Texture-based CT Image analysis of asthma

Harishwaran Hariharan; Sally Wenzel; Bin Zheng; Bruce Whiting; Jiantao Pu; David Gur; J. Ken Leader

doi:10.1117/12.2006887

29 March 2013 Texture-based CT Image analysis of asthma

Harishwaran Hariharan, Sally Wenzel, Bin Zheng, Bruce Whiting, Jiantao Pu, David Gur, J. Ken Leader

Proceedings Volume 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging; 86720D (2013) https://doi.org/10.1117/12.2006887
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

This study was motivated by anecdotal reports from our clinicians that the lung parenchyma appears “different” (more heterogeneous) in asthmatics compared to non-asthmatics. We investigated whether traditional texture features were different between severe asthmatics and non-asthmatics. CT examinations from 76 subjects classified as “severe asthma” (n = 51) and “normal control” (n = 25) based on Severe Asthma Research Program (SARP) criteria were used in this study. The CT exams were performed on a 64-detector or 16-detector GE scanner at a radiation exposure of 96.6 (±30.7) mAs with the subjects holding their breath at end-normal-expiration (functional residual capacity). The CT images were reconstructed at 0.625 or 1.25 mm thickness using either GE’s “standard” or “detail” kernels. Air trapping was computed as the percentage of voxels with a value less than -856 HU. Gray level co-occurrence matrices (GLCM) were computed from the CT images, and 15 Haralick texture descriptors were computed from the GLCM. Air trapping was significantly greater in the severe asthma subjects compared to the normal control subjects. Seven of the 15 texture features were significantly different between the severe asthma and normal control subjects. Our findings provide some validity to anecdotal reports of differences between the parenchyma of asthmatic and non-asthmatics. The significant texture features may ultimately be used to classify individuals as asthmatic or non-asthmatic, which should improve the limited performance of air trapping alone.

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Harishwaran Hariharan, Sally Wenzel, Bin Zheng, Bruce Whiting, Jiantao Pu, David Gur, and J. Ken Leader "Texture-based CT Image analysis of asthma", Proc. SPIE 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging, 86720D (29 March 2013); https://doi.org/10.1117/12.2006887

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KEYWORDS

Computed tomography

Lung

Image analysis

Image segmentation

Statistical analysis

Analytical research

Image processing

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