{"title":"基于改进变换的量化超声图像重建","authors":"Chris Miller, B. Babb, F. Moore, M. R. Peterson","doi":"10.1109/WACV.2011.5711511","DOIUrl":null,"url":null,"abstract":"State-of-the-art lossy compression schemes for medical imagery utilize the 9/7 wavelet. Recent research has established a methodology for using evolutionary computation (EC) to evolve wavelet and scaling numbers describing novel reconstruction transforms that outperform the 9/7 under lossy conditions. This paper describes an investigation into whether evolved transforms could automatically compensate for the detrimental effects of quantization for ultrasound (US) images. Results for 16:1, 32:1, and 64:1 quantization consistently demonstrate superior performance of evolved transforms in comparison to the 9/7 wavelet; in general, this advantage increases in proportion to the selected quantization level.","PeriodicalId":424724,"journal":{"name":"2011 IEEE Workshop on Applications of Computer Vision (WACV)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Evolving improved transforms for reconstruction of quantized ultrasound images\",\"authors\":\"Chris Miller, B. Babb, F. Moore, M. R. Peterson\",\"doi\":\"10.1109/WACV.2011.5711511\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"State-of-the-art lossy compression schemes for medical imagery utilize the 9/7 wavelet. Recent research has established a methodology for using evolutionary computation (EC) to evolve wavelet and scaling numbers describing novel reconstruction transforms that outperform the 9/7 under lossy conditions. This paper describes an investigation into whether evolved transforms could automatically compensate for the detrimental effects of quantization for ultrasound (US) images. Results for 16:1, 32:1, and 64:1 quantization consistently demonstrate superior performance of evolved transforms in comparison to the 9/7 wavelet; in general, this advantage increases in proportion to the selected quantization level.\",\"PeriodicalId\":424724,\"journal\":{\"name\":\"2011 IEEE Workshop on Applications of Computer Vision (WACV)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE Workshop on Applications of Computer Vision (WACV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WACV.2011.5711511\",\"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 IEEE Workshop on Applications of Computer Vision (WACV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WACV.2011.5711511","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evolving improved transforms for reconstruction of quantized ultrasound images
State-of-the-art lossy compression schemes for medical imagery utilize the 9/7 wavelet. Recent research has established a methodology for using evolutionary computation (EC) to evolve wavelet and scaling numbers describing novel reconstruction transforms that outperform the 9/7 under lossy conditions. This paper describes an investigation into whether evolved transforms could automatically compensate for the detrimental effects of quantization for ultrasound (US) images. Results for 16:1, 32:1, and 64:1 quantization consistently demonstrate superior performance of evolved transforms in comparison to the 9/7 wavelet; in general, this advantage increases in proportion to the selected quantization level.