Demons kernel computation with single-pass stream processing on FPGA

Abstract

Non-rigid registration is crucial in imaging, in particular, to adjust deformities produced during image acquisition and improve the accuracy of datasets. However, conventional imaging systems lack the desired speed and computational bandwidth for additional non-rigid registration of the deformed images. Therefore, such functionality is usually unavailable in time-critical settings. Expensive computations and memory intensive characteristics of non-rigid image registration algorithms such as the Demons algorithm further limits the realization of such systems. In response, we propose an alternative and efficient custom hardware-based Demons registration algorithm which utilizes pipelined streaming models to minimize memory fetches for computation. Designed for highly customizable hardware, our design only requires single-pass of images to compute the Demons kernel. Implementation results on the Xilinx ML605 FPGA system is presented and quantitatively evaluated in clock cycle counts in contrast with a software-based implementation.