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2d87191d9618-0 | ResearchArticle
Chagas Parasite Detection in Blood Images Using AdaBoost
Víctor Uc-Cetina,1Carlos Brito-Loeza,1and Hugo Ruiz-Piña2
1FacultaddeMatem ´aticas,UniversidadAut ´onomadeYucat ´an,AnilloPerif ´ericoNorte,TablajeCatastral,13615M ´erida,YUC,Mexico
2CentrodeInvestigacionesRegionalesDr.HideyoNoguchi,UniversidadAut... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-1 | solution to the task of Chagas parasite detection in blood images. We give details of the algorithm and our experimental setup.With this method, we get 100% and 93.25% of sensitivity and specificity, respectively. A ROC comparison with the method mostcommonlyusedforthedetectionofmalariaparasitesbasedonsupportvectormach... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-2 | The Chagas disease presents itself in two phases. The
initial,acutephaselastsforabouttwomonthsafterinfection.Duringtheacutephase,ahighnumberofparasitescirculatein the blood. When the Chagas disease is diagnosed earlyin this phase and a treatment is initiated, the patient can becured. During the chronic phase, the paras... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-3 | Hindawi Publishing Corporation
Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 139681, 13 pages
http://dx.doi.org/10.1155/2015/1396812 ComputationalandMathematicalMethodsinMedicine
A peripheral blood smear is basically a glass microscope
s l i d ec o a t e do no n es i d ew i t hat h i nl a y... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-4 | neighbors binary classifier is trained and the performancesare 0.98 and 0.80 in sensitivity and specificity terms, respec-tively. The results reported in this paper using AdaBoost +SVMhaveasignificantimprovementoverthepreviousones.
A p a r tf r o mt h e s et w op a p e r s ,n oo t h e rs t u d yu s i n g
machine learni... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-5 | 𝑘-means clustering [ 17], and other methods [ 14,16]. Brief
summariesoftheapproachesmorerelevantforourownworkarepresentednext.
In [10], an image classification system implementing
a two-stage tree classifier using back-propogation neuralnetworks is introduced. Such a system identifies malariaparasites present in thin ... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-6 | In [12], the parasite detector uses a Bayesian pixel classi-
fier to mark stained pixels. The class conditional probabilitydensity functions of the stained and the nonstained classesareestimatedusinganonparametrichistogrammethod.Thestained pixels are further processed to extract features suchasHumoments,relativeshapeme... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-7 | acquisition unit and (2)image analysis module. The authors
have developed an image acquisition system that can beeasily mounted on most conventional light microscopes. Itautomatically controls the movement of microscope stagein 3-directional planes. The vertical adjustment (focusing)can be made in a nanometer range (7–... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-8 | 22]. We provide an AdaBoost and a SVM learning solution
to the task of Chagas parasite detection in blood images.Our AdaBoost solution includes the definition of a newset of Haar-like features specially designed for learning theChagas parasite’s morphology pattern. We give details ofthe algorithms and our experimental ... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-9 | istrainedusingadifferentsubsetofexamples.Thenewtrain-ingsubsetcontainsexamplesthatareincorrectlyclassifiedbythe current ensemble. By doing such an iterative selection ofdifficult examples, boosting methods improve the accuracyof any supervised machine learning algorithm. Althougheach component classifier has an accurac... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-10 | tively.
(ii) Initializeweights 𝑤1,𝑖= 1/2𝑚, 1/2𝑙 for𝑦𝑖=0 , 1resp-
ectively, where 𝑚and𝑙are the number of negatives
andpositives,respectively.
(iii) For 𝑡=1 ,...,𝑇 :
(1) Normalizetheweights,
𝑤𝑡,𝑖←𝑤𝑡,𝑖
∑𝑛
𝑗=1𝑤𝑡,𝑗(1)
sothat 𝑤𝑡isaprobabilitydistribution.4 ComputationalandMathematicalMethodsinMedicin... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-11 | (4) Updatetheweights:
𝑤𝑡+1,𝑖=𝑤𝑡,𝑖𝛽1−𝑒𝑖
𝑡, (2)
where 𝑒𝑖=0if, for example, 𝑥𝑖is classified
correctly, 𝑒𝑖=1otherwise,and 𝛽𝑡=𝜖𝑡/(1 − 𝜖𝑡).
