Medical Image Denoising Based On Wavelet-domain Hidden Markov Tree

Wavelet image denoising has been well acknowledged as an important method of image denoising. Although it can preserve edge information, present wavelet methods ignore the relativity of wavelet coefficients. According to the deficiency, a new image denoising method was proposed based on hidden markov tree.This paper presents a technique for denoising digital radiographic images based upon the wavelet-domain Hidden Markov Tree (HMT) model. Wavelet domain HMT models the dependencies of multiscale wavelet coefficients through the state probabilities of the wavelet coefficients, who sedist ribution densities can be approximated by Gaussian mixture model. The proposed algorithm specifies the prior dist ribution of reaworld images through wavelet domain HMT model. The method uses the Anscombe's transformation to adjust the original image 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 methods were used to denoise the wavelet coefficients. Finally, the modified wavelet coefficients are transformed back into the original domain to get the denoised image.The proposed method could keep natural images edges from damaging and increase PSNR.Quantitative and qualitative DR images assessment showed that the proposed algorithm outperforms the traditional Gaussian filter in terms of noise reduction, quality of details and bone sharpness.