FPGA based system for automatic cDNA microarray image processing Computerized Medical Imaging and Graphics

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Computerized Medical Imaging and Graphics

j ou rna l h o m e pa g e:w w w . e l s e v i e r . c o m / l o c a t e / c o m p m e d i m a g

FPGA based system for automatic cDNA microarray image processing

Bogdan Belean

, Monica Borda

, Bertrand Le Gal

, Romulus Terebes

aTechnicalUniversityofCluj-Napoca,DepartmentofCommunication,71-73Dorobantilor,400609Cluj-Napoca,Romania

bNationalInstituteforResearchandDevelopmentofIsotopicandMolecularTechnologies,65-103Donath,400293Cluj-Napoca,Romania

cUniversityofBordeaux1,IMSLaboratory,351CoursdelaLibération,TalenceCedex,France

a r t i c l e i n f o

Articlehistory:

Received5July2011 Receivedinrevisedform 23November2011 Accepted26January2012

Keywords:

Microarray Geneexpression Imageprocessing Parallelcomputation FPGAtechnology

a b s t r a c t

AutomationisanopensubjectinDNAmicroarrayimageprocessing,aimingreliablegeneexpressionesti- mation.Thepaperpresentsanovelshockfilterbasedapproachforautomaticmicroarraygridalignment.

Theproposedmethodbringsupsignificantlyreducedcomputationalcomplexitycomparedtostateofthe artapproaches,whilesimilarresultsintermsofaccuracyareachieved.Basedonthisapproach,wealso proposeanFPGAbasedsystemformicroarrayimageanalysisthateliminatestheshortcomingsofexist- ingsoftwareplatforms:userintervention,increasedcomputationaltimeandcost.Oursystemincludes application-specificarchitectureswhichinvolvealgorithmparallelization,aimingfastandautomated cDNAmicroarrayimageprocessing.Theproposedautomatedimageprocessingchainisimplemented bothonageneralpurposeprocessorandusingthedevelopedhardwarearchitecturesasco-processorsin aFPGAbasedsystem.Thecomparativeresultsincludedinthelastsectionshowthatanimportantgain intermsofcomputationaltimeisobtainedusinghardwarebasedimplementations.

© 2012 Elsevier Ltd. All rights reserved.

1. IntroductionincDNAmicroarraytechnology

Measurementofgeneexpressioncanprovidecluesaboutreg- ulatory mechanism,biochemical pathwaysand broader cellular function.Molecularbiologyandbioinformaticsareusingmicroar- raytechnologyinordertoidentifygenesinbiologicalsequences andtodeterminetheirfunctionalityand theirexpressionlevels underdifferentconditions.GenesareknownasportionsofDNA moleculethatencodefor a typeof protein.By geneexpression weunderstandthetransformationofgene’sinformationintopro- teins.Theinformationalpathwayingeneexpressionisasfollows:

DNA→mRNA→protein.Theproteincodinginformationistrans- mittedbyanintermediatemoleculecalledmessengerribonucleic acid,mRNA.Thismoleculepassesfromnucleustocytoplasmcar- ryingtheinformationtobuildupproteins[1].ThismRNAacidis asinglestrandedmoleculefromtheoriginalDNAandissubject todegradation, soit is transformed into stablecDNA (comple- mentaryDNA)forfurtherexamination.Microarraytechnologyis basedoncreatingcDNAmicroarrayswhichrepresentsgenespe- cificprobesarrayedonamatrixsuchasaglassslideormicrochip [2].Usually,samplesfromtwosources(cDNAfromtargetsample

∗Correspondingauthorat:TechnicalUniversityofCluj-Napoca,Departmentof Communication,71-73Dorobantilor,400609Cluj-Napoca,Romania.

Tel.:+40264401575;fax:+40264401575.

E-mailaddresses:bogdan.belean@itim-cj.ro,bogdan.belean@com.utcluj.ro (B.Belean),monica.borda@com.utcluj.ro(M.Borda),

bertrand.legal@ims-bordeaux.fr(B.LeGal),romulus.terebes@com.utcluj.ro (R.Terebes).

andcDNAfromreferencesample)arelabelledwithtwodifferent fluorescentmarkers(cyanine3–Cy3andcyanine5–Cy5,respec- tively) and hybridized on the same array (glass slide). The hybridization process represents the tendency of two single strandedDNAmoleculestobindtogether.Afterhybridization,the arrayisscannedusingtwolightsourceswithdifferentwavelengths foreachmarker(redandgreen)todeterminetheamountoflabelled sampleboundtoeachspotthroughhybridizationprocess.Thelight sourcesinducefluorescenceinthespotswhich iscapturedbya scannerand a compositeimage isproduced [2] (Fig.1).In this way,microarrayscomparegenesfromnormalcells withabnor- malortreated cells,determiningand providinginformation for understandingthegenes involved in differentdiseases [3].The microarraytechnologyisusedalsointoxicologicalresearchand monitoringenvironmentaleffectsondifferentgenomes.

Classical genomic microarray experiments involve complex stepsincludingslideproductionandscanning.Abriefdescription ofamicroarrayexperimentcanbesummarizedasfollows:

1.GenerationofarrayreadycDNA(selectingspecificcellmaterial andusingPolymericChainReactionforDNAamplification);

2.cDNAselectionandmicroarrayslideprinting;

3.Selectionofspecificcellmaterialfromtargettissuestobetested andfluorescentlabelling;

4.Hybridizationofthetargetmaterialonthemicroarrayslide;

5.Microarrayimagescanning;

6.Imagefilteringandspotdetection;

7. Intensityextractioninordertoevaluategeneexpression;

8.Highorderprocessing(Clusteringandinterpretation,genereg- ulatorynetworkestimation).

0895-6111/$–seefrontmatter© 2012 Elsevier Ltd. All rights reserved.

doi:10.1016/j.compmedimag.2012.01.002

Fig.1.CompositecDNAmicroarrayimagetogetherwithitsrawdataparameters determinedafterperformingstep6and7.

