What is it about?
Abstract: 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. Keywords: Adaptive filter, cat swarm optimization, neural network, nature inspired technique, medical image, noise.
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Why is it important?
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. 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.
Perspectives
Writing this article was a great pleasure as it has co-authors Dr. Sudhansu Kumar Mishra, Sumit Kumar Choubey, Sanjay Shankar Tripathy, Dilip Kumar Choubey, Dinesh Das with whom I have had long standing collaborations. Especially, I would like to thank Sumit Kumar Choubey who help me to code entire algorithm. I would like This article also lead pre-processing tasks and real-time denoising of the digital images. This work appeals researchers who are working in the area digital image processing, computational intelligence, machine learning and signal processing to rare disease groups contacting me and ultimately to a greater involvement in rare disease research.
Manish Kumar
Mody University of Science and Technology
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This page is a summary of: Cat Swarm Optimization based Functional Link Multilayer Perceptron for Suppression of Gaussian and Impulse Noise from Computed Tomography Images, Current Medical Imaging Formerly Current Medical Imaging Reviews, May 2020, Bentham Science Publishers,
DOI: 10.2174/1573405614666180903115336.
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