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Classification of Brain Magnetic Resonance Images using 2 Level Decision Tree Learning  

Kim, Hyung-Il (동국대학교 컴퓨터공학과)
Kim, Yong-Uk (동국대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.34, no.1, 2007 , pp. 18-29 More about this Journal
Abstract
In this paper we present a system that classifies brain MR images by using 2 level decision tree learning. There are two kinds of information that can be obtained from images. One is the low-level features such as size, color, texture, and contour that can be acquired directly from the raw images, and the other is the high-level features such as existence of certain object, spatial relations between different parts that must be obtained through the interpretation of segmented images. Learning and classification should be performed based on the high-level features to classify images according to their semantic meaning. The proposed system applies decision tree learning to each level separately, and the high-level features are synthesized from the results of low-level classification. The experimental results with a set of brain MR images with tumor are discussed. Several experimental results that show the effectiveness of the proposed system are also presented.
Keywords
Medical Image; Content-based Image Retrieval; Magnetic Resonance Image; Decision Tree; Machine Learning;
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