Medical image enhancement using genetic optimization algorithm

Abstract

The visual interpretation of the damaged areas of the human body may not be possible
using low-quality medical photographs. Therefore, adaptive histogram equalization, a novel
adaptive picture improvement technique based on genetic algorithms, has been proposed in
this research to improve the image visions and to give computational support.The study made
use of several fitness functions. The fitness function included image entropy, energy, sharpness, peak signal-to-noise ratio, structural similarity index, gray level cooccurrence matrix, and
Sobel edge feature extraction techniques.The modified probability density function (PDF), histogram sub-division, and genetic algorithm are all part of the suggested framework. The exposure threshold and the ideal threshold for maintaining brightness and minimizing information
loss are used in a histogram subdivision technique. Using the idea of a genetic algorithm
and the suggested multi-objective fitness function as a guide, the threshold parameters are
modified to make the suggested method more adaptive. To improve the image quality, each
sub-histogram’s PDF is then altered. The findings of the trial demonstrate that the suggested
method outperforms alternative enhancement methods currently in use.