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学科主题: 临床医学
题名:
Mass retrieval in mammogram based on hashing theory and linear neighborhood propagation
作者: Li Yan-Feng1; Chen Hou-Jin1; Cao Lin1; Han Zhen-Zhong1; Cheng Lin2
关键词: mammogram ; mass retrieval ; relevance feedback ; Hashing theory
刊名: ACTA PHYSICA SINICA
发表日期: 2014-10-20
DOI: 10.7498/aps.63.208701
卷: 63, 期:20
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology
类目[WOS]: Physics, Multidisciplinary
研究领域[WOS]: Physics
英文摘要:

Mass detection in mammograms usually has high false positive (FP) rate. Content based mass retrieval can effectively 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 representation, 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.

语种: 中文
所属项目编号: 61271305 ; 61201363 ; 20110009110001
项目资助者: National Natural Science Foundation of China ; Specialized Research Fund for the Doctoral Program of Higher Education of China
WOS记录号: WOS:000344616500050
Citation statistics:
内容类型: 期刊论文
版本: 出版稿
URI标识: http://ir.bjmu.edu.cn/handle/400002259/61661
Appears in Collections:北京大学第二临床医学院_乳腺外科_期刊论文

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作者单位: 1.Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
2.Peking Univ, Peoples Hosp, Breast Ctr, Beijing 100044, Peoples R China

Recommended Citation:
Li Yan-Feng,Chen Hou-Jin,Cao Lin,et al. Mass retrieval in mammogram based on hashing theory and linear neighborhood propagation[J]. ACTA PHYSICA SINICA,2014,63(20).
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