Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Performance Analysis of Glioma Brain Tumor Segmentation using Ridgelet Transform and CANFES Methodology

Version 1 : Received: 3 March 2020 / Approved: 4 March 2020 / Online: 4 March 2020 (10:22:09 CET)

How to cite: Srinivasan, S.; Ponnuchamy, T. Performance Analysis of Glioma Brain Tumor Segmentation using Ridgelet Transform and CANFES Methodology. Preprints 2020, 2020030058 (doi: 10.20944/preprints202003.0058.v1). Srinivasan, S.; Ponnuchamy, T. Performance Analysis of Glioma Brain Tumor Segmentation using Ridgelet Transform and CANFES Methodology. Preprints 2020, 2020030058 (doi: 10.20944/preprints202003.0058.v1).

Abstract

Objective:The Glioma brain tumor detection and segmentation methods are proposed in this paper using machine learning approaches. Methods:The boundary edge pixels are detected using Kirsch’s edge detectors and then contrast adaptive histogram equalization method is applied on the edge detected pixels. Then, Ridgelet transform is applied on this enhanced brain image in order to obtain the Ridgelet multi resolution coefficients. Further, features are derived from the Ridgelet transformed coefficients and the features are optimized using Principal Component Analysis (PCA) method and these optimized features are classified into Glioma or non-Glioma brain images using Co-Active Adaptive Neuro Fuzzy Expert System (CANFES) classifier.Results:The proposed method with PCA and CANFES classification approach obtains 97.6% of se, 98.56% of sp, 98.73% of Acc, 98.85% of Pr, 98.11% of FPR and 98.185 of FNR, then the proposed Glioma brain tumor detection method using CANFES classification approach only.

Subject Areas

Glioma; tumors; features; Ridgelet transform; enhancement

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