Removing Noise from Medical CR Image Using Multineural Network Filter Based on Noise Intensity Distribution

In this paper, a new type of multineural networks filter (MNNF) is presented that is trained for restoration and enhancement of the medical CR images. In medical CR image, noise has been categorized as quantum mottle, which is related to the incident X-ray exposure and artificial noise, which is caused by the grid, etc. MNNF consists of several neural network filters (NNFs). A novel analysis method is proposed to make the characteristics of the trained MNNF clearly. In the proposed method, a characteristics judgement system is presented to decide which NNF will be executed through the estimation of noise intensity calculated by maximum penalized likelihood estimator (MPLE). The new approach was tested on clinical medical X-ray image, synthesized noisy X-ray image and natural image. In all cases, the proposed MNNF produced better results in terms of mean square error (MSE) measure than MPLE, NNF and conventional wavelet BayesShrink (BS) methods.