Title: Segmentation and morphometric analysis of subcortical regions in autistic MR brain images using fuzzy Gaussian distribution model-based distance regularised multi-phase level set

Authors: A.R. Jac Fredo; G. Kavitha; S. Ramakrishnan

Addresses: Department of Electronics Engineering, MIT campus, Anna University, Chennai, India ' Department of Electronics Engineering, MIT campus, Anna University, Chennai, India ' Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India

Abstract: In this work, subcortical regions of autism spectrum disorder are analysed using fuzzy Gaussian distribution model-based distance regularised multi-phase level set method in autistic MR brain images. The fuzzy Gaussian distribution model is used as the intensity discriminator. The segmented images are validated with the ground truth using geometrical measure area. The results show that the fuzzy Gaussian distribution model-based multi-phase level set method is able to extract the subcortical tissue boundaries. The subcortical regions segmented using this method gives high correlation with ground truth. The corpus callosum area gives very high (R = 0.94) correlation. The brain stem and cerebellum present high correlations of 0.89 and 0.84, respectively. Also, it is found the segmented autistic subcortical regions have reduced area and are statistically significant (p < 0.0001). The ratio metric analysis proves the relation in reduction of the area in subcortical regions with total brain area.

Keywords: autism; autistic subcortical regions; multi-phase level sets; fuzzy Gaussian distribution models; ratio metric analysis; image segmentation; morphometric analysis; MRI scans; brain images; magnetic resonance imaging.

DOI: 10.1504/IJBET.2014.064647

International Journal of Biomedical Engineering and Technology, 2014 Vol.15 No.3, pp.211 - 223

Received: 08 Mar 2014
Accepted: 26 May 2014
Published online: 21 Oct 2014 *

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