Brain tumor detection and diagnosis using ANFIS classifier. Issue 2 (June 2016)

• Record Type: Journal Article
• Authors: Thirumurugan, P.; Shanthakumar, P.
• Is Part Of: International journal of imaging systems and technology. Volume 26:Issue 2(2016:Jun.)
• Page Start: 157
• Page End: 162
• Publication Date: 2016-06
• Subjects: gray matter -- white matter -- CSF -- brain tumor -- brain tissue; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22170
• Languages: English
• ISSNs: 0899-9457

Abstract

ABSTRACT: In this article, the segmented brain tumor region is diagnosed into mild, moderate, and severe case based on the presence of tumor cells in the brain components such as Gray Matter (GM), White Matter (WM), and cerebrospinal fluid (CSF). The modified spatial fuzzy c mean algorithm is used to segment brain tissues. The feature Local binary pattern is extracted from segmented tissues, which is trained and classified by ANFIS Classifier. The performance of the proposed brain tissues segmentation system is analyzed in terms of sensitivity, specificity, and accuracy with respect to manually segmented ground truth images. The severity of brain tumor is diagnosed into mild case if the segmented brain tumor is present in the grey matter. The severity of brain tumor is diagnosed into moderate case if the segmented brain tumor is present in the WM. The severity of brain tumor is diagnosed into severe case if the segmented brain tumor is present in the CSF region. The immediate surgery is required for severe case and medical treatment is preferred for mild and moderate case.