Steps1–5arecarriedoutbycompaniesproducingmicroarray slidesandspeciallaboratoryconditionsneedtobemetinorderto beaccomplished.Regardingsteps6and7,theyrepresentachainof imageprocessingtechniquesintegratedinexistingsoftwareplat- forms(e.g.AgilentFeatureExtractionsoftware,GenePixProetc.).

Theclassicalflowofprocessinga microarrayimageisgenerally separatedin the following tasks: pre-processing,for improving imagequalityandenhancingweaklyexpressedspots,addressing, segmentationandintensityextraction[4].Addressingassociates logicalcoordinatestoeachspotoftheimageandsegmentation classifiespixelseitherasforeground,representingtheDNAspots, eitherasbackground.Thelaststepcalculatestheintensitiesofeach spotand alsoestimates backgroundintensityvalues. Following allthesestepsinformationregardingthearraylayout,spotsizes andshapes,spotintensitiesandbackgroundintensityvalues, is obtainedforfurtherinterpretation.

Themaindisadvantageinmicroarrayimageprocessingisuser intervention which brings up the need of a workstation with acostlyprocessing platformwhich willslowdowntheprocess ofmicroarrayanalysisifa largenumber ofsubjectsis involved.

Besidesthat,theequipmentisnotportableandcannotbeused in field applications where a fast decision on a specific anal- ysis result may be crucial, as in embarked crews and remote areas,orwhere e-healthisan objective[5].In thiscontext, we designashockfilter-basedapproachforimageaddressing,within acompletemicroarrayimageprocessingsystem,robustandinde- pendentofoperatorlasttimeadjustments.Theproposedautomatic imageaddressingmethodiscomparedwithexistingapproachesin termsofcomputationalcomplexityandaccuracyinthepresence ofartefacts. Moreover,tovalidatetheproposedimage process- ingtechniques,wecompareourresultswithresultstakenfrom theGeneExpressionOmnibus,apublicfunctionalgenomicsdata repositorycontainingmicroarrayimageprocessingresultsdeliv- eredbysoftwareplatformslikeAgilentFeatureExtraction,GenePix Pro[6]andAffymetrix[7].Thelastmentionedsoftwareplatforms provideraw-datawithmicroarrayimagecharacteristicswhichare

usedfurtheroninhighorderanalyseslikeclusteringandgenereg- ulatorynetworkestimation.Anexampleofraw-datainformation deliveredbyAgilentFeatureExtractionsoftwareasaresultofa microarrayexperiment isdescribed inFig.1,where each infor- mationlinecorrespondstoamicroarrayspotwhichhasaprecise locationandrepresentsaspecificgene.

Foreach of theproposedmicroarray imageprocessingtech- niques,usingFPGAtechnologyandtakingadvantageofitsparallel computationcapabilities,wedesignedapplicationspecifichard- ware architectures. All together they describe an FPGA based systemforfastandautomatedmicroarrayimageprocessingand acquisition.Theproposedsystemcanbeeitherintegratedintothe microarrayscannertoautomaticallydeliverresultsorintoanother devicedesignedforremotemicroarrayscanningandprocessing.

2. AutomaticimageprocessingtechniquesforcDNA microarrayimages

Spotdetectionandintensityextraction,includedinamicroar- ray experiment workflow, are fulfilled using image processing techniques.Recentresearchdevelopedseveralmicroarrayimage processingmethodsspecifictocDNAmicroarrayanalysiswhich provide grid alignment, spot segmentation and spot intensity extraction.ThissectiondetailseachstepofcDNAmicroarrayimage processingby presenting thestateof theartand alsoourpro- posedimageprocessingtechniques.Thenovelshockfilterbased approachforautomaticimageaddressingiscomparedregarding thecomputationalcomplexitytothestateoftheartapproaches.

Moreover,theaccuracyoftheproposedmethodinthepresence ofartefactsisillustratedcomparedtoSVMandOMTGapproaches reportedin[8]and[9]respectively.Inordertovalidateourresults intermsofaccuracyandreliabilityofspotdetection,GEO(Gene ExpressionOmnibus),aMIAMEcompliantdatabasewasusedto provideforcomparisonbetweendifferentmicroarrayimagesand thecorrespondentresultsdeliveredbyexistingsoftwareplatforms formicroarrayimageprocessing.

2.1. Microarrayimageenhancement

Awell-knowncharacteristicofmicroarrayimagesdeliveredby existingscannersisthelowlevelofexpressionformicroarrayspots, determinedbytheirpixelintensity.Thus,themicroarrayimage processingworkflowcommonlystartswithapoint-wisenonlin- eartransformations,usedinordertoimproveimagequalityand toenhanceweaklyexpressedspots[5,10].Onecanusealogarithm transformationasshowninEq.(1).Theoutput,foramicroarray imageI(x,y)with(x,y)denotingthecoordinatesofapixelandn thenumberofbitsforluminance/chrominancefunctionrepresen- tation,isdescribedby:

IL(x,y)=ln(I(x,y)+1)

nln2 2ⁿ (1)

Alternatively,anarctangenthyperbolicbasedtransformationcan beusedforimageenhancement[11].Incaseofsuchanonlinear transformationonlyforeground(spot)information isselectively enhanced.

2.2. Automaticmicroarrayimageaddressing(gridalignment) Thefirstoperationperformedonmicroarrayimageisknownas addressingorgridalignment.Thisoperationaimsregisteringaset ofhorizontalandverticallineswhichdescribeatwo-dimensional arrayofspots.Theexistingsoftwareplatformsformicroarrayimage analysistogetherwithlateresearchimposetwoapproachesforgrid alignment, template-basedand, respectively,data-driven meth- ods[12].CurrentlyavailablesoftwarelikeGenePixPro(Molecular

Devices, Inc.), ScanAlyze, Dapple [13] or ImaGene (Biodiscov- ery,Inc.)usetheprocedureofmanualtemplatematchingwhich involvesdefiningthenumberofspots(alongrowsandcolumns), spotdiameterandspotspacing.Thedefinedtemplateisoverlaid onthemicroarray imageandthepreviously mentionedparam- etersareadjustedinordertomatchthespotsinthemicroarray imageofinterest.Automatictemplateadjustmentsareintroduced byGenePixandQuantArray[14]butevenso,ifgridgeometrydevi- ationis increased,themethodisnot efficient.For eachtype of microarraytechnologydifferenttemplatedefinitionsarenecessary, thusthemethodisnotfullyautomated.

