3D extension of Haralick texture features for medical image analysis

Authors:
Ludvik Tesar;Daniel Smutek;Akinobu Shimizu;Hidefume Kobatake
Affiliations:
Tokyo University of Agriculture and Technology, Japan;Charles University, Prague, Czech Republic;Tokyo University of Agriculture and Technology, Japan;Tokyo University of Agriculture and Technology, Japan
Venue:
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Year:
2007

Citing 1
Cited 1

Computer and Robot Vision

Computer and Robot Vision

Medical image segmentation using cooccurrence matrix based texture features calculated on weighted region

ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology

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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.