Research Progress on Content-Based Medical Image Retrieval

Content-based medical image retrieval method is a research hotspot in the field of computer vision in recent years, and has been widely used in the research of computer-aided diagnosis. This paper summarizes the research progress and significance of content-based medical image retrieval methods, introduces the current mainstream medical image retrieval algorithms and their advantages and disadvantages, and aims to guide researchers to quickly understand the research content in this field. The research of medical image retrieval is mainly divided into two parts: feature extraction and similarity measurement. This paper introduces the feature extraction method of medical images starting with the extraction of traditional features and the feature extraction based on deep learning emerging in recent years. The similarity measure part enumerates the Mahalanobis distance metric, vocabulary tree, and hash algorithm. Finally, the related feedback technology in the field of medical image retrieval and the commonly used image retrieval system are summarized. The possible research directions and related difficulties in medical image retrieval are discussed.