Automated screening of MRI brain scanning using grey level statistics
Hasan, A and Meziane, F 2016, 'Automated screening of MRI brain scanning using grey level statistics' , Computers & Electrical Engineering .
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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.
|Schools:||Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)|
|Journal or Publication Title:||Computers & Electrical Engineering|
|Funders:||Non funded research|
|Depositing User:||Prof Farid Meziane|
|Date Deposited:||29 Feb 2016 13:42|
|Last Modified:||19 Apr 2016 13:30|
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