(iv) Thefinalstrongclassifieris
ℎ(𝑥)={{
{{
{1𝑇
∑
𝑡=1𝛼𝑡ℎ𝑡(𝑥)≥1
2𝑇
∑
𝑡=1𝛼𝑡,
0otherwise ,(3)
where 𝛼𝑡=log(1/𝛽𝑡).
2.2. Support Vector Machines. As u p p ... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-12 | 3. Chagas Parasites Detection
The whole process of Chagas detection is divided into fourstages, as illustrated in Figure3. This process has been spe-
cially designed to allow the automatization of the diagnosisas much as possible. The robustness of our methodologyrelies on the four modular stages that can be implemente... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-13 | image is scanned using our previously trained AdaBoost
classifier in order to detect subwindows of pixels containingpossibleparasites.Finally,inthefourthstep,weusethegreencomponent of the original RGB image to extract some threefeatures related to the number of pixels representing a highDNA (deoxyribonucleic acid) cont... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-14 | Chagasparasite’smorphology.
such asT. Cruzi,Leishmania sp., andPlasmodium sp,. After
the staining process the blood smears were placed verticallyand were left to dry. Finally, an optical Nikon Eclipse E600microscope was used to take images, first at 10x and then at100x,witharesultingsizeincreaseof1000times.
3.2. Stage ... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-15 | color components of the image: red, green, and blue. Toconvert any RGB image to a grayscale representation of itsluminance, first one must obtain the values of its red, green,andbluecomponents.Then,weneedtoaddtogether30%oft h er e dv a l u e ,5 9 %o ft h eg r e e nv a l u e ,a n d1 1 %o ft h eb l u e
value. These perce... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-16 | The nine features in Figure4(a) were inspired by the
originalHaar-likefeaturesproposedbyViolaandJonesin[ 7].
ThesetoffourHaar-likefeaturesin Figure4(b) weredesigned
specifically to represent the shrimp-like shape adopted byChagas parasites. After running ten times the AdaBoostalgorithm and examining the number of times... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-17 | row𝑟andcolumn 𝑐.Notethatthenumberofrowsincreases
intheimagefromtoptobottomandthenumberofcolumnsincreasesfromlefttoright.
Now, given the same image 𝐼and four points
𝑝
1(𝑟1,𝑐1), 𝑝2(𝑟2,𝑐2), 𝑝3(𝑟3,𝑐3),a n d 𝑝4(𝑟4,𝑐4)as illustrated
inFigure6(a) , and using the definition of integral image of
(4),w ec a nc... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-18 | (a)
R1R2
R3
R4R5
(b)
Figure6:(a)Genericintegralimagecomputation;(b)computationofoneChagas-specificintegralimage.
For𝑆2and𝑆3wecompute,respectively,
𝜎( 𝑆2)=𝑖𝑖 ( 𝑟2,𝑐2)−𝑖𝑖 ( 𝑟1,𝑐1),
𝜎( 𝑆3)=𝑖𝑖 ( 𝑟3,𝑐3)−𝑖𝑖 ( 𝑟1,𝑐1).(6)
Finally,for 𝑆4,wecalculate
𝜎( 𝑆4)=𝑖𝑖 ( 𝑟4,𝑐4)−𝑖𝑖 ( 𝑟3,𝑐3)−𝑖𝑖 ( 𝑟2,𝑐2)... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-19 | 𝑟of subwindows required to cover it, this number
being 𝑛𝑟=3for the simplest one and 𝑛𝑟=5for the most
complex. The computation of one Chagas-specific integralimagewith5subwindowsisillustratedin Figure6(b) ,where
theHaar-likefeaturetemplatehasbeendividedinto5regions,namely, 𝑅
1,...,𝑅5andinordertocom putethefea tur... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-20 | an accumulation of DNA of the Chagas parasite. When the
imageisplottedasasurface,itcanbeseenthatsuchDNAspothas one particular valley-like shape, shown in Figure7(b) .
