DocumentCode
3349678
Title
Image-based detection of Corpus Callosum variability for more accurate discrimination between autistic and normal brains
Author
Elnakib, Ahmed ; El-Baz, Ayman ; Casanova, Manuel F. ; Gimel´farb, Georgy ; Switala, Andrew E.
Author_Institution
Bioeng. Dept., Univ. of Louisville, Louisville, KY, USA
fYear
2010
fDate
26-29 Sept. 2010
Firstpage
4337
Lastpage
4340
Abstract
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 CC.
Keywords
biomedical MRI; brain; diseases; image segmentation; medical image processing; neurophysiology; patient diagnosis; 3D MRI; CC surface; Corpus Callosum variability; abnormal anatomy; autism diagnosis; autistic brain; communication skills; image segmentation; image-based detection; neuropathological study; normal brain; personal behavior; three-dimensional magnetic resonance image; Autism; Image segmentation; Magnetic resonance imaging; Mathematical model; Shape; Solid modeling; Three dimensional displays; Autism; Corpus Callosum; Modeling; Segmentation;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location
Hong Kong
ISSN
1522-4880
Print_ISBN
978-1-4244-7992-4
Electronic_ISBN
1522-4880
Type
conf
DOI
10.1109/ICIP.2010.5652409
Filename
5652409
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