{"title":"基于多尺度纹理字典的冠状血管自动检测","authors":"A. Zifan, B. Chapman","doi":"10.1109/HISB.2012.40","DOIUrl":null,"url":null,"abstract":"In this paper we present a new automatic method for coronary artery vessel detection. We employ a texture modelling approach based on image textons as texture features, in the context of a classification experiment, where we attempt to discriminate between vessel and non-vessel like shapes in X-ray angiogram images. Experiments were conducted on a real patient database. The results show that the proposed model can perform well and distinguish vessel areas from others in an efficient manner, and outperforms other existing methods.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"161 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Automatic Detection of Coronary Vessels Using Mutli-scale Texture Dictionaries\",\"authors\":\"A. Zifan, B. Chapman\",\"doi\":\"10.1109/HISB.2012.40\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a new automatic method for coronary artery vessel detection. We employ a texture modelling approach based on image textons as texture features, in the context of a classification experiment, where we attempt to discriminate between vessel and non-vessel like shapes in X-ray angiogram images. Experiments were conducted on a real patient database. The results show that the proposed model can perform well and distinguish vessel areas from others in an efficient manner, and outperforms other existing methods.\",\"PeriodicalId\":375089,\"journal\":{\"name\":\"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology\",\"volume\":\"161 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HISB.2012.40\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HISB.2012.40","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Detection of Coronary Vessels Using Mutli-scale Texture Dictionaries
In this paper we present a new automatic method for coronary artery vessel detection. We employ a texture modelling approach based on image textons as texture features, in the context of a classification experiment, where we attempt to discriminate between vessel and non-vessel like shapes in X-ray angiogram images. Experiments were conducted on a real patient database. The results show that the proposed model can perform well and distinguish vessel areas from others in an efficient manner, and outperforms other existing methods.