{"title":"基于形状的水平集分割x射线血管造影图像中的冠状动脉","authors":"J. Brieva","doi":"10.1109/PAHCE.2013.6568258","DOIUrl":null,"url":null,"abstract":"This paper presents a level set technique to extract vascular structures in X-Ray Angiographic images. It makes uses of the Chan and Vese model applied to images of non-uniform illumination and uses a shape-based model to perform the segmentation. The shape model is computed using string matching techniques. Its performance, using different metrics, has been evaluated on a image sequence of 64 angiographic images by comparison with expert delineation. A sensitivity of 81% and a specificity of 94% were found in the quantitative validation analysis.","PeriodicalId":151015,"journal":{"name":"2013 Pan American Health Care Exchanges (PAHCE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Coronary artery segmentation in X-Ray Angiographic image by means of a shape based level set method\",\"authors\":\"J. Brieva\",\"doi\":\"10.1109/PAHCE.2013.6568258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a level set technique to extract vascular structures in X-Ray Angiographic images. It makes uses of the Chan and Vese model applied to images of non-uniform illumination and uses a shape-based model to perform the segmentation. The shape model is computed using string matching techniques. Its performance, using different metrics, has been evaluated on a image sequence of 64 angiographic images by comparison with expert delineation. A sensitivity of 81% and a specificity of 94% were found in the quantitative validation analysis.\",\"PeriodicalId\":151015,\"journal\":{\"name\":\"2013 Pan American Health Care Exchanges (PAHCE)\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Pan American Health Care Exchanges (PAHCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PAHCE.2013.6568258\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Pan American Health Care Exchanges (PAHCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PAHCE.2013.6568258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Coronary artery segmentation in X-Ray Angiographic image by means of a shape based level set method
This paper presents a level set technique to extract vascular structures in X-Ray Angiographic images. It makes uses of the Chan and Vese model applied to images of non-uniform illumination and uses a shape-based model to perform the segmentation. The shape model is computed using string matching techniques. Its performance, using different metrics, has been evaluated on a image sequence of 64 angiographic images by comparison with expert delineation. A sensitivity of 81% and a specificity of 94% were found in the quantitative validation analysis.
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