Abstract
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.
Keywords: Adaptive filter, artificial neural network, denoising, medical image, nature-inspired algorithms, optimization.
Graphical Abstract
[1]
Babu JJJ, Sudha GF. Adaptive speckle reduction in ultrasound images using fuzzy logic on coefficient of variation. Biomed Signal Process Control 2016; 23: 93-103.
[http://dx.doi.org/10.1016/j.bspc.2015.08.001]
[http://dx.doi.org/10.1016/j.bspc.2015.08.001]
[2]
Barletta L, Magarini M, Spalvieri A. Bridging the gap between kalman filter and wiener filter in carrier phase tracking. IEEE Photonics Technol Lett 2013; 25(11): 1035-8.
[http://dx.doi.org/10.1109/LPT.2013.2259476]
[http://dx.doi.org/10.1109/LPT.2013.2259476]
[3]
Zhou Y-T, Chellappa R, Vaid A, Jenkins BK. Image restoration using a neural network. IEEE Trans Acoust 1988; 36(7): 1141-51.
[http://dx.doi.org/10.1109/29.1641]
[http://dx.doi.org/10.1109/29.1641]
[4]
Ma L, Staunton RC. Integration of multiresolution image segmentation and neural networks for object depth recovery. Pattern Recognit 2005; 38(7): 985-96.
[http://dx.doi.org/10.1016/j.patcog.2005.01.005]
[http://dx.doi.org/10.1016/j.patcog.2005.01.005]
[5]
Rogers SK, Colombi JM, Martin CE, et al. Neural networks for automatic target recognition. Neural Netw 1995; 8(7–8): 1153-84.
[http://dx.doi.org/10.1016/0893-6080(95)00050-X]
[http://dx.doi.org/10.1016/0893-6080(95)00050-X]
[6]
Shou YW, Lin CT. Image descreening by GA-CNN-based texture classification. IEEE Trans Circuits Syst I Regul Pap 2004; 51(11): 2287-99.
[http://dx.doi.org/10.1109/TCSI.2004.836861]
[http://dx.doi.org/10.1109/TCSI.2004.836861]
[7]
Li Y, Lu J, Wang L, Ya T. Removing noise from radiological image using multineural network filter. In: International Conference on Industrial Technology. 2005 14-17 Dec; Hong Kong, China.
IEEE 2006;, pp. 1365-70.
[8]
Choubey A, Sinha GR, Choubey S. A hybrid filtering technique in medical image denoising: Blending of neural network and fuzzy inference. In: 3rd International Conference on Electronics Computer Technology. 2011 8-10 April; Kanyakumari, India. IEEE 2011;, pp.
170-7.
[http://dx.doi.org/10.1109/ICECTECH.2011.5941584]
[http://dx.doi.org/10.1109/ICECTECH.2011.5941584]
[9]
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]
[http://dx.doi.org/10.3233/BME-151425] [PMID: 26405887]
[10]
Yang XS, Deb S, Fong S, He X, Zhao YX. From swarm intelligence to metaheuristics: nature-inspired optimization algorithms. Computer 2016; 49(9): 52-9.
[http://dx.doi.org/10.1109/MC.2016.292]
[http://dx.doi.org/10.1109/MC.2016.292]
[11]
Jia X, Yuan P, Shi Z, et al. An effective self-adaptive mean filter for mixed noise. In: International Conference on Advanced Robotics and Mechatronics (ICARM). 2016 18-20 Aug; Macau, China.
IEEE 2016; pp. 484-9.
[http://dx.doi.org/10.1109/ICARM.2016.7606968]
[http://dx.doi.org/10.1109/ICARM.2016.7606968]
[12]
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]
[http://dx.doi.org/10.1109/TIP.2014.2317985] [PMID: 24760906]
[13]
Jiang J, Shen J. An effective adaptive median filter algorithm for removing salt & pepper noise in images. In: Symposium on Photonics and Optoelectronics. 2010 19-21 June; Hengdu, China. IEEE 2010;, pp. 1-4.
