Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm

[Display omitted] •Kapur entropy method measuring the homogeneity of segmented classes.•T2 weighted axial MR brain images are considered for segmentation.•Higher entropy value and low standard deviation are explain the better and consistent performance of the algorithm. Medical image analysis is one of the major research areas in the last four decades. Many researchers have contributed quite good algorithms and reported results. In this paper, real coded genetic algorithm with Simulated Binary Crossover (SBX) based multilevel thresholding is used for the segmentation of medical brain images. The T2 weighted Magnetic Resonance Imaging (MRI) brain images are considered for image segmentation. The optimum multilevel thresholding is found by maximizing the entropy. The results are compared with the results of the existing algorithms like Nelder–Mead simplex, PSO, BF and ABF. The statistical performances of the 100 independent runs are reported. The results reveal that the performance of real coded genetic algorithm with SBX crossover based optimal multilevel thresholding for medical image is better and has consistent performance than already reported methods.