Fully automated glioma tumour segmentation using anatomical symmetry plane detection in multimodal brain MRI

Abstract

Automatic brain abnormality detection is a major challenge in medical image processing. Manual lesion delineation techniques are susceptible to subjective errors, and therefore, computer aided preliminary screening of a lesion is necessary. This study introduces an efficient and automated algorithm based on the symmetry of the brain structures in the two hemispheres for brain tumour segmentation using multimodal brain magnetic resonoce imaging. Symmetry is a vital clue for determining intensity-based lesion difference in the two hemispheres of brain. A reliable method is proposed for extracting the cancerous region in order to improve the speed and accuracy of brain tumour segmentation. First, a symmetry plane is detected and then through features extracted from both sides of the brain, a similarity measure for comparing the hemisphere is defined. The cancerous region is extracted using similarity measurement, and the accuracy is improved using postprocessing operation. This algorithm is evaluated against the BRATS datasets including high- and low-grade glioma brain tumours. The performance indices are calculated and comparative analysis is implemented as well. Experimental results demonstrate accuracy close to manual lesion demarcation with performance indices.