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COPD detection using three-dimensional Gaussian Markov random fields based on binary features

COPD detection using three-dimensional Gaussian Markov random fields based on binary features
COPD detection using three-dimensional Gaussian Markov random fields based on binary features
This paper proposes new descriptors based on three-dimensional
Gaussian Markov random fields (3D-GMRF) for volumetric texture classification. The estimated parameters of 3DGMRF are decomposed into sign and magnitude components and then are encoded into a single binary code to describe the local texture. Our experiments on a synthetic dataset of volumetric texture show that this approach leads to significant reduction in descriptor size, while preserving the discriminative power of 3D-GMRF features. The descriptors proposed here demonstrate strong performance in distinguishing between healthy and chronic obstructive pulmonary disease (COPD) subjects, using a medical dataset. These descriptors are successfully employed to measure the differences between various groups from the medical dataset, in order to determine which group is at risk of COPD.
Volumetric texture classification, 3D texture, 3D-GMRF, COPD
IEEE
Al Makady, Yasseen, Hamad
8125c167-d6ef-45bf-ac22-b9a7e72d36fd
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
b0c687ac-b468-4575-8f08-af9940dacda3
Al Makady, Yasseen, Hamad
8125c167-d6ef-45bf-ac22-b9a7e72d36fd
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
b0c687ac-b468-4575-8f08-af9940dacda3

Al Makady, Yasseen, Hamad, Mahmoodi, Sasan and Bennett, Michael (2020) COPD detection using three-dimensional Gaussian Markov random fields based on binary features. In Proceeding of IEEE International Conference on Image Processing. IEEE. 5 pp . (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

This paper proposes new descriptors based on three-dimensional
Gaussian Markov random fields (3D-GMRF) for volumetric texture classification. The estimated parameters of 3DGMRF are decomposed into sign and magnitude components and then are encoded into a single binary code to describe the local texture. Our experiments on a synthetic dataset of volumetric texture show that this approach leads to significant reduction in descriptor size, while preserving the discriminative power of 3D-GMRF features. The descriptors proposed here demonstrate strong performance in distinguishing between healthy and chronic obstructive pulmonary disease (COPD) subjects, using a medical dataset. These descriptors are successfully employed to measure the differences between various groups from the medical dataset, in order to determine which group is at risk of COPD.

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Yasseen_Accepted_ICIP2020_18May
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More information

Accepted/In Press date: 16 May 2020
Venue - Dates: IEEE International Conference on Image Processing, Abu Dabi, Abu Dabi, United Arab Emirates, 2020-10-25 - 2020-10-28
Keywords: Volumetric texture classification, 3D texture, 3D-GMRF, COPD

Identifiers

Local EPrints ID: 441003
URI: http://eprints.soton.ac.uk/id/eprint/441003
PURE UUID: d819c471-c42b-4f7d-9c68-e45445618652
ORCID for Yasseen, Hamad Al Makady: ORCID iD orcid.org/0000-0002-1583-1777

Catalogue record

Date deposited: 27 May 2020 16:53
Last modified: 30 Oct 2020 05:01

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Contributors

Author: Yasseen, Hamad Al Makady ORCID iD
Author: Sasan Mahmoodi
Author: Michael Bennett

University divisions

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