WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 12, 2013
A Novel Approach for the Prediction of Epilepsy from 2D Medical Images Using Case Based Reasoning Classification Model
Authors: , ,
Abstract: Corpus Callosum is a highly visible structure in brain imaging whose function is to connect the left and right hemisphere of the brain. Epilepsy is the sudden alterations in behaviour or motion function caused by an electrical discharge from the brain. Such electrical activity that starts from one side of the brain spread to the other side through the Corpus Callosum. Epilepsy occurs in 2% of the general population and it is the oldest known brain disorder. Approaches for the classification of Corpus Callosum are described for the specific application of epilepsy detection. The proposed technique includes the improved classification for the diagnosis of epilepsy. The technique includes the following phases: (i) Pre-processing the 2D MR Brain Image using threshold interval method and Min-Max Normalization (ii) Segmentation of brain image using Multiscale segmentation method to obtain the segments of corpus callosum. Multiscale segmentation proves to be better in curvature segmentation with less execution time and 91% of accuracy based on entropy (iii) Shape features such as corpus callosum bending angle, Genu thickness and Intelligent Quotient (IQ) are extracted from the segmented corpus callosum (iv) Diagnosis of epilepsy using Case Based Reasoning (CBR). The performance of the proposed CBR classification reduces the false positive rate and results in 96.7% of prediction accuracy when compared to the conventional classification models.
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Keywords: Corpus callosum, Epilepsy, Multiscale Segmentation, Pre-processing, shape features, Case Based Reasoning