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Changes in Brain White Matter Assessed Via Textural Features Using a Neural Network

Changes in Brain White Matter Assessed Via Textural Features Using a Neural Network

R. Kalpana, S. Muttan, B. Agrawala
Copyright: © 2012 |Pages: 10
ISBN13: 9781466617551|ISBN10: 1466617551|EISBN13: 9781466617568
DOI: 10.4018/978-1-4666-1755-1.ch009
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MLA

Kalpana, R., et al. "Changes in Brain White Matter Assessed Via Textural Features Using a Neural Network." Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs, edited by Joseph Tan, IGI Global, 2012, pp. 144-153. https://doi.org/10.4018/978-1-4666-1755-1.ch009

APA

Kalpana, R., Muttan, S., & Agrawala, B. (2012). Changes in Brain White Matter Assessed Via Textural Features Using a Neural Network. In J. Tan (Ed.), Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs (pp. 144-153). IGI Global. https://doi.org/10.4018/978-1-4666-1755-1.ch009

Chicago

Kalpana, R., S. Muttan, and B. Agrawala. "Changes in Brain White Matter Assessed Via Textural Features Using a Neural Network." In Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs, edited by Joseph Tan, 144-153. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1755-1.ch009

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Abstract

Diffusion Tensor Magnetic Resonance Imaging (DTMRI) has proved useful for microstructure characterization of the brain. This technique also helps determining complex connectivity of fiber tracts. The brain white matter (BMW) changes with respect to age and corresponding appearance of white-matter lesions among the brain’s message-carrying axons affects cognitive functions in old age. In this paper, the observed morphology in BWM on ageing is analyzed using statistical parameters extracted from DTMR images of different age groups. The gray level co-occurrence matrix (GLCM) obtained from the segmented images gives 14 textural features, subsets of which are adopted as the input sets in a backpropagation neural network classifier. The network is trained to predict the age based on BMW details used as the inputs. The proposed method helps in understanding the age-related changes in white matter. This is useful for the physician in understanding miscorrelation in motor activities and relevant causes in elderly subjects.

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