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
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;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.73