D. Boukerroui, O. Basset, A. Baskurt, A. Hernandez, N. Guérin, G. Giménez
{"title":"基于纹理的乳腺三维病灶分割自适应聚类算法","authors":"D. Boukerroui, O. Basset, A. Baskurt, A. Hernandez, N. Guérin, G. Giménez","doi":"10.1109/ULTSYM.1997.661836","DOIUrl":null,"url":null,"abstract":"A specific algorithm is presented for the automatic extraction of breast tumors. This algorithm involves 3D adaptive K-means clustering of the gray-scale and texture features images. The segmentation problem is formulated as a Maximum A Posterior (MAP) estimation problem. The MAP estimation is achieved using Besag's Iterated Conditional Modes algorithm for the minimization of an energy function. This function has three components. The first one constrains the region to be close to the data, the second imposes spatial continuity and the third takes into consideration the texture of the various regions. This segmentation technique is demonstrated on in vivo breast data. The method revealed very efficient. The results are compared with the manual segmentation of lesions by an expert.","PeriodicalId":6369,"journal":{"name":"1997 IEEE Ultrasonics Symposium Proceedings. An International Symposium (Cat. No.97CH36118)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1997-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Texture based adaptive clustering algorithm for 3D breast lesion segmentation\",\"authors\":\"D. Boukerroui, O. Basset, A. Baskurt, A. Hernandez, N. Guérin, G. Giménez\",\"doi\":\"10.1109/ULTSYM.1997.661836\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A specific algorithm is presented for the automatic extraction of breast tumors. This algorithm involves 3D adaptive K-means clustering of the gray-scale and texture features images. The segmentation problem is formulated as a Maximum A Posterior (MAP) estimation problem. The MAP estimation is achieved using Besag's Iterated Conditional Modes algorithm for the minimization of an energy function. This function has three components. The first one constrains the region to be close to the data, the second imposes spatial continuity and the third takes into consideration the texture of the various regions. This segmentation technique is demonstrated on in vivo breast data. The method revealed very efficient. The results are compared with the manual segmentation of lesions by an expert.\",\"PeriodicalId\":6369,\"journal\":{\"name\":\"1997 IEEE Ultrasonics Symposium Proceedings. An International Symposium (Cat. No.97CH36118)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1997 IEEE Ultrasonics Symposium Proceedings. An International Symposium (Cat. No.97CH36118)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ULTSYM.1997.661836\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1997 IEEE Ultrasonics Symposium Proceedings. An International Symposium (Cat. No.97CH36118)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ULTSYM.1997.661836","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Texture based adaptive clustering algorithm for 3D breast lesion segmentation
A specific algorithm is presented for the automatic extraction of breast tumors. This algorithm involves 3D adaptive K-means clustering of the gray-scale and texture features images. The segmentation problem is formulated as a Maximum A Posterior (MAP) estimation problem. The MAP estimation is achieved using Besag's Iterated Conditional Modes algorithm for the minimization of an energy function. This function has three components. The first one constrains the region to be close to the data, the second imposes spatial continuity and the third takes into consideration the texture of the various regions. This segmentation technique is demonstrated on in vivo breast data. The method revealed very efficient. The results are compared with the manual segmentation of lesions by an expert.