Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

Efficient Restoration of Magnetic Resonance Images Corrupted with Impulse Noise Using Spatial Constraints Based Fuzzy Decision Filter


Affiliations
1 Electronics & Communication Engineering Department, Birla Institute of Technology, Mesra, Ranchi, Jharkhand 835 215, India
 

Magnetic Resonance (MR) images are subject to unavoidable noises during the data acquisition due to imperfections of device components and trade-offs in the scan parameters. The study proposes a two-step Fuzzy Decision-Based Filter (FDBF) as a post-reconstruction technique to mitigate random valued impulse noise from MR images. The FDBF employs a Spatial Fuzzy C-means (SFCM) clustering for detection and an Intensity Based Fuzzy Estimation (IBFE) technique for restoration. Firstly, SFCM integrates the spatial relation of the adjacent pixels into the membership function to form three separate clusters. The IBFE technique leaves the noise-free cluster unaltered while restoring the remaining in the second step. IBFE incorporates neighbor pixel correlation to restore the corrupted pixel leading to edge preservation. To assess the efficacy of the intended method both the quality metrics and the observed quality of the restored images are considered. The suggested detection strategy using SFCM performs very well, up to a 93% corruption level with zero false and miss detection rates even when there is intensity in homogeneity among pixels. Compared to other existing filtering techniques, the proposed two-step restoration method significantly improves the perceived image quality and other image quality metrics of the restored image without obliterating more intricate details and finer structures. FDBF considers the spatial information of the nearby pixels during the detection and restoration processes, which is essential for MR image restoration.

Keywords

Decision-Based Filter, Edge Preservation, Image Restoration, Intensity-Based Fuzzy Estimation, Spatial Fuzzy C-Means Clustering.
User
Notifications
Font Size

  • Gerig G, Kubler O, Kikinis R & Jolesz F A, Nonlinear anisotropic filtering of MRI data, IEEE Trans Med Imaging, 11(2) (1992) 221–232, DOI:10.1109/42.141646.

  • Saxena P & Kumar R S, An effective filter for noise removal in magnetic resonance images using multilevel fuzzy reasoning concept, J Clin Eng, 42(3) (2017) 121–135, DOI: 10.1097/JCE.0000000000000224.

  • Lin P H, Chen B H, Cheng F C & Huang S C, A morphological mean filter for impulse noise removal, J Display Technol, 12(4) (2016) 344–350, DOI:10.1109/JDT.2015.2487559.

  • Yeganli S F, Demirel H & Yu R, Noise removal from MR images via iterative regularization based on higher-order singular value decomposition, Signal Image Video Process, 11 (2017) 1477–1484, https://doi.org/10.1007/s11760-017-1110-y.

  • Gonzalez R C & Woods R E, Digital Image Processing, 3rd edn (Prentice-Hall, Englewood Cliffs NJ) 2008.

  • Huang T, Yang G & Tang G, A fast two-dimensional median filtering algorithm, IEEE Trans Signal Process, 27(1) (1979) 13–18, DOI: 10.1109/TASSP.1979.1163188.

  • Brownrigg D R, The weighted median filter, Commun, ACM, 27(8) (1984) 807–818.

  • Ko S J & Lee Y H, Center weighted median filters and their applications to image enhancement, IEEE Trans Circuits Syst, 38(9) (1991) 984–993, DOI: 10.1109/31.83870.

  • Hwang H & Haddad R A, Adaptive median filters: new algorithms and results, IEEE Trans Image Process, 4(4) (1995) 499–502, DOI: 10.1109/83.370679.

  • Chen T, Ma K K & Chen L H, Tri-state median filter for image denoising, IEEE Trans Image Process, 8(12) (1999) 1834–1838, DOI: 10.1109/83.806630

  • Zhang S & Karim M A, A new impulse detector for switching median filters, IEEE Signal Process Lett, 9(11) (2002) 360–363, DOI:10.1109/LSP.2002.805310.

  • Sun T & Neuvo Y, Detail-preserving median based filters in image processing, Pattern Recognit Lett, 15(4) (1994) 341–347, DOI:10.1016/0167-8655(94)90082-5.

