23 February 2012 An application to pulmonary emphysema classification based on model of texton learning by sparse representation
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Abstract
We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.
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Min Zhang, Xiangrong Zhou, Satoshi Goshima, Huayue Chen, Chisako Muramatsu, Takeshi Hara, Ryojiro Yokoyama, Masayuki Kanematsu, Hiroshi Fujita, "An application to pulmonary emphysema classification based on model of texton learning by sparse representation", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831534 (23 February 2012); doi: 10.1117/12.912454; https://doi.org/10.1117/12.912454
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