A content-based digital mammography retrieval using inexact graph matching

Content-Based Image Retrieval (CBIR) is becoming one of the most vivid research area in computer vision. It is widely used in medical applications especially in computer aided diagnostic systems (CAD). CBIR systems in digital mammography take an important part of these works. The work presented in this paper aims to propose a CBIR approach based on inexact graph matching algorithm for mammographic images. To achieve this task, we represent a mammogram as an Attributed Relational Graph (ARG) based on ImageMap approach where each node of the graph represents a semantic object. Objects that are considered in mammogram are: Background, Breast, Pectoral Muscle, Masses and Calcifications. Then, for each node, we compute a signature that describes the selected object. In order to retrieve the most similar images to a query one, a graph matching technique is applied based on the Hungarian algorithm. To Evaluate our approach 100 mammographic images from the MIAS database were used and six metrics were computed. Experiments demonstrate that the proposed method using Hamming distance gives the most promising results.