[http://dx.doi.org/10.1109/SOPO.2010.5504337]
[http://dx.doi.org/10.1109/SOPO.2010.5504337]
[14]
Rekha CK, Manjunathachari K, Subha Rao GV. Speckle noise reduction in 3D ultrasound images-a review. In: International Conference on Signal Processing and Communication Engineering Systems. 2015 2-3 Jan; Guntur, India. IEEE 2015;, pp. 257-9.
[15]
Kotropoulos C, Magnisalis X, Pitas I, Strintzis MG. Nonlinear ultrasonic image processing based on signal-adaptive filters and self-organizing neural networks. IEEE Trans Image Process 1994; 3(1): 65-77.
[http://dx.doi.org/10.1109/83.265980] [PMID: 18291909]
[http://dx.doi.org/10.1109/83.265980] [PMID: 18291909]
[16]
He L, Greenshields IR. A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images. IEEE Trans Med Imaging 2009; 28(2): 165-72.
[http://dx.doi.org/10.1109/TMI.2008.927338] [PMID: 19188105]
[http://dx.doi.org/10.1109/TMI.2008.927338] [PMID: 19188105]
[17]
Zhang D, Mabu S, Hirasawa K. Noise reduction using genetic algorithm based PCNN method. In: International Conference on Systems, Man and Cybernetics. 2010 Oct 10-13; Istanbul, Turkey. IEEE 2010;, pp. 2627-33.
[http://dx.doi.org/10.1109/ICSMC.2010.5641902]
[http://dx.doi.org/10.1109/ICSMC.2010.5641902]
[18]
Borse S, Bora PK. A novel approach to image edge detection using Kalman filtering. In: 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). 2016 Oct 13-15; Vancouver, BC, Canada. IEEE 2016; pp. 1-5.
[http://dx.doi.org/10.1109/IEMCON.2016.7746258]
[http://dx.doi.org/10.1109/IEMCON.2016.7746258]
[19]
Liu Y, Xi B. Iterative Wiener filter. Electron Lett 2013; 49(5): 343-4.
[http://dx.doi.org/10.1049/el.2010.3742]
[http://dx.doi.org/10.1049/el.2010.3742]
[20]
Ahn CW, Yoo J-C. Image restoration by blind-Wiener filter. IET Image Process 2014; 8(12): 815-23.
[http://dx.doi.org/10.1049/iet-ipr.2013.0693]
[http://dx.doi.org/10.1049/iet-ipr.2013.0693]
[21]
Xiong S, Zhou Z, Member S. Neural filtering of colored noise based on kalman filter structure. IEEE Trans Instrum Meas 2003; 52(3): 742-7.
[http://dx.doi.org/10.1109/TIM.2003.814669]
[http://dx.doi.org/10.1109/TIM.2003.814669]
[22]
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.
[http://dx.doi.org/10.1109/TIP.2005.860311] [PMID: 16435548]
[http://dx.doi.org/10.1109/TIP.2005.860311] [PMID: 16435548]
[23]
Lin Y, Chang H. Automatic noise removal in MR images using bilateral filtering associated with artificial neural networks. Int J Pharm Med Bio Sci 2015; 4(1): 39-43.
[24]
Chen S, Suzuki K. Bone suppression in chest radiographs by means of anatomically specific multiple massive-training ANNs combined with total variation minimization smoothing and consistency processing. IEEE Trans Med Imaging 2014; 33(2): 211-35.
[25]
Gargouri A. New digital pulse-mode neural network based image denoising. AEU Int J Electron Commun 2013; 67(6): 513-20.
[http://dx.doi.org/10.1016/j.aeue.2012.11.011]
[http://dx.doi.org/10.1016/j.aeue.2012.11.011]
[26]
Liu X, Mei W, Du H. Biomedical signal processing and control multimodality medical image fusion algorithm based on gradient minimization smoothing filter and pulse coupled neural network. Biomed Signal Process Control 2016; 30: 140-8.
[http://dx.doi.org/10.1016/j.bspc.2016.06.013]
[http://dx.doi.org/10.1016/j.bspc.2016.06.013]
[27]
Sondes T, Hassene S, Zouhair M, Ezzedine BB. RGB image de-noising using new low-pas filter with variable Gaussian core real time optimized by neural networks. In: International Conference on Electrical Engineering and Software Applications. 2013 March 21-23; Hammamet, Tunisia. IEEE 2013; pp. 1-6.
