{"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}
引用次数: 15
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