Accurate Automated Detection of Autism Related Corpus Callosum Abnormalities

被引:0
作者
Ayman El-Baz
Ahmed Elnakib
Manuel F. Casanova
Georgy Gimel’farb
Andrew E. Switala
Desha Jordan
Sabrina Rainey
机构
[1] University of Louisville,BioImaging Laboratory, Department of Bioengineering
[2] University of Louisville,Department of Psychiatry and Behavioral Science
[3] University of Auckland,Department of Computer Science
来源
Journal of Medical Systems | 2011年 / 35卷
关键词
Segmentation; Modeling; Autism; Corpus callosum;
D O I
暂无
中图分类号
学科分类号
摘要
The importance of accurate early 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. We propose a new approach to quantitative analysis of three-dimensional (3D) magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of autistic and normal subjects. It consists of three main processing steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting a centerline of the CC; and (iii) cylindrical mapping of the CC surface for its comparative analysis. Our 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.
引用
收藏
页码:929 / 939
页数:10
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