Brain Tumor Segmentation based on Red-Bellied Woodpecker Mating Optimization Algorithm

Abstract

Earlier, many researchers proposed various segmentation algorithms to segment tumor from MRI Brain image. The method of a nature-inspired meta heuristic-based woodpecker characteristics approach is used to segment the tumored area of this proposed study. In this automated MRI brain tumor segmentation, the MRI brain image gets enhanced for improving the performance of the segmentation accompanied by the skull elimination phase to eliminate the morphological operations of all non-brain tissues. In the end, the RBWMOA (Red-Bellied Woodpecker Mating Optimization Algorithm) is suggested for the segmentation of tumor. An assessment of the experimental outcomes of the methodology suggested was focused on the coefficient of dice similarity, Hausdorff distance, Jaccard coefficient, Precision, Recall, Accuracy and F-measure. The experimental result of RBWMOA obtain better performance and shows0.845 Dice Similarity Coefficient, 7.231 Hausdorff distance in mm, 0.6981 Jaccard Coefficient, 95.67 % Precision, 94.72 % Recall, 98.29 % Accuracy and 95.19 % F-measure.