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Digital radiographic image denoising via wavelet-based hidden Markov model estimation.

Ricardo J Ferrari¹, Robin Winsor

¹Department of Computing Science, University of Alberta, 221 Athabasca Hall, Edmonton, Alberta, Canada, T6G 2E8. ferrari@cs.ualberta.ca

Journal of Digital Imaging

|April 14, 2005

Summary

This study introduces a wavelet-domain Hidden Markov tree (HMT) model for denoising digital radiographic images. The advanced technique improves noise reduction and image detail compared to traditional filters.

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Area of Science:

Medical Imaging
Signal Processing
Computer Vision

Background:

Digital radiography is susceptible to Poisson noise, degrading image quality.
Traditional denoising methods like Gaussian filters may obscure fine details.

Purpose of the Study:

To develop and evaluate a novel wavelet-domain Hidden Markov tree (HMT) based denoising technique for digital radiographic images.
To enhance image quality by reducing noise while preserving crucial details and bone sharpness.

Main Methods:

Anscombe's transformation to convert Poisson noise to Gaussian noise.
Dual-tree complex wavelet transform for image decomposition.
Wavelet-domain Hidden Markov tree (HMT) model for coefficient distribution.
Application of correction functions for wavelet coefficient shrinkage.

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Main Results:

The proposed HMT algorithm demonstrated superior noise reduction compared to Gaussian filters.
Improved preservation of image details and bone sharpness was observed.
Minor artifacts near edges were noted in some radiographic images.

Conclusions:

The wavelet-domain HMT model offers a promising approach for effective radiographic image denoising.
This technique enhances diagnostic quality by improving signal-to-noise ratio and detail preservation.
Further refinement may be needed to mitigate edge artifacts.