A Comprehensive Review on Nature Inspired Neural Network based Adaptive Filter for Eliminating Noise in Medical Images.

BACKGROUND: Various kind of medical imaging modalities are available for providing noninvasive view and for analyzing any pathological symptoms of human beings. Different noise may appear in those modalities at the time of acquisition, transmission, scanning, or at the time of storing. The removal of noises from the digital medical images without losing any inherent features is always considered a challenging task because a successful diagnosis relies on them. Numerous techniques have been proposed to fulfill this objective, and each having their own benefits and limitations. DISCUSSION: In this comprehensive review article, more than 65 research articles are investigated to illustrate the applications of Artificial Neural Networks (ANN) in the field of biomedical image denoising. In particular, the zest of this article is to highlight the hybridized filtering model using nature-inspired algorithms and artificial neural networks for suppression of noise. Various other techniques, such as fixed filter, linear adaptive filters and gradient descent learning based neural network filter are also included. CONCLUSION: This article envisages how to train ANN using derivative free nature-inspired algorithms, and its performance in various medical images modalities and noise conditions.

Cat Swarm Optimization based Functional Link Multilayer Perceptron for Suppression of Gaussian and Impulse Noise from Computed Tomography Images.

BACKGROUND: The Gaussian and impulse noises corrupt the Computed Tomography (CT) images either individually or collectively, and the conventional fixed filters do not have the potential to suppress these noise. OBJECTIVES: These spurious noises affect the inherent features of CT image awkwardly. Hence, to handle such a situation adaptive Cat Swarm Optimization based Functional Link Multilayer Perceptron (CSO-FLMLP) has been proposed in this paper to get rid of unwanted noise from the CT images. METHODS: Here, the nature-inspired CSO technique which is an optimization algorithm has been employed to assist in updating the weights of FLMLP network. In this work, the cost function considered for CSO is the error between noisy and contextual pixels of reference images which need to minimize. For examining the efficiency of CSO-FLMLP filter, it is compared with the other six competitive adaptive filters. RESULTS: The performance of proposed approach and other state-of-the-art filters are compared on the basis of performance metrics like the structural similarity index (SSIM), peak signal to noise ratio (PSNR), computational time and convergence rate. Supremacy of CSO-FLMLP among the considered adaptive filters is validated through Friedman statistical test. CONCLUSION: The CSO-FLMLP adaptive filter could successfully re-move the dominant Gaussian, impulse or combination of both noises from the clinical CT images.