  • Aizenberg I & Butakoff C, Effective impulse detector based on rank-order criteria, IEEE Signal Process Lett, 11(3) (2004) 363–366, DOI: 10.1109/LSP.2003.822925.

  • Garnett R, Huegerich T, Chui C & He W, A universal noise removal algorithm with an impulse detector, IEEE Trans Image Process, 14(11) (2005) 1747–1754, DOI:10.1109/TIP.2005.857261.

  • Zhou Y Y, Ye Z F & Huang J J, Improved decision-based detail-preserving variational method for removal of random-valued impulse noise, IET Image Process, 6(7) (2012) 976–985, DOI:10.1049/iet-ipr.2011.0312.

  • Kayhan S K, An effective 2-stage method for removing impulse noise in images, J Vis Commun Image Represent, 25(2) (2014) 478–486, DOI:10.1016/j.jvcir.2013.12.016.

  • Horng S J, Hsu L Y, Li T, Qiao S, Gong X, Chou H H & Khan M K, Using sorted switching median filter to remove high-density impulse noises, J Vis Commun Image Represent, 24(7) (2013) 956–967, DOI:10.1016/j.jvcir.2013.06.012

  • Zhang J, An efficient median filter-based method for removing random-valued impulse noise, Digit Signal Process, 20(4) (2010) 1010–1018, DOI: 10.1016/j.dsp.2009.11.003.

  • Abreu E, Lightstone M, Mitra S K & Arakawa K, A new efficient approach for the removal of impulse noise from highly corrupted images, IEEE Trans Image Process, 5(6) (1996) 1012–1025, DOI:10.1109/83.503916.

  • Eng H L & Ma K K, Noise adaptive soft-switching median filter, IEEE Trans Image Process, 10(2) (2001) 242–251, DOI:10.1109/83.902289.

  • Ng P E & Ma K K, A switching median filter with boundary discriminative noise detection for extremely corrupted images, IEEE Trans Image Process, 15(6) (2006) 1506–1516, DOI: 10.1109/TIP.2005.871129.

  • Jafar I F, AlNa'mneh R A & Darabkh K A, Efficient improvements on the BDND filtering algorithm for the removal of high-density impulse noise, IEEE Trans Image Process, 22(3) (2012) 1223–1232, DOI:10.1109/TIP.2012. 2228496.

  • Roy A, Chandra S & Rabidas R, Improved switching vector median filter for removal of impulse noise from color images, Proc Adv Smart Commun Imag Syst: Lect Notes Electr Eng (Springer Singapore) 721 (2020) 11–19, DOI:10.1007/978-981-15-9938-5_2.

  • Mélange T, Nachtegael M, Schulte S & Kerre E E, A fuzzy filter for the removal of random impulse noise in image sequences, Image Vis Comput, 29(6) (2011) 407–419, DOI: https://doi.org/10.1016/j.imavis.2011.01.005.

  • Schulte S, Nachtegael M, De Witte V, Van der Weken D & Kerre E E, A fuzzy impulse noise detection and reduction method, IEEE Trans Image Process, 15(5) (2006) 1153–1162, DOI:10.1109/TIP.2005.864179.

  • Nair M S & Raju G, A new fuzzy-based decision algorithm for high-density impulse noise removal, Signal Image Video Process, 6 (2012) 579–595, DOI:10.1007/s11760-010-0186-4.

  • Toprak A & Güler İ, Suppression of impulse noise in medical images with the use of a fuzzy adaptive median filter, J Med Syst, 30 (2006) 465–471, DOI: 10.1007/s10916-006-9031-2.

  • Schulte S, De Witte V, Nachtegael M, Van der Weken D & Kerre E E, Fuzzy two-step filter for impulse noise reduction from color images, IEEE Trans Image Process, 15(11) (2006) 3567–3578, DOI: 10.1109/TIP.2006.877494.

  • Nadeem M, Hussain A, Munir A, Habib M & Naseem M T, Removal of random valued impulse noise from grayscale images using quadrant based spatially adaptive fuzzy filter, Signal Process, 169 (2020) 107403, DOI: 10.1016/j.sigpro.2019.107403.

