Multiple classifier systems in texton-based approach for the classification of CT images of lung

Authors:
Mehrdad J. Gangeh;Lauge Sørensen;Saher B. Shaker;Mohamed S. Kamel;Marleen de Bruijne
Affiliations:
Department of Electrical and Computer Engineering, University of Waterloo, Canada;Department of Computer Science, University of Copenhagen, Denmark;Department of Respiratory Medicine, Gentofte University Hospital, Hellerup, Denmark;Department of Electrical and Computer Engineering, University of Waterloo, Canada;Department of Computer Science, University of Copenhagen, Denmark and Biomedical Imaging Group Rotterdam, Erasmus MC, The Netherlands
Venue:
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Year:
2010

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Abstract

In this paper, we propose using texton signatures based on raw pixel representation along with a parallel multiple classifier system for the classification of emphysema in computed tomography images of the lung. The multiple classifier system is composed of support vector machines on the texton signatures as base classifiers and combines their decisions using product rule. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. Textonbased approach in texture classification mainly has two parameters, i.e., texton size and k value in k-means. Our results show that while aggregation of single decisions by SVMs over various k values using multiple classifier systems helps to improve the results compared to single SVMs, combining over different texton sizes is not beneficial. The performance of the proposed system, with an accuracy of 95%, is similar to a recently proposed approach based on local binary patterns, which performs almost the best among other approaches in the literature.