Automatic classification of medical x-ray images

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

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Abstract

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.

Item Type: Article
Additional Information: Times Cited: 0 Zare, Mohammad Reza Seng, Woo Chaw Mueen, Ahmed
Uncontrolled Keywords: Classification; X-Ray; Image Processing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Dept of Artificial Intelligence
Depositing User: Miss Nur Jannatul Adnin Ahmad Shafawi
Date Deposited: 11 Jul 2013 01:37
Last Modified: 29 Oct 2014 00:42
URI: http://eprints.um.edu.my/id/eprint/7108

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