The similarity measure is an essential part of medical image retrieval systems for assisting in radiological diagnosis. Attempts have been made to use distance metric learning approaches to improve the retrieval performance while decreasing the semantic gap. However, existing approaches did not resolve the problem of dependency between images (e.g. normal and abnormal images are compared with the same distance). This affects the semantic and the visual similarity. Thus, this work aims at learning a distance metric which preserves both visual resemblance and semantic similarity and modeling this distance in order to treat each query independently. The proposed method is described in three stages: (1) low-level image feature extraction, (2) offline distance metric modeling, and (3) online retrieval. The first stage exploits transform-domain texture descriptors based on local binary pattern histogram Fourier, shearlet, and curvelet transforms. The second stage is carried out using low-level features and machine learning. Given a query image, the online retrieval is based on the evaluation of the similarity between this image and each image within the dataset, while using a distance that is dynamically defined according to the query image. Realized experiments on the challenging Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets prove the effectiveness of the proposed method in determining dynamically the adequate distance and retrieving the most semantically similar images, while investigating single low-level features as well as fused ones.

相似性度量是医学图像检索系统中协助放射诊断的必要部分。已经尝试使用距离度量学习方法来改善检索性能，同时减小语义差距。但是，现有方法无法解决图像之间的依赖性问题（例如将正常和异常图像的距离进行比较）。这会影响语义和视觉相似性。因此，这项工作旨在学习一种既保留视觉相似性又保留语义相似性的距离度量，并对该距离建模以独立对待每个查询。该方法分为三个阶段：（1）低层图像特征提取；（2）离线距离度量建模；（3）在线检索。第一阶段利用基于局部二进制模式直方图的傅立叶变换，剪切波变换和曲线波变换的变换域纹理描述符。第二阶段使用低级功能和机器学习进行。给定查询图像，在线检索基于该图像与数据集中每个图像之间的相似性评估，同时使用根据查询图片动态定义的距离。在具有挑战性的乳腺图像分析学会（MIAS）和用于筛查乳腺X射线照片的数字数据库（DDSM）数据集上进行的实验证明了该方法在动态确定适当距离并检索语义上最相似的图像的同时调查单个低级特征的有效性以及融合的