Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images
Joint Authors
Chen, Yen-Wei
Wang, Jian
Han, Xian-Hua
Xu, Yingying
Lin, Lanfen
Hu, Hongjie
Jin, Chongwu
Source
International Journal of Biomedical Imaging
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-13
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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 K-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.
American Psychological Association (APA)
Wang, Jian& Han, Xian-Hua& Xu, Yingying& Lin, Lanfen& Hu, Hongjie& Jin, Chongwu…[et al.]. 2017. Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images. International Journal of Biomedical Imaging،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1159601
Modern Language Association (MLA)
Wang, Jian…[et al.]. Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images. International Journal of Biomedical Imaging No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1159601
American Medical Association (AMA)
Wang, Jian& Han, Xian-Hua& Xu, Yingying& Lin, Lanfen& Hu, Hongjie& Jin, Chongwu…[et al.]. Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images. International Journal of Biomedical Imaging. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1159601
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1159601