Blood Cell Image Denoising Based on Tunicate Rat Swarm Optimization with Median Filter

The significant challenge that occurs due to medical image processing is to acquire the image without the loss of any crucial data. The image data can be degraded by noise or other factors while acquiring or processing the image. This noise affects the image quality since the contrast of medical images is already very low, and it is hard for the experts to identify the infections from the images. Henceforth, image denoising is an essential process in the medical imaging systems. In this paper, a hybrid tunicate rat swarm optimization with median filter (TRSWOAMF) has been proposed to remove or reduce the noise from the medical blood cell image, thus to restore a high-quality image. The proposed TRSWOAMF method uses median filter to remove the noise from the blood cell images, which then optimizes the parameters by using the tunicate rat swarm optimization algorithm. This TRSWOAMF method detects the noise in the blood cell image, wherein the median filter computes the median value for every pixel and best value is replaced. Then the parameters in the image are optimized by the tunicate rat swarm optimization algorithm to retain the original quality of the image. The result shows that the proposed TRSWOAMF method produces high-quality denoised image with a significantly reduced error rate.