[http://dx.doi.org/10.1109/ICEESA.2013.6578483]
[http://dx.doi.org/10.1109/ICEESA.2013.6578483]
[28]
Wen H, Wen J. Image denoising and restoration using pulse coupled neural networks. In: 6th International Congress on Image and Signal Processing (CISP). 2013 Dec 16-18; Hangzhou, China. IEEE 2014; pp. 282-7.
[http://dx.doi.org/10.1109/CISP.2013.6744003]
[http://dx.doi.org/10.1109/CISP.2013.6744003]
[29]
Rao DH, Panduranga PP. A survey on image enhancement techniques: Classical spatial filter, neural network, cellular neural network and fuzzy filter. In: International Conference on Industrial Technology. 2006 Dec 15-17; Mumbai, India. IEEE 2007;, pp.
2821-6.
[http://dx.doi.org/10.1109/ICIT.2006.372671]
[http://dx.doi.org/10.1109/ICIT.2006.372671]
[30]
Sun Y. Hopfield neural network based algorithms for image restoration and reconstruction. I. Algorithms and simulations. IEEE Trans Signal Process 2000; 48(7): 2105-18.
[http://dx.doi.org/10.1109/78.847794]
[http://dx.doi.org/10.1109/78.847794]
[31]
Sivakumar K. Image restoration using a multilayer perceptron with a multilevel sigmoidal function. IEEE Trans Signal Process 1993; 41(5): 2018-22.
[http://dx.doi.org/10.1109/78.215329]
[http://dx.doi.org/10.1109/78.215329]
[32]
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.
[http://dx.doi.org/10.17706/jcp.10.2.68-80]
[http://dx.doi.org/10.17706/jcp.10.2.68-80]
[33]
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. 82-3.
[http://dx.doi.org/10.1109/IJCNN.2007.4371018]
[http://dx.doi.org/10.1109/IJCNN.2007.4371018]
[34]
Zhao H, Zhang J. Functional link neural network cascaded with Chebyshev orthogonal polynomial for nonlinear channel equalization. Signal Processing 2008; 88(8): 1946-57.
[http://dx.doi.org/10.1016/j.sigpro.2008.01.029]
[http://dx.doi.org/10.1016/j.sigpro.2008.01.029]
[35]
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]
[http://dx.doi.org/10.1109/TASL.2011.2136336]
[36]
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]
[http://dx.doi.org/10.1016/j.eswa.2010.06.070]
[37]
Sukhaswami MB, Pujari AK. Restoration of geometrically aberrated images using a self-organising neural network. Pattern Recognit Lett 1996; 17: 1-10.
[http://dx.doi.org/10.1016/0167-8655(95)00053-4]
[http://dx.doi.org/10.1016/0167-8655(95)00053-4]
[38]
Boublil D, Elad M, Shtok J, Zibulevsky M. Spatially-adaptive reconstruction in computed tomography using neural networks. IEEE Trans Med Imaging 2015; 34(7): 1474-85.http://arxiv.org/abs/1311.7251
[http://dx.doi.org/10.1109/TMI.2015.2401131] [PMID: 25675453]
[http://dx.doi.org/10.1109/TMI.2015.2401131] [PMID: 25675453]
[39]
Tarasia N, Mishra MK, Dash PC, Samal SK. A parallel approach to train FLANN for an adaptive filter. In: 3rd International Conference on Advanced Computer Control. 2011 Jan 18-20; Harbin, China. IEEE 2011; pp. 242-6.
[http://dx.doi.org/10.1109/ICACC.2011.6016406]
[http://dx.doi.org/10.1109/ICACC.2011.6016406]
[40]
Goel AK, Saxena SC, Bhanot S. Modified Functional Link Artificial Neural Network. IJSRIT 2006; 1(1): 22-30.
[PMID: 17010571]
[PMID: 17010571]
[41]
Yuanhua G. Functional link artificial neural networks filter for Gaussian noise. Appl Mech Mater 2013; 2013: 2027-31.
