title: Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images.
abstract: Characterization and individual trait analysis of the focal liver lesions (FLL) is a challenging task in medical image processing and clinical site. The character analysis of a unconfirmed FLL case would be expected to benefit greatly from the accumulated FLL cases with experts' analysis, which can be achieved by content-based medical image retrieval (CBMIR). CBMIR mainly includes discriminated feature extraction and similarity calculation procedures. Bag-of-Visual-Words (BoVW) (codebook-based model) has been proven to be effective for different classification and retrieval tasks. This study investigates an improved codebook model for the fined-grained medical image representation with the following three advantages: (1) instead of SIFT, we exploit the local patch (structure) as the local descriptor, which can retain all detailed information and is more suitable for the fine-grained medical image applications; (2) in order to more accurately approximate any local descriptor in coding procedure, the sparse coding method, instead ofK-means algorithm, is employed for codebook learning and coded vector calculation; (3) we evaluate retrieval performance of focal liver lesions (FLL) using multiphase computed tomography (CT) scans, in which the proposed codebook model is separately learned for each phase. The effectiveness of the proposed method is confirmed by our experiments on FLL retrieval.
authors.affiliation: Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
authors.forename: Jian
authors.identifier: 0000-0003-4317-5022
authors.initials: J
authors.lastname: Wang
authors.affiliation: National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.
authors.forename: Xian-Hua
authors.initials: XH
authors.lastname: Han
authors.affiliation: College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
authors.forename: Yingying
authors.initials: Y
authors.lastname: Xu
authors.affiliation: College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
authors.forename: Lanfen
authors.initials: L
authors.lastname: Lin
authors.affiliation: Department of Radiology, Sir Run Run Shaw Hospital, Hangzhou, China.
authors.forename: Hongjie
authors.initials: H
authors.lastname: Hu
authors.affiliation: Department of Radiology, Sir Run Run Shaw Hospital, Hangzhou, China.
authors.forename: Chongwu
authors.initials: C
authors.lastname: Jin
authors.affiliation: Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
authors.forename: Yen-Wei
authors.identifier: 0000-0002-5952-0188
authors.initials: YW
authors.lastname: Chen