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Automated Segmentation and Analysis of Corpus Callosum in Autistic MR Brain Images Using Fuzzy-c-Means-Based Level Set Method

  • A. R. Jac FredoEmail author
  • G. Kavitha
  • S. Ramakrishnan
Original Article

Abstract

In this work, the segmentation and analysis of the corpus callosum (CC) in autistic MR brain images is carried out using a fuzzy c-means (FCM)-based level set method. Initially, the images are skull-stripped using the geodesic active contour method. The CC is extracted from the skull-stripped images using the FCM-based level set method. FCM clustering forms the initial contour. The evolution of the curve is then regularized using a distance function in the level sets. The segmented CC is divided into five segments, whose areas are measured. The subjective results show that the proposed method is able to extract the CC from skull-stripped images. It is demonstrated that the level set with the FCM as the initial contour gives better results than those obtained with a manual initial contour. It is found that the autistic subjects have a reduced CC area compared to that of control subjects. The total CC area of autistic subjects gives a correlation of R = 0.39 with the verbal intelligence quotient (IQ) values. Further analysis shows that the anterior third region of the CC gives significant discrimination of the control and autistic subjects compared to the other segments. Its correlation (R) with verbal IQ is found to be 0.27 in autistic subjects. The feature area extracted from the CC and its segments are significant, hence the results may be clinically helpful in the mass screening of autistic subjects.

Keywords

Autism Corpus callosum Fuzzy c-means Level set Intelligence quotient 

Notes

Acknowledgments

The first author, A. R. Jac Fredo, is receiving a fellowship from Maulana Azad National Fellowship for Minority students (F1-17.1/2011/MANF-CHR-TAM-1826) for his research work.

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Copyright information

© Taiwanese Society of Biomedical Engineering 2015

Authors and Affiliations

  1. 1.Department of Electronics EngineeringAnna UniversityChennaiIndia
  2. 2.Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied MechanicsIndian Institute of Technology MadrasChennaiIndia

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