[42]
Mirjalili S, Mohd Hashim SZ, Moradian Sardroudi H. Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl Math Comput 2012; 218(22): 11125-37.
[http://dx.doi.org/10.1016/j.amc.2012.04.069]
[http://dx.doi.org/10.1016/j.amc.2012.04.069]
[43]
Naik B, Nayak J, Behera HS. A TLBO based gradient descent learning-functional link higher order ANN: An efficient model for learning from non-linear data. J King Saud Univ-Comput Inf Sci 2018; 30(1): 120-39.
[44]
Ostrowski T. Computing with genetic algorithms in the context of adaptive neural filtering. Pattern Recognit Lett 1995; 16(2): 125-32.
[http://dx.doi.org/10.1016/0167-8655(94)00080-M]
[http://dx.doi.org/10.1016/0167-8655(94)00080-M]
[45]
Mishra S, Bisoi R. Image denoising using neural network based accelerated particle swarm optimization. In: Power Communication and Information Technology Conference (PCITC). 2015 Oct 15-17; 2015; Bhubaneswar, India. IEEE 2016; pp. 1-4.
[http://dx.doi.org/10.1109/PCITC.2015.7438124]
[http://dx.doi.org/10.1109/PCITC.2015.7438124]
[46]
Aizenberg I, Aizenberg N, Hiltner J, Moraga C, Meyer E. Cellular neural networks and computational intelligence in medical image processing. Image Vis Comput 2001; 19: 177-83.
[47]
Ding W. A new method for image noise removal using Chaos-PSO and nonlinear ICA. Procedia Eng 2011; 24: 111-5.
[http://dx.doi.org/10.1016/j.proeng.2011.11.2611]
[http://dx.doi.org/10.1016/j.proeng.2011.11.2611]
[48]
Duan H, Wang X. Echo state networks with orthogonal pigeon-inspired optimization for image restoration. IEEE Trans Neural Netw Learn Syst 2016; 27(11): 2413-25.
[http://dx.doi.org/10.1109/TNNLS.2015.2479117] [PMID: 26529785]
[http://dx.doi.org/10.1109/TNNLS.2015.2479117] [PMID: 26529785]
[49]
Saadi S, Guessoum A, Bettayeb M. ABC optimized neural network model for image deblurring with its FPGA implementation. Microprocess Microsyst 2013; 37(1): 52-64.
[http://dx.doi.org/10.1016/j.micpro.2012.09.013]
[http://dx.doi.org/10.1016/j.micpro.2012.09.013]
[50]
Goh CK, Teoh EJ, Tan KC, Tan KC. Hybrid multiobjective evolutionary design for artificial neural networks. IEEE Trans Neural Netw 2008; 19(9): 1531-48.
[http://dx.doi.org/10.1109/TNN.2008.2000444] [PMID: 18779086]
[http://dx.doi.org/10.1109/TNN.2008.2000444] [PMID: 18779086]
[51]
Dash PP, Patra D. 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]
[http://dx.doi.org/10.1504/IJCVR.2011.042839]
[52]
Kumar M, Mishra SK. In: Computational Vision and Robotics.
Sethi IK, Ed. Berlin, Germany: Springer 2015.
[http://dx.doi.org/10.1007/978-81-322-2196-8_13]
[http://dx.doi.org/10.1007/978-81-322-2196-8_13]
[53]
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]
[http://dx.doi.org/10.1155/2016/6304915]
[54]
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 Dec 22-24; Jalgaon, India. IEEE 2017; pp 103-7.
[http://dx.doi.org/10.1109/ICGTSPICC.2016.7955278]
[http://dx.doi.org/10.1109/ICGTSPICC.2016.7955278]
[55]
Kumar M, Mishra SK. Jaya-FLANN based adaptive filter for mixed noise suppression from ultrasound images. Biomed Res (Aligarh) 2017; 28(9): 4159-64.
