期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 66, 期 -, 页码 148-158出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2017.01.002
关键词
Medical image retrieval; Content-based image retrieval; Lung CT images; Semantic information; Visual information; Shortest path; Common CT Imaging Signs of Lung Diseases (CISLs)
资金
- National Natural Science Foundation of China [60973059, 81171407]
- New Century Excellent Talents in University of China [NCET-10-0044]
This paper proposes a new method of content based medical image retrieval through considering fused, context-sensitive similarity. Firstly, we fuse the semantic and visual similarities between the query image and each image in the database as their pairwise similarities. Then, we construct a weighted graph whose nodes represent the images and edges measure their pairwise similarities. By using the shortest path algorithm over the weighted graph, we obtain a new similarity measure, context-sensitive similarity measure, between the query image and each database image to complete the retrieval process. Actually, we use the fused pairwise similarity to narrow down the semantic gap for obtaining a more accurate pairwise similarity measure, and spread it on the intrinsic data manifold to achieve the context-sensitive similarity for a better retrieval performance. The proposed method has been evaluated on the retrieval of the Common CT Imaging Signs of Lung Diseases (CISLs) and achieved not only better retrieval results but also the satisfactory computation efficiency. (C) 2017 Elsevier Inc. All rights reserved.
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