{"title":"医学图像分割的一种替代方法","authors":"F. Deniz, B. Alagoz, M. Tagluk","doi":"10.1109/SIU.2010.5653146","DOIUrl":null,"url":null,"abstract":"In this study a segmentation algorithm controlled by a dissimilarity function composed of a few criterion for segmentation of medical images, starting from a seed point and growing toward the boundary of the considered anatomical tissue, is proposed. It has been shown that the algorithm can be controlled to overcome the complications such as noise, variations in the pixel values and weak connectivity encountered on the targeted anatomical region in the medical image.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"33 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An alternative approach for medical image segmentation\",\"authors\":\"F. Deniz, B. Alagoz, M. Tagluk\",\"doi\":\"10.1109/SIU.2010.5653146\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study a segmentation algorithm controlled by a dissimilarity function composed of a few criterion for segmentation of medical images, starting from a seed point and growing toward the boundary of the considered anatomical tissue, is proposed. It has been shown that the algorithm can be controlled to overcome the complications such as noise, variations in the pixel values and weak connectivity encountered on the targeted anatomical region in the medical image.\",\"PeriodicalId\":152297,\"journal\":{\"name\":\"2010 IEEE 18th Signal Processing and Communications Applications Conference\",\"volume\":\"33 3\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE 18th Signal Processing and Communications Applications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2010.5653146\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 18th Signal Processing and Communications Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2010.5653146","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An alternative approach for medical image segmentation
In this study a segmentation algorithm controlled by a dissimilarity function composed of a few criterion for segmentation of medical images, starting from a seed point and growing toward the boundary of the considered anatomical tissue, is proposed. It has been shown that the algorithm can be controlled to overcome the complications such as noise, variations in the pixel values and weak connectivity encountered on the targeted anatomical region in the medical image.