[56]
Neves CMM, Silva VRM, Barros GAB, Lopes RVV. A variation of the genetic algorithm of Holland to support analysis of balance sheet and income statement for the fiscal year. In: 6th Joint International Information Technology and Artificial Intelligence Conference. 2011 Aug 20-22; Chongqing, China. IEEE 2011; pp.311-5
[http://dx.doi.org/10.1109/ITAIC.2011.6030338]
[http://dx.doi.org/10.1109/ITAIC.2011.6030338]
[57]
Yamany SM, Khiani KJ, Farag AA. Application of neural networks and genetic algorithms in the classification of endothelial cells. Pattern Recognit Lett 1997; 18(11): 1205-10.
[http://dx.doi.org/10.1016/S0167-8655(97)00140-2]
[http://dx.doi.org/10.1016/S0167-8655(97)00140-2]
[58]
Teixeira CA, Ruano MG, Ruano AE, Pereira WCA. A soft-computing methodology for noninvasive time-spatial temperature estimation. IEEE Trans Biomed Eng 2008; 55(2 Pt 1): 572-80.
[http://dx.doi.org/10.1109/TBME.2007.901029] [PMID: 18269992]
[http://dx.doi.org/10.1109/TBME.2007.901029] [PMID: 18269992]
[59]
Mohanta DK, Sadhu PK, Chakrabarti R. Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results. Reliab Eng Syst Saf 2007; 92(2): 187-99.
[http://dx.doi.org/10.1016/j.ress.2005.11.062]
[http://dx.doi.org/10.1016/j.ress.2005.11.062]
[60]
Kennedy J. Encyclopedia of machine learning. Sammut C, Webb
GI, Ed. Berlin, Germany: Springer 2011; pp. 760-6.
[61]
Chu S, Tsai P, Pan J. Trends in artificial intelligence. Yang Q,
Webb G, Ed. Berlin, Germany: Springer 2006.
[http://dx.doi.org/10.1007/978-3-540-36668-3_94]
[http://dx.doi.org/10.1007/978-3-540-36668-3_94]
[62]
Ansar W, Bhattacharya T. A new gray image segmentation algorithm using cat swarm optimization. In: International Conference on Communication and Signal Processing (ICCSP). 2016 Apr 6-8; Madras, India. IEEE 2016; pp. 1004-8.
[http://dx.doi.org/10.1109/ICCSP.2016.7754300]
[http://dx.doi.org/10.1109/ICCSP.2016.7754300]
[63]
Rao RV, Savsani VJ, Vakharia DP. Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Comput Des (Winchester) 2011; 43(3): 303-15.
[http://dx.doi.org/10.1016/j.cad.2010.12.015]
[http://dx.doi.org/10.1016/j.cad.2010.12.015]
[64]
Rao RV, Patel V. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 2012; 3(4): 535-60.
[http://dx.doi.org/10.5267/j.ijiec.2012.03.007]
[http://dx.doi.org/10.5267/j.ijiec.2012.03.007]
[65]
Liu S, Mernik L. A note on teaching – learning-based optimization algorithm. Inf Sci (Ny) 2012; 212: 79-93.
[http://dx.doi.org/10.1016/j.ins.2012.05.009]
[http://dx.doi.org/10.1016/j.ins.2012.05.009]
[66]
Rao RV. Jaya : A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 2016; 7(1): 19-34.
[67]
Suraj S, Sinha RK, Ghosh S. Jaya based ANFIS for monitoring of two class motor imagery task IEEE Access 2016; 4: 9273-82.
[http://dx.doi.org/10.1109/ACCESS.2016.2637401]
[http://dx.doi.org/10.1109/ACCESS.2016.2637401]
[68]
Rao RV, More KC, Taler J, Ocłoń P. Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl Therm Eng 2016; 103: 572-82.
[http://dx.doi.org/10.1016/j.applthermaleng.2016.04.135]
[http://dx.doi.org/10.1016/j.applthermaleng.2016.04.135]
[69]
Nanda SJ, Panda G. A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 2014; 16: 1-18.
[http://dx.doi.org/10.1016/j.swevo.2013.11.003]
[http://dx.doi.org/10.1016/j.swevo.2013.11.003]
[70]
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]
[http://dx.doi.org/10.3233/BME-171702] [PMID: 29171969]
Article Metrics
9