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 ´onomadeYucat ´an,Avenida,Itz ´aesNo.490x59,
ColoniaCentro,97000M ´erida,YUC,Mexico
CorrespondenceshouldbeaddressedtoV ´ıctorUc-Cetina;uccetina@uady.mx
Received17October2014;Revised20February2015;Accepted20February2015
AcademicEditor:IriniDoytchinova
Copyright©2015V ´ıctorUc-Cetinaetal. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
TheChagasdiseaseisapotentiallylife-threateningillnesscausedbytheprotozoanparasite, Trypanosoma cruzi. Visualdetectionof
suchparasitethroughmicroscopicinspectionisatediousandtime-consumingtask.Inthispaper,weprovideanAdaBoostlearning


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 mostcommonlyusedforthedetectionofmalariaparasitesbasedonsupportvectormachines(SVM)isalsoprovided.Ourexperimental
work shows mainly two things: (1) Chagas parasites can be detected automatically using machine learning methods with high
accuracy and (2) AdaBoost + SVM provides better overall detection performance than AdaBoost or SVMs alone. Such resultsare the best ones known so far for the problem of automatic detection of Chagas parasites through the use of machine learning,
computervision,andimageprocessingmethods.
1. Introduction
The Chagas disease, also known as American trypanosomi-
asis, is a potentially life-threatening illness caused by theprotozoan parasite, Trypanosoma cruzi (T. cruzi).According
to the World Health Organization [ 1], it is found mainly in
Latin America, where it is mostly transmitted to humans by
thefaecesoftriatominebugs.Morethan25millionpeopleare
at risk of the disease and an estimated 10 million people areinfected worldwide, mostly in Latin America where Chagasdiseaseisendemic.Approximately20,000deathsattributabletoChagasdiseaseoccurannually[ 2].
The Chagas disease presents itself in two phases. The


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 parasites are hiddenmainly in the heart and digestive muscle. In later years theinfectioncanleadtosuddendeathorheartfailurecausedbyprogressivedestructionoftheheartmuscle.Sometestscanbeusefulformakingadiagnosis,depend-
ing on the phase of the disease. According to [ 3], the most
typical tests used for the diagnosis of the Chagas disease
are blood culture, chest X-ray echocardiogram, electrocar-
diogram (ECG), enzyme-linked immunoassay (ELISA), and
peripheralbloodsmears.Uptodate,oneofthemosteffective
ways of detecting the Chagas disease in its initial phase isthrough the ELISA test. Another commonly used method is
theChagasStat-Pakrapidimmunochromatographictest[ 4],
which provides a performance comparable to that obtained
withELISA.
Screening blood donors for Chagas disease is of much
concerninallLatinAmericancountries.AlthoughtheW orldHealth Organization (WHO) expert committee and some
guidelines recommend a single ELISA test to screen blood
donors[5],insomecountries,suchasBrazil,thereisamore
restrictiveregulation,recommendingtwosimultaneoustests
ofdifferenttechniques[ 6],performedinparallel.Oneofthe
tests that can be performed in parallel is the inspection ofperipheralbloodsmears.
Hindawi Publishing Corporation


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 e ro fv e n o u sb l o o d .The slide is stained with a dye, usually Wright’s stain, andexaminedunderamicroscope.Eventhoughvisualdetectionof the Chagas parasite through microscopic inspection ofp e r i p h e r a lb l o o ds m e a r si st h em o s tw i d e l yu s e dt e c h n i q u e
for parasitemia determination, it is a time-consuming and
laborious process. When the number of blood screeningsperformed in a laboratory increases, it becomes a problem.To cope with this problem, we introduce an automaticcomputational method for the detection of Chagas parasitesbasedonmachinelearningandimageprocessingalgorithms.Chagas detection using automatic image analysis is, to thebest of our knowledge, not yet studied as it is evidenced bythelackofpublicationsonthistopic.
Currently, there is only a couple of papers reporting
resultsonChagasparasitesdetectionusingmachinelearningmethods [ 8,9]. In the former, a Gaussian discriminant
analysis is implemented and the resulting performance ratesare0.0167false-negatives,0.1563false-positives,0.8437true-negatives,and0.9833true-positives.Inthelatter,a 𝑘-nearest


