Acceleration of Medical Image Registration Using Graphics Process Units in Computing Normalized Mutual Information

This paper presents a computational performance analysis of an accelerated medical image registration using Graphics Processing Units (GPUs). In our previous work, a multi-resolution approach using normalized mutual information (NMI) has proven to be useful in medical image registration. In this paper, we propose an acceleration of the NMI procedure using GPU implementation because of the parallel processing capabilities. Registration algorithms were implemented on NVIDIA's GeForece 9600 GT graphic processor with the Compute Unified Device Architecture (CUDA) programming environment. Experimental results showed that the GPU implementation improves the registration computational performance with a speedup factor of 23.4x. In addition, the maximum speedup can be achieved with diligent data profiling.