A leader Harris hawks optimization for 2-D Masi entropy-based multilevel image thresholding

Abstract

The multilevel image thresholding is one of the important steps in multimedia tools to understand and interpret the object in the real world. Nevertheless, 1-D Masi entropy is quite new in the thresholding application. However, the 1-D Masi entropy-based image thresholding fails to consider the contextual information. To address this problem, we propose a 2-D Masi entropy-based multilevel image thresholding by utilizing a 2-D histogram, which ensures the contextual information during the thresholding process. The computational complexity in multilevel thresholding increases due to the exhaustive search process, which can be reduced by a nature-inspired optimizer. In this work, we propose a leader Harris hawks optimization (LHHO) for multilevel image thresholding, to enhance the exploration capability of Harris hawks optimization (HHO). The increased exploration can be achieved by an adaptive perching during the exploration phase together with a leader-based mutation-selection during each generation of Harris hawks. The performance of LHHO is evaluated using the standard classical 23 benchmark functions and found better than HHO. The LHHO is employed to obtain optimal threshold values using 2-D Masi entropy-based multilevel thresholding objective function. For the experiments, 500 images from the Berkeley segmentation dataset (BSDS 500) are considered. A comparative study on state-of-the-art algorithm-based thresholding methods, using segmentation metrics such as – peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the feature similarity index (FSIM), is performed. The experimental results reveal a remarkable difference in the thresholding performance. For instance, the average PSNR values (computed over 500 images) for the level 5 are increased by 2% to 4% in case of 2-D Masi entropy over 1-D Masi entropy.