Real-time wavelet denoising with edge enhancement for medical x-ray imaging

X-ray image visualized in real-time plays an important role in clinical applications. The real-time system design requires that images with the highest perceptual quality be acquired while minimizing the x-ray dose to the patient, which can result in severe noise that must be reduced. The approach based on the wavelet transform has been widely used for noise reduction. However, by removing noise, high frequency components belonging to edges that hold important structural information of an image are also removed, which leads to blurring the features. This paper presents a new method of x-ray image denoising based on fast lifting wavelet thresholding for general noise reduction and spatial filtering for further denoising by using a derivative model to preserve edges. General denoising is achieved by estimating the level of the contaminating noise and employing an adaptive thresholding scheme with variance analysis. The soft thresholding scheme is to remove the overall noise including that attached to edges. A new edge identification method of using approximation of spatial gradient at each pixel location is developed together with a spatial filter to smooth noise in the homogeneous areas but preserve important structures. Fine noise reduction is only applied to the non-edge parts, such that edges are preserved and enhanced. Experimental results demonstrate that the method performs well both visually and in terms of quantitative performance measures for clinical x-ray images contaminated by natural and artificial noise. The proposed algorithm with fast computation and low complexity provides a potential solution for real-time applications.