Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 29, No. 1 (January 2016) 31-39    Article in Press

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  AN ADAPTIVE HIERARCHICAL METHOD BASED ON WAVELET AND ADAPTIVE FILTERING FOR MRI DENOISING
 
V. Hajihashemi and K. Borna
 
( Received: December 08, 2015 – Accepted: January 07, 2016 )
 
 

Abstract    MRI is one of the most powerful techniques to study the internal structure of the body. MRI image quality is affected by various noises. Noises in MRI are usually thermal and mainly due to the motion of charged particles in the coil. Noise in MRI images also cause a limitation in the study of visual images as well as computer analysis of the images. In this paper, first, it is proved that probability density function (PDF) of MRI Images is rician because of the process of image capturing and MRI hardware. Based on the review of later works in this area, it is determined that rician denoising in wavelet domain is better. It was concluded that the remaining noise in the final output of the conventional methods of wavelet domain, is Gaussian and can be greatly reduced with a Gaussian adaptive filter. In the proposed method the histogram of input and output image difference in first step of denoising routine is using for an adaptive estimation of remained Gaussian noise in output. Based on this estimation, a Gaussian filter designed and the output image was filtered again. The results showed that the final image quality will improve considerably. Rather than visual criteria, for proving the improvement the SSIM between main and filtered image is shown. In similar situations, the proposed algorithm is always better than the others.

 

Keywords    adaptive filtering, denoising, Gaussian pdf, MRI, rician pdf, SSIM

 

چکیده    MRI یکی از قویترین تکنیک های مطالعه ساختاری قسمت های داخلی بدن می باشد. کیفیت عکس های MRI تحت تاثیر نویزهای مختلفی هستند. نویز در MRI عمدتاً از نوع نویز حرارتی می باشد که به خاطر حرکت ذرات باردار در فرکانس رادیویی سیم پیچ بوجود می آید. نویز در تصاویر MRI باعث ایجاد محدودیت در بررسی ظاهری تصاویر و همچنین آنالیز این تصاویر توسط کامپیوتر می شود. در این مقاله ابتدا اثبات می شود بر اساس فرآیند رخ داده در سخت افزار عکسبرداری تصویر MRI، نویز موجود در این تصاویر از تابع چگالی احتمال رایسین پیروی می کند. بر این اساس و با مرور مجموعه کارهای انجام شده در این زمینه مشخص شد حذف نویز با توجه به تابع چگالی احتمال رایسین در فضای موجک بهتر صورت می گیرد. با تحلیل های صورت گرفته این گونه نتیجه گیری شد که نویز باقی مانده در خروجی نهایی روشهای مرسوم حوزه موجک، به دلیل خطای فیلتری ماهیت گوسی دارد که می توان آن را با یک فیلتر تطبیقی گوسی تا حد زیادی کاهش داد. بر اساس هیستوگرام های موجود در تصاویر تفاضل و برآورد تطبیقی نویز گوسی موجود در تصویر خروجی نهایی، یک فیلتر گوسی طراحی و با استفاده از آن تصویر خروجی حذف نویز شده دوباره فیلتر گردید. نتایج نشان می دهد که تصویر نهایی از نظر کیفیت بهبود قابل ملاحظه ای پیدا می کند. علاوه بر آن برای اثبات صحت و کارآیی روش علاوه بر معیار چشمی با استفاده از معیار SSIM ، تصویر نهایی با تصویر اولیه بدون نویز و بهترین خروجی روشهای قبل مورد مقایسه قرار گرفت و نشان داده شد در حالت یکسان کیفیت خروجی روش پیشنهادی همواره از نتایج قبلی بهتر خواهد بود.

References   

 

1.     McVeigh, E., Henkelman, R. and Bronskill, M., "Noise and filtration in magnetic resonance imaging", Medical physics,  Vol. 12, No. 5, (1985), 586-591.

