Classification of Brain MRI Image of Normal And Convulsion Pediatric By Using Texture Analysis

Abstract

This study is an effort to study brain - white matter (WM ), gray matter (GM ), cerebrospinal fluid (CSF) - in M RI images which shows normal appearance if the patients were normal or had convulsion, through computerized analysis. The main objective of this study was to identify brain tissue as normal and abnormal “convulsion” pediatric using textural analysis. The textural features were extracted from the whole M RI image using a window of 3×3pixel wide, where the first order statistics (mean, standard deviation, Coefficient of variation etc...) were extracted using one pixel interlace. The features in the window were classified as GM or WM or CSF as well as normal or abnormal using K-means and discriminate analysis. The images were collected from M RI brain scans for 41 patients presented for examination in the period from July 2014 to December 2014, 5 patients is control and 36 patients as cases underwent epilepsy protocol and T1, T2 and FLAIR images obtained. Segmentation analysis and ratios are compared between structures of the both diseased and control groups. the classification technique were adopted as a method of pattern identification the image into normal or elliptic classes. Linear discriminate analysis using steps wise were used to classify the WM , GM and CSF into the predefined classes normal and convulsion one. The result of this classification showed that the white matter of the convulsion cases showed a high classification accuracy of 99.5% and demarked as separate class from the rest of the class’s weather it normal or abnormal. In conclusion the white matter of the convulsion cases texturally were very different from the normal one while from visual perception point of view they appear similar