Computer aided automatic detection of glioblastoma tumour in the brain using CANFIS classifier. (27th June 2019) - Ravichandran, C.G.
Detection and diagnosis of brain tumour is complicated due to its similar characteristics between tumour pixels and non-tumour pixels in brain image. This paper proposes an efficient methodology for the detection and segmentation of glioblastoma tumour region in the brain. The proposed methodology for glioblastoma tumour classifications has the following stages as noise reduction, image fusion, feature extraction and classification. The median filter is used to remove the noises in the brain images and pixel level image fusion is applied to obtain the enhanced brain image. The features are extracted from the fused image and co-active neuro fuzzy inference system (CANFIS) classifier is used to classify the brain image into either benign or malignant. Further, morphological operations are applied on the classified malignant brain image in order to segment the glioblastoma tumour region. The proposed methodology achieves 96.43% sensitivity, 99.99% specificity and 99.91% accuracy with respect to ground truth images.