Automatic classification of medical x-ray images
Image representation is one of the major aspects of automatic classification algorithms. In this paper, different feature extraction techniques have been utilized to represent medical X-ray images. They are categorized into two groups; (i) low-level image representation such as Gray Level Co-occurre...
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| Format: | Article |
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2013
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| _version_ | 1796945528637882368 |
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| author | Zare, M.R. Seng, W.C. Mueen, A. |
| author_facet | Zare, M.R. Seng, W.C. Mueen, A. |
| author_sort | Zare, M.R. |
| collection | UM |
| description | Image representation is one of the major aspects of automatic classification algorithms. In this paper, different feature extraction techniques have been utilized to represent medical X-ray images. They are categorized into two groups; (i) low-level image representation such as Gray Level Co-occurrence Matrix(GLCM), Canny Edge Operator, Local Binary Pattern(LBP), pixel value, and (ii) local patch-based image representation such as Bag of Words (BoW). These features have been exploited in different algorithms for automatic classification of medical X-ray images. We then analyzed the classification performance obtained with regard to the image representation techniques used. These experiments were evaluated on ImageCLEF 2007 database consists of 11000 medical X-ray images with 116 classes. Experimental results showed the classification performance obtained by exploiting LBP and BoW outperformed the other algorithms with respect to the image representation techniques used. |
| first_indexed | 2024-03-06T05:18:03Z |
| format | Article |
| id | um.eprints-7108 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:18:03Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | um.eprints-71082014-10-29T00:42:35Z http://eprints.um.edu.my/7108/ Automatic classification of medical x-ray images Zare, M.R. Seng, W.C. Mueen, A. QA75 Electronic computers. Computer science Image representation is one of the major aspects of automatic classification algorithms. In this paper, different feature extraction techniques have been utilized to represent medical X-ray images. They are categorized into two groups; (i) low-level image representation such as Gray Level Co-occurrence Matrix(GLCM), Canny Edge Operator, Local Binary Pattern(LBP), pixel value, and (ii) local patch-based image representation such as Bag of Words (BoW). These features have been exploited in different algorithms for automatic classification of medical X-ray images. We then analyzed the classification performance obtained with regard to the image representation techniques used. These experiments were evaluated on ImageCLEF 2007 database consists of 11000 medical X-ray images with 116 classes. Experimental results showed the classification performance obtained by exploiting LBP and BoW outperformed the other algorithms with respect to the image representation techniques used. 2013 Article PeerReviewed Zare, M.R. and Seng, W.C. and Mueen, A. (2013) Automatic classification of medical x-ray images. Malaysian Journal of Computer Science, 26 (1). pp. 9-22. ISSN 0127-9084, http://mjcs.fsktm.um.edu.my/document.aspx?FileName=1343.pdf |
| spellingShingle | QA75 Electronic computers. Computer science Zare, M.R. Seng, W.C. Mueen, A. Automatic classification of medical x-ray images |
| title | Automatic classification of medical x-ray images |
| title_full | Automatic classification of medical x-ray images |
| title_fullStr | Automatic classification of medical x-ray images |
| title_full_unstemmed | Automatic classification of medical x-ray images |
| title_short | Automatic classification of medical x-ray images |
| title_sort | automatic classification of medical x ray images |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT zaremr automaticclassificationofmedicalxrayimages AT sengwc automaticclassificationofmedicalxrayimages AT mueena automaticclassificationofmedicalxrayimages |