{"title":"Optical fundus image segmentation methods","authors":"S.A. Andrikevych, S.E. Tuzhanskyi","doi":"10.31649/1681-7893-2024-47-1-155-165","DOIUrl":null,"url":null,"abstract":"The paper presents a comparative analysis and evaluation of methods for segmenting optical fundus images in order to study their efficiency, accuracy, completeness, and computational complexity in Matlab. The methods analyzed are Otsu, adaptive thresholding, Watershed, K-means, maximum expectation algorithm (EM), and Frangi method. The features, advantages and disadvantages in the context of application for the diagnosis of fundus diseases are considered.","PeriodicalId":509753,"journal":{"name":"Optoelectronic Information-Power Technologies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optoelectronic Information-Power Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31649/1681-7893-2024-47-1-155-165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The paper presents a comparative analysis and evaluation of methods for segmenting optical fundus images in order to study their efficiency, accuracy, completeness, and computational complexity in Matlab. The methods analyzed are Otsu, adaptive thresholding, Watershed, K-means, maximum expectation algorithm (EM), and Frangi method. The features, advantages and disadvantages in the context of application for the diagnosis of fundus diseases are considered.
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