Automated Segmentation of Tumours in MRI Brain Scans

The research reported in this paper concerns the development of a novel automated algorithm to identify and segment brain tumours in MRI scans. The input is the patient's scan slices and the output is a subset of the slices that includes the tumour. The proposed method is called Bounding 3D Box Based Genetic Algorithm (BBBGA) and is based on the use of Genetic Algorithm (GA) to search for the most dissimilar regions between the left and right hemispheres of the brain. The process involves randomly generating a hundred of 3D boxes with different sizes and locations in the left hemisphere of the brain and compared with the corresponding 3D boxes in the right hemisphere of the brain through the objective function. These 3D boxes are moved and updated during the iterations of the GA towards the region of maximum dissimilarity between the two hemispheres which represent the approximate position of the tumour. The dataset includes 88 pathological patients provided by the MRI Unit of Al-Kadhimiya Teaching Hospital in Iraq. The achieved accuracy of the BBBGA and 3D segmentation of the tumour were 95% and 90% respectively.

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