1. bookVolume 13 (2013): Issue 4 (August 2013)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
access type Open Access

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

Published Online: 24 Aug 2013
Volume & Issue: Volume 13 (2013) - Issue 4 (August 2013)
Page range: 177 - 186
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English

In this paper, a new filtering method based on neutrosophic set (NS) approach of wiener filter is presented to remove Rician noise from magnetic resonance image. A neutrosophic set, a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image is transformed into NS domain, described using three membership sets: True (T), Indeterminacy (I) and False (F). The entropy of the neutrosophic set is defined and employed to measure the indeterminacy. The ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. The experiments have conducted on simulated Magnetic Resonance images (MRI) from Brainweb database and clinical MR images corrupted by Rician noise. The results show that the NS wiener filter produces better denoising results in terms of visual perception, qualitative and quantitative measures compared with other denoising methods, such as classical wiener filter, the anisotropic diffusion filter, the total variation minimization scheme and non local means filter.

Keywords

[1] Wright, G. (1997). Magnetic resonance imaging. IEEE Signal Process Magazine, 14 (1), 56-66.10.1109/79.560324Search in Google Scholar

[2] Gudbjartsson, H., Patz, S. (1995). The Rician distribution of noisy MRI data. Magetic Resonance inMedcine, 34 (6), 910-914.10.1002/mrm.191034061822541418598820Search in Google Scholar

[3] Henkelman, R.M. (1985). Measurement of signal intensities in the presence of noise in MR images. Medical Physics, 12 (2), 232-233.10.1118/1.5957114000083Search in Google Scholar

[4] McVeigh, E.R., Henkelman, R.M., Bronskill, M.J. (1985). Noise and filtration in magnetic resonance imaging. Medical Physics, 12 (5), 586-591.10.1118/1.59567921692004046992Search in Google Scholar

[5] Gerig, G., Kubler, O., Kikinis, R., Jolesz, F.A. (1992). Nonlinear anisotropic filtering of MRI data. IEEETransaction on Medical Imaging, 11 (2), 221-232.10.1109/42.14164618218376Search in Google Scholar

[6] Krissian, K., Aja-Fernández, S. (2009). Noise driven anisotropic diffusion filtering of MRI. IEEETransaction on Image Processing, 18 (10), 2265-2274.10.1109/TIP.2009.202555319546041Search in Google Scholar

[7] Samsonov, A., Johnson, C. (2004). Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels. Magnetic Resonance inMedicine, 52 (4), 798-806.10.1002/mrm.2020715389962Search in Google Scholar

[8] Weaver, J.B., Xu, Y., Healy, D.M., Cromwell, L.D. (1991). Filtering noise from images with wavelet transforms. Magnetic Resonance Imaging, 21 (2), 288-295.10.1002/mrm.19102102131745127Search in Google Scholar

[9] Nowak, R.D. (1999). Wavelet-based Rician noise removal for magnetic resonance imaging. IEEETransaction on Image Processing, 8 (10), 1408-1419.10.1109/83.79196618267412Search in Google Scholar

[10] Pizurica, A., Philips, W., Lemahieu, I., Acheroy, M. (2003). A versatile wavelet domain noise filtration technique for medical imaging. IEEE Transaction onMedical Imaging, 22 (3), 323-331.10.1109/TMI.2003.80958812760550Search in Google Scholar

[11] Bao, P., Zhang, L. (2003). Noise reduction for magnetic resonance images via adaptive multiscale products thresholding. IEEE Transaction on MedicalImaging, 22 (9), 1089-1099.10.1109/TMI.2003.81695812956264Search in Google Scholar

[12] Yang, X., Fei, B. (2011). A wavelet multiscale denoisng algorithm for magentic resonance images, Measurement Science and Technology, 22 (2), 1-12.10.1088/0957-0233/22/2/025803370751623853425Search in Google Scholar

[13] Rabbani, H., (2011). Statistical modeling of low SNR magnetic resonance images in wavelet domain using Laplacian prior and two-sided Rayleigh noise for visual quality improvement. Measurement ScienceReview, 11 (4), 125-130.10.2478/v10048-011-0023-0Search in Google Scholar

[14] Buades, A., Coll, B., Morel, J.M. (2005). A review of image denoising algorithms with a new one. Multiscale Modeling and Simulation, 4 (2), 490-530.10.1137/040616024Search in Google Scholar

