{"title":"三维磁共振脑图像的自动脑分割算法","authors":"J. G. Park, T. Jeong, Chulhee Lee","doi":"10.1109/SOFA.2007.4318305","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a new brain segmentation method for 3D magnetic resonance (MR) brain images. The proposed method consists of four steps: background rejection, image normalization, initial slice segmentation, and brain segmentation. In the image normalization step, intensity non-uniformity is removed. In the brain segmentation step, we use mathematical morphological operators and masking. The proposed algorithm was tested with twenty 3D MR normal brain image sets. Experiment results showed the proposed algorithm is fast and provides robust and satisfactory results.","PeriodicalId":205589,"journal":{"name":"2007 2nd International Workshop on Soft Computing Applications","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Automated Brain Segmentation Algorithm for 3D Magnetic Resonance Brain Images\",\"authors\":\"J. G. Park, T. Jeong, Chulhee Lee\",\"doi\":\"10.1109/SOFA.2007.4318305\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a new brain segmentation method for 3D magnetic resonance (MR) brain images. The proposed method consists of four steps: background rejection, image normalization, initial slice segmentation, and brain segmentation. In the image normalization step, intensity non-uniformity is removed. In the brain segmentation step, we use mathematical morphological operators and masking. The proposed algorithm was tested with twenty 3D MR normal brain image sets. Experiment results showed the proposed algorithm is fast and provides robust and satisfactory results.\",\"PeriodicalId\":205589,\"journal\":{\"name\":\"2007 2nd International Workshop on Soft Computing Applications\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 2nd International Workshop on Soft Computing Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SOFA.2007.4318305\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 2nd International Workshop on Soft Computing Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SOFA.2007.4318305","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated Brain Segmentation Algorithm for 3D Magnetic Resonance Brain Images
In this paper, we propose a new brain segmentation method for 3D magnetic resonance (MR) brain images. The proposed method consists of four steps: background rejection, image normalization, initial slice segmentation, and brain segmentation. In the image normalization step, intensity non-uniformity is removed. In the brain segmentation step, we use mathematical morphological operators and masking. The proposed algorithm was tested with twenty 3D MR normal brain image sets. Experiment results showed the proposed algorithm is fast and provides robust and satisfactory results.
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