A new method for medical image retrieval based on Markov random field

Abstract

The development of medical images acquisition and storage technology has led to the rapid growth of the relevant data. Retrieval of similar medical images can effectively help doctors to diagnose diseases more accurately. But because of the particularity of medical images, traditional content-based image retrieval (CBIR) method such as bag-of-words (BOW) cannot be applied to medical images. For example, when retrieving a diseased image, we should not only consider the similar characteristics but also need to consider the type of lesion. And for medical images, images with the same lesion may have different image features, similar images may have different types of lesions. In this paper, a Markov random field (MRF) is structured, and an approximate belief propagation algorithm is used to retrieval images. An adjust-ranking step after initial retrieval is incorporated to further improve the retrieval performance. This paper uses the real brain CT images. The experimental results show that the proposed method can significantly improve the retrieval accuracy and has good efficiency.