title: Content-based medical image classification using a new hierarchical merging scheme.
abstract: 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%.
authors.affiliation: School of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran. h poorghasem@modares.ac.ir
authors.forename: Hossein
authors.initials: H
authors.lastname: Pourghassem
authors.forename: Hassan
authors.initials: H
authors.lastname: Ghassemian
mesh_terms: D002965:Classification / Q000379:methods*; D016000:Cluster Analysis; D003661:Decision Support Techniques; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods