Title: Computer aided automatic detection of glioblastoma tumour in the brain using CANFIS classifier
Authors: C.G. Ravichandran; K. Rajesh
Addresses: SCAD Institute of Technology, SCAD Knowledge City, Palladam – Pollachi Highway, 641 664, Tirupur District, Tamil Nadu, India ' Department of Electronics and Communication Engineering, SSM Institute of Engineering and Technology, India
Abstract: 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.
Keywords: glioblastoma tumour; median filter; malignant; features; classification.
International Journal of Biomedical Engineering and Technology, 2019 Vol.30 No.2, pp.179 - 194
Received: 21 Nov 2016
Accepted: 18 Dec 2016
Published online: 27 Jun 2019 *