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

Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification
Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification
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.
3D-GMRF, COPD, Classification, Volumetric texture
1-14
Almakady, Yasseen
f183c2d0-f78f-4c38-a9db-0fa8e7054300
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Conway, Joy
bbe9a2e4-fb85-4d4a-a38c-0c1832c32d06
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7
Almakady, Yasseen
f183c2d0-f78f-4c38-a9db-0fa8e7054300
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Conway, Joy
bbe9a2e4-fb85-4d4a-a38c-0c1832c32d06
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7

Almakady, Yasseen, Mahmoodi, Sasan, Conway, Joy and Bennett, Michael (2020) Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification. Computer Vision and Image Understanding, 194, 1-14, [102931]. (doi:10.1016/j.cviu.2020.102931).

Record type: Article

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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Accepted/In Press date: 6 February 2020
e-pub ahead of print date: 12 February 2020
Published date: May 2020
Keywords: 3D-GMRF, COPD, Classification, Volumetric texture

Identifiers

Local EPrints ID: 437971
URI: http://eprints.soton.ac.uk/id/eprint/437971
PURE UUID: 372e0e22-635f-4393-907b-4d6eae91e7bd
ORCID for Joy Conway: ORCID iD orcid.org/0000-0001-6464-1526

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Date deposited: 25 Feb 2020 17:30
Last modified: 15 Sep 2021 04:37

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Contributors

Author: Yasseen Almakady
Author: Sasan Mahmoodi
Author: Joy Conway ORCID iD
Author: Michael Bennett

University divisions

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