CR image filter methods research based on wavelet-domain hidden markov models

In the procedure of computed radiography imaging, we should firstly get across the characters of kinds of noises and the relationship between the image signals and noises. Based on the specialties of computed radiography (CR) images and medical image processing, we have study the filtering methods for computed radiography images noises. On the base of analyzing computed radiography imaging system in detail, the author think that the major two noises are Gaussian white noise and Poisson noise. Then, the different relationship of between two kinds of noises and signal were studied completely. By considering both the characteristics of computed radiography images and the statistical features of wavelet transformed images, a multiscale image filtering algorithm, which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model, has been used to decrease the Gaussian white noise in computed images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and obtain power spectrum matrix, then this carried through the syncretized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM, according to scale size, and achieve the experiments as well as the comparison with other denoising methods were presented at last.