Indata-drivenapproach,imageprocessingtechniquesareused todeterminethegridalignment.First,horizontalandverticalpro- jectionsvectorsarecomputedbysummingupthepixelsintensities onmicroarrayimagerowsand,respectively,columns[12].Based onstatisticalanalysesof1Dimageprojections,localextremarep- resentingspotscentreslocationsarethanestimated[15].Dueto theprofileirregularities,autocorrelationisusedtodeterminespot spacingandspotdimensions[16].Thisapproachisefficientinspots centreslocationscomputation,butontheotherhand,duetopro- filesirregularities, additionalpowerfulsegmentationtechniques areneededtodetectspotdimensionsandspotspacing.

Forhandlingalltheabovementioned issues,we proposean accuratelow-complexityautomatedgridalignmentmethod.Itisto bementionedthatournovelshockfilterbasedapproach,besides addressinginformationalsoprovidessegmentation information, thenextstepinmicroarrayimageprocessing.Thecomputational complexityoftheproposedmethodiscomparedwiththestateof theartapproaches.Resultsandadvantagesobtainedbyapplying shockfiltersformicroarrayimageaddressingarepointedoutin Section2.4.

Inimageprocessing, shockfiltersgenerallyserve asanedge enhancingalgorithm.Aimingblurryedgeenhancement,Osherand Rudinproposedthefirstshock filterformulation in[17]. Based on a hyperbolic partial differential equation, the general one- dimensional(1D)shockfiltermodelisdescribedbyEq.(2),under theinitialconditionsU(x,0)=U0(x)andwiththeoperatorFfulfilling thefollowingconditions:F(0)=0andF(s)×sign(s)≥0.

∂U

∂t +F(Uxx)

InEq.(2)UxandUxxaredenotingrespectively,thefirstandthe secondorderderivativesofthefunctionU.BychoosingF(s)=sign(s), weobtaintheclassicalshockfilterequation:

Ut=−sign(Uxx)

Thefollowingdiscreteschemeisusedfor1Dshockfilterapproxi- mation[17]:

U_iⁿ⁺¹=U_iⁿ−t·

i

where:

DU_iⁿ= m(+U_iⁿ,−Uin)

h D²Uⁿ_i =(+−U_iⁿ)

h² (5)

andm(x,y)isthe“minmod”function:

m(x,y)=[sign(x)+sign(y)]·min(|x|,|y|) (6) and

±=±(U_i±1−Ui) (7)

aretheforwardandbackwarddifferenceoperators.

Formicroarrayimageaddressing,weappliedshockfilterson thehorizontalandtheverticalimageprofiles,usingthepreviously 1Ddiscreteshockfiltermodel,Eq.(4).Thehorizontalandvertical profilecomputationisdescribedby(8)and(9),withVPrepresent- ingtheverticalprofileandHPthehorizontalone.Wedenoteby

I(x,y)thegreyscalemicroarrayimage,and,respectively withX theheightandwithYthewidthoftheimage(y=0,1,...,Y−1and x=0,1,...,X−1).

VP(x)= 1 Y

Y−1

y=0

I(x,y) (8)

HP(y)= 1 X

I(x,y) (9)

Byapplyingshockfilters,imageprofilesevolveasdescribedbythe Eq.(3),anexamplecanbeseeninFig.2aandbwhereasection oftheoriginalprofileanditscorrespondingresultafterapplying shockfiltersaredescribed.Abetterviewoftheshockfiltereffect canbeseeninFig.2c,wherethethincontinuouslinerepresentthe originalimageprofilewhichevolvesinthedirectionpointedbythe arrows.Theresultisthethickcontinuousline,thedottedlinesbeing intermediatestepsinimageprofilesevolution.Themainadvantage isthatshockfiltercreatesstrongdiscontinuitiesattheinflexion pointsoftheprofiles,thus,basedontheresultedprofilesdescribed inFig.2c,gridalignmentisperformedasfollows.Pairsofperpen- dicularlinescanbedrawnover thepictureasshowninFig.2d ande,consideringtheinflexionpointsonbothimageprofiles.Spot locationissimplydeterminedasthecentreofthesquaredefined byupperleftcornerA(2i,2j)andlowerrightcornerB(2(j+1)+1, 2(i+1)+1).Moreover,aregioncanbedefinedasbeinglocalback- groundinformation aroundthespot(theareabetweenthetwo squaresinthesinglespotcroppedimagefromFig.2d).Separating eachspotfromitslocalbackgroundisconsideredasinformation regardingsegmentation.

2.3. Microarrayimagesegmentation

Theoutcomeofthepreviouslydescribedimageprocessingtech- niqueisanautomatedapproximationofspotlocations,definedas arectangularareaenclosingonespot.Thenextstepistoidentify pixelsthatbelongtothemicroarrayspotand pixelsthat repre- sentbackgroundinformation.Thus,takingintoaccountthegrid alignment,a templatewhichdefines areaswithforegroundand backgroundpixels is overlaidonthemicroarray spot.Different softwareplatformsusedifferentstrategiestodefinethetemplate Fig.3a[18];ScanAlyzeusesallthepixelsthatarenotwithinthe spotmaskbutwithinthedottedsquarecenteredasaregionfor backgroundestimation,ArrayVisionconsidersanarealiketheone betweenthetwocirclesasbackgroundandGenePixestimatesthe backgroundonthebasisofthepixelvaluesinthediamondareas.

Incaseofourautomatedimageprocessingchain,shockfilterbased approachforgridalignmentintroducessegmentationinformation.

Thus,backgroundareaisdelimitedbytwosquaresasshowninthe singlespotimagefromFig.3b.

Fortheforegroundinformationedgedetectionisappliedinside thesmallersquaretoseparatebackgroundfromspotpixels.For edgedetectionahigh-passfilterinFourierdomaincanbeapplied, orconvolutionwithanappropriatekernel(Sobel,Prewittetc.)in thespatialdomainissuitable[19].Thealgorithmaddedtoourauto- matedprocessingchainforimagesegmentationisCannyfilter[20]

duetoitsoptimalresultsintermsofaccuracyandcomputational complexity.

