IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



An Improved Denoising Of Medical Images Based On Hybrid Filter Approach And Assess Quality Metrics

[Full Text]

 

AUTHOR(S)

Mopidevi Suneetha , Mopidevi Subbaraoz

 

KEYWORDS

Denoising, MRI, CT, Non-local means filter, wavelet domain, medical image, Noise

 

ABSTRACT

Degradation of images and segmentation are the two most demanding fields for medical image processing, particularly when explicitly applied. The involvement of noise not only deteriorates the visual quality but also the precision of the segmentation which is vital to the medical diagnosis process of development. The complicated and monotonous main task is to manually denoise medical images such as CT, ultrasound and large numbers of clinical routine MRI images. The medical image must be denoised automatically. The proposed approach is associated with less complexity, this follows from the fact that, the design of system and time for optimization. Results show their efficacy for noise removal in medical ultrasound and MRI images .The final results of the proposed scheme in terms of noise reduction and structural preservation are excellent. However the proposed scheme is compared with existing methods and the performance of the proposed method in terms of visual quality, image quality index, peak SNR and PSNR is shown to be superior to existing methods.

 

REFERENCES

[1] L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation-based noise removal algorithms,” Physica D: Nonlinear Phenomena, vol. 60, no. 1-4, pp. 259–268, 1992.
[2] G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing, Partial Differential Equations and the Calculus of Variations, vol. 147 of Applied Mathematical Sciences, Springer Science + Business Media, LLC, New York, NY, USA, 2ndedition, 2006.
[3] T. F. Chan and J. Shen, Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA,2005.
[4] J. Xu, A. Feng, Y. Hao, X. Zhang, and Y. Han, “Image deblurring and denoising by an improved variational model,” International Journal of Electronics and Communications, vol. 70, no. 9, pp.1128–1133, 2016.
[5] L. Jiang, J. Huang, X.-G. LV, and J. Liu, “Alternating direction method for the high-order total variation-based Poisson noise removal problem,” Numerical Algorithms, vol. 69, no. 3, pp. 495–516, 2015.
[6] M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Transactions on Image Processing, vol. 15, no. 12, pp. 3736–3745,2006.
[7] N. Mustafa, J. Ping, S. Ahmed, and M. Giess, “Medical image de-noising schemes using wavelet transform with fixed formthresholding,” International Journal of Advanced ComputerScience and Applications, vol. 6, no. 10, pp. 173–178, 2015.
[8] W. Jifara, F. Jiang, S. Rho, M. Cheng, and S. Liu, “Medicalimage denoising using convolutional neural network: a residual learning approach,” The Journal of Supercomputing, pp. 1–15,2017.
[9] F. Zhang, N. Cai, J. Wu, G. Cen, H. Wang, and X. Chen, “Image denoising method based on a deep convolution neuralnetwork,” IET Image Processing, vol. 12, no. 4, pp. 485–493, 2018.
[10] K. Isogawa, T. Ida, T. Shiodera, and T. Takeguchi, “Deep shrinkage convolutional neural network for adaptive noise reduction,” IEEE Signal Processing Letters, vol. 25, no. 2, pp. 224–228, 2018.
[11] Liu C, Szeliski R, Kang SB, Zitnick CL, Freeman WT. Automatic estimation and removal of noise from a single image. IEEE Transactions on Pattern Analysis and MachineIntelligence. 2008 Feb; 30(2).
[12] Anutam, Rajni. Performance analysis of image denoisingwith wavelet thresholding methods for different levels ofdecomposition. The International Journal of Multimediaand Its Applications. 2014 Jun; 6(3).