{"title":"基于Kohonen图的头部MRI组织分割","authors":"S. Congorto, S. D. Penna, S. Erne","doi":"10.1109/IEMBS.1996.652721","DOIUrl":null,"url":null,"abstract":"The authors developed a new method in order to automatically segment magnetic resonance images (MRIs) of the head. The main tissues, such as scalp, brain and skull, are recognised. The method is based on a Kohonen self organising feature map which performs a cluster of the image areas into three main classes. The network, after being trained, is successfully operated on the test set. The network performances do not depend on the MRI apparatus producing the images set. The network classes are properly matched and processed in order to obtain slices containing the desired tissues. The proposed method has been developed in the frame of a project for the 3-dimensional reconstruction of selected surfaces.","PeriodicalId":20427,"journal":{"name":"Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","volume":"81 1","pages":"1087-1088 vol.3"},"PeriodicalIF":0.0000,"publicationDate":"1996-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Tissue segmentation of MRI of the head by means of a Kohonen map\",\"authors\":\"S. Congorto, S. D. Penna, S. Erne\",\"doi\":\"10.1109/IEMBS.1996.652721\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors developed a new method in order to automatically segment magnetic resonance images (MRIs) of the head. The main tissues, such as scalp, brain and skull, are recognised. The method is based on a Kohonen self organising feature map which performs a cluster of the image areas into three main classes. The network, after being trained, is successfully operated on the test set. The network performances do not depend on the MRI apparatus producing the images set. The network classes are properly matched and processed in order to obtain slices containing the desired tissues. The proposed method has been developed in the frame of a project for the 3-dimensional reconstruction of selected surfaces.\",\"PeriodicalId\":20427,\"journal\":{\"name\":\"Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society\",\"volume\":\"81 1\",\"pages\":\"1087-1088 vol.3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMBS.1996.652721\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMBS.1996.652721","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tissue segmentation of MRI of the head by means of a Kohonen map
The authors developed a new method in order to automatically segment magnetic resonance images (MRIs) of the head. The main tissues, such as scalp, brain and skull, are recognised. The method is based on a Kohonen self organising feature map which performs a cluster of the image areas into three main classes. The network, after being trained, is successfully operated on the test set. The network performances do not depend on the MRI apparatus producing the images set. The network classes are properly matched and processed in order to obtain slices containing the desired tissues. The proposed method has been developed in the frame of a project for the 3-dimensional reconstruction of selected surfaces.