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 learning and computer vision methods has beenreported for the detection of the Chagas parasite, to the besto fo u rk n o w l e d g e .H o w e v e r ,s u c hk i n do ft e c h n i q u e sh a v ebeen extensively used for the detection of Malaria parasites
[10–16]. All the approaches we reviewed utilize supervised
and/orunsupervisedlearningmethodstodetect,classifyandq u a n t i f yt h en u m b e ro fm a l a r i ap a r a s i t e so nb l o o di m a g e s .The general process commonly reported can be divided inthreestages: (1)segmentation; (2)featuresextraction;and (3)
classification.
In the first stage, the segmentation is obtained through
differentmethodsbasedonimagehistrogramcomputations.The second stage is performed through the computation ofdifferent features that can be classified in four categories[15]: texture features, color features, geometric features, and
features obtained from human expert knowledge. The thirdand final stage makes the biggest difference among themethods, some applied neural networks [ 10], some others
applied statistical measures [ 11,13], bayesian classifiers [ 12],
𝑘-means clustering [ 17], and other methods [ 14,16]. Brief


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 blood smears and classifies themaccording to their different species. Image features based oncolor,texture,andthegeometryallowthesystemtoperform
morphological and threshold selection of possible parasites,
distinguishingthemfromsaneerythrocytesandothercells.
Similarly, the study presented in [ 11] provides a method
for quantification and classification of erythrocytes instained thin blood films infected with malaria parasites.Thisapproachiscomposedofthreemainphases.First,there
isapreprocessingstep,whichcorrectsluminancedifferences.Second,thereisasegmentationstepthatusesthenormalizedRGBcolorspaceforclassifyingpixelseitheraserythrocyteorbackground. Third, there is a two-step classification processidentifies infected erythrocytes and diagnoses the infection
stage, using a trained bank of classifiers. An interesting
characteristic of this method is that user intervention isallowed when the approach cannot make a proper decision.Automatic identification of infected erythrocytes showed aspecificity of99.7% and a sensitivityof94%. Meanwhile, theinfectionstagewasdeterminedwithanaveragesensitivityof78.8%andaveragespecificityof91.2%.
In [12], the parasite detector uses a Bayesian pixel classi-


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,relativeshapemeasurements,andcolorforaparasiteornonparasiteclassifier. Finallya distanceweighted𝑘-nearest neighbor classifier is trained with the extracted
features achieving 74% of sensitivity, 98% of specificity, 88%ofpositiveprediction,and95%ofnegativepredictionvaluesforthemalariaparasitedetection.
Another malaria study based on pattern matching and
parameter optimization is presented in [ 13]. In this case, the
parasitaemia measurements are carried out by partitioningtheuninfectedandinfectedcellsusingunsupervisedmachinelearning. A comparison of the performance is done witha training-based method which improves the classification
ratesgiving92%ofprecisionand95%ofrecall.
Even when some machine learning methods have been
successfully applied to the detection, classification, andquantification of Malaria parasites, these methods cannotbe employed in a straightforward way to detect Chagasparasites for one powerful reason: their morphology isdifferent. Malaria parasites have a ring shape ( Figure1(a) )
while Chagas parasites have a curved shape, similar to ashrimp (Figure1(b) ). This difference in their morphology
prevents us from using exactly the same features during theveryimportantstageoffeaturesselection.
In addition to detection algorithms, some authors have
developed complete automated systems. In [ 18], an auto-
mated system to identify and analyze parasite species onthick blood films by image analysis techniques is presented.The system comprises two main components: (1)image
acquisition unit and (2)image analysis module. The authors


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 (7–9nm). Images areacquiredwithadigitalcamerathatisinstalledatthetopofthemicroscope. The captured images are analyzed by an image
analysis software which utilizes computer vision algorithms
todetectandidentifymalariaparasites.
Otherworksrelatedtotheapplicationofpatternrecogni-
tion methods for images taken with microscopes are those
used for the detection of special types of cells such asComputationalandMathematicalMethodsinMedicine 3
(a)
 (b)
 (c)
Figure 1: (a) Malaria parasites in early form (inside green squares); (b) Chagas parasite (inside red square); (c) blood cell that looks very
similartoaChagasparasite(insidebluesquare).
cancerous cells [ 19] and cervical cells [ 20]. In the first work
t h ea u t h o r su s e dt h es h a p e ,s i z e ,a n dt e x t u r eo ft h ec e l l sto perform a classification, meanwhile in the second worka𝑘-means clustering algorithm for designing binary tree
classifiers is used, along with the Bhattacharyya distancemetric.
Inthispaper,wepresentacomparisonoftwoofthemost
robust algorithms for binary classification. Both methodshavebeenextensivelystudiedandtestedwithdifficultpatternrecognition problems on images such as face detection [ 21,
22]. We provide an AdaBoost and a SVM learning solution


