{"title":"基于先验信息的脑MRI关节分割与配准模糊框架","authors":"M. El-Melegy, H. Mokhtar","doi":"10.1109/ICCES.2010.5674904","DOIUrl":null,"url":null,"abstract":"This paper introduces a fuzzy framework for the simultaneous segmentation and registration of MRI datasets. The framework utilizes prior information which may be available about the class center and class's pixels distribution through the datasets. The algorithm is evaluated using phantom and real medical MRI brain volume. The results show that the algorithm has considerable accuracy for segmentation and affine registration. The algorithm needs a small number of iterations to reach convergence compared with other similar algorithms.","PeriodicalId":124411,"journal":{"name":"The 2010 International Conference on Computer Engineering & Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Fuzzy framework for joint segmentation and registration of brain MRI with prior information\",\"authors\":\"M. El-Melegy, H. Mokhtar\",\"doi\":\"10.1109/ICCES.2010.5674904\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a fuzzy framework for the simultaneous segmentation and registration of MRI datasets. The framework utilizes prior information which may be available about the class center and class's pixels distribution through the datasets. The algorithm is evaluated using phantom and real medical MRI brain volume. The results show that the algorithm has considerable accuracy for segmentation and affine registration. The algorithm needs a small number of iterations to reach convergence compared with other similar algorithms.\",\"PeriodicalId\":124411,\"journal\":{\"name\":\"The 2010 International Conference on Computer Engineering & Systems\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2010 International Conference on Computer Engineering & Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCES.2010.5674904\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2010 International Conference on Computer Engineering & Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCES.2010.5674904","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy framework for joint segmentation and registration of brain MRI with prior information
This paper introduces a fuzzy framework for the simultaneous segmentation and registration of MRI datasets. The framework utilizes prior information which may be available about the class center and class's pixels distribution through the datasets. The algorithm is evaluated using phantom and real medical MRI brain volume. The results show that the algorithm has considerable accuracy for segmentation and affine registration. The algorithm needs a small number of iterations to reach convergence compared with other similar algorithms.