Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis

Abstract

This paper presents an efficient skull stripping method to improve the decision-making process. Extended weiner filtering (EWF) is used for removing the noise and enhancing the quality of images. Further, laplacian lion optimization algorithm (LXLOA) is implemented. LXLOA utilizes the Otsu's and Tsallis entropy fitness function to determine an optimal solution. The implemented LXLOA provides a threshold value required for performing the segmentation on the brain MRI images. The extracted features are selected using fuzzy weighted k-means embedding LDA (linear discriminant analysis) method for improving training of the classification model. The proposed LXLOA is extensively tested on standard benchmark functions CEC 2017 and outperforms the existing state-of-the-art algorithm. Rigorous statistical analysis is conducted to determine the statistical significance. Three-fold performance comparison is performed by considering (a) the quality of the segmented image; (b) accuracy, sensitivity, and specificity; and (c) computational cost of convergence for finding an optimal solution. Result reveals that LXLOA gives promising results and demonstrate effective outcomes on the standard quality measures (a) accuracy (97.37%); (b) sensitivity (85.8%); (c) specificity (90%); and (d) precision (91.92%).