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Autism Diagnostics by 3D Shape Analysis of the Corpus Callosum

Autism Diagnostics by 3D Shape Analysis of the Corpus Callosum

Ahmed Elnakib, Manuel F. Casanova, Georgy Gimel’farb, Ayman El-Baz
Copyright: © 2012 |Pages: 21
ISBN13: 9781466600591|ISBN10: 1466600594|EISBN13: 9781466600607
DOI: 10.4018/978-1-4666-0059-1.ch015
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MLA

Elnakib, Ahmed, et al. "Autism Diagnostics by 3D Shape Analysis of the Corpus Callosum." Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, edited by Kenji Suzuki, IGI Global, 2012, pp. 315-335. https://doi.org/10.4018/978-1-4666-0059-1.ch015

APA

Elnakib, A., Casanova, M. F., Gimel’farb, G., & El-Baz, A. (2012). Autism Diagnostics by 3D Shape Analysis of the Corpus Callosum. In K. Suzuki (Ed.), Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis (pp. 315-335). IGI Global. https://doi.org/10.4018/978-1-4666-0059-1.ch015

Chicago

Elnakib, Ahmed, et al. "Autism Diagnostics by 3D Shape Analysis of the Corpus Callosum." In Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, edited by Kenji Suzuki, 315-335. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0059-1.ch015

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Abstract

The importance of accurate diagnostics of autism that severely affects personal behavior and communication skills cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in autistic brains. This chapter proposes a new approach to quantitative analyses of three-dimensional (3D) Magnetic Resonance Images (MRI) of the brain that ensures a more accurate quantification of the anatomical differences between the CC of autistic and normal subjects. It consists of three main processing steps: (1) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (2) extracting a centerline of the CC; and (3) cylindrical mapping of the CC surface for its comparative analysis. The authors’ experiments revealed significant differences (at the 95% confidence level) between 17 normal and 17 autistic subjects in four anatomical divisions (i.e. splenium, rostrum, genu, and body of their CCs). Moreover, the initial classification results suggest that the proposed centerline-based shape analysis of the CC is a promising supplement to the current techniques for diagnosing autism.

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