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Characterization Of Corpus Collosum In Mr Images Using Texture Analysis

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Author: 
Amna M. Ahmed, Suhaib Alameen, Caroline Edward Ayad and Mohamed E. M. Gar-Elnabi
Abstract: 

This study concern to characterize the corpus collosum parts to splenium, trunk and genu using first order statistic and extract classification features from MR images. The FOS techniques included eight’s features. To find the gray level variation in MR images it complements the FOS features extracted from MR images with variation of gray level in pixels and estimate the size variated of the sub patterns. analyzing the image with Interactive Data Language IDL software to measure the grey level variation of images. The results show that the first order statistic and features give classification accuracy of corpus collosum parts for splenium 100.0%, trunk 76.5% and the genu classification accuracy 97.0%. The overall classification accuracy of corpus collosum area 96.2%. These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new MR images with the appropriate corpus collosum area names.

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