{"title":"用分区树表示医学图像","authors":"K. Subramanian, B. Naylor","doi":"10.1109/VISUAL.1992.235214","DOIUrl":null,"url":null,"abstract":"The binary space partitioning tree is a method of converting a discrete space representation to a particular continuous space representation. The conversion is accomplished using standard discrete space operators developed for edge detection, followed by a Hough transform to generate candidate hyperplanes that are used to construct the partitioning tree. The result is a segmented and compressed image represented in continuous space suitable for elementary computer vision operations and improved image transmission/storage. Examples of 256*256 medical images for which the compression is estimated to range between 1 and 0.5 b/pixel are given.<<ETX>>","PeriodicalId":164549,"journal":{"name":"Proceedings Visualization '92","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Representing medical images with partitioning trees\",\"authors\":\"K. Subramanian, B. Naylor\",\"doi\":\"10.1109/VISUAL.1992.235214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The binary space partitioning tree is a method of converting a discrete space representation to a particular continuous space representation. The conversion is accomplished using standard discrete space operators developed for edge detection, followed by a Hough transform to generate candidate hyperplanes that are used to construct the partitioning tree. The result is a segmented and compressed image represented in continuous space suitable for elementary computer vision operations and improved image transmission/storage. Examples of 256*256 medical images for which the compression is estimated to range between 1 and 0.5 b/pixel are given.<<ETX>>\",\"PeriodicalId\":164549,\"journal\":{\"name\":\"Proceedings Visualization '92\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Visualization '92\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VISUAL.1992.235214\",\"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 Visualization '92","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VISUAL.1992.235214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Representing medical images with partitioning trees
The binary space partitioning tree is a method of converting a discrete space representation to a particular continuous space representation. The conversion is accomplished using standard discrete space operators developed for edge detection, followed by a Hough transform to generate candidate hyperplanes that are used to construct the partitioning tree. The result is a segmented and compressed image represented in continuous space suitable for elementary computer vision operations and improved image transmission/storage. Examples of 256*256 medical images for which the compression is estimated to range between 1 and 0.5 b/pixel are given.<>
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