A computer‐aided approach for meningioma brain tumor detection using CANFIS classifier. Issue 3 (September 2017)

• Record Type: Journal Article
• Authors: Kathirvel, R.; Batri, K.
• Is Part Of: International journal of imaging systems and technology. Volume 27:Issue 3(2017)
• Page Start: 193
• Page End: 200
• Publication Date: 2017-09
• Subjects: accuracy -- brain tumor -- classifier -- contourlet transform -- features; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22223
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Abnormal growth of cells in brain leads to the formation of tumors in brain. The earlier detection of the tumors in brain will save the life of the patients. Hence, this article proposes a computer‐aided fully automatic methodology for brain tumor detection using Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classifier. The internal region of the brain image is enhanced using image normalization technique and further contourlet transform is applied on the enhanced brain image for the decomposition with different scales. The grey level and heuristic features are extracted from the decomposed coefficients and these features are trained and classified using CANFIS classifier. The performance of the proposed brain tumor detection is analyzed in terms of classification accuracy, sensitivity, specificity, and segmentation accuracy.