title: A new method of content based medical image retrieval and its applications to CT imaging sign retrieval.
abstract: 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.
authors.affiliation: Beijing Lab of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Ling
authors.initials: L
authors.lastname: Ma
authors.affiliation: Beijing Lab of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China. Electronic address: liuxiabi@bit.edu.cn.
authors.forename: Xiabi
authors.initials: X
authors.lastname: Liu
authors.affiliation: Beijing Lab of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Dept. of Imaging Diagnosis, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing 100021, China.
authors.forename: Yanfeng
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Dept. of Imaging Diagnosis, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing 100021, China.
authors.forename: Xinming
authors.initials: X
authors.lastname: Zhao
authors.affiliation: Dept. of Imaging Diagnosis, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing 100021, China.
authors.forename: Chunwu
authors.initials: C
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008490:Medical Informatics; D012660:Semantics; D014057:Tomography, X-Ray Computed
keywords: Common CT Imaging Signs of Lung Diseases (CISLs); Content-based image retrieval; Lung CT images; Medical image retrieval; Semantic information; Shortest path; Visual information