GPU Accelerated Bilateral Filter for MR Image Denoising

Abstract

Background: Magnetic resonance (MR) imaging plays a significant role in the computer- aided diagnostic systems for remote healthcare. In such systems, the soft textures and tissues within the denoised MR image are classified by the segmentation stage using machine learning algorithms like Hidden Markov Model. Thus, the quality of the MR image is of extreme importance and is decisive in the accuracy of the process of classification and diagnosis. Objective: To provide real-time medical diagnostics in the remote healthcare intelligent setups, the research work proposes CUDA GPU based accelerated bilateral filter for fast denoising of 2D high- resolution knee MR images. Methods: To achieve optimized GPU performance with better speed-up, the work implements an improvised technique that uses on-chip shared memory in combination with a constant cache. Results: The speed-up of 382x is achieved with the new proposed optimization technique which is 2.7x as that obtained with the shared memory only approach. The superior speed-up is along with 90.6%occupancy index indicating effective parallelization. The work here also aims at justifying the appropriateness of bilateral filter over other filters for denoising magnetic resonance images. All the patents related to GPU based image denoising are revised and uniqueness of the proposed technique is confirmed. Conclusion: The results indicate that even for a 64Mpixel image, the execution time of the proposed implementation is 334.91 msec only, making the performance almost real time. This will surely contribute to the real-time computer-aided data diagnostics requirement under remote critical conditions.