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

• Published in: Applied Computational Intelligence and Soft Computing Vol. 2016; no. 2016; pp. 205 - 210
• Main Authors: Kumar, M., Sahu, S. S., Mishra, S. K.
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2016; Hindawi Publishing Corporation; John Wiley & Sons, Inc; Hindawi Limited; Wiley
• Subjects: Adaptive filters; Artificial neural networks; Computer aided tomography; CT imaging; Gaussian; Image processing; Links; Mathematical optimization; Methods; Neural networks; Noise; Noise control; Optimization; Swarm intelligence; Technology application
• ISSN: 1687-9724; 1687-9732
• DOI: 10.1155/2016/6304915

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.