Automated screening of MRI brain scanning using grey level statistics
|Hasan, A and Meziane, F.
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.
|Linear discriminant analysis; ; Magnetic resonance imaging; ; Modified grey level Co-occurrence matrix; ; Multi-layer perceptron neural network; ; Principle component analysis; ; Support vector machine
|Computers & Electrical Engineering
|Vol 53 (Jul 2016), pp. 276-291
|Digital Object Identifier (DOI)
|Web address (URL)
|16 Apr 2016
|29 Sep 2016
|Publication process dates
|14 Mar 2016
|05 Jun 2023
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