2.     Bird, R.E., Wallace, T.W. and Yankaskas, B.C., "Analysis of cancers missed at screening mammography", Radiology,  Vol. 184, No. 3, (1992), 613-617.

3.     Arodź, T., Kurdziel, M., Popiela, T.J., Sevre, E.O. and Yuen, D.A., "Detection of clustered microcalcifications in small field digital mammography", computer methods and programs in biomedicine,  Vol. 81, No. 1, (2006), 56-65.

4.     Perona, P. and Malik, J., "Scale-space and edge detection using anisotropic diffusion", Pattern Analysis and Machine Intelligence, IEEE Transactions on,  Vol. 12, No. 7, (1990), 629-639.

5.     Gerig, G., Kübler, O., Kikinis, R. and Jolesz, F., "Nonlinear anisotropic filtering of mri data", Medical Imaging, IEEE Transactions on,  Vol. 11, No. 2, (1992), 221-232.

6.     Coakley, K., Quintarelli, F., Van Doorn, T. and Hirst, C., "Classification of equivocal mammograms through digital analysis", the breast,  Vol. 3, No. 4, (1994), 222-226.

7.     Aja-Fernández, S., Alberola-López, C. and Westin, C.-F., "Noise and signal estimation in magnitude mri and rician distributed images: A lmmse approach", Image Processing, IEEE Transactions on,  Vol. 17, No. 8, (2008), 1383-1398.

8.     Tang, J., Sun, Q., Liu, J. and Cao, Y., "An adaptive anisotropic diffusion filter for noise reduction in mr images", in Mechatronics and Automation, 2007. ICMA 2007. International Conference on, IEEE. Vol., No. Issue, (2007), 1299-1304.

9.     Krissian, K. and Aja-Fernández, S., "Noise-driven anisotropic diffusion filtering of mri", Image Processing, IEEE Transactions on,  Vol. 18, No. 10, (2009), 2265-2274.

10.   Senra Filho, D.S., Carlos, A., Jinzenji Duque, J., Junior, M. and Luiz, O., "Isotropic anomalous filtering in diffusion-weighted magnetic resonance imaging", in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, IEEE. Vol., No. Issue, (2013), 4022-4025.

11.   Zhang, F. and Ma, L., "Mri denoising using the anisotropic coupled diffusion equations", in Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on, IEEE. Vol. 1, No. Issue, (2010), 397-401.

12.   You, Y.-L. and Kaveh, M., "Fourth-order partial differential equations for noise removal", Image Processing, IEEE Transactions on,  Vol. 9, No. 10, (2000), 1723-1730.

13.   Lysaker, M., Lundervold, A. and Tai, X.-C., "Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time", Image Processing, IEEE Transactions on,  Vol. 12, No. 12, (2003), 1579-1590.

14.   Samsonov, A.A. and Johnson, C.R., "Noiseadaptive nonlinear diffusion filtering of mr images with spatially varying noise levels", Magnetic Resonance in Medicine,  Vol. 52, No. 4, (2004), 798-806.

15.   Tomasi, C. and Manduchi, R., "Bilateral filtering for gray and color images", in Computer Vision, 1998. Sixth International Conference on, IEEE. Vol., No. Issue, (1998), 839-846.

16.   Gudbjartsson, H. and Patz, S., "The rician distribution of noisy mri data", Magnetic Resonance in Medicine,  Vol. 34, No. 6, (1995), 910-914.

17.   Awate, S.P. and Whitaker, R.T., "Nonparametric neighborhood statistics for mri denoising", in Information Processing in Medical Imaging, Springer. Vol., No. Issue, (2005), 677-688.

18.   Awate, S.P. and Whitaker, R.T., "Feature-preserving mri denoising: A nonparametric empirical bayes approach", Medical Imaging, IEEE Transactions on,  Vol. 26, No. 9, (2007), 1242-1255.

