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

在乳腺X线图像肿块检测中存在较高的假阳性率,通过基于内容的肿块检索,将待判定肿块与已确诊肿块进行相似性分析,可有效降低假阳性率.本文提出了一种结合可区分锚点图哈希和线性近邻传递的乳腺图像肿块检索方法.针对传统锚点图哈希在相似度定义中没有考虑病理相关性的问题,引入病理类别至锚点图哈希图像相似度计算,提出了可区分锚点图哈希以重新表示图像.利用线性近邻传递作为相关反馈技术,基于图像底层特征表达与图像高层语义间的学习机制,实现交互式肿块图像检索.采用北京大学人民医院乳腺中心提供的临床图像作为实验数据,实验结果表明,引入病理类别的可区分锚点图哈希图像表达在肿块相似性分析上优于传统锚点图哈希.相比于现有方法,本文提出的方法在肿块检索性能上得到明显提高.

Mass retrieval in mammogram based on hashing theory and linear neighborhood propagation

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.
Keywords： mammogram mass retrieval relevance feedback Hashing theory