Top-k Medical Images Query Based on Association Graph

Patient-to-patient comparison, especially image-to-image comparison plays an important role in the medical domain since doctors invariably make diagnoses based on prior experiences of similar cases. It is very significant for doctors to find similar medical images from the database as similar pathological changes in prior patients’ images and corresponding reports can assist doctors to make diagnoses for current patients. Therefore, advanced medical image retrieval techniques have been widely studied to improve the accuracy in recent years. However, the processing time has become another problem in medical image retrieval domain because of the increasing number of medical images. As doctors are only interested in the most similar k results, a novel model of association graph is proposed for medical image top-k query in this paper. The fuzzy expression in a association graph can describe the similarity between images effectively. Moreover, a series of correlation measurements are proposed for similarity reasoning. Then the medical image top-k query method is represented based on the characters of correlation measurements. Furthermore, four walk strategies are studied to accelerate and stabilize the top-k process. Experimental results show that its efficiency and effectiveness are higher in comparison with state of the art.