Baâzaoui Abir, Abderrahim Marwa, Barhoumi Walid
Université de Tunis El Manar, Institut Supérieur d'Informatique d'El Manar, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de recherche en Informatique, Modélisation et Traitement de l'Information et de la Connaissance (LIMTIC), 2 Rue Abou Rayhane Bayrouni, 2080 Ariana, Tunisia.
Université de Tunis El Manar, Institut Supérieur d'Informatique d'El Manar, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de recherche en Informatique, Modélisation et Traitement de l'Information et de la Connaissance (LIMTIC), 2 Rue Abou Rayhane Bayrouni, 2080 Ariana, Tunisia; Université de Carthage, Ecole Nationale d'Ingénieurs de Carthage, 45 Rue des Entrepreneurs, 2035 Tunis-Carthage, Tunisia.
Comput Biol Med. 2020 Jul;122:103833. doi: 10.1016/j.compbiomed.2020.103833. Epub 2020 May 26.
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辅助放射诊断的重要组成部分。人们已尝试使用距离度量学习方法来提高检索性能，同时缩小语义鸿沟。然而，现有方法并未解决图像之间的依赖问题（例如，正常图像和异常图像以相同的距离进行比较）。这影响了语义和视觉相似性。因此，这项工作旨在学习一种既能保持视觉相似性又能保持语义相似性的距离度量，并对该距离进行建模，以便独立处理每个查询。所提出的方法分为三个阶段进行描述：（1）低级图像特征提取，（2）离线距离度量建模，以及（3）在线检索。第一阶段利用基于局部二值模式直方图傅里叶变换、剪切波变换和曲波变换的变换域纹理描述符。第二阶段使用低级特征和机器学习来完成。对于给定的查询图像，在线检索基于对该图像与数据集中每个图像之间相似性的评估，同时使用根据查询图像动态定义的距离。在具有挑战性的乳腺X线图像分析协会（MIAS）和乳腺X线筛查数字数据库（DDSM）数据集上进行的实验证明了所提出方法在动态确定合适距离和检索语义上最相似图像方面的有效性，同时研究了单个低级特征以及融合特征。
