An Enhancement Visual Quality of Digital Medical Image Based on Hybrid Genetic Algorithm and Salp-Swarm Optimization.

Abstract

This paper proposed a method to denoise medical images using a Hybrid Adaptive Algorithm based on the Genetic Algorithm (GA) and Salp-Swarm Algorithm (SSA). Medical images can be often affected by different kinds of noise that decrease the precision of any automatic system for analysis. Therefore, the noise reduction methods are frequently utilized for increasing the Peak signal-to-noise ratio PSNR and the structural similarity index measure SSIM to optimize the originality. Gaussian noise speckle noise, Poisson noise, and salt & pepper noise corrupted the used medical data, separately with the different noise levels to medical images were added noise variance from 0.1 to 0.9. In the analytical study, we apply different kinds of noise like Gaussian noise (GN), speckle noise, Poisson noise, and Salt-andpepper noise (SPN) to medical images for making these images noisy. The hybrid GA-SSA model was applied on medical noisy images and the performances have been determined by the statistical analyses such as PSNR values are gotten (54.84967, 51.98685, 43.57169, 45.4709), MSE equal (0.99075, 0.997525, 1.14925, 2.218438) Gaussian noise (GN), salt-andpepper noise (SPN), Poisson noise, and speckle noise (SN) on order.