{"title":"腰椎x线图像自动分类系统","authors":"Soontharee Koompairojn, K. Hua, Chutima Bhadrakom","doi":"10.1109/CBMS.2006.54","DOIUrl":null,"url":null,"abstract":"Existing computer-based spinal stenosis diagnosis systems are not fully automatic. Their performance depends on the knowledge and experience of the user. Such a system is typically intended for specialists such as radiologists. We present in this paper a fully automatic system, more suitable for general practitioners for use in screening and initial diagnosis. To evaluate the performance of the proposed techniques, we build a system prototype with two environments - one for managing training images and building the classifiers, and the other environment for diagnosis use in practice. Our experimental results, based on an X-ray image database NHANES II available from the National Library of Medicine, indicates that the proposed system is effective for screening purposes","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Automatic Classification System for Lumbar Spine X-ray Images\",\"authors\":\"Soontharee Koompairojn, K. Hua, Chutima Bhadrakom\",\"doi\":\"10.1109/CBMS.2006.54\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Existing computer-based spinal stenosis diagnosis systems are not fully automatic. Their performance depends on the knowledge and experience of the user. Such a system is typically intended for specialists such as radiologists. We present in this paper a fully automatic system, more suitable for general practitioners for use in screening and initial diagnosis. To evaluate the performance of the proposed techniques, we build a system prototype with two environments - one for managing training images and building the classifiers, and the other environment for diagnosis use in practice. Our experimental results, based on an X-ray image database NHANES II available from the National Library of Medicine, indicates that the proposed system is effective for screening purposes\",\"PeriodicalId\":208693,\"journal\":{\"name\":\"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS.2006.54\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2006.54","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Classification System for Lumbar Spine X-ray Images
Existing computer-based spinal stenosis diagnosis systems are not fully automatic. Their performance depends on the knowledge and experience of the user. Such a system is typically intended for specialists such as radiologists. We present in this paper a fully automatic system, more suitable for general practitioners for use in screening and initial diagnosis. To evaluate the performance of the proposed techniques, we build a system prototype with two environments - one for managing training images and building the classifiers, and the other environment for diagnosis use in practice. Our experimental results, based on an X-ray image database NHANES II available from the National Library of Medicine, indicates that the proposed system is effective for screening purposes