Multiple Classifier Systems in Texton-Based Approach for the Classification of CT Images of Lung
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. Texton-based 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.
Unable to display preview. Download preview PDF.
- 9.Gangeh, M.J., Sørensen, L., Shaker, S.B., Kamel, M.S., de Bruijne, M., Loog, M.: 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.) MICCAI 2010. LNCS, vol. 6363, pp. 596–603. Springer, Heidelberg (2010)Google Scholar
- 13.Bach, F.R., Lanckriet, G.R.G., Jordan, M.I.: Multiple Kernel Learning, Conic Duality, and the SMO Algorithm. In: Proceedings of 21st International Conference of Machine Learning, ICML (2004)Google Scholar
- 19.Webb, W.R., Müller, N., Naidich, D.: High-Resolution CT of the Lung, 3rd edn. Lippincott Williams & Wilkins (2001)Google Scholar
- 24.Zhong, C., Sun, Z., Tan, T.: Robust 3D Face Recognition Using Learned Visual Codebook. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–6 (2007)Google Scholar