3D extension of Haralick texture features for medical image analysis

Abstract

In this paper, we propose a new approach to segmentation of 3D CT images, which is aimed at texture-based segmentation of organs or disease diagnosing. The extension of Haralick 2D texture feature to the 3D domain was studied. 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 rectangular window around the voxel. This makes it possible to segment given 3D image as opposed to calculating the feature for the pre-segmented regions of an image. Consequently, 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 algorithm is used for learning the parameters of mixture model from training data-set.