Objective To denoise digital radiographic images well.Methods A technique was presented that used the Anscombe's transformation to adjust the original image to a Gaussian noise model based upon the wavelet denoising method and the wavelet-domain Hidden Markov Tree(HMT) model.Wavelet domain HMT models were used to determine the dependencies of multiscale wavelet coefficients through the state probabilities of the wavelet coefficients,whose sedistribution densities could be approximated by Gaussian mixture model.Results The proposed method could keep natural images edges from damaging and increase PSNR.Conclusion Quantitative and qualitative DR images assessment shows that the proposed algorithm outperforms the traditional Gaussian filter in terms of noise reduction,quality of details and bone sharpness.

It is very difficult to use the conventional methods that are based on area and on border for extracting a region of intered such as liver tumor region with vague and irregular boundaries. This paper introduces Poisson matting that uses transparency (alpha value) as self-property of image and seeks its best result value to achieve the aim of extracting object. Meanwhile, this method is applied to the liver tumor CT picture experimental results. It makes improvement in two steps: computing image source a value; calculating foreground image F and background image B. Consequently, successful result is obtained.
Algorithms ; Humans ; Image Enhancement ; methods ; Image Processing, Computer-Assisted ; methods ; Liver Neoplasms ; diagnostic imaging ; pathology ; Pattern Recognition, Automated ; methods ; Tomography, X-Ray Computed