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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Acknowledgement
The CT data used in this work were acquired as a part of a study into the application of imaging to the characterization of the phenotypes of COPD. The written informed consent was given and signed by all subjects. The study was approved by the Southampton and South West Hampshire local research ethics committee (LREC number: 09/H0502/91) and the University Hospital Southampton Foundation Trust Research and Development Department. The study was conducted in the Southampton NIHR Respiratory Biomedical Research Unit. The research in this paper is funded by Technical and Vocational Training Corporation (TVTC) in Saudi Arabia.
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Almakady, Y., Mahmoodi, S., Conway, J., Bennett, M. (2018). Volumetric Texture Analysis Based on Three-Dimensional Gaussian Markov Random Fields for COPD Detection. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_16
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