Automatic classification of medical x-ray images

• Main Authors: Zare, M.R., Seng, W.C., Mueen, A.
• Format: Article
• Published: 2013
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.um.edu.my/7108/; http://mjcs.fsktm.um.edu.my/document.aspx?FileName=1343.pdf
• Institution: Universiti Malaya

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.