Medical image retrieval based on texture and shape feature co-occurrence

Abstract

With the rapid development and wide application of medical imaging technology, explosive volumes of medical image data are produced every day all over the world. As such, it becomes increasingly challenging to manage and utilize such data effectively and efficiently. In particular, content-based medical image retrieval has been intensively researched in the past decade or so. In this work, we propose a novel approach to content-based medical image retrieval utilizing the co-occurrence of both the texture and the shape features in contrast to most previous algorithms that use purely the texture or the shape feature. Specifically, we propose a novel form of representation for the co-occurrence of the texture and the shape features in an image, i.e., the gray level and edge direction co-occurrence matrix (GLEDCOM). Based on GLEDCOM, we define eleven features forming a feature vector that is used to measure the similarity between images. As a result, it consistently yields outstanding performance on both images rich in texture (e.g., image of brain) and images with dominant smooth regions and sharp edges (e.g., image of bladder). As demonstrated by experiments, the mean precision of retrieval with GLEDCOM algorithm outperforms a set of representative algorithms including the gray level co-occurrence matrix (GLCM) based, the Hu's seven moment invariants (HSMI) based, the uniformity estimation method (UEM) based and the the modified Zernike moments (MZM) based algorithms by 10%-20%.