Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

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

Author(s): Manish Kumar*, Sudhansu Kumar Mishra, Sumit Kumar Choubey, Sanjay Shankar Tripathy, Dilip Kumar Choubey and Dinesh Das

Volume 16, Issue 4, 2020

Page: [329 - 339] Pages: 11

DOI: 10.2174/1573405614666180903115336

Price: $65

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.

Graphical Abstract

[1]
Jiang J, Zhang L, Yang J. Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE Trans Image Process 2014; 23(6): 2651-62.
[http://dx.doi.org/10.1109/TIP.2014.2317985] [PMID: 24760906]
[2]
Luisier F, Blu T, Unser M. Image denoising in mixed Poisson-Gaussian noise. IEEE Trans Image Process 2011; 20(3): 696-708.
[http://dx.doi.org/10.1109/TIP.2010.2073477] [PMID: 20840902]
[3]
Dokur Z, Olmez T. Segmentation of ultrasound images by using a hybrid neural network. Pattern Recognit Lett 2002; 23: 1825-36.
[http://dx.doi.org/10.1016/S0167-8655(02)00155-1]
[4]
Chang YN, Chang HH. Automatic brain MR image denoising based on texture feature-based artificial neural networks. Biomed Mater Eng 2015; 26(Suppl. 1): S1275-82.
[http://dx.doi.org/10.3233/BME-151425] [PMID: 26405887]
[5]
Sudha S, Suresh GR, Sukanesh R. Speckle noise reduction in ultrasound images using context-based adaptive wavelet thresholding. IETE J Res 2009; 55(3): 135-43.
[6]
Zhang D, Mabu S, Hirasawa K. Noise reduction using genetic algorithm based PCNN method. In: IEEE International Conference on Systems, Man and Cybernetics. 2010 Oct 10-13; Istanbul, Turkey: IEEE pp 2627-33.
[7]
Giakoumis I, Nikolaidis N, Pitas I. Digital image processing techniques for the detection and removal of cracks in digitized paintings. IEEE Trans Image Process 2006; 15(1): 178-88.
[8]
Joseph J, Sivaraman J, Periyasamy R, Simi VR. Noise based computation of decay control parameter in nonlocal means filter for MRI restoration. J Med Imaging Health Inform 2016; 6(4): 1027-37.
[9]
Debakla M, Djemal K, Benyettou M. A novel approach for medical images noise reduction based RBF neural network filter. J Comp 2015; 10(2): 68-80.
[10]
Shou YW, Lin CT. Image descreening by GA-CNN-based texture classification. IEEE Trans Circ Syst I 2004; 51(11): 2287-99.
[11]
Ma L, Staunton RC. Integration of multiresolution image segmentation and neural networks for object depth recovery. Pattern Recognit 2005; 38(7): 985-96.
[12]
Alilou VK, Yaghmaee F. Application of GRNN neural network in non-texture image inpainting and restoration. Pattern Recognit Lett 2015; 62: 24-31.
[13]
Sivakumar K. Image restoration using a multilayer perceptron with a multilevel sigmoidal function. IEEE Trans Signal Process 1993; 41(5): 2018-22.
[14]
Pao Y. Adaptive pattern recognition and neural networks. 1st ed. Boston, MA: Addison-Wesley 1989.
[15]
Sicuranza GL, Carini A. A generalized FLANN filter for nonlinear active noise control. IEEE Trans Audio Speech Lang Process 2011; 19(8): 2412-7.
[http://dx.doi.org/10.1109/TASL.2011.2136336]
[16]
Zhang X, Tay ALP. Fast Learning Artificial Neural Network (FLANN) Based Color Image Segmentation in R-G-B-S-V Cluster Space. In: International Joint Conference on Neural Networks. 2007 Aug 12-17; Orlando, FL, USA. IEEE 2007: pp. 563-8.
[17]
Majhi B, Panda G. Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique. Expert Syst Appl 2011; 38(1): 321-33.
[http://dx.doi.org/10.1016/j.eswa.2010.06.070]
[18]
Majhi B, Sa PK. FLANN-based adaptive threshold selection for detection of impulsive noise in images 2007. 61: 478-84.
[http://dx.doi.org/10.1016/j.aeue.2006.08.007]
[19]
Mohapatra S. Adaptive Digital image filter using functional link artificial. Neural Netw 2012; 46(6): 1-9.
[20]
Gaurav K, Sahoo AK, Mishra SK. Nonlinear system identification using functional link multilayer perceptron artificial neural networks. Intrnational J Appl Eng Res 2015; 10(44): 31542-6.
[21]
Yaghini M, Khoshraftar MM, Fallahi M. A hybrid algorithm for artificial neural network training. Eng Appl Artif Intell 2013; 26(1): 293-301.
[http://dx.doi.org/10.1016/j.engappai.2012.01.023]
[22]
Patra D, Das PP, Mishra SK. Evolutionary neural network for noise cancellation in image data. Int J Comput Vis Robot 2011; 2(3): 206-17.
[http://dx.doi.org/10.1504/IJCVR.2011.042839]
[23]
Kumar M, Mishra SK. Particle swarm optimization-based functional link artificial neural network for medical image denoising. Computa Vis Rob 2015; 2015: 105-11.
[http://dx.doi.org/10.1007/978-81-322-2196-8_13]
[24]
Upadhyaya A, Sagar P, Kumar M, Mishra SK. Particle swarm optimization-functional link multilayer perceptron for Rician noise suppression from MRI images. In: International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). . 2016; Jalgaon, India. IEEE 2017: pp. 103-7.
[http://dx.doi.org/10.1109/ICGTSPICC.2016.7955278]
[25]
Kennedy J. Encyclopedia of machine learning. Berlin, Germany: Springer 2011; pp. 760-6.
[26]
Kumar M, Mishra SK. Jaya-FLANN based adaptive filter for mixed noise suppression from ultrasound images. Biomed Res (Aligarh) 2017; 28(9): 4159-64.
[27]
Kumar M, Mishra SK. Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image. Biomed Mater Eng 2017; 28(6): 643-54.
[http://dx.doi.org/10.3233/BME-171702] [PMID: 29171969]
[28]
Kumar M, Mishra SK, Sahu SS. Cat swarm optimization based functional link artificial neural network filter for gaussian noise removal from computed tomography images. Appl Comput Intell Soft Comput 2016; 2016: 1-6.
[http://dx.doi.org/10.1155/2016/6304915]
[29]
Chu S, Tsai P, Pan J. In: Yang Q, Webb G, Eds. Trends in artificial intelligence. Berlin, Germany: Springer 2006; pp. 854-9.
[http://dx.doi.org/10.1007/978-3-540-36668-3_94]
[30]
Brunet D, Vrscay ER, Wang Z. On the mathematical properties of the structural similarity index. IEEE Trans Image Process 2012; 21(4): 1488-99.
[http://dx.doi.org/10.1109/TIP.2011.2173206] [PMID: 22042163]
[31]
Kumar M, Mishra SK. Jaya based functional link multilayer perceptron adaptive filter for poisson noise from X-ray images. Multimedia Tools Appl 2018; 77(18): 24405-25.
[http://dx.doi.org/10.1007/s11042-017-5592-y]

Rights & Permissions Print Cite
Article Metrics
17
3
© 2024 Bentham Science Publishers | Privacy Policy