DocumentCode
2510982
Title
Dyslexia Diagnostics by Centerline-Based Shape Analysis of the Corpus Callosum
Author
Elnakib, Ahmed ; El-Baz, Ayman ; Casanova, Manuel F. ; Switala, A.E.
Author_Institution
Bioeng. Dept., Univ. of Louisville, Louisville, KY, USA
fYear
2010
fDate
23-26 Aug. 2010
Firstpage
261
Lastpage
264
Abstract
Dyslexia severely impairs learning abilities, so that improved diagnostic methods are called for. Neuropathological studies have revealed abnormal anatomy of the Corpus Callosum (CC) in dyslexic brains. We explore a possibility of distinguishing between dyslexic and normal (control) brains by quantitative CC shape analysis in 3D magnetic resonance images (MRI). Our approach consists of the three steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting the centerline of the CC; and (iii) classifying the subject as dyslexic or normal based on the estimated length of the CC centerline using a _-nearest neighbor classifier. Experiments revealed significant differences (at the 95% confidence level) between the CC centerlines for 14 normal and 16 dyslexic subjects. Our initial classification suggests the proposed centerline-based shape analysis of the CC is a promising supplement to the current dyslexia diagnostics.
Keywords
biomedical MRI; image classification; image segmentation; medical image processing; patient diagnosis; 3D MRI; 3D magnetic resonance images; CC classification; CC segmentation; CC shape analysis; Neuropathological studies; centerline-based shape analysis; corpus callosum; dyslexia diagnosis; dyslexic brains; k-nearest neighbor classifier; Accuracy; Image segmentation; Magnetic resonance imaging; Shape; Solid modeling; Three dimensional displays; Training; Diagnosis; Dyslexia; and Segmentation;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location
Istanbul
ISSN
1051-4651
Print_ISBN
978-1-4244-7542-1
Type
conf
DOI
10.1109/ICPR.2010.73
Filename
5597588
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