{"title":"基于掩模的MRI病变图像分割","authors":"A. De, R. Das, A. Bhattacharjee, D. Sharma","doi":"10.1109/ICISA.2010.5480274","DOIUrl":null,"url":null,"abstract":"We have devised a new technique to segment an diseased MRI image wherein the diseased part is segregated using a masking based thresholding technique together with entropy maximization. The particle swarm optimization technique (PSO) is used to get the region of interest (ROI) of the MRI image. The mask used is a variable mask. The rectangular mask is grown using an algorithm provided in the subsequent sections using similarity of neighbourhood pixels. Tests on various diseased MRI images show that small diseased objects are successfully extracted irrespective of the complexity of the background and difference in intensity levels and class sizes. Previous works are based on bimodal images whereas our work is based on multimodal images.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Masking Based Segmentation of Diseased MRI Images\",\"authors\":\"A. De, R. Das, A. Bhattacharjee, D. Sharma\",\"doi\":\"10.1109/ICISA.2010.5480274\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We have devised a new technique to segment an diseased MRI image wherein the diseased part is segregated using a masking based thresholding technique together with entropy maximization. The particle swarm optimization technique (PSO) is used to get the region of interest (ROI) of the MRI image. The mask used is a variable mask. The rectangular mask is grown using an algorithm provided in the subsequent sections using similarity of neighbourhood pixels. Tests on various diseased MRI images show that small diseased objects are successfully extracted irrespective of the complexity of the background and difference in intensity levels and class sizes. Previous works are based on bimodal images whereas our work is based on multimodal images.\",\"PeriodicalId\":313762,\"journal\":{\"name\":\"2010 International Conference on Information Science and Applications\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Information Science and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISA.2010.5480274\",\"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 International Conference on Information Science and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISA.2010.5480274","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We have devised a new technique to segment an diseased MRI image wherein the diseased part is segregated using a masking based thresholding technique together with entropy maximization. The particle swarm optimization technique (PSO) is used to get the region of interest (ROI) of the MRI image. The mask used is a variable mask. The rectangular mask is grown using an algorithm provided in the subsequent sections using similarity of neighbourhood pixels. Tests on various diseased MRI images show that small diseased objects are successfully extracted irrespective of the complexity of the background and difference in intensity levels and class sizes. Previous works are based on bimodal images whereas our work is based on multimodal images.