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 setup. With the bestresulting method, AdaBoost, we get 100% and 93.25% ofsensitivity and specificity, respectively. Our experimentalwork shows mainly two things: (1)Chagas parasites can be
detectedautomaticallyusingmachinelearningmethodsand(2)AdaBoost+SVMprovidesbetterdetectionperformance
thanplainAdaBoostorSVMs.
In the next section, we provide a brief review of the
two compared machine learning methods: AdaBoost andSVM.Section3 describesthefourstagesrequiredtoanalyze
t h ei m a g e sl o o k i n gf o rs u b w i n d o w sc o n t a i n i n gaC h a g a sparasite. The experimental work is detailed in Section4,
which contains the experimental methodology we followedand the experimental results. In Section5,w ed i s c u s st h e
results and point out the issues that need to be addressed ifthesystemistobeestablishedanddeployedinreality.Finally,ourconclusionsareexplainedin Section6.
2. AdaBoost and SVM
AdaBoost is the short for Adaptive Boosting which is cur-
r e n t l yt h em o s tu s e db o o s t i n gm e t h o d .Th eg o a lo fb o o s t i n gis to improve the accuracy of any given learning algorithm.Boosting creates an ensemble of classifiers by training andaddingonecomponentclassifieratatime.Eachnewclassifier


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 accuracy just greater thanaverage, the joint decision rule of the ensemble has a highaccuracywithallpreviouslyselectedtrainingexamples[ 23].
2.1. Boosted Cascade of Simple Features. In 2001, Viola and
Jones [7] introduced an extremely rapid approach for visual
objectdetectionmotivatedbythetaskoffacedetection.Suchanapproachhasasitscentralcomponentanalgorithmbasedon the standard AdaBoost learning method. In addition tot h eu s eo fb o o s t i n g ,t w oa r et h ec h a r a c t e r i s t i c st h a tm a k ethis approach very fast and efficient. First, the use of theso-called integral image allows the features used by thedetector to be computed very quickly. Second, a methodfor combining the increasingly more complex classifiers in acascade,asillustratedin Figure2,allowsbackgroundregions
o ft h ei m a g et ob eq u i c k l yd i s c a r d e dw h i l es p e n d i n gm o r e
computingresourcesonpromisingregions.
ThealgorithmintroducedbyViolaandJonesispresented
next.
(i) Given example images (𝑥
1,𝑦1) ,...,( 𝑥𝑛,𝑦𝑛)where
𝑦𝑖=0 , 1for negative and positive examples, respec-
tively.


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 ComputationalandMathematicalMethodsinMedicine
Cascade of classifiers
All
subwindows
subwindows
subwindowsAcceptedTrue True True
False False False
Discardedh1 h2 hT···
Figure 2: The detection cascade. Classifiers with increasing com-
plexity are arranged in a cascade scheme to allow background
regions of the image to be quickly discarded while spending morecomputingresourcesonpromisingregions.
(2) For each feature, 𝑗, train a classifier ℎ𝑗which is
restricted to using a single feature. The error isevaluatedwithrespectto 𝑤
𝑡,𝜖𝑗=∑𝑖𝑤𝑖|ℎ𝑗(𝑥𝑖)−
𝑦𝑖|.
(3) Choosetheclassifier, ℎ𝑡,withthelowesterror 𝜖𝑡.
(4) Updatetheweights:


(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 o r tv e c t o rm a c h i n e
(SVM)isalearningtoolthatoriginatedinmodernstatisticallearning theory [ 24]. In recent years, SVM learning has
found a wide range of real-world applications, includinghandwritten digit recognition, object recognition, speakeridentification, face detection in images, and text catego-rization. The formulation of SVM learning is based on theprinciple of structural risk minimization. SVM tends toperform well when applied to data outside the training setand it has been reported that SVM-based approaches areabletosignificantlyoutperformcompetingmethodsinmanyapplications. SVM achieves this by focusing on the trainingexamples that are most difficult to classify. These trainingexamplesarecalledthesupportvectors.
3. Chagas Parasites Detection


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 implemented
Image
acquisition Preprocessing
Learning-based
postprocessingAdaBoost
detection
Figure 3: The process of parasite detection consists of four stages:
(1)image adquisition via microscope; (2)image conversion from
RGBcolortograyscaleformat; (3)possibleparasitesdetectionusing
AdaBoost; and (4)amount of DNA pixels used to further discard
falsepositives.
a ss e p a r a t e dp r o g r a m m i n gc l a s s e s ,w h e r ee v e np a r a l l e lp r o -
gramming,thatis, usingGraphics Processing Units(GPUs),c a na l s ob ee m p l o y e dt or e d u c et h et i m eo fd e t e c t i o n[25]. This methodology is generic enough to be applied in
otherobjectdetectionsystems,particularlyfromthebiologydomain where samples observed through a microscope arestained.
The first step consists in obtaining a digital image using
ac a m e r aa n dam i c r o s c o p e .O n c ew eh a v et h ei m a g ei nRGB format, in a second step we convert it to a grayscaleimagetoreducetheamountofinformationthattheAdaBoostalgorithm needs to process. In the third stage, the grayscale
image is scanned using our previously trained AdaBoost