2.4. Resultsanddiscussion

Uptothispoint,imageprocessingtechniqueswerepresented inordertorealizearobustdetectionofmicroarrayimagespots (features).Anovelshockfilterbasedapproachwasintroducedin microarrayimageprocessingchain,whichprovidesautomatedgrid

Fig.2.Automaticmicroarraygridalignmentusingshockfilters:(a)sectionofthemicroarrayimagehorizontalprofile,(b)resultedprofileafterapplyingshockfilters,(c) profileevolution,(d)microarrayspotaddressingusingshockfiltersonbothhorizontalandverticalprofiles,and(e)automaticmicroarrayimageaddressingonrealsize Agilentmicroarrayimage.

Table1

Accuracyestimationforspotdetectionincaseofourshock-filterbasedapproachforautomatedimageaddressingappliedondifferentmicroarrayimages(Imagecharacter- istics:pixelresolution6100×2160,16bits/pixel,22,575spots).

ComputedMSEforspotposition

ExperimentID AgilentscannerID MSEXposition MSEYposition MeanEuclideandistance(d)

US451028671 11,472 0.418 0.598 0.881

US45102867 2 11,868 0.256 0.576 0.778

US451028673 11,978 0.450 0.442 0.832

GSM135598 11,978 0.295 0.998 1.006

GSM135599 11,978 0.129 0.982 0.921

GSM102718 11,978 0.342 0.517 0.822

GSM102721 11,978 0.411 0.730 0.927

GSM207313 11,521 0.256 0.902 0.935

GSM207316 11,521 0.372 0.979 1.033

GSM207320 11,521 0.595 0.906 1.073

alignmentandalsodeliversinformationregardingspotsegmen- tation.In order tovalidatetheproposed automatedaddressing methodintermsofaccuracyandefficiency,wecompareourresults withtheonesdrawnfromGEOdatabaseandweestimatethecom- putationalcomplexityofourshockfilterbasedapproachcompared withexistingstateoftheartapproaches.

2.4.1. Accuracyofspotlocationestimation

Thedatasetusedtoevaluateourshock-filterbasedapproach forlocalisingmicroarrayspotsconsistsof45 microarrayimages whichcontainsover1millionmicroarrayspots.The16-bitgrey scaleimagesof22,575microarrayspotseacharestoredasTIFFfiles witharesolutionof6100×2160pixels.Theaccuracyisestimated bycomparingourobtainedresultswiththeonesavailableonGene expressionOmnibus.Thus,foreachmicroarrayimagethedeter- minedlocationsforeachmicroarrayspotiaredefinedbypairs(Xi, Y_i)representingspotcentrecoordinates.Thepairs(X_i^G,Y_i^G)repre- sentspotlocationexpressedinpixelsdeliveredbyexistingsoftware platformsdrawnfromGEOdatarepository.Meansquarederrors MSE_XandMSE_Yarecomputedbetweenthetwo(Xi,X_i^G)and(Yi,Y_i^G) spotscoordinates.Moreover,themeandistancedbetweenspot centresdrawnfromGEOdatabaseandtheonesobtainedbyour approachisestimatedincaseofeachmicroarrayimage.InTable1, resultsoftheaforementionedcomparisonarelistedconsidering asubsetof10microarrayimages.Consideringthewholedataset, eachspotresidesinsideitsdeterminedgridcell,whereasthemean distancebetweentheestimatedspotpositionandtheonedrawn fromthedatabaseis1.08pixels.

2.4.2. Computationalcomplexityevaluation

Furtheron,currentstateoftheartapproachesforautomaticgrid alignmentaresummarisedandtheircomputationalcomplexityis estimated(Table2),consideringMandNbeingtheimagedimen- sions.Themostcommonlyusedmethodforgridalignmentisbased oncomputinghorizontalandverticalprofilesandusingautocorre- lationtodeterminatespotdimensionanddistancebetweenspots [15,16,21].ReducedcomplexityisachievedasshowninTable2, but,ontheotherhand,majordisadvantagesareintroduced.Spot locationsaredeterminedusingprofilespeaksandvalleys,which incaseofirregularpixelintensitydistributionalongamicroarray spotarenotaccurate.Moreover,afixedspotdimensionandfixed spotspacingareestimatedbyautocorrelation,whichfailsinterms of accuracy in case of irregular image profiles and spots with different radii. Thus, more complex approaches for automatic gridalignmentwereproposedintheliterature.InRefs.[22,23]a hillclimbingalgorithmisusedforgridalignmentwithincreased computationalcomplexity.Morphological operatorsare usedin [24]andfor[25]automaticmicroarrayimageaddressing,witha computationalcomplexity of O(2SeMN) where Se is the size in pixels (1k) of the structural element for dilation and erosion.

Fig.3. Exampleofbackgroundandforegroundseparationmethods.

Moreover,basedona selectionofmicroarrayspots,in [26]and [8]aSVM(supportvectormachine)approachisreportedforauto- maticmicroarraygridingwhichovercomesthegeneticalgorithm presentedin[27]withnearlyoneorderofmagnitudeintermsof computationalcomplexity.Parameterkrepresentsthenumberof selectedmicroarrayspotstotraintheSVM,inrealcasemicroarray

Fig.4. Computationalstepsperformedbytheshockfiltersforgridalignment.

Table2

Computationalcomplexityfor(M,N)dimensionmicroarrayimagesincaseofexistingapproachesforautomaticgridalignment.

Computationalcomplexityforautomaticgridalignment

Autocorrelation[15,16,21] Hillclimbingapproach[22,23] Morphologicaloperators[24,25] SVM[8,26] OMTG[9] Shockfiltersbasedapproach

O(M+N) O(M²N²) O(2SeMN) O(MN(M+k)) O(tsN²) O(MN+˛(M+N))

imagestheorderofkbeing10³.In[9]fullyautomaticmicroarray gridalignmentisperformedusinganoptimalmultilevelthreshold approach. The reported computational complexity is O(tsN²), wheretsdenotesthethresholdsetsize.

