Denoising by using multineural networks for medical X-ray imaging applications

In this paper, a new type of multineural network filter (MNNF) is presented that is trained for restoration and enhancement of the digital radiological images. In medical radiographices, 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 standard deviation value of pixels in the input region. The new approach was tested on nine clinical medical X-ray images and five synthesized noisy X-ray images. In all cases, the proposed MNNF produced better results in terms of peak signal-to-noise ratio (PSNR), mean-to-standard-deviation ratio (MSR) and contrast to noise ratio (CNR) measures than the original NNF, linear inverse filter and nonlinear median filter.