{"title":"基于分割的体积医学图像数据无损压缩方案","authors":"Qiusha Min, R. Sadleir","doi":"10.1109/IMVIP.2011.26","DOIUrl":null,"url":null,"abstract":"It is not acceptable to use loss less techniques when compressing medical image data, and as a result, it is difficult to achieve high compression ratios. We have developed a novel segmentation based compression scheme to overcome this problem and our experimental results indicate that this scheme is capable of achieving a high level of compression without sacrificing the quality of the patient data.","PeriodicalId":179414,"journal":{"name":"2011 Irish Machine Vision and Image Processing Conference","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A Segmentation Based Lossless Compression Scheme for Volumetric Medical Image Data\",\"authors\":\"Qiusha Min, R. Sadleir\",\"doi\":\"10.1109/IMVIP.2011.26\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is not acceptable to use loss less techniques when compressing medical image data, and as a result, it is difficult to achieve high compression ratios. We have developed a novel segmentation based compression scheme to overcome this problem and our experimental results indicate that this scheme is capable of achieving a high level of compression without sacrificing the quality of the patient data.\",\"PeriodicalId\":179414,\"journal\":{\"name\":\"2011 Irish Machine Vision and Image Processing Conference\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Irish Machine Vision and Image Processing Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMVIP.2011.26\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Irish Machine Vision and Image Processing Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMVIP.2011.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Segmentation Based Lossless Compression Scheme for Volumetric Medical Image Data
It is not acceptable to use loss less techniques when compressing medical image data, and as a result, it is difficult to achieve high compression ratios. We have developed a novel segmentation based compression scheme to overcome this problem and our experimental results indicate that this scheme is capable of achieving a high level of compression without sacrificing the quality of the patient data.
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