Thenumberofcomputationalstepsperformedbyourproposed shockfilter based approach for horizontal grid lines alignment isillustratedinFig.4.Consideringthenumberofstepsforver- tical grid lines determination computed in a similar manner, the total computational cost is denoted by the upper bound functionf(M,N)=2MN·s+6˛(M+N)sonthealgorithmcomplexity, where s represents one computational step. The computa- tional complexity of the proposed grid alignment algorithm is O(f(M, N))=MN+˛(M+N), representing the order of growth for the computational cost. The ˛ parameter represents the number of iteration necessary for profiles evolution to deliver accurate results. We empirically determined the optimal ˛ value(˛=100).

AspresentedinTable2,thenovelshockfilterbasedapproach bringsupsignificantly reduced computationalcomplexity com- paredtostateoftheartapproaches,whilesimilarresultsinterms ofaccuracyareachieved.Moreover,gridalignmentisautomatically performedwhileinformationregardingsegmentationisdelivered fornextprocessingsteps.Thelargesizeofmicroarrayimagesis alsotakenintoaccountwhileapplyingshockfilters.Thus,inspite oftheiriterativenature,shockfiltersprovedtobeefficientsince theyareappliedonimageprofiles.

2.4.3. Efficiencyevaluationinthepresenceofartefacts

Fig.5illustratesthegridalignmentperformedbyourapproach inthepresenceof artefacts.More specifically,a setofmicroar- rayimages(AT-20392-ch1,A20T-13036325-ch1,1302-ch2-OD370, 1311-ch2-OD080)containingbrightartefactsduetothemicroar- rayslideprintingandscanningprocessesisanalysed.In[9],failure todetectsomespotregionsduetotheextremelycontaminated

Fig.5.Evaluationofourgridalignmentapproachinthepresenceofartefacts:(a)accurategriddingperformedonAT-20392-ch1comparedwiththeapproachpresentedin [OMTG];(b)–(d)accurategridinginthepresenceofbrightartefacts.

imagesisreportedincaseoftheAT-20392-ch1microarrayimage fromtheSMDdataset.Forthesameimage,ourapproachdelivers accurateresultsforthespotsenclosedinthedottedsquaredarea fromFig.5a.Meanwhile,theaforementionedgroupofspotsisnot detectedbythegridalignmentmethodproposedin[9].Despite thepresenceofartefactsandnoise,fortheimagesillustratedin Fig.5b–dtheproposedmethodachievessuccessfulgriddingeven inthevicinityoftheartefacts.Itistobementionedthat,thesame typeofartefactswereconsideredforaccuracyevaluationasthe onesreferredinRefs.[8,26].

Toconclude,experimentalresultsprovedthatourautomated image processing techniques are accurately determining spots locationsandcharacteristics,motivatingthedevelopmentofappli- cationspecificarchitecturesinordertoeliminateuserintervention andreducecomputationalcosts.

3. FPGAbasedsystemformicroarrayimageprocessing FPGAtechnologyusespre-builtlogicblocksandprogrammable routingresourcesforconfigurationandforimplementingcustom hardwarefunctionality.Itsmainbenefitsarethelowcost,theshort timetomarketandtheeaseofreconfiguration.Moreover,FPGA technologyexploitsspatialandtemporalparallelismaimingalgo- rithmparallelizationforfastprocessing.Alltheseadvantagesare usedtoimplementapplication-specificarchitecturesforourpro- posedautomatedimageprocessingtechniquesdescribedinSection 2.

Imagescanning,spotdetectionandintensityextraction,steps 5–7fromamicroarrayexperimentaresusceptibletobeintegrated inaFPGAbasedsysteminordertoreducecomputationalcosts,to eliminateuserinterventionneededbytheexistingsoftwareplat- formsand todecreasetheprocessing time.Fig.6 describesthe proposedFPGAbasedsystemformicroarrayimageacquisitionand

Fig.6.ProposedFPGAbasedarchitectureformicroarrayimageacquisitionand processing.

Fig.7. HardwaredesignformicroarrayimageprocessingonVirtex5platform.

processing.TheproposedarchitectureisdevelopedaroundanFPGA basedProcessingUnitandaChargedCoupledDevice(CCD)image sensor.TheCCDtransformsthefluorescencelevels(incomingpho- tons)producedbythedouble-laserscanningdeviceintoelectrons whicharestoredaselectricalcharge.Thechargeisamplifiedresult- inginananalogoutputsignal,whichisdigitizedusingan(Analog toDigitalConverter)ADCandstoredintheDDRAMmemoryasa microarrayimage.

TheFPGAbasedProcessingUnitimplementsthefollowingfunc- tions:scannercontrol,CCDcontrol,storage controlthroughthe ControlUnit(CU),robustmicroarrayimageprocessingusingFPGA basedapplicationspecificarchitectures(describedinthenextsec- tion),andalsothecontrolforthetwocommunicationinterfaces COM,andCOM2.Thefirstone,COM,usesageneralpurposecom- municationinterfaceUSBwhichallowsthetransferofprocessed microarrayimagestogetherwithraw-datainformationdescribing microarrayimagecharacteristicsforstorage.Theproblemsposed by acquisition and storage werediscussed in [28,29]; we refer tothesepublications for moredetails. Thesecond one,COM2, useswirelesscommunicationspecificforlastgenerationhand-held devicesinordertosendthemicroarrayimageparameters,likethe onesinFig.1,forhigh-ordermicroarrayimageprocessing.

Themaingoalofthisapproachformicroarrayimageprocessing istoobtainadevicewhichwillbeabletoextractandquantifygene expression.

3.1. FPGAbasedapplicationspecificarchitecturesformicroarray imageprocessing

Thehardwareimplementationsofmicroarrayimageprocess- ingtechniquesmakeuseoftheFPGAfeaturesinordertoevaluate theperformancesofourproposedsystemontargetdevices.Indeed, FPGAtechnologyoffersthepossibilitytoexploitspatialandtempo- ralparallelismformicroarrayimageprocessinginordertocreatea fastautomatedprocesswhichdeliversraw-datainformationabout microarrayimagecharacteristics.Asaconsequence,FPGAareeffi- cientforprocessingmicroarrayimagesasshownin[28].

