{"title":"An effective computer aided diagnosis system using B-Mode and color Doppler flow imaging for breast cancer","authors":"Songbo Liu, Heng-Da Cheng, Yan Liu, Jianhua Huang, Yingtao Zhang, Xianglong Tang","doi":"10.1109/VCIP.2013.6706400","DOIUrl":null,"url":null,"abstract":"To improve the diagnostic accuracy of breast ultrasound classification, a novel computer-aided diagnosis (CAD) system based on B-Mode and color Doppler flow imaging is proposed. Several new features are modeled and extracted from the static images and color Doppler image sequences to study blood flow characteristics. Moreover, we proposed a novel classifier ensemble strategy for obtaining the benefit of mutual compensation of classifiers with different characteristics. Experimental results demonstrate that the proposed CAD system can improve the true-positive and decrease the false positive detection rate, which is useful for reducing the unnecessary biopsy and death rate.","PeriodicalId":407080,"journal":{"name":"2013 Visual Communications and Image Processing (VCIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Visual Communications and Image Processing (VCIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VCIP.2013.6706400","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
To improve the diagnostic accuracy of breast ultrasound classification, a novel computer-aided diagnosis (CAD) system based on B-Mode and color Doppler flow imaging is proposed. Several new features are modeled and extracted from the static images and color Doppler image sequences to study blood flow characteristics. Moreover, we proposed a novel classifier ensemble strategy for obtaining the benefit of mutual compensation of classifiers with different characteristics. Experimental results demonstrate that the proposed CAD system can improve the true-positive and decrease the false positive detection rate, which is useful for reducing the unnecessary biopsy and death rate.