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 human behavior 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. The traditional classification methods have less average prediction accuracy of 84.15% in diagnosing epilepsy. The proposed technique includes the improved classification approach for the diagnosis of epilepsy. The technique includes the following phases: pre-processing the 2D MR Brain Image using Threshold Interval Method and Min.-Max. Normalization 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 shape features such as corpus callosum bending angle, Genu thickness and Intelligent Quotient (IQ) are extracted from the segmented corpus callosum diagnosis of epilepsy using Case Based Reasoning (CBR) and genetic classification. The performance of the proposed optimized CBR classification reduces the false positive rate. The CBR classification model results in 96.7% of prediction accuracy and the optimized classification approach results in 97.3% of prediction accuracy.
P. Tamijeselvy, V. Palanisamy and S. Elakkiya, 2013. Corpus Callosum Classification Using Case Based Reasoning and Genetic Classifier for the Prediction of Epilepsy from 2D Medical Images. Asian Journal of Information Technology, 12: 117-125.