[15] Manjon, J.V., Robles, M., Thacker, N.A. (2007). Multispectral MRI de-noising using non-local means. In Medical Image Understanding and Analysis 2007(MIUA), 17-18 July 2007. University of Wales Aberystwyth, 41-46.Search in Google Scholar

[16] Manjón, J.V., Carbonell-Caballero, J., Lull, J.J., García-Martí, G., Martí-Bonmatí, L., Robles, M. (2008). MRI denoising using non-local means. Medical Image Analysis, 12 (4), 514-523.10.1016/j.media.2008.02.00418381247Search in Google Scholar

[17] Coupe, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C. (2008). An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Transaction on MedicalImaging, 27 (4), 425-441.10.1109/TMI.2007.906087288156518390341Search in Google Scholar

[18] Manjón, J.V., Coupe, P., Martí-Bonmatí, L., Collins D.L., Robles, M., (2010). Adaptive non-local means denoising of MR images with spatially varying noise levels. Magnetic Resonance Imaging, 31 (1), 192-203.10.1002/jmri.2200320027588Search in Google Scholar

[19] Manjón, J.V., Coupe, P., Buades, A., Collins, D.L., Robles, M. (2012). New methods for MRI denoising based on sparseness and self-similarity. Medical ImageAnalysis, 16 (1), 18-27.10.1016/j.media.2011.04.00321570894Search in Google Scholar

[20] Lopez-Rubio, E., Florentin-Nunez, M.N. (2011). Kernel regression based feature extraction for 3D MR image denoising. Medical Image Analysis, 15 (4), 498-513.10.1016/j.media.2011.02.00621414834Search in Google Scholar

[21] Samarandache, F. (2003). A Unifying Field in Logics:Neutrosophic Logic. Neutrosophy, Neutrosophic Set,Neutrosophic Probability (third edition). American Research Press.Search in Google Scholar

[22] Cheng, H.D., Guo, Y. (2008). A new neutrosophic approach to image thresholding. New Mathematics andNatural Computation, 4 (3), 291-308.10.1142/S1793005708001082Search in Google Scholar

[23] Guo, Y., Cheng, H.D. (2009). New neutrosophic approach to image segmentation. Pattern Recognition, 42 (5), 587-595.10.1016/j.patcog.2008.10.002Search in Google Scholar

[24] Sengur, A., Guo, Y. (2011). Color texture image segmentation based on neutrosophic set and wavelet transformation. Computer Vision and ImageUnderstanding, 115 (8), 1134-1144.10.1016/j.cviu.2011.04.001Search in Google Scholar

[25] Guo, Y., Cheng, H.D., Zhang, Y. (2009). A new neutrosophic approach to image denoising. NewMathemetics and Natural Computation, 5 (3), 653-662.10.1142/S1793005709001490Search in Google Scholar

[26] Mohan, J., Krishnaveni, V., Guo, Y. (2011). A neutrosophic approach of MRI denoising. In International Conference on Image InformationPocessing (ICIIP), 3-5 November 2011. IEEE, 1-6.10.1109/ICIIP.2011.6108880Search in Google Scholar

[27] Mohan, J., Krishnaveni, V., Guo, Y., Kanchana, J. (2012). MRI denoising based on neutrosophic wiener filtering. In IEEE International Conference onImaging Systems and Techniques (IST), 16-17 July 2012. IEEE, 327-331.10.1109/IST.2012.6295518Search in Google Scholar

[28] Kwan, R.K., Evans, A.C., Pike, G.B. (1999). MRI simulation based evaluation of image processing and classification methods. IEEE Transaction on MedicalImaging, 18 (11), 1085-1097.10.1109/42.816072Search in Google Scholar

[29] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncell, E.P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transaction onImage Processing, 13 (4), 600-612.10.1109/TIP.2003.819861Search in Google Scholar

[30] Gonzalez, R.C., Woods, R.E. (2002). Digital ImageProcessing (second edition). Prentice Hall.Search in Google Scholar

[31] Perona, P., Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEETransaction on Pattern Analysis and MachineInteligence, 12 (7), 629-639.10.1109/34.56205Search in Google Scholar

[32] Rudin, L.I., Osher, S., Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60 (1-4), 259-268.10.1016/0167-2789(92)90242-FSearch in Google Scholar

[33] Mathworks. The Matlab image processing toolbox. http://www.mathworks.com/access/helpdesk/help/toolbox/images/ Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo