Automated screening of MRI brain scanning using grey level statistics

Hasan, A; Meziane, F

Abstract

This paper describes the development of an algorithm for detecting and classifying MRI brain slices into normal and abnormal by relying on 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 abnormality with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 patients having different brain abnormalities whilst the remainder do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 100 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumours detection was 97.8% using a Multi-Layer Perceptron Neural Network.

Citation

Hasan, A., & Meziane, F. (2016). Automated screening of MRI brain scanning using grey level statistics. Computers and Electrical Engineering, 53, 276-291. https://doi.org/10.1016/j.compeleceng.2016.03.008

• Journal Article Type: Article
• Acceptance Date: Mar 14, 2016
• Online Publication Date: Apr 16, 2016
• Publication Date: Apr 16, 2016
• Deposit Date: Feb 29, 2016
• Publicly Available Date: Apr 16, 2018
• Journal: Computers & Electrical Engineering
• Print ISSN: 0045-7906
• Publisher: Elsevier
• Volume: 53
• Pages: 276-291
• DOI: https://doi.org/10.1016/j.compeleceng.2016.03.008
• Publisher URL: http://dx.doi.org/10.1016/j.compeleceng.2016.03.008
• Related Public URLs: http://www.journals.elsevier.com/computers-and-electrical-engineering/