Implementation of a nonlinear gradient adaptive filter for processing of large-size medical sequences on general-purpose hardware

Abstract

To achieve significant noise reduction in medical images while at the same time preserving fine structures of diagnostic value, a non-linear filter called the multi-resolution gradient adaptive filter (MRGAF) was developed. Though the algorithm is well suited for its task of noise reduction in medical images, it is still limited to the application of offline processing in medical workstations due to its computational complexity. The aim of our study is to reach real-time processing of data from low-cost x-ray systems on a standard PC without additional hardware. One major drawback of the original MRGAF procedure is its irregular memory access behavior caused by the intermediate multi-resolution representation of the image (Laplacian pyramid). This is addressed by completely re-arranging the computation. The image is divided into super-lines carrying all relevant information of all pyramidal levels, which allow to apply the complete MRGAF procedure in a single pass. This way, the cache utilization is improved considerably, the total number of memory accesses is reduced, and the use of super-scalar processing capabilities of current processors is facilitated. The current implementation allows applying advanced multi-resolution non-linear noise reduction to images of 768 × 564 pixels at a rate of more than 30 frames per second on a workstation. This shows that high-quality real-time image enhancement is feasible from a technical as well as from an economical point of view.