An extended non-local means algorithm: Application to brain MRI
ABSTRACT Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic r...
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| Published in | International journal of imaging systems and technology Vol. 24; no. 4; pp. 293 - 305 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, NJ
Blackwell Publishing Ltd
01.12.2014
Wiley Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0899-9457 1098-1098 |
| DOI | 10.1002/ima.22106 |
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| Abstract | ABSTRACT
Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic resonance (MR) imaging modality. Moreover, for improved denoising, a wavelet coefficient mixing procedure is used in XNLM to mix wavelet sub‐bands of two IANLM‐filtered images, which are obtained using different parameters of IANLM. Finally, XNLM includes a novel parameter‐free pixel preselection procedure for improving computational efficiency of the algorithm. The proposed algorithm is validated on T1‐weighted, T2‐weighted and Proton Density (PD) weighted simulated brain MR images (MRI) at several noise levels. Optimal values of different parameters of XNLM are obtained for each type of MRI sequence, and different variants are investigated to reveal the benefits of different extensions presented in this work. The proposed XNLM algorithm outperforms several contemporary denoising algorithms on all the tested MRI sequences, and preserves important pathological information more effectively. Quantitative and visual results show that XNLM outperforms several existing denoising techniques, preserves important pathological information more effectively, and is computationallyefficient. |
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| AbstractList | Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic resonance (MR) imaging modality. Moreover, for improved denoising, a wavelet coefficient mixing procedure is used in XNLM to mix wavelet sub‐bands of two IANLM‐filtered images, which are obtained using different parameters of IANLM. Finally, XNLM includes a novel parameter‐free pixel preselection procedure for improving computational efficiency of the algorithm. The proposed algorithm is validated on T1‐weighted, T2‐weighted and Proton Density (PD) weighted simulated brain MR images (MRI) at several noise levels. Optimal values of different parameters of XNLM are obtained for each type of MRI sequence, and different variants are investigated to reveal the benefits of different extensions presented in this work. The proposed XNLM algorithm outperforms several contemporary denoising algorithms on all the tested MRI sequences, and preserves important pathological information more effectively. Quantitative and visual results show that XNLM outperforms several existing denoising techniques, preserves important pathological information more effectively, and is computationallyefficient. ABSTRACT Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic resonance (MR) imaging modality. Moreover, for improved denoising, a wavelet coefficient mixing procedure is used in XNLM to mix wavelet sub‐bands of two IANLM‐filtered images, which are obtained using different parameters of IANLM. Finally, XNLM includes a novel parameter‐free pixel preselection procedure for improving computational efficiency of the algorithm. The proposed algorithm is validated on T1‐weighted, T2‐weighted and Proton Density (PD) weighted simulated brain MR images (MRI) at several noise levels. Optimal values of different parameters of XNLM are obtained for each type of MRI sequence, and different variants are investigated to reveal the benefits of different extensions presented in this work. The proposed XNLM algorithm outperforms several contemporary denoising algorithms on all the tested MRI sequences, and preserves important pathological information more effectively. Quantitative and visual results show that XNLM outperforms several existing denoising techniques, preserves important pathological information more effectively, and is computationallyefficient. |
| Author | Rathore, Saima Hussain, Mutawarra Jalil, Abdul Iftikhar, Muhammad Aksam Ali, Ahmad |
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| CitedBy_id | crossref_primary_10_1016_j_cmpb_2018_04_024 crossref_primary_10_3390_jimaging1010060 crossref_primary_10_1016_j_asoc_2016_02_043 crossref_primary_10_1016_j_bspc_2018_08_031 crossref_primary_10_1093_imamat_hxw026 |
| Cites_doi | 10.1002/ima.22034 10.1109/18.382009 10.1097/00006123-199708000-00013 10.1002/ima.22057 10.1007/BFb0046947 10.1109/TIP.2007.891064 10.1109/TIP.2008.925382 10.1117/1.1525793 10.1137/040605412 10.1109/ICCV.1998.710815 10.1109/TIP.2011.2172799 10.1111/j.1467-8659.2008.01242.x 10.1002/jmri.22003 10.1109/TIP.2007.901238 10.1109/34.56205 10.1016/j.cmpb.2013.10.012 10.1002/ima.22079 10.1109/TPAMI.2004.47 10.1137/040616024 10.1214/aos/1024691081 10.1117/1.1426077 10.1016/j.media.2008.02.004 10.1109/83.791966 10.1016/j.cmpb.2011.07.014 10.1109/42.712135 10.1155/2008/590183 10.1109/TSP.2004.826174 10.1117/1.JEI.22.4.043016 10.1109/83.862633 10.1109/TMI.2007.906087 |
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| Keywords | Brain Computer vision Mixing Subband decomposition Noise reduction brain MRI Central nervous system Subband Nonlocal means Nuclear magnetic resonance imaging Adaptive method Rician noise Encephalon Noise level denoising Wavelet transformation Efficiency wavelet Rician distribution Signal to noise ratio |
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| References_xml | – reference: S.A. Fernandez, C.A. Lopez, and C.F. Westin, Noise and signal estimation in magnitude mri and rician distributed images: A lmmse approach, IEEE Trans Image Process 17 (2008), 1383-1398. – reference: K. Dabov, A. Foi, V. Katkovnik, and K.O. Egiazarian, Image denoising by sparse 3-d transform-domain collaborative filtering, IEEE Trans Image Process 16 (2007), 2080-2095. – reference: M.A. Iftikhar, A. Jalil, S. Rathore, A. Ali, and M. Hussain, Brain MRI denoizing and segmentation based improved adaptive non-local means, Int J Imaging Syst Technol 23 (2013), 235-248. – reference: M.A. Iftikhar, A. Jalil, S. Rathore, and M. Hussain, Robust brain mri denoising and segmentation using enhanced non-local means algorithm, Int J Imaging Syst Technol 24 (2014), 52-66. – reference: D. Gupta, R.S. Anand, and B. Tyagi, Edge preserved enhancement of medical images using adaptive fusion-based denoising by shearlet transform and total variation algorithm, J Electron Imaging 22 (2013), 043016. – reference: G. Gilboa, N. Sochen, and Y.Y. Zeevi, Image enhancement and denoising by complex diffusion processes, IEEE Trans Pattern Anal Mach Intell 26 (2004), 1020-1036. – reference: S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, An iterative regularization method for total variation-based image restoration, Multiscale Model Simul 4 (2005), 460-489. – reference: D.L. Collins, A.P. Zijdenbos, V. Kollokian, J.G. Sled, N.J. Kabani, C.J. Holmes, and A.C. Evans, Design and construction of a realistic digital brain phantom, IEEE Trans Med Imaging 17 (1998), 463-468. – reference: F. Luisier, T. Blu, and M. Unser, A new sure approach to image denoising: Interscale orthonormal wavelet thresholding, IEEE Trans Image Process 16 (2007), 593-606. – reference: J. Wen, Y. Li, and W. 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Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window... Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this... |
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| SubjectTerms | Algorithms Applied sciences Artificial intelligence Biological and medical sciences brain MRI Computer science; control theory; systems denoising Exact sciences and technology Investigative techniques, diagnostic techniques (general aspects) Medical sciences Nervous system nonlocal means Pattern recognition. Digital image processing. Computational geometry Radiodiagnosis. Nmr imagery. Nmr spectrometry Rician noise wavelet |
| Title | An extended non-local means algorithm: Application to brain MRI |
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