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

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 t...

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Bibliographic Details
Published in:Journal of medical signals and sensors 2015-10, Vol.5 (4), p.220-229
Main Authors: Fadaee, Mojtaba, Shamsi, Mousa, Saberkari, Hamidreza, Sedaaghi, Mohammad Hossein
Format: Article
Language:English
Subjects:
Algorithms
Computed Tomography Images
Noise
Original
Principal Component Analysis
Signal-to-Noise Ratio
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Summary: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.
ISSN:2228-7477
2228-7477
DOI:10.4103/2228-7477.168651