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) content. Based on the amountofDN Aitispossibletodiscardfalseparasites.ThislaststageisimplementedwithaSVManditisveryimportantbecauseitallowsustoinclude,aspartoftheclassificationprocedure,aprioriknowledgeabouttheDNAoftheparasites.
It is important to mention that the DNA from both
parasitesandnonparasitessuchaswhitebloodcellstendstoabsorbdifferentamountsofthestainemployed.However,thepattern generated by the stained DNA of parasites is clearlydifferent from the pattern generated by the stained DNAof white blood cells. Such a difference is recognized by thepatternrecognitionalgorithms.
3.1.Stage1:ImageAcquisition. Agroupofmicewereinfected
with an inoculation of 5 × 104blood trypomastigotes of T.
cruzivia intraperitoneal. Once the mice were infected, the
parasitaemia detection started in average between 11 and 15daysafterwards.Atthistime,thebloodsmearswerepreparedandstainedusingWrightstain,whichallowstheobservationofthemorphologyofdifferentbloodcells,aswellasparasitesComputationalandMathematicalMethodsinMedicine 5
(a) (b)
Figure4:(a)GenericHaar-likefeaturesinspiredbythoseproposedbyViolaandJones[ 7];(b)Haar-likefeaturesspeciallydesignedtocapture
Chagasparasite’smorphology.


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 2: RGB to Grayscale Format. The conversion from
an RGB image to a grayscale image involves a simple mani-pulation of matrices and is performed to reduce the amountofinformationinvolvedinthelearninganddetectionprocess.The RGB color model is an additive color model in whichred, green, and blue light are added together in differentproportions to produce a wide array of colors. A color inthe RGB color model is described by indicating how muchof each of the red, green, and blue is included. The color isexpressedasatriplet (𝑟, 𝑔, 𝑏)whereeachcomponentcanvary
from zero to a defined maximum value, which in our casewas256.R G Bi m a g e sa r es t o r e di nm e m o r ya s 𝑤×ℎ×3
matrices, where 𝑤is the width and ℎis the height of the
imagemeasuredinnumberofpixels.Thethirddimensionofthe matrix which is of size 3corresponds to the 3different


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 percentages are regarded as typical values. The
resultingimageisstoredina 𝑤×ℎimagecontainingpixelsof
differentintensitiesofthegraycolor.
3.3. Stage 3: Parasites Detection. The Chagas detection pro-
cess employs a set of Haar-like features and one AdaBoost
binary classifier. Haar-like features provide informationabout clear and dark regions in images. Such informationis very useful to detect types of objects that share thesame morphological pattern. Since Chagas parasites can beconsidered objects that share the same shrimp-like shape,t h e yc a nb ed e t e c t e du s i n gt h ea p p r o p r i a t es e to fH a a r - l i k efeatures.
Givenadetectionwindowtakenfromaspecificlocation
ofthegrayscaleimage,aHaar-likefeatureconsidersadjacentrectangularregions,sumsupthepixelintensitiescontainedineachrectangularregionandcalculatesthedifferencebetweenthese sums. This difference is then compared to alearnedthreshold that separates nonparasites from parasites. The
rectangular regions are defined in such a way that relevantdark pixels fall into one same region, meanwhile clearpixels fallintoanotherone. Figure4illustratestheHaar-like
featuresutilizedtodetectChagasparasites.
The nine features in Figure4(a) were inspired by the


