Particle Swarm Optimization-based Functional Link Artificial Neural Network for Medical Image Denoising

Abstract

In this paper, a new computationally fast and efficient adaptive digital image filter has been proposed for denoising of digital medical image corrupted with additive white Gaussian noise. A particle swarm optimization-based functional link artificial neural network (FLANN) has been applied for this interesting and challenging problem. The three others competitive networks based on artificial neural network such as multilayer perceptron (MLP), direct linear artificial feed-through neural network (DLFANN), and LMS-based FLANN have also been applied for this purpose. The quantitative analysis of the proposed algorithm has been carried out by taking the peak signal to noise ratio (PSNR) and mean square error (MSE) as two parameters. Experimental results demonstrate the efficiency and effectiveness of the proposed algorithm.