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Volumetric texture analysis based on three-dimensional Gaussian Markov random fields for COPD detection

Volumetric texture analysis based on three-dimensional Gaussian Markov random fields for COPD detection
Volumetric texture analysis based on three-dimensional Gaussian Markov random fields for COPD detection
This paper proposes a 3D GMRF-based descriptor for volumetric texture image classification. In our proposed method, the estimated parameters of the GMRF model in volumetric texture images are employed as texture features in addition to the mean of a processed image region. The descriptor of the volumetric texture is then constructed by computing the histograms of each feature element to characterize the local texture. The evaluation of this descriptor achieves a high classification accuracy on a 3D synthetic texture database. Our method is then applied on a clinical dataset to exploit its discriminatory power, achieving a high classification accuracy in COPD detection. To demonstrate the performance of the descriptor, a comparison is carried out against a 2D GMRF-based method using the same dataset, variables, and settings. The descriptor outperforms the 2D GMRF-based method by a significant margin.
COPD 3D GMRF Volumetric texture
153-164
Springer
Al Makady, Yasseen
8125c167-d6ef-45bf-ac22-b9a7e72d36fd
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Conway, Joy
04b19151-4c9a-48f8-b594-c224c699c45c
Bennett, Michael
e42f5213-4410-4284-9e96-74e359df6b19
Nixon, M.
Mahmoodi, S.
Zwiggelaar, R.
Al Makady, Yasseen
8125c167-d6ef-45bf-ac22-b9a7e72d36fd
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Conway, Joy
04b19151-4c9a-48f8-b594-c224c699c45c
Bennett, Michael
e42f5213-4410-4284-9e96-74e359df6b19
Nixon, M.
Mahmoodi, S.
Zwiggelaar, R.

Al Makady, Yasseen, Mahmoodi, Sasan, Conway, Joy and Bennett, Michael (2018) Volumetric texture analysis based on three-dimensional Gaussian Markov random fields for COPD detection. Nixon, M., Mahmoodi, S. and Zwiggelaar, R. (eds.) In Annual Conference on Medical Image Understanding and Analysis: MIUA 2018: Medical Image Understanding and Analysis. vol. 894, Springer. pp. 153-164 . (doi:10.1007/978-3-319-95921-4_16).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper proposes a 3D GMRF-based descriptor for volumetric texture image classification. In our proposed method, the estimated parameters of the GMRF model in volumetric texture images are employed as texture features in addition to the mean of a processed image region. The descriptor of the volumetric texture is then constructed by computing the histograms of each feature element to characterize the local texture. The evaluation of this descriptor achieves a high classification accuracy on a 3D synthetic texture database. Our method is then applied on a clinical dataset to exploit its discriminatory power, achieving a high classification accuracy in COPD detection. To demonstrate the performance of the descriptor, a comparison is carried out against a 2D GMRF-based method using the same dataset, variables, and settings. The descriptor outperforms the 2D GMRF-based method by a significant margin.

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More information

e-pub ahead of print date: 21 August 2018
Published date: 2018
Keywords: COPD 3D GMRF Volumetric texture

Identifiers

Local EPrints ID: 425323
URI: http://eprints.soton.ac.uk/id/eprint/425323
PURE UUID: 29cf99b3-8502-4e22-8cb7-ebc7995783c1
ORCID for Yasseen Al Makady: ORCID iD orcid.org/0000-0002-1583-1777

Catalogue record

Date deposited: 12 Oct 2018 16:30
Last modified: 17 Dec 2019 02:05

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