Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification

Abstract

This paper proposes a set of rotation invariant features based on three dimensional Gaussian Markov Random Fields (3D-GMRF) for volumetric texture image classification. In the method proposed here, the mathematical notion of spherical harmonics is employed to produce a set of features which are used to construct the rotation invariant descriptor. Our proposed method is evaluated and compared with other method in the literature for datasets containing synthetic textures as well as medical images. The results of our experiments demonstrate excellent classification performance for our proposed method compared with state-of-the-art methods. Furthermore, our method is evaluated using a clinical dataset and show good performance in discriminating between healthy individuals and COPD patients. Our method also performs well in classifying lung nodules in the LIDC-IDRI dataset. Our results indicate
that our 3D-GMRF-based method enjoys more superior performance compared with other methods in the literature.

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ORCID for Joy Conway: orcid.org/0000-0001-6464-1526

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