Automatic classification of medical x-ray images
Document Type
Article
Publication Date
1-1-2013
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.
Keywords
Classification, X-Ray, Image Processing
Divisions
ai
Publication Title
Malaysian Journal of Computer Science
Volume
26
Issue
1
Additional Information
Times Cited: 0 Zare, Mohammad Reza Seng, Woo Chaw Mueen, Ahmed