OurFPGAbasedsystemonachipincludingcustomprocessing elementsformicroarrayimageprocessingtechniquesisdescribed inFig.7.Eachcustomprocessingelementfromtheproposeddesign isconnectedtotheFastSimpleLink-FSLdatabusasaco-processor forthesoft-coreMicroBlaze100MHzmicroprocessor.TheFSLbus implementsapointtopointFIFO(FirstInFirstOut)basedcom- municationbetweenprocessingunitsandmicroprocessor.Awrite operationtoaFSLinputFIFOisperformedbyMicroBlazeinasingle clockcycle.AreadoperationtransfersthecontentofaFSLbustoa generalpurposeregisterin2clockcycles.Ourcustomprocessing elements,usingparallelcomputing,actashardwareaccelerators;

inputdataissequentiallywrittenintheFSLinputFIFOandthe resultsaredeliveredthroughtheFSLoutputFIFO.For example, incaseofthelogarithmbasedenhancementarchitecture,theFSL inputFIFOis loadedwithpixel intensities,whilethecomputed resultsaresequentiallydeliveredtotheFSL outputFIFO aftera 3T_clkcyclesdelay.TheMicroBlazesoft-coremicroprocessorisused asaninterfacebetweenourprocessingelementsandinputs/otputs.

AProcessorLocalBusIPblockwithcachesystemisusedtostore resultsinRAMmemory.

Thecustomprocessingelementsincludedinoursystemimple- ment in a pipeline manner the independent processing steps specifictomicroarrayimageprocessing:imageenhancement,grid alignmentandsegmentation.Thus,usingspatialandtemporalpar- allelism,theproposed hardware architecturesrealizes a robust microarray image processing, aiming tospeed upcomputation andtoreducepowerconsumptioncomparedtoageneralpurpose microprocessorimplementation.Customprocessingelementscor- respondingtoimageenhancement,automaticgridalignmentand segmentationaredescribedinthefollowingsections.Timingcon- siderationsandhardwareresourceusagearepresentedinSections 3.2and3.3.

3.1.1. Imageenhancementarchitecture

Spatiallogarithmtransformationisusedformicroarrayimage enhancement.ThelogicblocfromFig.8dcomputesthelogarithm oftheluminancefunction(Ycomponent)foreach pixel(x,y)of themicroarrayimage.Thehardwareimplementationofthelog- arithmtransformation is based onlinear approximation of the logarithmfunctionandisefficientlydesignedaccordingtoalgorith- micconstraints(fixedpointrequirements).Thelogarithmfunction iscalculatedinannnumberofpointsAn(x,y)storedinamem- orynamedROMLOG.Alsotheslopemforeachlinedescribedby twoadjacentpointsiscalculatedandstoredinamemorycalled ROMSLOPE.Inordertocalculatethelogarithmoftheluminance, weareusingEq.(10)whichrepresentstheequationofalinewith slopemandpassestroughthepointAi(xi,yi)fromtheinitialAn

points,wherey_iandmrepresentthememorycontentatYmodulo naddress.

ylog=m(y−xi)+yi (10) MSE= 1

YMAX

y

[ln(y)−lnest(y)]²=1.807×10⁻⁵ (11)

Inordertoevaluatethelogfunctionestimation,meansquareerror wascalculatedforyvaluesbetween1andYMAX=2¹⁶withanum- berofn=2¹⁰A_ipointsandtheresultisshowninEq.(11).Afull pipelinedarchitecturewasdevelopedtomaximizetheprocessing throughput.Thischoicereducesthecomputationaltimeforthelog- arithmunitto1pixel/clockcyclewithaninitial3clockcyclesdelay.

Logarithmaccuracycanbeimprovedbyincreasingthenumberof A_ipointsforestimationwhichinvolveshighermemorystoragefor theROMmemories.

3.1.2. Automaticgridalignmentarchitecture

The custom processing element for grid alignment involves computingthehorizontalandverticalimageprofilesofthelog- arithm transformed microarray image. Thus, the 16 bits Y_Log intensityfromthepreviousarchitectureisthecurrentinput.The

˙Xand˙YRAMmemoriesandthetwoadders(32bits)areusedas accumulatorsforcomputinghorizontalandverticalprofileswhile thewholeimage isscanned onlyonce.The architectureshown inFig.8a,isfullypipelined,eachlogarithm datareceivedbeing summedwiththecorrespondingrawandcolumndataaccording totheaddressprocessingunit. Prefetchread anddelayedwrite operationsarerealizedonmemoriestosupporthighthroughput

requirements.ThestoreddatainthetwomemoriesRAM˙Xand RAM˙Yisfurtheronusedinthenextprocessingstep,gridalign- ment,whichaimstoextractspotlocations.

WeconsidertheimageprofilesbeingstoredinaRAMmemory (ImageprofileRAM).Thenovelarchitectureproposedforshockfil- tercomputationisshowninFig.8c.Thearchitecturesdividesthe profilesinblockshavingthedimensionn,thesameasthedimen- sionofBuffer1andBuffer2whichworkasshiftregisters.Inorder tofill uptheBuffer1,nxCLKcyclesarenecessarysincedatais readfromRAM.WhentheBuffer1isfull,theProfiledividergener- ates“loadctrl”signalinordertoperformaparallelloadofBuffer1 registerintoOut⁽ⁱ⁻¹⁾register.Whilenewn–valuesareloadedin Buffer1,Out⁽ⁱ⁻¹⁾andOut⁽ⁱ⁾registersimplementedontoFPGAwill usespatialparallelismtocomputethefollowing:

Loopclk=1...n

r_k⁽ⁱ⁾⇐r_k⁽ⁱ⁻¹⁾+dt·sgn(r_k−⁽ⁱ⁻¹⁾−r_k+⁽ⁱ⁻¹⁾)·min(r_k−⁽ⁱ⁻¹⁾,r_k+⁽ⁱ⁻¹⁾) (12)

r_k⁽ⁱ⁻¹⁾⇐r_k⁽ⁱ⁾, (13)

Endloopwherenrepresentsthenumberofclock-cyclesforwhich theOutregistersevolveastheEqs.(12)and(13)andr_k±=r_k−r_k±1. ConsideringpandqtheclockcyclesneededforcomputingEqs.(12) and(13)respectively,thenumberofiterationsperformedbythe proposedapplicationspecificarchitectureisgivenbyi=n/(p+q).

