A Robust Retinal Blood Vessel Segmentation using Bendlets

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dc.contributor.author Kushol, Rafsanjany
dc.date.accessioned 2020-09-23T09:38:32Z
dc.date.available 2020-09-23T09:38:32Z
dc.date.issued 2018-11-15
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dc.identifier.uri http://hdl.handle.net/123456789/350
dc.description Supervised by Prof. Dr. Md. Hasanul Kabir en_US
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh en_US
dc.title A Robust Retinal Blood Vessel Segmentation using Bendlets en_US
dc.type Thesis en_US


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