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 that thealgorithmchosetheHaar-likefeaturesasweaklearners,itwaspossible to effectively discard those Haar-like features thatfailed to capture important patterns of the Chagas parasites,while keeping only the best ones. Figure5illustrates how
specially designed Haar-like features adapt better to themorphologyoftheparasite.
Moreover, the position of these rectangular regions is
defined relative to a detection window that acts like abounding box to the target parasite. In the detection phase,a window of the target size is moved over the input image,andforeachsubsectionoftheimage,theHaar-likefeatureis
computed.
The Haar-like features can be computed very fast using
the concept of integral image. Given an image 𝐼of size 𝑅×
𝐶,w h e r e 𝑅is the number of rows and 𝐶is the number of
columns, the integral image of a point located in row 𝑟and
column 𝑐isdefinedas
𝑖𝑖(𝑟, 𝑐)=∑
𝑟󸀠≤𝑟,𝑐󸀠≤𝑐𝐼( 𝑟󸀠,𝑐󸀠),(4)
where 𝐼(𝑟󸀠,𝑐󸀠)is the gray intensity value of pixels located in


row𝑟󸀠andcolumn 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 o m p u t et h er e g i o n s 𝑆1,𝑆2,𝑆3,a n d 𝑆4as follows.
To calculate the sum of pixels in subwindow 𝑆1, denoted by
𝜎(𝑆1),wesimplycompute
𝜎( 𝑆1)=𝑖𝑖 ( 𝑝1)=𝑖𝑖 ( 𝑟1,𝑐1). (5)6 ComputationalandMathematicalMethodsinMedicine
Figure 5: Haar-like features on top can capture more information abou tthemorphology oftheChagas parasite,given their circular shape,
thanthegenericHaar-likefeaturesproposedbyViolaandJones,shownonthebottom.
S1S2
S3S4(r1,c1) (r2,c2)
(r3,c3)( r4,c4)(1,C)
(R,C ) (R,1)(1,1)
(a)
R1R2
R3
R4R5
(b)


(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)+𝑖𝑖 ( 𝑟1,𝑐1).(7)
In general, for an image of size 𝑅×𝐶,thewho lein t egral
image 𝑖𝑖(𝑅, 𝐶)c a nb ec o m p u t e di no n l yo n ep a s so v e rt h e
imageandcanbestoredina2-dimensionalarray.Therefore,tocomputeasubwindowlike 𝑆
4inFigure6(a) ,weonlyneed
toaccesstheintegralimagearrayfourtimes.Furthermore,tocomputetheintegralimageofourChagasHaar-likefeatures,
wejustneedtosegmenteachHaar-likefeatureintheminimalnumber 𝑛
𝑟of subwindows required to cover it, this number


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 turevalue
weneedtocalculate 𝜎(𝑅1)+𝜎 ( 𝑅2)+𝜎 ( 𝑅3)+𝜎 ( 𝑅4)−𝜎 ( 𝑅5).
Inconclusion,thecomputationoftheintegralimageforourspeciallydesignedHaar-likefeaturesisstillveryfast.
3.4. Stage 4: Postprocessing. The cascade application of the
AdaBoost algorithm together with the appropriate set ofComputationalandMathematicalMethodsinMedicine 7
DNA
(a)
180
140
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004812
(b)
Figure7:(a)DarkspotofpixelsgeneratedbyanaccumulationofDNA;(b)aDNAspotseenasasurfaceof 11 × 11pixels.
Haar-like features is highly effective in finding most of the
trueChagasparasites.However,italsodetectsasmallnumberof false parasites. The postprocessing filter described in thissectionhelpstodiscardfalseparasitesanddecreasesthefalsepositiverateofourmethod.
Thepostprocessingfilterisfocusedontheanalysisofone
dark spot of pixels appearing in all parasites’ bodies. Thisspot, indicated by an arrow in Figure7(a) , corresponds to


an accumulation of DNA of the Chagas parasite. When the
imageisplottedasasurface,itcanbeseenthatsuchDNAspothas one particular valley-like shape, shown in Figure7(b) .
Sincetheshapeofthisdarkspotandthelowintensityvaluestakenbytheircorresponingpixelscreateapatternveryusefultodiscriminatebetweenparasitesandnonparasites,theywereusedasasourceforfeatureextractionasitisdescribednext.
Given that the stained pixels have low intensities, these
values being not greater than 80, in the green component oftheRGBimage,thefollowingthreefeatureswereusedtotrainaSVM.
(i) Feature 1 . Given a subwindow detected by AdaBoost, the
percentage of pixels that have intensities at most 80 wascomputed. This percentage, which represents the size of thestainedregionoftheparasite,isthefirstfeature.
(ii) Feature 2 . The mean of the intensities of all pixels with
individual intensities at most 80 was encountered in thesubwindowdetectedbyAdaBoost.
(iii) Feature 3 . The standard deviation of the intensities of all
pixelswithindividualintensitiesatmost80wasencounteredinthesubwindowdetectedbyAdaBoost.
The choice of the green component over the red and the
blue ones was decided after a careful visual examination ofa subset of images in the original RGB version and in all
3 individual components: red, green, and blue. After such
visualexamination,itwasclearthatthegreencomponentwassuperiortotheothersintermsofdiscriminativeinformation.In other words, the information provided by the green
channelallowsustoperformamoreaccurateclassificationofparasitesandnonparasites.
4. Experimental Work