Whenthesecondseries ofn values fromtheimageprofileare loadedinBuffer1,resultsoftheshockfiltercomputationareloaded inBuffer2.NextstepisloadingtheresultedprofileintheShockfil- terresultRAM,whichtakesnclockcycles.Itistobementioned theparameternischosendependingonthenumberofiterationsi neededbyshockfilter.Anempiricalapproachisusedtocompute theparameteri.Accurateresultsareobtainedwithi=100,which leadston=400,tackingintoaccountthatusingspatialparallelism, p+qislessthan4clockcycles.

3.1.3. Imagesegmentationarchitecture

This section presents a custom processing element which implementsCannyedgedetectoraimingmicroarrayimageseg- mentation.Taking intoaccountthat spotslocationareprovided bythegridalignmentstep,edgedetectionbasedsegmentationis appliedonlyonspotarea.ApplyingCannyfilteroneachspotloca- tiondeterminedbygridalignmentinvolvesthefollowingsteps:

image smoothing, image gradient computation, non-maximum suppressiontoeliminatethepixelsthatrepresentfalseedges.The lastmentionedimageprocessingoperationsarebasedonimage convolution,classifiedasa spatialfilter.Convolutionisusedfor implementingimageoperatorswhichhaveasoutputpixelvalue alinearcombinationbetweenpixelsoftheoriginalimage.Con- ceptually,eachpixelintheoutputimageisproducedbyslidingan N×Mwindowovertheinputimageandcomputinganoperation accordingtotheinputpixelsunderthewindowandthechosen windowoperator.Thehardwareapproachforconvolutionispre- sentedasfollows:theentireinputimage,inourcaseamicroarray spot(width×heightpixels),isstoredintotheFSLFIFO;M×Npix- elsvaluesarerequiredtocalculateoneoutputpixelvalue.Memory bandwidthconstraintsmakeobtainingallthesepixelseachclock cycleimpossible,solocalcachingisperformed[30,31].Inthisway, N−1rowsarecachedusingashiftregisterwhichleadstotheblock diagram fromFig.8b. Thus, insteadofsliding a window across theimage,theimplementationfeedstheimageofwidth×height dimensionthroughthewindow. Correspondingpixelsfromthe resultedimageafterconvolutionaredeliveredsequentiallytothe outputafteraninitialdelayoft=(M−1)width+N.

Using the previous approach for image convolution, logic blocksareimplementedforsmoothingtheimage,forcomputing thefirstorder derivativeandfor non-maximum suppression.A

Table3

Computationaltimefortheproposedimageprocessingtechniques.Computational timewasestimatedforeachmicroarrayimageprocessingtechniquesbothonPC (IntelDualcoreT2370processor,2GBRAM)andVirtex5platform(MicroBlazepro- cessor,256MBRAM).EstimationswereperformedonAgilentUS45102867 1image (pixelresolution6100×2160,16bits/pixelincluding22,575microarrayspots).

Griddingmethod Processingplatform Processingtime M³G Athlon×2–3.8GHz,3GBRAM 10s

Geneticalgorithm – 92s

Proposedshockfilter Virtex5,100MHz,512MBRAM 192ms Proposedshockfilter T2370–2×1.7GHz,2GBRAM 313ms

Table4

HDLsynthesisreport.

HDLsynthesisreport

No.ofmultipliers 4(16×16bit) 3(16×16bit) 9(16×16bit) No.ofadders 3(16bit) 3(32bit) 8(32bit)

1(16bit) 5(17bit) 2(10bit) No.ofsingleportRAMs - 2(4k×32bit)

2(8k×32bit)

No.ofcounters 2(12bitup) 1(7bitup)

2(13bitup)

pipelinearchitectureincludingallthree logicblocksisdesigned fortheCannyfilter,theresultedimagebeingstoredinaFSLFIFO Out.

3.2. Computationtimeconsiderations

Thecomputationaltimeneededforperformingautomaticgrid alignmentincaseoftheSVMapproachpresentedin[26]isreported as10msfor a450×450pixel sizemicroarrayimageblock.The sametypeofmicroarrayimagewasconsideredinordertoevalu- atetheprocessingtimeofourproposedshockfilterbasedapproach forautomaticgridalignment.Themicroarrayimagewasprocessed usingCcode,whileclockfunctionwasusedfortimemeasurement.

Computational time isestimated alsofor eachof thehardware architecturesdevelopedforautomaticmicroarrayimageprocess- ing.InTable3,resultsarepresentedforprocessinga450×450pixel microarrayimageblock(16bits/pixelrepresentation),usingboth stateoftheartapproachesandtheproposedapproachondifferent processingplatforms(PCandVirtex5platform).

Fig.9 illustratesthecomputationaltime forperforminggrid alignment in case of ourproposedapproach applied ondiffer- entblocksofspotsofthesamemicroarrayimagesUS451028671, deliveredbyAgilentscanners.Computationaltimewasmeasured onbothPC(IntelDualcoreT2370processor,witha1.73GHzclock frequency,2GBRAM)andVirtex5platforms.Yaxisrepresentsthe

Fig.8.(a)Hardwarearchitectureforimageprofilecomputation.(b)Hardwarearchitectureforimageconvolution.(c)Hardwarearchitectureforshockfiltercomputation.

(d)Hardwarearchitectureforlogarithmtransformationbaseonlogfunctionlinearapproximation.

Table5

Deviceutilizationsummary.Deviceutilizationforxc5vlx110tFPGA(Virtex5platform)ispresentedforeachoftheproposedhardwarearchitecture.

