Abstract — We propose a nonparametric statistical snake technique that is based on the minimization of the stochastic complexity (minimum description length principle). The probability distributions of the gray levels in the different regions of the image
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0.012FREQUENCY
IEEETRANSACTIONSONIMAGEPROCESSING,VOL.??.NO.??,????
HISTOGRAM (256 BINS)STEP FUNCTION (q=20)
OBJECT
0.004
ANMP(%)
50
100
150
200
250
BINS’ INDEXSTEP FUNCTION (q=20)BACKGROUND
0.008
(a) 0 0
BHATTACHARYYA DISTANCE
Fig.3.ANMPasafunctionoftheBhattacharyyadistanceBforsegmentationresultsobtainedwiththelevelsetsnakeandthetwostrategiesontheimageFig.2apertubedwithGaussian,GammaandPoissonnoises.EachANMPhavebeenestimatedon20noiserealizations.
250
50 100 150 200
BINS’ INDEX
(b)NOISY IMAGE WITH INITIAL CONTOUR
ENCY
0.016 0.012 0.008 0.004
0 0
50
HISTOGRAM (256 BINS)STEP FUNCTION (q=4)
OBJECT
thatthesimpleproposedapproachesforestimatingthegraylevelprobabilitydistributionandfortheoptimizationstrategyprovidegoodresultsandleadtoasimpleandfastsegmentationtechnique.
A.In uenceoftheoptimizationstrategy
100 150 200BINS’ INDEX
HISTOGRAM (256 BINS) 250
0.008
FREQUENCBACKGROUND
(c)INITIAL CONVERGENCE
0.006 0.004 0.002
0 0
50
100 150 200BINS’ INDEX
250
(d)FINAL CONVERGENCE(e)
Fig.2.Illustrationofthe3-stagestrategy.(a)Syntheticimagewithoutnoise(115×83pixels).(b)NoisyversionquantizedwithQ=256levelsandwithinitialcontour.Onecanseethehistogramsandthestepfunctionsofparameterq=20associatedtotheobjectandthebackgroundoftheimage(b).(c)Initialconvergenceperformedconsideringstepfunctionsofparameterq=20.Onecanseethehistogramsassociatedtotheimage(c)andthestepfunctionswithnumberofstepsestimated(q=4)aftertheinitialconvergence.(d)Finalconvergenceperformedfromthecontourobtainedattheendoftheinitialconvergenceandwiththe
estimatedGLPDs.(e)Finalcontourobtainedconsideringq=Q=256.
Inordertocomparethetwooptimizationstrategiesin-troducedinII-E,theaveragenumberofmisclassi edpix-els(ANMP)aftersegmentationiscomputed.ThesyntheticimagesconsideredarenoisyversionsoftheimageFig.2aperturbedwithGaussian,GammaandPoissonnoisesfordifferentvaluesofthecontrastbetweenthetworegionsintheimage.ThiscontrastcanbemeasuredwiththeKullback-Leiblerdivergence[23]ortheBhattacharyya[23]distancebetweenthedistributionsofthebackgroundandtargetgraylevels.However,ithasbeenshown[36]that,forsmalltargets,theBhattacharyyadistanceisabettermeasureofcontrast.Inparticular,differentnoisycon gurationswiththesamevalueofBleadtosimilarvaluesoftheANMPfordifferenttypesofnoisesforpolygonalsnakes[36]andforlevelsetsnakeswithparametricpdfmodels[16].Inthecontinuouscase,theBhattacharyyadistancebetweenpdfPtandPbreads
B= logPt(z)Pb(z).
TheNMPafterasegmentationisdeterminedfromthe nalcontourbycountingthenumberofpixelsthatbelongtothetruetargetshapebutlieoutsidethecontourΓ,andthosethatbelongtothetruebackgroundbutlieinsidethecontourΓ.Inthefollowing,thevaluesoftheNMPwillbenormalizedbythenumberofpixelsinthetrueshapeofthetarget.
Forthelevelsetsnakeimplementation,onecanseeinFig.3thatthetwooptimizationstrategiesleadtoequivalentsegmen-tationresults.Thisresulthasbeencon rmedwithdifferentexperimentsandwithpolygonalsnakeimplementations.The3-stagestrategyismuchfaster(afewtenssecondsinsteadofmanyminutes).Forexample,theimageFig.2bhasbeensegmentedin12minuteswiththefull-iterativestrategyandonlyin41secondswiththe3-stagestrategy.So,thisfasterstrategywillonlybeconsideredinthefollowing.B.Evaluationofthecontourregularity
Weshowinthissubsectionthattheminimizationofthestochasticcomplexityleadstocontourwiththeappropriate
functionswithq=20suchthateachstepisofequallength.Then,noconvergenceofthecontourisperformedbetweenfusionofstepsoftheprobabilitydistribution.However,whentheajandqhavebeenestimated,a nalconvergenceofthecontourisperformedfromthecontourobtainedbeforethestepfusions.
III.EXPERIMENTALRESULTS
Thissectionprovidesanevaluationofourmethod.Syntheticimagesare rstconsideredsincetheyallowonetogetaprecisedeterminationofthenumberofmisclassi edpixels(NMP).Realimagesarealsoconsideredtoshedlightintheperformanceinapracticalcase.Theseresultsdemonstrate