ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2015, Vol. 52 ›› Issue (9): 2033-2045.doi: 10.7544/issn1000-1239.2015.20140692

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Top-k Medical Images Query Based on Association Graph

Li Pengyuan1, Pan Haiwei1, Li Qing2, Han Qilong1, Xie Xiaoqin1, Zhang Zhiqiang1   

  1. 1(College of Computer Science and Technology, Harbin Engineering University, Harbin 150001); 2(Department of Computer Engineering and Information Technology, City University of Hong Kong, Hong Kong)
  • Online:2015-09-01

Abstract: 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.

Key words: association graph, Top-k query, walk strategy, image retrieval, medical image

CLC Number: