Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images
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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2017/1413297 |
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| _version_ | 1832567127007035392 |
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| author | Jian Wang Xian-Hua Han Yingying Xu Lanfen Lin Hongjie Hu Chongwu Jin Yen-Wei Chen |
| author_facet | Jian Wang Xian-Hua Han Yingying Xu Lanfen Lin Hongjie Hu Chongwu Jin Yen-Wei Chen |
| author_sort | Jian Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-be2f5b82ff774fc09b1eeb9fcf8d9f5b |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-be2f5b82ff774fc09b1eeb9fcf8d9f5b2025-02-03T01:02:18ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962017-01-01201710.1155/2017/14132971413297Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical ImagesJian Wang0Xian-Hua Han1Yingying Xu2Lanfen Lin3Hongjie Hu4Chongwu Jin5Yen-Wei Chen6Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, JapanNational Institute of Advanced Industrial Science and Technology, Tokyo, JapanCollege of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaDepartment of Radiology, Sir Run Run Shaw Hospital, Hangzhou, ChinaDepartment of Radiology, Sir Run Run Shaw Hospital, Hangzhou, ChinaGraduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, JapanCharacterization 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.http://dx.doi.org/10.1155/2017/1413297 |
| spellingShingle | Jian Wang Xian-Hua Han Yingying Xu Lanfen Lin Hongjie Hu Chongwu Jin Yen-Wei Chen Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images International Journal of Biomedical Imaging |
| title | Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images |
| title_full | Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images |
| title_fullStr | Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images |
| title_full_unstemmed | Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images |
| title_short | Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images |
| title_sort | sparse codebook model of local structures for retrieval of focal liver lesions using multiphase medical images |
| url | http://dx.doi.org/10.1155/2017/1413297 |
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