A texton-based approach for the classification of lung parenchyma in CT images

Authors:
Mehrdad J. Gangeh;Lauge Sørensen;Saher B. Shaker;Mohamed S. Kamel;Marleen de Bruijne;Marco Loog
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;Pattern Recognition Laboratory, Delft University of Technology, The Netherlands
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

Citing 7
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Abstract

In this paper, a texton-based classification system based on raw pixel representation along with a support vector machine with radial basis function kernel is proposed for the classification of emphysema in computed tomography images of the lung. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. The results show the superiority of the proposed approach to common techniques in the literature including moments of the histogram of filter responses based on Gaussian derivatives. The performance of the proposed system, with an accuracy of 96.43%, also slightly improves over a recently proposed approach based on local binary patterns.