Automated detection of glioblastoma tumor in brain magnetic imaging using ANFIS classifier. Issue 2 (June 2016)

• Record Type: Journal Article
• Authors: Thirumurugan, P.; Ramkumar, D.; Batri, K.; Sundhara Raja, D.
• Is Part Of: International journal of imaging systems and technology. Volume 26:Issue 2(2016:Jun.)
• Page Start: 151
• Page End: 156
• Publication Date: 2016-06
• Subjects: contourlet transform -- classifier -- glioblastoma tumor -- feature extraction; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22169
• Languages: English
• ISSNs: 0899-9457

Abstract

ABSTRACT: This article proposes a novel and efficient methodology for the detection of Glioblastoma tumor in brain MRI images. The proposed method consists of the following stages as preprocessing, Non‐subsampled Contourlet transform (NSCT), feature extraction and Adaptive neuro fuzzy inference system classification. Euclidean direction algorithm is used to remove the impulse noise from the brain image during image acquisition process. NSCT decomposes the denoised brain image into approximation bands and high frequency bands. The features mean, standard deviation and energy are computed for the extracted coefficients and given to the input of the classifier. The classifier classifies the brain MRI image into normal or Glioblastoma tumor image based on the feature set. The proposed system achieves 99.8% sensitivity, 99.7% specificity, and 99.8% accuracy with respect to the ground truth images available in the dataset.