3D Extension of Haralick Texture Features for Medical Image Analysis

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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