The week's pick
Random Articles
Reseach Article
An Efficient MRI Noise Removal Technique using Linear and Nonlinear Filters
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 15 |
| Year of Publication: 2018 |
| Authors: Suhas S., C. R. Venugopal |
10.5120/ijca2018915777
|
Suhas S., C. R. Venugopal . An Efficient MRI Noise Removal Technique using Linear and Nonlinear Filters. International Journal of Computer Applications. 179, 15 ( Jan 2018), 17-20. DOI=10.5120/ijca2018915777
Abstract
For the study of anatomical structure and image processing of MRI medical images techniques of noise removal have become an important practice in medical imaging application. In medical image processing, precise images need to be obtained to get accurate observations for the given application. The goal of any de-noising technique is to remove noise from an image which is the first step in any image processing. The noise removal method should be applied watchful manner otherwise artefacts can be introduced which may blur the image. In this paper, performance evaluation of the of MRI image de-noising techniques is provided. The techniques used are namely the median and Gaussian filter, Max filter [11], Min filter [11], and Arithmetic Mean filter [8]. All the above filters are applied on MRI brain and spinal cord images and the results are noted. A new method is proposed which modifies the existing median filter by adding features. The experimental result of the proposed method is then analyzed with the other three image filtering algorithms. The output image efficiency is measured by the statistical parameters like root mean square error (RMSE), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR).
References
- Nguyen Thanh Binh and Ashish Khare, "Adaptive complex wavelet technique for medical image denoising" in proceedings of the third International Conference on the development of Biomedical Engineering, pp. 195-198, Vietnam, January 11-14, 2010.
- Bhausaheb Shinde, Dnyandeo Mhaske, Machindra Patare, A.R. Dani,A.R. Dani “Apply Different Filtering Techniques To Remove The Speckle Noise Using Medical Images” International Journal of Engineering Research and Applications, Vol. 2, Issue 1,Jan-Feb 2012, pp.1071-1079
- L. Gagnon and A. Jouan, Speckle filtering of SAR images – A comparative study between complex-wavelet-based and standard filters SPIE Proc. 3169, 80 (1997).
- Herlidou-Meme S, Constans JM, and Carsin B,"MRI texture analysis on texture test objects, normal brain and intracranial tumours", Magn Reson Imaging 2003; 21:989-93.
- Mahmoud-Ghoneim D, Toussaint G, Constans JM, and De Certaines JD, "Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas", Magn Reson Imaging 2003; 21:983-7.
- B. Goossens, A. Pizurica, and W. Philips. Image denoising using mixtures of projected Gaussian scale mixtures, IEEE Transactions on Image Processing, Vol.18, Issue. 8, pp.1689-1702, 2009.
- M. Lysaker, A. Lundervold, and X. Tai.Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans. Imag. Proc., 2003.
- M. C. Motwani, M. C. Gadiya, R. C. Motwani and F. C. Harris.“Survey of Image Denoising Techniques”. University of Nevada, Reno Dept of Comp. Sci. & Engr., Reno, NV 89557 USA (775) 784-6571.
- B.Deepa and Dr. M.G.Sumithra, “Comparative Analysis of Noise Removal Techniques in MRI Brain Images”, 978-1-4799-7849-6/15 2015 IEEE.
- G.Castellano, L.Bonilha, L.M.Li, and F.Cendes, "Texture analysis of medical images", Clinical Radiology (2004) 59, 1061 -1069.
- R. C. Gonzalez and R. E. Woods, Digital Image Processing, Third Edition (Prentice-Hall, 2007) ISBN-10:013168728.
Index Terms
Keywords