物理学报Issue(20)：367-375,9.DOI:10.7498/aps.63.208701
基于哈希理论和线性近邻传递反馈的乳腺X线图像肿块检索方法
Mass retrieval in mammogram based on hashing theory and linear neighb orhood propagation
摘要
Abstract
Mass detection in mammograms usually has high false positive (FP) rate. Content based mass retrieval can effec-tively reduce the FP rate by comparing the image which is to be determined with mass images which have already been diagnosed. In this paper, a method combining discriminating anchor graph hashing (DAGH) and linear neighborhood propagation (LNP) is proposed for mammogram mass retrieval. Original AGH image representation does not consider pathological relevance in defining image similarity. To solve this problem, DAGH is put forward as a new image repre-sentation, which introduces the pathological class into image similarity. Furthermore, LNP is employed as a relevance feedback technique. Finally, interactive retrieval for mammogram masses is implemented based on the learning strategy between the underlying features and high-level semantic for images. Mammograms provided by the Breast Center of Peking University People’s Hospital (BCPKUPH) are used to test the proposed method. Experimental results show that the DAGH image representation introducing pathological class is superior to original AGH in analyzing the similarity of mass images. Compared with existing methods, the proposed method shows obvious improvement in mass retrieval performance.关键词
乳腺X线图像/肿块检索/相关反馈/哈希理论Key words
mammogram/mass retrieval/relevance feedback/Hashing theory引用本文复制引用
李艳凤,陈后金,曹霖,韩振中,程琳..基于哈希理论和线性近邻传递反馈的乳腺X线图像肿块检索方法[J].物理学报,2014,(20):367-375,9.基金项目
国家自然科学基金(批准号:61271305,61201363)和高等学校博士学科点专项科研基金(批准号:20110009110001)资助的课题.* Project supported by the National Natural Science Foundation of China (Grant Nos.61271305,61201363) and the Specia-lized Research Fund for the Doctoral Program of Higher Education of China (Grant No.20110009110001) （批准号:61271305,61201363）