Deviceutilizationsummary

Logarithmtransform. Profilesandshockfiltercomputation Segmentation Total Available

No.ofslicereg. 18 355 1068 1441 69,120

No.ofsliceLUT – 8525 1736 10,261 69,120

No.ofBlockRAM 2 – – 2 148

Fig.9.Computationaltimeforgridalignmentperformedondifferentblocksof spotsofthesamemicroarrayimageUS45102867 1(pixelresolution6100×2160, 16bits/pixelincluding22,575microarrayspots)usingtheVirtex5platformandPC.

processingtimeinmilliseconds,whileXaxisreferstothedimen- sionofeachblockofspotsspecifiedbythenumberofmicroarray spotsenclosed.Itcanbeseenthat,inspiteofhigherperformances providedbythePC,theapplicationspecificarchitecturesaddedto aMicroBlazesystemonVirtex5FPGAbringupbetterresults.

3.3. Hardwareresourceusage

Thetargetdeviceusedfortheimplementationsoftheproposed hardwarearchitectureisXilinxxc5vlx110tFPGAfoundonVirtex5 platform.ThearchitectureswerecompletelydescribedusingVHDL andtheVHDLcodewasbehaviourallyandpostplace-and-route validatedthroughsimulations.HDLsynthesisreportstogetherwith deviceutilizationsummaryaredetailedinTables4and5,respec- tively,foreachmicroarrayimageprocessingstep.

Besides spatial and temporal parallelism introduced by our implementations,futureworkaimsdesigningahighthroughput systemwhichusemultipleinstancesoftheproposedapplication specifichardwarearchitectures.

4. Conclusions

Thepaper proposes a novelshock filter basedapproach for automatedgridalignmentonmicroarrayimages,integratedinthe classicalflow ofa microarrayexperiment. Theobtainedresults, comparedwiththeonesdeliveredbytheexistingsoftwareplat- formsformicroarrayimageprocessing,provedtobereliableboth intermsofautomationandaccuracy.Computationalcomplexity provedtobesignificantlyreduced comparedtothestateofthe artapproachesforautomaticmicroarrayimageprocessing.Taking intoaccountautomationintroducedbytheproposedimagepro- cessingtechniques,anFPGAbasesystemaimingtoreduceboth userinterventionandcomputationalcostsisdescribed.Thesys- temincludesapplicationspecifichardwarearchitecturesforimage enhancement, grid alignment and segmentation makinguse of

spatialandtemporalparallelismofferedbyFPGAtechnology.Mea- surementsforthecomputationaltimewereperformedontheFPGA platformandonapersonalcomputer.Experimentalresultsbring upalowerprocessingtimeontheFPGAplatform,inspiteofhigher resourcesavailableonPC.Moreover,theSVMapproachproposed in[26]requiresaprocessingtimewithmorethanoneorderofmag- nitudelargerthanourreducedcomplexityapproachforautomatic microarrayimageprocessing.

Toconclude,theexperimentalresultsprovedthatourshockfil- terbasedapproachforautomaticgridalignmenttogetherwiththe FPGAbasedhardwarearchitecturesareanefficientsolutionforfast andautomatedmicroarrayimageprocessing,overcomingthedis- advantagesofexistingsoftwareplatformsincaseofalargenumber ofmicroarrayanalysesareneeded.

Acknowledgemets

Theauthorswouldliketothanktheanonymousreviewersfor theirconstructiveandinsightfulcommnets.Thisworkwasfunded bytheRomanianResearchCouncilofHigherEducationthrough PNIIIDEIprogram,contractno.332/2007.

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BogdanBeleanwasborninTargu-Mures,Romania,onFebruary7,1983.Hereceived hisB.E.andPh.Ddegreeinelectronicsandcommunicationengineeringfromthe

TechnicalUniversityofCluj-Napoca,Romaniain2006and2010,respectively.He iscurrentlyascientificresearcherwithintheNationalInstituteforResearchand DevelopementofIsotopicandMolecularTechnologies,departmentofMassSpec- trometry,Cluj-Napoca.Hisresearchinterestsincludesignalandimageprocessing, bioinformaticsandapplicationspecifichardwarearchitecturesforparallelcomput- ing.

MonicaBordareceivedthePh.Ddegreeinelectronicsandtelecommunications from“Politehnica”UniversityofBucharest,Romania,in1987.Shehasheldfac- ultypositionsattheTechnicalUniversityofCluj-Napoca,Romania,whereshe is theDirectoroftheData ProcessingandSecurity ResearchCenter.She has conducted researchin codingtheory, nonlinear signal and imageprocessing, image watermarking, genomic signal processing and computervision having authoredandcoauthoredmorethan150researchpapersinreferrednationaland internationaljournalsandconferenceproceedings.Herresearchinterestsarein theareasofinformationtheoryandcoding,signal,imageandgenomicsignal processing.

BertrandLeGalwasbornin1979,inLorientFrance.HereceivedhisPh.Ddegreein informationandengineeringsciencesandtechnologiesfromtheUniversitédeBre- tagneSud,Lorient,France,in2005andtheDEA(MSDegree)inElectronicsin2002.

HeiscurrentlyanAssociateProfessorintheIMSLaboratory,ENSEIRBEngineering School,Talence,France.Hisresearchfocusesonsystemdesign,applicationspecific processors,high-levelsynthesis,SoCsdesignmethodologiesandsecurityissuesin embeddeddevicessuchasVirtualComponentProtection(IPP).

RomulusTerebeswasborninLivada,Romania,onDecember13,1969.Hereceived theB.S.degreeinelectronicsandtelecommunicationsfromtheTechnicalUniver- sityofCluj-Napoca(TUC-N),Romaniain1994,andthePh.D.degreefromUniversite Bordeaux1,FranceandtheTUC-N(co-advisedPh.D.thesis)in2004.Thesubject ofhisPh.D.thesisdealtwithpartialderivativesequationsbasedimageprocessing techniques.HeiscurrentlyanAssociateProfessorintheDepartmentofCommunica- tionsattheFacultyofElectronics,TelecommunicationsandInformationTechnology, TUC-N.Hisresearchinterestsareintheareaofimageprocessingandcomputer vision.