19.   López-Rubio, E. and Florentín-Núñez, M.N., "Kernel regression based feature extraction for 3d mr image denoising", Medical image analysis,  Vol. 15, No. 4, (2011), 498-513.

20.   Zhu, H., Li, Y., Ibrahim, J.G., Shi, X., An, H., Chen, Y., Gao, W., Lin, W., Rowe, D.B. and Peterson, B.S., "Regression models for identifying noise sources in magnetic resonance images", Journal of the American Statistical Association,  Vol. 104, No. 486, (2009), 623-637.

21.   Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C. and Barillot, C., "An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images", Medical Imaging, IEEE Transactions on,  Vol. 27, No. 4, (2008), 425-441.

22.   Anand, C.S. and Sahambi, J., "Mri denoising using bilateral filter in redundant wavelet domain", in TENCON 2008-2008 IEEE Region 10 Conference, IEEE. (2008), 1-6.

23.   Anand, C.S. and Sahambi, J.S., "Wavelet domain non-linear filtering for mri denoising", Magnetic Resonance Imaging,  Vol. 28, No. 6, (2010), 842-861.

24.   Zaroubi, S. and Goelman, G., "Complex denoising of mr data via wavelet analysis: Application for functional mri", Magnetic Resonance Imaging,  Vol. 18, No. 1, (2000), 59-68.

25.   Placidi, G., Alecci, M. and Sotgiu, A., "Post-processing noise removal algorithm for magnetic resonance imaging based on edge detection and wavelet analysis", Physics in medicine and biology,  Vol. 48, No. 13, (2003), 1987.

26.   Pižurica, A., Philips, W., Lemahieu, I. and Acheroy, M., "A versatile wavelet domain noise filtration technique for medical imaging", Medical Imaging, IEEE Transactions on,  Vol. 22, No. 3, (2003), 323-331.

27.   Ashamol, V., Sreelekha, G. and Sathidevi, P., "Diffusion-based image denoising combining curvelet and wavelet", in Systems, Signals and Image Processing, 2008. IWSSIP 2008. 15th International Conference on, IEEE. Vol., No. Issue, (2008), 169-172.

28.   Do, M.N. and Vetterli, M., "The contourlet transform: An efficient directional multiresolution image representation", Image Processing, IEEE Transactions on,  Vol. 14, No. 12, (2005), 2091-2106.

29.   Parthiban, L. and Subramanian, R., "Medical image denoising using x-lets", in India Conference, 2006 Annual IEEE, IEEE. Vol., No. Issue, (2006), 1-6.

30.   Bamberger, R.H. and Smith, M.J., "A filter bank for the directional decomposition of images: Theory and design", Signal Processing, IEEE Transactions on,  Vol. 40, No. 4, (1992), 882-893.

31.   Monir, S.M.G. and Siyal, M.Y., "Denoising functional magnetic resonance imaging time-series using anisotropic spatial averaging", Biomedical Signal Processing and Control,  Vol. 4, No. 1, (2009), 16-25.

32.   Coupé, P., Manjón, J.V., Gedamu, E., Arnold, D., Robles, M. and Collins, D.L., An object-based method for rician noise estimation in mr images, in Medical image computing and computer-assisted intervention–miccai 2009. 2009, Springer. p. 601-608.

33.   A wong, a k mishra, quasi-monte carlo “estimation approach for denoising mri data based on regional statistics”, ieee trans. Biomedical eng, vol. 58, (2011), 1076–1083

34.   Sijbers, J. and Den Dekker, A., "Maximum likelihood estimation of signal amplitude and noise variance from mr data", Magnetic Resonance in Medicine,  Vol. 51, No. 3, (2004), 586-594.

35.   Sijbers, J., Poot, D., den Dekker, A.J. and Pintjens, W., "Automatic estimation of the noise variance from the histogram of a magnetic resonance image", Physics in medicine and biology,  Vol. 52, No. 5, (2007), 1335.

