A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization

In this paper, a novel krill herd (KH) based optimized contrast and sharp edge enhancement framework is introduced for medical images. Plateau limit and fitness function are proposed in this paper to achieve the best-enhanced image. A new plateau limit is applied to clip the histogram using minimum, maximum, mean, and median of the histogram with a tunable parameter. The residue pixels are reallocated to the relative vacancy available on histogram bins. This method explores KH meta-heuristic algorithm to automatically adjust the tunable parameter based on a novel fitness function. Fitness function contains two different objective functions, which use edge, entropy, gray level co-occurrence matrix (GLCM) contrast, and GLCM energy of image for best visual, contrast enhancement and improved different characteristic information of the anatomical images. This method is compared with a different state of the art methods to check the viability and vigorous of the scheme and salp swarm algorithm (SSA) optimization is also used for the fair comparison of the proposed approach. The results show that the proposed framework is having superior performance compared to all the existing methods, both qualitatively and quantitatively, in terms of contrast, information content, edge details, and structure similarity. (C) 2019 Elsevier Ltd. All rights reserved.