Sage Journals: Discover world-class research

Abstract

Background:

The clinical magnetic resonance imaging (MRI) images may get corrupted due to the presence of the mixture of different types of noises such as Rician, Gaussian, impulse, etc. Most of the available filtering algorithms are noise specific, linear, and non-adaptive.

Objective:

There is a need to develop a nonlinear adaptive filter that adapts itself according to the requirement and effectively applied for suppression of mixed noise from different MRI images.

Methods:

In view of this, a novel nonlinear neural network based adaptive filter i.e. functional link artificial neural network (FLANN) whose weights are trained by a recently developed derivative free meta-heuristic technique i.e. teaching learning based optimization (TLBO) is proposed and implemented.

Results:

The performance of the proposed filter is compared with five other adaptive filters and analyzed by considering quantitative metrics and evaluating the nonparametric statistical test. The convergence curve and computational time are also included for investigating the efficiency of the proposed as well as competitive filters.

Conclusion:

The simulation outcomes of proposed filter outperform the other adaptive filters. The proposed filter can be hybridized with other evolutionary technique and utilized for removing different noise and artifacts from others medical images more competently.

Keywords

MRI adaptive filter functional link artificial neural network teaching learning optimization

Get full access to this article

View all access options for this article.

References

and Greenshields

I.R.

, A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images, IEEE Trans Med Imaging. 28(2) (2009), 165–172. doi:10.1109/TMI.2008.927338.

Zhou

Zhang

and Wan

, FASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter, Bio-Medical Materials and Engineering. 26 (2015), 1409–1415. doi:10.3233/BME-151439.

Chang

and Chang

, Automatic brain MR image denoising based on texture feature-based artificial neural networks, Bio-Medical Material and Engineering. 26 (2015), 1275–1282. doi:10.3233/BME-151425.

Zhang

and Mabu

S.K.

, Noise reduction using genetic algorithm based PCNN method, in: IEEE Conf. Systems Man and Cybernetics (SMC), 2010, pp. 2627–2633.

Zhou

Y.-T.

Chellappa

Vaid

and Jenkins

B.K.

, Image restoration using a neural network, IEEE Trans Acoust. 36(7) (1988), 1141–1151. doi:10.1109/29.1641.

Das

D.P.

and Panda

, Active mitigation of nonlinear noise processes using a novel filtered-s LMS algorithm, IEEE Trans Speech Audio Process. 12(3) (2004), 313–322. doi:10.1109/TSA.2003.822741.

Jafarifarmand

and Badamchizadeh

M.A.

, Artifacts removal in EEG signal using a new neural network enhanced adaptive filter, Neurocomputing. 103 (2013), 222–231. doi:10.1016/j.neucom.2012.09.024.

Kumar

and Mishra

S.K.

, Jaya-FLANN based adaptive filter for mixed noise suppression from ultrasound images, Biomedical Research 28(9) (2017), 4159–4164.

Sicuranza

G.L.

and Carini

, A generalized FLANN filter for nonlinear active noise control, IEEE Trans Audio, Speech Lang Process. 19(8) (2011), 2412–2417. doi:10.1109/TASL.2011.2136336.

10.

Zhang

and Tay

A.L.P.

, Fast learning artificial neural network (FLANN) based color image segmentation in R-G-B-S-V cluster space, in: 2007 International Joint Conference on Neural Networks, 2007, pp. 563–568. doi:10.1109/IJCNN.2007.4371018.

11.

George

N.V.

and Panda

, A particle-swarm-optimization-based decentralized nonlinear active noise control system, IEEE Trans Instrum Meas. 61(12) (2012), 3378–3386. doi:10.1109/TIM.2012.2205492.

12.

Mazwin

and Hassim

, Solving a classification task using functional link neural networks with modified artificial bee colony, in: IEEE Int Conf. on Natural Computation (ICNC), 2013, pp. 189–193.

13.

Gaurav

Sahoo

A.K.

and Mishra

S.K.

, Nonlinear system identification using functional link multilayer perceptron artificial neural networks, Int J of Applied Engineering Research 10(44) (2015), 31542–31546.

14.

Majhi

and Panda

, Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique, Expert Syst Appl. 38(1) (2011), 321–333. doi:10.1016/j.eswa.2010.06.070.

15.

Yaghini

Khoshraftar

M.M.

and Fallahi

, A hybrid algorithm for artificial neural network training, Eng Appl Artif Intell. 26(1) (2013), 293–301. doi:10.1016/j.engappai.2012.01.023.

16.

Dash

P.P.

and Patra

, Evolutionary neural network for noise cancellation in image data, Int J Comput Vis Robot. 2(3) (2011), 206–217. doi:10.1504/IJCVR.2011.042839.

17.

Kumar

Mishra

S.K.

and Sahu

S.S.

, 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.

18.

Yogi

Subhashini

K.R.

and Satapathy

J.K.

, A PSO based functional link artificial neural network training algorithm for equalization of digital communication channels, in: Ind Inf Syst (ICIIS), 2010 Int Conf., 2010, pp. 107–112.

19.

Kumar

and Mishra

S.K.

, Particle swarm optimization-based functional link artificial neural network for medical image denoising, in: Computational Vision and Robotics, 2015, pp. 105–111.

20.

Duan

Member

and Wang

, Echo state networks with orthogonal pigeon-inspired optimization for image restoration, IEEE Trans on Neural Networks and Learning Systems. 27(11) (2015), 1–13.

21.

Ding

, A new method for image noise removal using chaos-PSO and nonlinear ICA, Procedia Eng. 24 (2011), 111–115. doi:10.1016/j.proeng.2011.11.2611.

22.

Rao

R.V.

K.C.

Taler

and Ocłoń

, Dimensional optimization of a micro-channel heat sink using Jaya algorithm, Appl Therm Eng. 103 (2016), 572–582. doi:10.1016/j.applthermaleng.2016.04.135.

23.

Rao

R.V.

Savsani

V.J.

and Vakharia

D.P.

, Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems, Comput Des. 43(3) (2011), 303–315.

24.

Naik

Nayak

and Behera

H.S.

, A TLBO based gradient descent learning-functional link higher order ANN: An efficient model for learning from non-linear data, J King Saud. (2016). doi:10.1016/j.jksuci.2016.01.001.

25.

Wang

Bovik

A.C.

Sheikh

H.R.

and Simoncelli

E.P.

, Image quality assessment: From error visibility to structural similarity, IEEE Trans Image Process. 13(4) (2004), 600–612. doi:10.1109/TIP.2003.819861.

26.

Friedman’s Test [Internet], MathWorks^®. [cited 2016 Jun 25]. Available from: https://in.mathworks.com/help/stats/friedman.html.

Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image

Abstract

Background:

Objective:

Methods:

Results:

Conclusion:

Keywords

Get full access to this article

References