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 these
images were specially selected to contain a Chagas parasite.Meanwhile, the other 60 remaining images were selectedto contain nonparasites. Machine learning was employed togenerate our basic classifier algorithm, which was then usedfor the more general Chagas parasite detection process. Theresultsweprovideinthispaperwereobtainedusingatypicalpattern recognition methodology [ 26], using training and
testingsetsofimages.
A 10-fold cross-validation procedure was used for train-
ing and testing the AdaBoost and the SVM + FeatureExtraction learning methods. Since AdaBoost is sensible torotation, during the training phase, we generated a total of840 positive examples of parasites images by rotating the
original images in increasing amounts of 15 degrees. For
every experiment, we used 756 positive images for training,leaving 84 positive images for testing. Even though we werelimited by the number of positive examples available, thenumber of negative examples could be defined as desired.All negative examples were generated by random samplingfrom the original 256 × 256 pixels images. For training,


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 proper analysis of8 ComputationalandMathematicalMethodsinMedicine
a256 ×256image,whichmeansthatthealgorithmisactually
lookingforparasites,isperformedinrealtime.Wearetalkingabout a few milliseconds, using a commercial laptop ordesktopcomputer.Therefore,severalhundredimagescanbeanalyzedinamatterofminutes.Moreover,ourprogramwasimplementedinMatlabusing2publiclibraries:fdtool,forthe
AdaBoostimplementation,andlibsvm,forthesupportvector
machineimplementation.
4.1.1.ComparisonwithSupportVectorMachines. Theapplica-
tionofSVMlearningrequiresthecomputationofonefeaturevector for each subwindow that needs to be classified asparasiteornonparasite.Suchfeaturesvectorsarethenpassedto the SVM classifier which makes the final decision. Thes e l e c t i o no ft h ef e a t u r e si sak e yp a r to ft h ec l a s s i fi c a t i o nprocess, simply because by using the wrong features thelearning algorithm cannot extract the patterns required tobuild a confident classifier, resulting in a high classificationerror. In order to obtain the best results with the applicationof SVMs, we reviewed the most relevant published worksinvolving SVM learning and malaria parasite detection. Sothatwecanuseforourexperimentalworkthosefeaturesthathavebeenpreviouslyreportedtoprovidehighqualityresults.
Color,shape,andtexturefeatureswereselectedtoforma


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 of the
application of the sobel operator horizontally and vertically
over the RGB image. Once the 7 histograms are computed,5 values of each histrogram form the first 35 features. Thevaluestakenfromthehistogramsarethemean,thestandarddeviation, kurtosis, skewness, and entropy. Finally, 3 morefeatures known as Tamura texture parameters are computedfrom the original RGB images. Those final parameters arecoarseness, contrast, and direction. All theses features areclassified in [ 15] aspartoftheirworkinautomaticdetection
ofMalariaparasitesandtheyaredescribedin[ 27].
4.1.2.Sensitivity,Specificity,and 𝐹-Measure. Inordertoeval-
uate the performance of our implemented AdaBoost clas-sifier, we used three different statistical metrics: sensitivity,specificity, and effectiveness. Sensitivity is the probability ofa positive test given that the patient is ill and it is computedasfollows:
sensitivity
=number of true positives
number of true positives +number of false negatives.
(8)
Specificityinturnistheprobabilityofanegativetestgiven
thatthepatientiswellanditiscomputedasfollows:
specificity
=number of true negatives
number of true negatives +number of false positives.
(9)Due to the unbalanced class distribution between sub-


(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 stands for the true positives, FN for the false
negatives, and FP for the false positives. The 𝛽coefficient
(0<𝛽<1 )allowstoassignrelativeweightstoboththetrue
positiveandprecisionrates.Intheseexperiments, 𝛽 = 0.05is
used,sothesearchwasaddressedtodetectionofTP.
4.2.ExperimentalResults
4.2.1. AdaBoost Results. Looking at the values provided in
Table 2, we see that AdaBoost methods are as good as SVM
methodsintermsofsensitivity,withmeanvalueof1andstan-
dard deviation of 0. All five methods were trained to detect
everyparasiteinthetestsets.Intermsofspecificity,AdaBoost+ Postprocessing is clearly the winner among the five meth-odsbeingcompared,withmeanvalueof0.9325andstandarddeviation of 0.0496, as given in Table 3. Tables4and5
also show that AdaBoost + Postprocessing is superior to theothers, in terms of 𝐹-measures. In Figure8,w ei l l u s t r a t e


