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International Journal of Biomedical Imaging
Volume 2017 (2017), Article ID 1413297, 13 pages
Research Article

Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images

1Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
2National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
3College of Computer Science and Technology, Zhejiang University, Hangzhou, China
4Department of Radiology, Sir Run Run Shaw Hospital, Hangzhou, China

Correspondence should be addressed to Yen-Wei Chen;

Received 23 September 2016; Revised 4 January 2017; Accepted 12 January 2017; Published 13 February 2017

Academic Editor: Yue Wang

Copyright © 2017 Jian Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 of -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.