Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

Abstract

Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT) image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN) to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN) and the Cat Swarm Optimization (CSO) is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR), have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

“…CSO outperformed GA, IGA, PSO, and CSPSO [64] Used CSO and FLANN to remove the unwanted Gaussian noises from CT images e proposed system outperformed mean filter and adaptive Wiener filter. [45] Used CSO with L-BFGS-B technique to register nonrigid multimodal images e system yielded satisfactory results [65] Used CSO in image enhancement to optimize parameters of the histogram stretching technique PSO outperformed CSO [66] Used CSO algorithm for IIR system identification CSO outperformed GA and PSO [67] Computational Intelligence and Neuroscience Used CSO and SVM for electrocardiograms signal classification…”

“…CSO outperformed GA and DE algorithms [105] CSO applied to optimize the network structure and learning parameters of an ANN model, which is used to predict an ASP flooding oil recovery index e system successfully forecast the ASP flooding oil recovery index [42] Applied CSO to build an identification model to detect early cracks in beam type structures CSO yields a desirable accuracy in detecting early cracks [106] Computational Intelligence and Neuroscience wavelet entropy, ANN, and CSO algorithm to develop an alcohol use disorder (AUD) identification system [64]. Kumar et al combined the CSO algorithm with functional link artificial neural network (FLANN) to remove the unwanted Gaussian noises from CT images [45]. Yang et al combined CSO with L-BFGS-B technique to register nonrigid multimodal images [65].…”

“…CS-FLANN. Kumar et al combined the CSO algorithm with functional link artificial neural network (FLANN) to develop an evolutionary filter to remove Gaussian noise[45].…”

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

“…The proposed system outperformed Mean Filter and Adaptive Wiener Filter. [46] Used CSO with L-BFGS-B technique to register non-rigid multi-modal images…”

A Regular k-Shrinkage Thresholding Operator for the Removal of Mixed Gaussian-Impulse Noise

“…Image restoration [1][2][3][4] attempts to recover a clear image from the observations of real scenes. As a fundamental procedure, it has been applied to various application areas, such as image fusion [5] and action recognition [6].…”