard statistical indices. The outcomes indicated that the developed SVM model can significantly improve the accuracy of monthly Ep projections. The achieved performance measures are, Z_$\\mathrm{R}^{2}=0.970$_Z, MAE=0.067, MSE=0.007, RMSE=0.087, RAE=0.163 and RSE=0.029.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Evaporation is a key element for irrigation system design, water resource management, and hydrological modelling. In this research work, monthly evaporation (Ep) was projected by utilising Support Vector Machine (SVM). Monthly meteorological statistics from a Malaysian weather station were utilised for training and testing the model by employing climatic aspects, such as mean temperature, minimum temperature, maximum temperature, wind speed, relative humidity, and solar radiation for the period 2000 to 2019. Various models were formulated by utilising diverse combination of input elements and other model parameters. The performance of the formulated model was assessed by utilising standard statistical indices. The outcomes indicated that the developed SVM model can significantly improve the accuracy of monthly Ep projections. The achieved performance measures are, $\\mathrm{R}^{2}=0.970$, MAE=0.067, MSE=0.007, RMSE=0.087, RAE=0.163 and RSE=0.029.",
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"normalizedAbstract": "Evaporation is a key element for irrigation system design, water resource management, and hydrological modelling. In this research work, monthly evaporation (Ep) was projected by utilising Support Vector Machine (SVM). Monthly meteorological statistics from a Malaysian weather station were utilised for training and testing the model by employing climatic aspects, such as mean temperature, minimum temperature, maximum temperature, wind speed, relative humidity, and solar radiation for the period 2000 to 2019. Various models were formulated by utilising diverse combination of input elements and other model parameters. The performance of the formulated model was assessed by utilising standard statistical indices. The outcomes indicated that the developed SVM model can significantly improve the accuracy of monthly Ep projections. The achieved performance measures are, -, MAE=0.067, MSE=0.007, RMSE=0.087, RAE=0.163 and RSE=0.029.",
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"Temperature Distribution",
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"Weather Forecasting",
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"authors": [
{
"affiliation": "Swinburne University of Technology,Department of Civil and Construction Engineering,Victoria,Australia",
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"title": "2020 International Conference on Urban Engineering and Management Science (ICUEMS)",
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"article": {
"id": "1lRlTcEuDFC",
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"title": "Research on Relations between Power Equipment Operating Status and Meteorological Data",
"normalizedTitle": "Research on Relations between Power Equipment Operating Status and Meteorological Data",
"abstract": "Based on meteorological disaster-meteorological correlation analysis of transmission lines, meteorological disaster condition model for transmission lines is established to determine whether transmission line meets meteorological conditions of meteorological disasters. Moreover, meteorological disaster risk coefficient covering parameters such as wind velocity and duration of meteorological disasters is proposed to measure meteorological disaster risk of transmission lines. In addition, according to mathematical statistics principle, risk coefficients of different meteorological disasters are classified to obtain different risk levels of meteorological disasters on the line that meet actual production needs during grid operation, and meteorological disaster warning model for transmission lines based on risk coefficients is established and verified with the help of historical meteorological data from power grid as well.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Based on meteorological disaster-meteorological correlation analysis of transmission lines, meteorological disaster condition model for transmission lines is established to determine whether transmission line meets meteorological conditions of meteorological disasters. Moreover, meteorological disaster risk coefficient covering parameters such as wind velocity and duration of meteorological disasters is proposed to measure meteorological disaster risk of transmission lines. In addition, according to mathematical statistics principle, risk coefficients of different meteorological disasters are classified to obtain different risk levels of meteorological disasters on the line that meet actual production needs during grid operation, and meteorological disaster warning model for transmission lines based on risk coefficients is established and verified with the help of historical meteorological data from power grid as well.",
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"normalizedAbstract": "Based on meteorological disaster-meteorological correlation analysis of transmission lines, meteorological disaster condition model for transmission lines is established to determine whether transmission line meets meteorological conditions of meteorological disasters. Moreover, meteorological disaster risk coefficient covering parameters such as wind velocity and duration of meteorological disasters is proposed to measure meteorological disaster risk of transmission lines. In addition, according to mathematical statistics principle, risk coefficients of different meteorological disasters are classified to obtain different risk levels of meteorological disasters on the line that meet actual production needs during grid operation, and meteorological disaster warning model for transmission lines based on risk coefficients is established and verified with the help of historical meteorological data from power grid as well.",
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"Meteorological Disaster Warning Model",
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"Power Transmission Lines",
"Wind Speed",
"Transmission Line Measurements",
"Data Models",
"Power Grids",
"Power Equipment Operation",
"Meteorological Data",
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"authors": [
{
"affiliation": "Ningxia Electrical Power of State Grid,Yinchuan,751100",
"fullName": "Guo Fei",
"givenName": "Guo",
"surname": "Fei",
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"affiliation": "Ningxia Electrical Power of State Grid,Yinchuan,751100",
"fullName": "Li Mei",
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"surname": "Mei",
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"affiliation": "Wuzhong Electrical Power of State Grid,Wuzhong,750011",
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"surname": "Bo",
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{
"affiliation": "Ningxia Electrical Power Information communication of State Grid,Yinchuan,751100",
"fullName": "Liu Siyao",
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"title": "2009 Second International Conference on Information and Computing Science",
