A Nonlinear Multi-Resolution Gradient-Adaptive Filter for Medical Images

We present a novel method for intra-frame image processing, which is applicable to a wide variety of medical imaging modalities, like X-ray angiography, X-ray fluoroscopy, magnetic resonance, or ultrasound. The method allows to reduce noise significantly- by about 4.5 dB and more - while preserving sharp image details. Moreover, selective amplification of image details is possible. The algorithm is based on a multi-resolution approach. Noise reduction is achieved by non-linear adaptive filtering of the individual band pass layers of the multi-resolution pyramid. The adaptivity is controlled by image gradients calculated from the next coarser layer of the multi-resolution pyramid representation, thus exploiting cross-scale dependencies. At sites with strong gradients, filtering is performed only perpendicular to the gradient, i.e. along edges or lines. The multi-resolution approach processes each detail on its appropriate scale so that also for low frequency noise small filter kernels are applied, thus limiting computational costs and allowing a real-time implementation on standard hardware. In addition, gradient norms are used to distinguish smoothly between "structure" and "noise only" areas, and to perform additional noise reduction and edge enhancement by selectively attenuating or amplifying the corresponding band pass coefficients.