An Improved Denoising of Medical Images Based on Hybrid Filter Approach and Assess Quality Metrics
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Published:2020-02
Issue:
Volume:44
Page:27-36
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ISSN:2296-9845
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Container-title:Journal of Biomimetics, Biomaterials and Biomedical Engineering
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language:
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Short-container-title:JBBBE
Author:
Suneetha Mopidevi1, Subbarao Mopidevi2
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.
Publisher
Trans Tech Publications, Ltd.
Reference17 articles.
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, p.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, p.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, p.495–516, (2015).
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