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
methodsusingdifferenttypesofkernels.
4.2.2. SVM + Feature Extraction Results. SVM results are
good especially using linear and polynomial kernels. Fea-tures used to train the SVM proved to contain the neededinformation to build good classifiers. Those features werecreated from color histograms and some other values thatencapsulatetextureinformationoftheimage.Thebestresultsforeachtypeofkernelwereobtainedwiththelibrarylibsvm,finding first the best pair of parameters 𝛾and𝐶for our
trainingexamples.Thoseparametersaregivenin Table 1.The
AdaBoost + Postprocessing method was implemented usinga linear kernel SVM with parameters 𝛾 = 0.002 and𝐶=
0.5.Theseparameterswereobtainedthroughmultiplecross-
validationexperimentsusingdifferentpairsofvaluesforthe𝛾and𝐶parameters.
4.2.3.AdaBoostversusSVM+FeatureExtractionComparison.
Results obtained with AdaBoost are more robust than thoseobtained with SVM. However, the sensitivity and specificityComputationalandMathematicalMethodsinMedicine 9
(a)
 (b)
 (c)
(d)
 (e)
 (f)
(g)
 (h)
 (i)
(j)
 (k)
 (l)


(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 classifier; in the third row we show the result of the postprocessing stage; and the fourth row shows the final result of thedetectionsystems.10 ComputationalandMathematicalMethodsinMedicine
0 0.005 0.01 0.015 0.02 0.025 0.03 0.03500.10.20.30.40.50.60.70.80.91
False positive rateTrue positive ratePolynomial SVM ROC curves
SVM_poly_ 2
SVM_poly_ 3
SVM_poly_ 4SVM_poly_ 5
SVM_poly_ 6
Figure 9: ROC curves with SVMs and polynomial kernels ranging
fromdegrees2to6.
0 0.005 0.01 0.015 0.02 0.025 0.03 0.035
False positive rate
SVM-linear
SVM-polynomial
SVM-RBFAdaBoost00.10.20.30.40.50.60.70.80.9True positive rate1
Ada+Postprocessing
Figure 10: ROC curves using AdaBoost and SVMs with three
differenttypesofkernel:linear,polynomial,andRBFs.
reached by using SVM cannot be considered bad, given the
difficultyofthetaskandtheirperformanceiscomparabletothe performance obtained in the malaria parasite detectiontask.
5. Discussion


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
Linear 0.00003 8
Polynomial 0.0078 0.0312
Radialbasisfunction 0.00003 8192
Table 2: Sensitivity by method.
Method Mean Std. Dev.
AdaBoost 1 0
AdaBoost+Postprocessing 1 0
SVMlinear+FeatureExtraction 1 0
SVMpolynomial+FeatureExtraction 1 0SVMRBF+FeatureExtraction 1 0
T able3:Specificitybymethod.
Method Mean Std. Dev.
AdaBoost 0.7550 0.1820AdaBoost+Postprocessing 0.9325 0.0496SVMlinear+FeatureExtraction 0.8218 0.0118SVMpolynomial+FeatureExtraction 0.6691 0.0035
SVMRBF+FeatureExtraction 0.7111 0.0068
Table 4: 𝐹-measure( 𝛽=1)bymethod.
Method Mean Std. Dev.
AdaBoost 0.7550 0.1820
AdaBoost+Postprocessing 0.9325 0.0496
SVMlinear+FeatureExtraction 0.8218 0.0118


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 0.9967 0.0000
SVMRBF+FeatureExtraction 0.9971 0.0001
to be the most robust way to go when we target a parasite
detection task. The main advantage in using AdaBoost withthe approach introduced by Viola and Jones is the fastcomputation of the features using the integral images trick,as opposed to any other method of image segmentation thatarecommonlyusedtogetherwiththeSVMapproach.Imagesegmentation is a time-consuming task and its use withoutparallel computation is almost impossible for applicationsthatrequiretoscanmanyimagesinashorttime.
The consideration of DNA pixels through a trained
SVM in the Ada + Postprocessing method is handled as apostprocessingprocedurebecauseitisalsoatime-consumingComputationalandMathematicalMethodsinMedicine 11
(a)
 (b)
(c)
 (d)
Figure11:Tworepresentativeimages.Inimage(a),thenumberofdarkpixelsmakesiteasyforthedetectionsystemtoconsideritasavalid


