Daniel Patel, M. Haidacher, Jean-Paul Balabanian, E. Gröller
{"title":"力矩曲线","authors":"Daniel Patel, M. Haidacher, Jean-Paul Balabanian, E. Gröller","doi":"10.1109/PACIFICVIS.2009.4906857","DOIUrl":null,"url":null,"abstract":"We define a transfer function based on the first and second statistical moments. We consider the evolution of the mean and variance with respect to a growing neighborhood around a voxel. This evolution defines a curve in 3D for which we identify important trends and project it back to 2D. The resulting 2D projection can be brushed for easy and robust classification of materials and material borders. The transfer function is applied to both CT and MR data.","PeriodicalId":133992,"journal":{"name":"2009 IEEE Pacific Visualization Symposium","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":"{\"title\":\"Moment curves\",\"authors\":\"Daniel Patel, M. Haidacher, Jean-Paul Balabanian, E. Gröller\",\"doi\":\"10.1109/PACIFICVIS.2009.4906857\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We define a transfer function based on the first and second statistical moments. We consider the evolution of the mean and variance with respect to a growing neighborhood around a voxel. This evolution defines a curve in 3D for which we identify important trends and project it back to 2D. The resulting 2D projection can be brushed for easy and robust classification of materials and material borders. The transfer function is applied to both CT and MR data.\",\"PeriodicalId\":133992,\"journal\":{\"name\":\"2009 IEEE Pacific Visualization Symposium\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"38\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Pacific Visualization Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PACIFICVIS.2009.4906857\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Pacific Visualization Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACIFICVIS.2009.4906857","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We define a transfer function based on the first and second statistical moments. We consider the evolution of the mean and variance with respect to a growing neighborhood around a voxel. This evolution defines a curve in 3D for which we identify important trends and project it back to 2D. The resulting 2D projection can be brushed for easy and robust classification of materials and material borders. The transfer function is applied to both CT and MR data.
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