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

Abstract. 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. Sec-ond, we compare the conventional bilateral filter with the recently in-troduced concept of guided image filtering for edge preserving denoising. 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 syn-thetic (real) data, at a total runtime of 40 ms.