Complexity prediction of automatic image registration: a case study on motion-compensated DSA

Real-time video and Quality-of-Service aspects play an increasing role in the development of medical imaging systems. To avoid resource overload and to guarantee the throughput of dynamic applications, we present a method for complexity prediction of image registration and motion-compensation algorithms, which can have a highly dynamic nature at runtime. As a case study, we explore a medical imaging function to reduce motion-artifacts in X-ray Digital Subtraction Angiography (DSA). Complexity prediction is based on motion estimation, prior to the actual image registration. Experimental results show that it is possible to model a dynamic content-dependent processing task with a high accuracy (95%, standard deviation 5%), thereby facilitating a higher quality for remaining tasks and well defined options for QoS.