A hybrid bat-genetic algorithm for improving the visual quality of medical images

Abstract

Efficient repression of noise in a medical image is a very significant issue. This paper proposed a method to denoise medical images by the use of a hybrid adaptive algorithm based on the bat algorithm (BA) and genetic algorithm (GA). 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 always utilized for increasing the Peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) of images to optimize the originality. Gaussian noise and salt and pepper noise corrupted the used medical data, separately. The noise level to medical images was added noise variance from 0.1 to 0.5 to compare the performance of the de-noising techniques. In the analytical study, we apply different kinds of noise like Gaussian noise and salt-and-pepper noise to medical images for making these images noisy. The hybrid BA-GA model was applied on medical noisy images to eliminate noise and the performances have been determined by the statistical analyses such as PSNR, values are gotten 63.04 dB and 59.75 dB for CT and MRI images.