Abstract:
Retinal vasculardiseasesaretheutmostcauseofvisibilitylossandblindness
where thebloodvesselsintheeyessomehowfailtocirculateappropriatelevelof
blood
ow.Diabeticretinopathy,Agerelatedmaculardegeneration,Hypertensive
retinopathy,Centralretinalarteryocclusion,andRetinalveinocclusionarethemost
common formofretinalvasculardisorderswhichcouldberecognizedbyanalyzingthe
structure ofretinalvasculature.Earlyandcorrectdetectionofretinalbloodvessel
facilitates humanstotakeexpedientremedyagainstmostoftheophthalmicdiseases
whichcansigni cantlyreducepossiblevisionloss.
This studypresentsane cientcontrastenhancementtechniquewheremorphologi-
cal operationsliketop-hatandbottom-hatareappliedtoenhancetheimage.Next,
edge content-basedcontrastmatrixismeasuredfordynamicallyselectingtheopti-
mal structuringelementsize.TheContrastenhancementmethoddevelopedherecan
also beappliedinanymedicalimageforbettervisualizationofanimage'sstruc-
ture andcontentlikeseparationofbones,bloodvessels,anddi erenttypesofsoft
tissue etc.Themajorcontributionofthisthesisistointroduceanewmultiscale
directional transformtechnique,namedBendlet,whichisabletocapturedirectional
information muchmoree cientlythanthetraditionalwaveletbasedapproaches.By
using Bendlets,itispossibletorepresenttheedgesalongcurvewithfewercoe -
cientvalueswhilereconstructingtheimage.Fortrainingpurpose,thefeaturevector
is constructedbytheoutcomeofBendlettransformatthreedi erentscalevalues.
Afterward,abunchofensembleclassi ersareappliedto ndoutthebestpossible
result whetherapixelfallsinsideavesselornon-vesselsegment.Ensemblemethods
are learningmodelsthatcombinevariousweaklearnerstogenerateastronglearnerin
order tominimizethee ectofover tting,variance,andbiasproblem.Finally,multi-
scale linedetectionalgorithmisutilizedto llthesmallgapsandbreakagealongthe
vesselsegmentconverselyareaopenoperationisperformedtoremoveunnecessary
noise orobjectfromtheoutputimage.
Extensiveexperimentshavebeenconductedontwobenchmarkandpubliclyavailable
retinal fundusimagedatasets(DRIVEandSTARE),wheretheproposedalgorithm
achievesapproximately95%averageaccuracyforvesselsegmentation.Furthermore,
comparison withotherpromisingworksontheaforementioneddatabasesdemon-
strates theenhancedperformanceande ciencyoftheproposedmethod.