High Performance GPU-Based Preprocessing for Time-of-Flight Imaging in Medical Applications

Time-of-Flight (ToF) imaging is a promising technology for real-time metric surface acquisition and has recently been proposed for a variety of medical applications. However, due to limitations of the sensor, range data from ToF cameras are subject to noise and contain invalid outliers. In this paper, we discuss a real-time capable framework for ToF preprocessing in a medical environment. The contribution of this work is threefold. First, we address the restoration of invalid measurements that typically occur with specular reflections on wet organ surfaces. Second, we compare the conventional bilateral filter with the recently introduced concept of guided image filtering for edge preserving de-noising. Third, we have implemented the pipeline on the graphics processing unit (GPU), enabling high-quality preprocessing in real-time. In experiments, the framework achieved a depth accuracy of 0.8 mm (1.4 mm) on synthetic (real) data, at a total runtime of 40 ms.