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"title": "Calculation of Mooring Tension of the Ship-Based Helicopter Based on FEM",
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"abstract": "The forces of the ship-based helicopter are analyzed comprehensively in the winds and waves. The parameterized FEM model about the ship-based helicopter body, the mooring cable and the mooring base is established with the language APDL. The parameterized FEM model is used to calculate the tension of the mooring cables for different sea conditions. The mooring cable will present strong nonlinearity under the action of external force. So the cable structure is disposed by the finite element method. The warp of parking position and change of sea conditions are taken into account for calculation. After defining the constraint conditions and applying external loads, the mooring tension is obtained. Compared with the theoretical and actual value, the result of the mooring tension is verified reliable. The new calculation method is supplied to improve the force state of the mooring facility.",
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"content": "The forces of the ship-based helicopter are analyzed comprehensively in the winds and waves. The parameterized FEM model about the ship-based helicopter body, the mooring cable and the mooring base is established with the language APDL. The parameterized FEM model is used to calculate the tension of the mooring cables for different sea conditions. The mooring cable will present strong nonlinearity under the action of external force. So the cable structure is disposed by the finite element method. The warp of parking position and change of sea conditions are taken into account for calculation. After defining the constraint conditions and applying external loads, the mooring tension is obtained. Compared with the theoretical and actual value, the result of the mooring tension is verified reliable. The new calculation method is supplied to improve the force state of the mooring facility.",
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"fno": "3634a111",
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"doi": "10.1109/ICHI.2016.42",
"title": "Supporting Patient Healing through ICUsmartCARE: Technologies that Enable Family Collaboration, Presence, and Information Flow",
"normalizedTitle": "Supporting Patient Healing through ICUsmartCARE: Technologies that Enable Family Collaboration, Presence, and Information Flow",
"abstract": "Research suggests that family members of ICU patients have not been meaningfully engaged in the care process, putting them at risk for experiencing fear, depression, stress, and disruptions of family relationships. To address this problem, we propose the design of a family-centered communication-information technology, ICUsmartCARE. Its purpose is to support collaboration by providing families increased (remote) access, presence, and interaction with the patient and patient data—without placing additional information-sharing burden on critical care clinicians. The approach will be novel in three ways: (1) Family-centered framework approach to accessing real-time bedside patient data and synchronized-video, (2) Mobile-health support for family-patient-clinician collaboration––using two integrated (and validated) technologies (CISCO Jabber Guest and Medical Information Visualization Assistant), and (3) Cognitive systems engineering methodology to conduct family-centered data collection/analysis, interaction design, and testing of the sociotechnical system. The commercial potential of ICUsmartCARE is considerable in supporting effective and efficient ICU family-centered care.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Research suggests that family members of ICU patients have not been meaningfully engaged in the care process, putting them at risk for experiencing fear, depression, stress, and disruptions of family relationships. To address this problem, we propose the design of a family-centered communication-information technology, ICUsmartCARE. Its purpose is to support collaboration by providing families increased (remote) access, presence, and interaction with the patient and patient data—without placing additional information-sharing burden on critical care clinicians. The approach will be novel in three ways: (1) Family-centered framework approach to accessing real-time bedside patient data and synchronized-video, (2) Mobile-health support for family-patient-clinician collaboration––using two integrated (and validated) technologies (CISCO Jabber Guest and Medical Information Visualization Assistant), and (3) Cognitive systems engineering methodology to conduct family-centered data collection/analysis, interaction design, and testing of the sociotechnical system. The commercial potential of ICUsmartCARE is considerable in supporting effective and efficient ICU family-centered care.",
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"normalizedAbstract": "Research suggests that family members of ICU patients have not been meaningfully engaged in the care process, putting them at risk for experiencing fear, depression, stress, and disruptions of family relationships. To address this problem, we propose the design of a family-centered communication-information technology, ICUsmartCARE. Its purpose is to support collaboration by providing families increased (remote) access, presence, and interaction with the patient and patient data—without placing additional information-sharing burden on critical care clinicians. The approach will be novel in three ways: (1) Family-centered framework approach to accessing real-time bedside patient data and synchronized-video, (2) Mobile-health support for family-patient-clinician collaboration––using two integrated (and validated) technologies (CISCO Jabber Guest and Medical Information Visualization Assistant), and (3) Cognitive systems engineering methodology to conduct family-centered data collection/analysis, interaction design, and testing of the sociotechnical system. The commercial potential of ICUsmartCARE is considerable in supporting effective and efficient ICU family-centered care.",
"fno": "6117a297",
"keywords": [
"Real Time Systems",
"Data Visualization",
"Collaboration",
"Hospitals",
"Systems Engineering And Theory",
"Human Computer Interaction",
"Cognitive Systems Engineering",
"Family Centered Care",
"Critical Care",
"Communication Information Technology",
"Human Computer Interaction"
],
"authors": [
{
"affiliation": null,
"fullName": "Anthony Faiola",
"givenName": "Anthony",
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{
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"fullName": "Elizabeth Lerner Papautsky",
"givenName": "Elizabeth Lerner",
"surname": "Papautsky",
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},
{
"affiliation": null,
"fullName": "Min Joo",
"givenName": "Min",
"surname": "Joo",
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}
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"pubDate": "2016-10-01T00:00:00",
"pubType": "proceedings",
"pages": "297-300",
"year": "2016",
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