Optic Disc Segmentation with Kapur-ScPSO Based Cascade Multithresholding

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dc.contributor.authorKoyuncu, Hasan
dc.contributor.authorCeylan, Rahime
dc.date.accessioned2020-03-26T19:25:30Z
dc.date.available2020-03-26T19:25:30Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesien_US
dc.description4th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO) -- APR 20-22, 2016 -- Grenada, SPAINen_US
dc.description.abstractThe detection of significant retinal regions (segmentation) constitutes an indispensible need for computer aided diagnosis of retinal based diseases. At this point, image segmentation algorithm is wanted to be quick in order to spare time for feature selection and classification parts. In this paper, we deal with the fast and accurate segmentation process of optic discs in retinal images. For this purpose, a cascade multithresholding (CMT) process is proposed by a novel optimization algorithm (Scout Particle Swarm Optimization) and an efficient cost function (Kapur). Scout Particle Swarm Optimization (ScPSO) is originated from Particle Swarm Optimization (PSO) and improves standard PSO by using a necessary part taken from Artificial Bee Colony (ABC) Optimization. In other words, the most important handicap of PSO (regeneration of useless particles) is eliminated via the formation of ScPSO that can be obtained by adding the scout bee phase from ABC into standard PSO. In this study, this novel method (ScPSO) constitutes the optimization part of multithresholding process. Kapur function is preferred as being the cost function to be used in ScPSO, since Kapur provides low standard deviations on output of optimization based multithresholding techniques in literature. In this manner, a well-combined structure (Kapur-ScPSO) is generated for cascade multithresholding. Optic disc images taken from DRIVE database are used for statistical and visual comparison. As a result, Kapur-ScPSO based CMT can define the optic disc quickly (7-8 s) with the rates of 77.08 % precision, 57.89 % overlap and 95.59 % accuracy.en_US
dc.description.sponsorshipUniv Granada, IEEE Computat Intelligence Soc, Spanish Chapter, BioMed Centen_US
dc.description.sponsorshipCoordinatorship of Selcuk University's Scientific Research ProjectsSelcuk Universityen_US
dc.description.sponsorshipThis work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects.en_US
dc.identifier.doi10.1007/978-3-319-31744-1_19en_US
dc.identifier.endpage215en_US
dc.identifier.isbn978-3-319-31744-1; 978-3-319-31743-4
dc.identifier.issn0302-9743en_US
dc.identifier.issn1611-3349en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage206en_US
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-319-31744-1_19
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33857
dc.identifier.volume9656en_US
dc.identifier.wosWOS:000401938000019en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER INTERNATIONAL PUBLISHING AGen_US
dc.relation.ispartofBIOINFORMATICS AND BIOMEDICAL ENGINEERING (IWBBIO 2016)en_US
dc.relation.ispartofseriesLecture Notes in Bioinformatics
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectScout particle swarm optimizationen_US
dc.subjectKapuren_US
dc.subjectOptic disc segmentationen_US
dc.subjectMultithresholdingen_US
dc.titleOptic Disc Segmentation with Kapur-ScPSO Based Cascade Multithresholdingen_US
dc.typeConference Objecten_US

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