Sincetheshapeofthisdarkspotandthelowintensityvaluestakenbytheircorresponingpixelscreateapatternveryusefultodiscriminatebetweenparasitesandnonparasites,th... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-21 | 4. Experimental Work
4.1.ExperimentalMethodology. W etestedtheproposedalgo-
rithm using a data collected in the Instituto de Investiga-ciones Regionales at the Universidad Aut ´onomadeYucat ´an,
M´exico. We had available for our study a total of 120
c o l o ri m a g e so fd i m e n s i o n2 5 6 ×256 pixels. Sixty of th... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-22 | AdaBoost used as many negative examples as possible toreducethefalsepositiverate,andthefinallearnedmodelwastested with 10800 negative images. In the case of the SVM +Feature Extraction methods, we used 1296 negative imagesfortrainingand10800fortesting.Itisworthmentioningthatonce the classifiers have been trained, the p... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-23 | Color,shape,andtexturefeatureswereselectedtoforma
38-dimensional feature vector. The color and shape featuresare computed from 7 different histograms that containinformation about the red channel and green channel of theRGBimage,thehueandsaturationcomponentfromtheHSVimage, the grayscale pixel intensities and the result... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-24 | (9)Due to the unbalanced class distribution between sub-
windowscontainingaChagasparasiteandsubwindowscon-tainingsomethingelse,theuseofstatisticalmetricsrelatedtotheeffectivenesswerealsoconsidered.Intheseexperiments,the𝐹-measureisused:
𝐹
𝛽=TPR∗PR
𝛽∗TPR+( 1−𝛽 )∗ PR(10)
with
TPR =TP
TP+FN,
PR=TP
TP+FP,(11)
where TP ... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-25 | t h er e s u l to fo u rA d a B o o s ta l g o r i t h mf o rC h a g a sp a r a s i t edetection.Theseresultswereobtainedwithacascadeofweakclassifiers of 11 stages. Figure9provides the ROC curves
for SVMs using different degrees of polynomial kernels andFigure10 comparestheROCcurvesforAdaBoostandSVMs
methodsusingdiffer... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-26 | (h)
(i)
(j)
(k)
(l)
Figure 8: Results of the Chagas parasite detection algorithm with a sample of images. Each column corresponds to a different example of
detection. The first row contains the color images in RGB format; the second row shows the grayscale images once the parasites have been
detected by the classifi... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-27 | 5. Discussion
B a s e do nt h er e s u l t so b t a i n e dw i t hb o t hm a c h i n el e a r n i n gmethods, AdaBoost and SVM, we can see that taking intoaccount features related to shape, color and texture seemsTable 1: Best 𝛾and𝐶parametersforSVMsaccordingtothetypeof
kernelemployedandourtrainingdata.
Kernel 𝛾𝐶
Li... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-28 | SVMlinear+FeatureExtraction 0.8218 0.0118
SVMpolynomial+FeatureExtraction 0.6691 0.0035SVMRBF+FeatureExtraction 0.7111 0.0068
Table 5: 𝐹-measure( 𝛽=1 0)bymethod.
Method Mean Std. Dev.
AdaBoost 0.9976 0.0018
AdaBoost+Postprocessing 0.9993 0.0004
SVMlinear+FeatureExtraction 0.9982 0.0001SVMpolynomial+FeatureExtraction ... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-29 | parasite. In image (b), the number of dark pixels is larger than average, which makes it difficult for the detection system to consider it as avalidparasite.Aneasybackgroundlookslike(c)andadifficultonelookslike(d).
procedure which should be applied a reduced number of
times. If we attempt to interwine this counting pro... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-30 | Failing to detect Chagas is certainly a more serious
situation than having a false positive error. Diagnosing apersonsufferingfromChagasasnothavingthediseasewouldp u th e rl i f ea tr i s k .Af a l s ep o s i t i v ea l a r mo nt h ec o n t r a r ywould only incur in a labor cost for the doctor in chargeof confirming w... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-31 | rithm, would motivate the laboratories and hospitals to
reconsider the usefulness of the blood smears analysis andtotrustthistestasaconfidentlaboratorydiagnosis.Further-more, the laboratory technicians would reduce the numberof hours they need to sit in front of a microscope to analyzeso many samples day after day. Sit... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-32 | parasiteswasimplementedandcomparedtoAdaBoost.This
method consists basically in the use of different kinds ofvalues taken from color histograms in order to form the
f e a t u r ev e c t o rt h a ta r el a t e ru s e dt ot r a i nas u p p o r tv e c t o r
m a c h i n e .B o t hs h a p ea n dc o l o rp r o v e dt ob ei m ... | https://downloads.hindawi.com/journals/cmmm/2015/139681.pdf |
2d87191d9618-33 | Acknowledgment
The authors thank the Universidad Autonoma de Yucatan,f o rs u p p o r t i n gt h er e s e a r c hp r o j e c to nC h a g a sp a r a s i t e sdectectionusingmachinelearningalgorithms.
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