{"title":"面向相对模糊连通性:理论、算法及其在图像分割中的应用","authors":"H. C. Bejar, P. A. Miranda","doi":"10.1109/SIBGRAPI.2014.38","DOIUrl":null,"url":null,"abstract":"Anatomical structures and tissues are often hard to be segmented in medical images due to their poorly defined boundaries, i.e., low contrast in relation to other nearby false boundaries. The specification of the boundary polarity can help to alleviate part of this problem. In this work, we discuss how to incorporate this property in the Relative Fuzzy Connectedness (RFC) framework. We include a theoretical proof of the optimality of the new algorithm, named Oriented Relative Fuzzy Connectedness (ORFC), in terms of an oriented energy function subject to the seed constraints, and show the obtained gains in accuracy using medical images of MRI and CT images of thoracic studies.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Oriented Relative Fuzzy Connectedness: Theory, Algorithms, and Applications in Image Segmentation\",\"authors\":\"H. C. Bejar, P. A. Miranda\",\"doi\":\"10.1109/SIBGRAPI.2014.38\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Anatomical structures and tissues are often hard to be segmented in medical images due to their poorly defined boundaries, i.e., low contrast in relation to other nearby false boundaries. The specification of the boundary polarity can help to alleviate part of this problem. In this work, we discuss how to incorporate this property in the Relative Fuzzy Connectedness (RFC) framework. We include a theoretical proof of the optimality of the new algorithm, named Oriented Relative Fuzzy Connectedness (ORFC), in terms of an oriented energy function subject to the seed constraints, and show the obtained gains in accuracy using medical images of MRI and CT images of thoracic studies.\",\"PeriodicalId\":146229,\"journal\":{\"name\":\"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIBGRAPI.2014.38\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIBGRAPI.2014.38","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Oriented Relative Fuzzy Connectedness: Theory, Algorithms, and Applications in Image Segmentation
Anatomical structures and tissues are often hard to be segmented in medical images due to their poorly defined boundaries, i.e., low contrast in relation to other nearby false boundaries. The specification of the boundary polarity can help to alleviate part of this problem. In this work, we discuss how to incorporate this property in the Relative Fuzzy Connectedness (RFC) framework. We include a theoretical proof of the optimality of the new algorithm, named Oriented Relative Fuzzy Connectedness (ORFC), in terms of an oriented energy function subject to the seed constraints, and show the obtained gains in accuracy using medical images of MRI and CT images of thoracic studies.
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