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.
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Keywords
- Chronic Obstructive Pulmonary Disease
- Local Binary Pattern
- Filter Bank
- Texture Classification
- Radial Basis Function Kernel
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Gangeh, M.J., Sørensen, L., Shaker, S.B., Kamel, M.S., de Bruijne, M., Loog, M. (2010). A Texton-Based Approach for the Classification of Lung Parenchyma in CT Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15711-0_74
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DOI: https://doi.org/10.1007/978-3-642-15711-0_74
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