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 procedurewith the typical AdaBoost algorithm using integral images,we simply nullify the major advantage of the integral imagetrick,whichisafastcomputationofthepixelsaddition.
InFigure11, we present some examples of images that
arerepresentativecasesofeaseanddifficultyofclassification.When an image contains a high percentage of pixels stainedindarkpurple,asin Figure11(d) ,thealgorithmfindsitmore
difficulttodiscardfalsepositives.
P a r a s i t e sa n do t h e rs t a i n e do b j e c t sl i k eb l o o dc e l l sa r e
nonrigid and therefore vary in shape and size. The color isimportantbutitsonlyusetodistinguishbetweentheChagasparasitesandotherthingsisnotenough.Ontheotherhand,rawimagescannotbeuseddirectlyasafeaturevectorfortwomain reasons: first the size of such a vector would increase
unnecesarily the time of computation and second the high
variationinshapeandsizewouldmakeitextremelydifficultfor any machine learning algorithm to learn the correctclassifier. In the ideal case, features must be able to capture
shapeandcolorcharacteristicsoftheobjectstobeclassified.
Failing to detect Chagas is certainly a more serious


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 whether the parasite is present in the bloodsample.
Inpractice,thedetectionof T.cruziby analysis of digital
images obtained from peripheral blood smears would bringm u l t i p l ebe n e fi t sa s soc i a t e dt oth eC h a g a sd i se a sed i a g n o s i s .First,itwouldmakepossiblethediagnosisofthediseaseinitsinitialphase,whichistheacutephase,becausethepresenceoftheparasiteinthebloodwouldindicatewithhighprobabilitythat the infection is present. As a consequence of the early
diagnosis, the human cases could be treated with drugs
such as Nifurtimox or Benznidazole, and the probability ofstopping the disease is very high. Second, given that human12 ComputationalandMathematicalMethodsinMedicine
c a s e so fC h a g a sa r ev e r yd i ffi c u l tt ob ed i a g n o s e dd u r i n g
the analysis routinely performed in national laboratories incountries such as Mexico, an automated Chagas detectionsystem would contribute to estimate the local impact of thediseaseandtodeterminetheexistenceofanyendemicregion.
Moreover, the successful implementation of this algo-
rithm, would motivate the laboratories and hospitals to


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. Situation that is currentlyoriginatingthemproblemssuchasvisualfatigueandfrequentpainintheback.
Finally, this algorithm can be integrated as part of one
automated microscopic system for the adquisitionof imagesfrom blood smears. Such a device would be composed ofoneopticalmicroscopewithacontrollableelectromechanicals u r f a c es p e c i a l l yd e s i g n e dt om o v et h eb l o o ds m e a r sa sneeded by the scanning software. The microscope would beequipped with a high-resolution camera in order to take the
images.Imageswouldbestoredinharddrivestoallowtheir
posterioranalysiswithourChagasdetectionalgorithm.Thisdevice is currently being built as part of one CONACYTresearch project on Chagas led by Dr. Ruiz-Pi ˜na in Mexico
(projectcode:salud-2009-01-113848).
6. Conclusions
In this paper, we have provided an approach to the Chagasparasite detection problem based on AdaBoost learning.
UsingtheapproachusedbyViolaandJonesforthetaskofface
detection,weobtainedhighsensitivityandspecificityvalues.
Chagasdetectionapproachesbasedonmachinelearningand
computer vision methods have been barely studied as it is
evidenced by the lack of literature on the topic. The mostpromising method proposed for the detection of malaria
parasiteswasimplementedandcomparedtoAdaBoost.This


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 p o r t a n ti n
the task of Chagas parasite detection when high level of
sensitivityandspecificityarerequired.ApplyingSVMforthetask of Chagas parasite detection requires the computation
of the 38-dimensional feature vector for each subwindow
that we want to classify as being a parasite or nonparasite.
However, if we proceed in this way with every subwindow
of the original image of size 256 ×256, the whole process
would be highly time-comsuming. One way to reduce thenumber of subwindows that are worth checking is by meansofsegmentation.Segmentationallowsustoidentifydifferentrelevantobjectsintheimageanddiscardthosethatdoesnotlook like parasites. Given that most of the objects detectedduringthesegmentationarediscarded,onlyaminornumberof subwindows are further check by the SVM. Although theuse of segmentation is very common for this kind of tasks,it can also be a time-consuming process. AdaBoost appliedwithintegralimagesdoesnotrequiresegmentation,because
everysubwindowcanbecheckedinconstanttime,regardlessofitssize.
Conflict of Interests
The authors declare that there is no conflict of interestsregardingthepublicationofthispaper.
Acknowledgment


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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