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Computed Tomography Images De-noising using a Novel Two Stage Adaptive Algorithm.

Fadaee M, Shamsi M, Saberkari H, Sedaaghi MH - J Med Signals Sens (2015 Oct-Dec)

Bottom Line: In local pixels grouping algorithm, blocks matching based on L (2) norm method is utilized, which leads to matching performance improvement.Implementation results show that the presented algorithm has a significant superiority in de-noising.Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

ABSTRACT
In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis. In local pixels grouping algorithm, blocks matching based on L (2) norm method is utilized, which leads to matching performance improvement. To evaluate the performance of our proposed algorithm, peak signal to noise ratio (PSNR) and structural similarity (SSIM) evaluation criteria have been used, which are respectively according to the signal to noise ratio in the image and structural similarity of two images. The proposed algorithm has two de-noising and cleanup stages. The cleanup stage is carried out comparatively; meaning that it is alternately repeated until the two conditions based on PSNR and SSIM are established. Implementation results show that the presented algorithm has a significant superiority in de-noising. Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods.

No MeSH data available.


Related in: MedlinePlus

Comparison of different methods for de-nosing vertebral column medical image. (a) Noiseless image. (b) The noisy image destructed by Gaussian white noise with standard deviation of σ = 36. (c) Locally learned dictionaries method, (d) local pixels grouping-principal component analysis method, (e) Bayes method, (f) patch based global principal component analysis method, (g) the proposed algorithm
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Figure 6: Comparison of different methods for de-nosing vertebral column medical image. (a) Noiseless image. (b) The noisy image destructed by Gaussian white noise with standard deviation of σ = 36. (c) Locally learned dictionaries method, (d) local pixels grouping-principal component analysis method, (e) Bayes method, (f) patch based global principal component analysis method, (g) the proposed algorithm

Mentions: In Figures 6, the comparison of the proposed algorithm and other methods in de-noising in vertebral column MRI images is demonstrated. Furthermore, in diagrams related to Figures 7 and 8, the comparison between our proposed algorithm with other de-noising methods in CT scan images are given for PSNR and SSIM measures. In these figures, the horizontal axis of Gaussian white noise with standard deviations higher than σ = 60 is for each image. Also, quantitative comparison of PSNR and SSIM values with other methods is given in Table 3. The proposed algorithm has considerably improved in PSNR and also SSIM amount in comparison to classical local pixels grouping algorithm and PCA (without a change in cleanup stages), methods based on PCA, wavelet transform and clustering.


Computed Tomography Images De-noising using a Novel Two Stage Adaptive Algorithm.

Fadaee M, Shamsi M, Saberkari H, Sedaaghi MH - J Med Signals Sens (2015 Oct-Dec)

Comparison of different methods for de-nosing vertebral column medical image. (a) Noiseless image. (b) The noisy image destructed by Gaussian white noise with standard deviation of σ = 36. (c) Locally learned dictionaries method, (d) local pixels grouping-principal component analysis method, (e) Bayes method, (f) patch based global principal component analysis method, (g) the proposed algorithm
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4759838&req=5

Figure 6: Comparison of different methods for de-nosing vertebral column medical image. (a) Noiseless image. (b) The noisy image destructed by Gaussian white noise with standard deviation of σ = 36. (c) Locally learned dictionaries method, (d) local pixels grouping-principal component analysis method, (e) Bayes method, (f) patch based global principal component analysis method, (g) the proposed algorithm
Mentions: In Figures 6, the comparison of the proposed algorithm and other methods in de-noising in vertebral column MRI images is demonstrated. Furthermore, in diagrams related to Figures 7 and 8, the comparison between our proposed algorithm with other de-noising methods in CT scan images are given for PSNR and SSIM measures. In these figures, the horizontal axis of Gaussian white noise with standard deviations higher than σ = 60 is for each image. Also, quantitative comparison of PSNR and SSIM values with other methods is given in Table 3. The proposed algorithm has considerably improved in PSNR and also SSIM amount in comparison to classical local pixels grouping algorithm and PCA (without a change in cleanup stages), methods based on PCA, wavelet transform and clustering.

Bottom Line: In local pixels grouping algorithm, blocks matching based on L (2) norm method is utilized, which leads to matching performance improvement.Implementation results show that the presented algorithm has a significant superiority in de-noising.Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

ABSTRACT
In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis. In local pixels grouping algorithm, blocks matching based on L (2) norm method is utilized, which leads to matching performance improvement. To evaluate the performance of our proposed algorithm, peak signal to noise ratio (PSNR) and structural similarity (SSIM) evaluation criteria have been used, which are respectively according to the signal to noise ratio in the image and structural similarity of two images. The proposed algorithm has two de-noising and cleanup stages. The cleanup stage is carried out comparatively; meaning that it is alternately repeated until the two conditions based on PSNR and SSIM are established. Implementation results show that the presented algorithm has a significant superiority in de-noising. Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods.

No MeSH data available.


Related in: MedlinePlus