Digital Radiographic Image Denoising Via Wavelet-Based Hidden Markov Model Estimation |
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Authors: | Ricardo J Ferrari Robin Winsor |
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Institution: | (1) Department of Computing Science, University of Alberta, 221 Athabasca Hall, Edmonton, Alberta, Canada, T6G 2E8;(2) Imaging Dynamics Company Ltd., 151, 2340 Pegasus Way N.E., Calgary, AB, Canada, T2E 8M5 |
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Abstract: | This paper presents a technique for denoising digital radiographic images based upon the wavelet-domain Hidden Markov tree (HMT) model. The method uses the Anscombe s transformation to adjust the original image, corrupted by Poisson noise, to a Gaussian noise model. The image is then decomposed in different subbands of frequency and orientation responses using the dual-tree complex wavelet transform, and the HMT is used to model the marginal distribution of the wavelet coefficients. Two different correction functions were used to shrink the wavelet coefficients. Finally, the modified wavelet coefficients are transformed back into the original domain to get the denoised image. Fifteen radiographic images of extremities along with images of a hand, a line-pair, and contrast–detail phantoms were analyzed. Quantitative and qualitative assessment showed that the proposed algorithm outperforms the traditional Gaussian filter in terms of noise reduction, quality of details, and bone sharpness. In some images, the proposed algorithm introduced some undesirable artifacts near the edges. |
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Keywords: | Medical image denoising digital radiography wavelet denoising hidden Markov model |
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