Journal
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
Volume 32, Issue 8, Pages 651-661Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2008.07.006
Keywords
Merging-based hierarchical classification; Merging scheme; Medical X-ray image; Content-based image retrieval
Funding
- Iran's Telecommunication Research Center (ITRC)
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Automatic medical image classification is a technique for assigning a medical image to a class among a number of image categories. Due to computational complexity, it is an important task in the content-based image retrieval (CBIR). In this paper, we propose a hierarchical medical image classification method including two levels using a perfect set of various shape and texture features. Furthermore, a tessellation-based spectral feature as well as a directional histogram has been proposed. In each level of the hierarchical classifier, using a new merging scheme and multilayer perceptron (MLP) classifiers (merging-based classification), homogenous (semantic) classes are created from overlapping classes in the database. The proposed merging scheme employs three measures to detect the overlapping classes: accuracy, miss-classified ratio, and dissimilarity. The first two measures realize a supervised classification method and the last one realizes an unsupervised Clustering technique. In each level, the merging-based classification is applied to a merged class of the previous level and splits it to several classes. This procedure is progressive to achieve more classes. The proposed algorithm is evaluated on a database consisting of 9100 medical X-ray images of 40 classes. It provides accuracy rate of 90.83% on 25 merged classes in the first level. If the correct class is considered within the best three matches, this Value Will increase to 97.9%. (C) 2008 Elsevier Ltd. All rights reserved.
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