Accelerating Volume Image Registration through Correlation Ratio Based Methods on GPUs

Volume image registration is a basic component of medical image processing which traditionally requires long computation time. In this paper, we propose five Correlation Ratio based schemes that explore the design space for Graphics Processing Unit (GPU) acceleration. Through comparisons among these five schemes, we present the trade-off between benefits and overheads of introducing shadow histograms on various storage (shared memory, global memory) by different level execution units (thread, warp, thread block). Compared to Mutual Information based methods, these Correlation Ratio based methods require less resources for shadow histograms, a faster storage therefore could be exploited to achieve better performance which is shown in our experiments. Particularly, the fifth scheme completely avoids updating conflicts of histogram calculation, leading to a substantial performance improvement (over 18x speedup) over the native FLIRT version. It reduces the registration time from over 100s to less than 6s for two typical 256x256x160 3D images.