Open Access

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising


Cite

[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

eISSN:
1335-8871
Language:
English
Publication timeframe:
6 times per year
Journal Subjects:
Engineering, Electrical Engineering, Control Engineering, Metrology and Testing