L. Tesar (Japan), D. Smutek (Czech Republic), A. Shimizu, and H. Kobatake (Japan)
texture features, Haralick features, 3D image analysis, im age segmentation, CT images, Gaussian mixture, model based decision-making, EM algorithm
In this paper, we propose a new approach to segmentation of 3D CT images, which is aimed at texture-based segmen tation of organs or disease diagnosing. The extension of Haralick 2D texture feature to the 3D domain was stud ied. Calculation of separate co-occurrence matrix for each voxel in the 3D image is proposed. The co-occurrence matrix is calculated from all voxels in a small rectangu lar window around the voxel. This makes it possible to segment given 3D image as opposed to calculating the fea ture for the pre-segmented regions of an image. Conse quently, such features can be used to search for very small regions with different texture properties (like tumours). A set of abdomen CT images is used for evaluation of the proposed approach. The segmentation method we used is model-based, using Gaussian Mixture Model. EM algo rithm is used for learning the parameters of mixture model from training data-set.
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