  • Azhar M, Dawood H, Dawood, H, Choudhary G I, Bashir A K & Chaudhary S H, Detail-preserving switching algorithm for the removal of random-valued impulse noise, J Ambient Intell Humaniz Comput, 10(10) (2019) 3925–3945, DOI: 10.1007/s12652-018-1153-0.

  • Turkmen I, A new method to remove random-valued impulse noise in images, Int J Electron Commun, 67(9) (2013) 771–779, DOI: https://doi.org/10.1016/j.aeue.2013.03.006.

  • Thakur R S, Chatterjee S, Yadav R N & Gupta L, Image de-noising with machine learning: A review, IEEE Access, 9 (2021) 93338–93363, DOI: 10.1109/ACCESS.2021.3092425.

  • Xing Y, Xu J, Tan J, Li D & Zha W, Deep CNN for removal of salt and pepper noise, IET Image Process, 13(9) (2019) 1550–1560, DOI:10.1049/iet-ipr.2018.6004.

  • Fu B, Zhao X, Li Y, Wang X & Ren Y, A convolutional neural networks denoising approach for salt and pepper noise, Multimed Tools Appl, 78 (2019) 30707–30721, DOI:10.1007/s11042-018-6521-4.

  • Sadrizadeh S, Otroshi-Shahreza H & Marvasti F, Impulsive noise removal via a blind CNN enhanced by iterative post-processing, Signal Process, 192 (2022) 108378, DOI:10.1016/j.sigpro.2021.108378.

  • Cai W, Chen S & Zhang D, Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognit, 40(3) (2007) 825–838, DOI: 10.1016/j.patcog.2006.07.011.

  • Choudhry M S & Kapoor R, Performance analysis of fuzzy C-means clustering methods for MRI image segmentation, Procedia Comput Sci, 89 (2016) 749–758, DOI: 10.1016/j.procs.2016.06.052

  • Wang Zhou, Alan C Bovik, Hamid R Sheikh & Eero P Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Trans Image Process, 13(4) (2004) 600–612, DOI: 10.1109/TIP.2003.819861


Abstract Views: 118

PDF Views: 85




  • Efficient Restoration of Magnetic Resonance Images Corrupted with Impulse Noise Using Spatial Constraints Based Fuzzy Decision Filter

Abstract Views: 118  |  PDF Views: 85

Authors

Priyank Saxena
Electronics & Communication Engineering Department, Birla Institute of Technology, Mesra, Ranchi, Jharkhand 835 215, India
R Sukesh Kumar
Electronics & Communication Engineering Department, Birla Institute of Technology, Mesra, Ranchi, Jharkhand 835 215, India

Abstract


Magnetic Resonance (MR) images are subject to unavoidable noises during the data acquisition due to imperfections of device components and trade-offs in the scan parameters. The study proposes a two-step Fuzzy Decision-Based Filter (FDBF) as a post-reconstruction technique to mitigate random valued impulse noise from MR images. The FDBF employs a Spatial Fuzzy C-means (SFCM) clustering for detection and an Intensity Based Fuzzy Estimation (IBFE) technique for restoration. Firstly, SFCM integrates the spatial relation of the adjacent pixels into the membership function to form three separate clusters. The IBFE technique leaves the noise-free cluster unaltered while restoring the remaining in the second step. IBFE incorporates neighbor pixel correlation to restore the corrupted pixel leading to edge preservation. To assess the efficacy of the intended method both the quality metrics and the observed quality of the restored images are considered. The suggested detection strategy using SFCM performs very well, up to a 93% corruption level with zero false and miss detection rates even when there is intensity in homogeneity among pixels. Compared to other existing filtering techniques, the proposed two-step restoration method significantly improves the perceived image quality and other image quality metrics of the restored image without obliterating more intricate details and finer structures. FDBF considers the spatial information of the nearby pixels during the detection and restoration processes, which is essential for MR image restoration.

Keywords


Decision-Based Filter, Edge Preservation, Image Restoration, Intensity-Based Fuzzy Estimation, Spatial Fuzzy C-Means Clustering.

References