Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms
We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/241396 |
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| _version_ | 1849409061167038464 |
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| author | Xian-Hua Han Yen-Wei Chen |
| author_facet | Xian-Hua Han Yen-Wei Chen |
| author_sort | Xian-Hua Han |
| collection | DOAJ |
| description | We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature
extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010. |
| format | Article |
| id | doaj-art-76fbf5af179546578fa29e2bce4ff562 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-76fbf5af179546578fa29e2bce4ff5622025-08-20T03:35:37ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/241396241396Biomedical Imaging Modality Classification Using Combined Visual Features and Textual TermsXian-Hua Han0Yen-Wei Chen1College of Information Science and Engineering, Ritsumeikan University, Kusatsu-Shi, 525-8577, JapanCollege of Information Science and Engineering, Ritsumeikan University, Kusatsu-Shi, 525-8577, JapanWe describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.http://dx.doi.org/10.1155/2011/241396 |
| spellingShingle | Xian-Hua Han Yen-Wei Chen Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms International Journal of Biomedical Imaging |
| title | Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms |
| title_full | Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms |
| title_fullStr | Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms |
| title_full_unstemmed | Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms |
| title_short | Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms |
| title_sort | biomedical imaging modality classification using combined visual features and textual terms |
| url | http://dx.doi.org/10.1155/2011/241396 |
| work_keys_str_mv | AT xianhuahan biomedicalimagingmodalityclassificationusingcombinedvisualfeaturesandtextualterms AT yenweichen biomedicalimagingmodalityclassificationusingcombinedvisualfeaturesandtextualterms |