Adaptive volumetric texture segmentation based on Gaussian Markov random fields features

Abstract

An adaptive method based on three dimensional Gaussian Markov Random fields (3D-GMRF) is proposed in this paper for volumetric texture segmentation. A feature vector is extracted for each voxel in a given volumetric texture image using an estimation cube. However, the selection of the size for this estimation cube causes some fundamental issues related to the uncertainty principle and the inability of the model to capture different texture patterns. These issues are tackled here by employing an adaptive method where the size of the estimation cube is adaptively varying to capture different patterns and also minimize the number of voxels that are related to different texture classes inside the estimation cube. The feature vectors that consist of the estimated parameters of the GMRF and form the parameter volume are hence employed to segment volumetric textures. These features are smoothed by applying an averaging filter using an adaptive averaging technique. Such an averaging filter improves the segmentation results considerably. Our method proposed here is evaluated on a
synthetic volumetric texture dataset and compared with other methods to demonstrate the superiority of our segmentation method.

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Identifiers

ORCID for Yasseen Hamad Al Makady: orcid.org/0000-0002-1583-1777

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