Automated screening of MRI brain scanning using grey level statistics

Automated algorithm for detecting normality or abnormality in MRI brain scans.A new Modified Grey level Co-occurrence Matrix (MGLCM) method is presented to extract second order statistical texture features for discriminating brain abnormality.MGLCM generates efficient texture feature that is used for measuring the symmetry of MRI brain scan than the tradition GLCM.An accuracy of 97.8% was achieved using MGLCM with 9 orientations and 1 distance. The paper describes the development of an algorithm for detecting and classifying MRI brain slices into normal and abnormal. The proposed technique relies on the prior-knowledge that the two hemispheres of a healthy brain have approximately a bilateral symmetry. We use the modified grey level co-occurrence matrix method to analyze and measure asymmetry between the two brain hemispheres. 21 co-occurrence statistics are used to discriminate the images. The experimental results demonstrate the efficacy of our proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 97.8% using a Multi-Layer Perceptron Neural Network. Display Omitted