36.   He, L. and Greenshields, I.R., "A nonlocal maximum likelihood estimation method for rician noise reduction in mr images", Medical Imaging, IEEE Transactions on,  Vol. 28, No. 2, (2009), 165-172.

37.   Tisdall, D. and Atkins, M.S., "Mri denoising via phase error estimation", in Medical imaging, International Society for Optics and Photonics. Vol., No. Issue, (2005), 646-654.

38.   Luo, J., Zhu, Y. and Hiba, B., "Medical image denoising using one-dimensional singularity function model", Computerized Medical Imaging and Graphics,  Vol. 34, No. 2, (2010), 167-176.

 

 

 

 

 

39.   Hu, J., Pu, Y., Wu, X., Zhang, Y. and Zhou, J., "Improved dct-based nonlocal means filter for mr images denoising", Computational and mathematical methods in medicine,  Vol. 2012, No., (2012), 232-685

40.   Coupé, P., Yger, P. and Barillot, C., Fast non local means denoising for 3d mr images, in Medical image computing and computer-assisted intervention–miccai 2006. 2006, Springer. p. 33-40.

41.   Rajan, J., Poot, D., Juntu, J. and Sijbers, J., "Noise measurement from magnitude mri using local estimates of variance and skewness", Physics in medicine and biology,  Vol. 55, No. 16, (2010), 441-449.

42.   Nadernejad, E., Hassanpour, H. and Miar, H., "Image restoration using a pde-based approach", IJE Transactions B: Applications,  Vol. 20, No. 3, (2007),225-236.

43.   Khosravi, M. and Hassanpour, H., "Image denoising using anisotropic diffusion equations on reflection and illumination components of image", International Journal of Engineering-Transactions C: Aspects,  Vol. 27, No. 9, (2014), 1339-1348.

44.   S. Ketabchi, M. Kianpour,  r.  Valizadeh, m.J.  Mahmoodabadi, a new  technique  for image zooming based on the moving least squares, international journal of engineering transactions c: Aspects, Vol. 25, No. 2, (2012), 105–109.

45.   Hassanpour, H. and Ghadi, A.R., "Image enhancement via reducing impairment effects on image components", International Journal of Engineering-Transactions B: Applications,  Vol. 26, No. 11, (2013), 1267-1274.

46.   E. Ehsaeyan, A robust image denoising technique in the contourlet transform domain, International Journal Of Engineering Transactions B: Applications, Vol. 28, No. 11 (2015), 1589-1596.

47.   Yu, H. and Zhao, L., "An efficient denoising procedure for magnetic resonance imaging", in Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on, IEEE. Vol., No. Issue, (2008), 2628-2630.

48.   Kwan, R.K., Evans, A.C. and Pike, G.B., "Mri simulation-based evaluation of image-processing and classification methods", Medical Imaging, IEEE Transactions on,  Vol. 18, No. 11, (1999), 1085-1097.

49.   Manjón, J.V., Coupé, P. and Buades, A., "Mri noise estimation and denoising using non-local pca", Medical image analysis,  Vol. 22, No. 1, (2015), 35-47.

50.   H Yu, L Zhao, “An efficient denoising procedure for magnetic resonance imaging”,  in: Proceedings of IEEE 2nd International Conference on Bioinformatics and Biomedical Engineering, (2008).

51.   C Shyam Anand, S Jyotinder Sahambi, “Wavelet domain non-linear filtering for MRI denoising”,  Magnetic Resonance Imaging, Vol. 28, ) 2010(, 842-86.

52.   RK Kwan, AC Evans, GB Pike, “MRI simulation-based evaluation of image-processing and classification methods”,  IEEE Trans. Med Image, Vol. 18, (1999), 1085-1097.

53.   V José Manjón, P Coupé, A Buades, “MRI noise estimation and denoising using non-local PCA”,  Medical Image Analysis, Vol.  22, (2015), 35-47.

 

 

 

 

 

 

 

 





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