{"title":"光学眼底图像分割方法","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":"{\"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}","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
摘要
本文对光学眼底图像分割方法进行了比较分析和评估，以研究它们在 Matlab 中的效率、准确性、完整性和计算复杂性。分析的方法包括大津法、自适应阈值法、分水岭法、K 均值法、最大期望算法（EM）和 Frangi 法。考虑了这些方法在眼底疾病诊断应用中的特点和优缺点。
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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