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Previous research has been done to classify different tissues/organs of interest present in medical images, in particular in Computed Tomography (CT) images. Most of the research used the anatomical structure present in the images in order to classify the tissues. In this paper, instead of using the anatomical structure, we propose a pixel-based texture approach for the representation and classification of the regions of interest. The approach incorporates various texture features and decision trees to accomplish tissue classification in normal Computed Tomography (CT) images of the chest and abdomen. First, we introduce a new “direction vs. displacement pairs” (DDP) approach to calculate a co-occurrence matrix for capturing all possible combination between directions and displacements necessary in calculating the texture features at the pixel-level. Second, we evaluate various different neighborhood sizes for the pixel-based texture representation in order to find the optimal window size for differentiating among 8 organs/tissues of interest: aorta, fat, kidney, liver, lung, muscle, spleen, and trabecular bone. For all organs/tissues (except for aorta), the optimal window was 13-by-13 allowing the classification sensitivity metric to be at least 96% for all organs/tissues. For aorta, the optimal window size was 9-by-9 with the classification sensitivity being 81%.
