为帮助医生进行乳腺X影像辅助诊断。针对乳腺X影像微钙化簇相似病灶检索问题，在分别研究单一特征和利用单距离相似性度量的特征融合的检索算法的基础上，提出一种基于多距离特征融合和相关反馈的乳腺X线影像钙化病灶检索方法，该方法针对不同特征采用多距离度量方法计算相似性，并结合用户的反馈信息动态调整各个特征分量的权值来完成查询。实验建立在由250幅包含微钙化簇的乳腺X线影像构成的数据库基础上，通过单一特征，特征融合及相关反馈图像检索的查准率-查全率（PVR）曲线验证该方法的检索性能。实验结果表明，该方法比传统的基于单一特征检索方法以及运用单一距离度量的基于特征融合的检索方法有更好的检索效果。

This research aims to assist doctors to detect micro-calcifications. In connection with the similar lesions retrieval problem of micro-calcification cluster in mammography, we develop a new algorithm with multi-feature fusion and relevance feedback based on the study of single feature and feature fusion using single distance measure image retrieving techniques, this method adopts multi-distance measure to calculate the similarity directing at different features. Experiment is based on mammography image database which contain 250 mammography images and each image contains calcification cluster, we verified the retrieval performance by the precision-recall ratio (PVR) of single feature, feature fusion and relevance feedback. Experimental results show that the method has a better retrieval result than these methods which are single feature and feature fusion based using single distance measurement.