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Beschreibung:
Nowadays, minimally invasive surgery is an essential part of medical interventions. In a typical clinical workflow, procedures are planned preoperatively with 3-dimensional (3-D) computed tomography (CT) data and guided intraoperatively by 2-dimensional (2-D) video data. However, accurate preoperative data acquired for diagnose and operation planning is often not feasible to deliver valid information for orientation and decisions within the intervention due to issues like organ movements and deformations. Therefore, innovative interventional tools are required to aid the surgeon and improve safety and speed for minimally invasive procedures. Augmenting 2-D color information with 3-D range data allows to use an additional dimension for developing novel surgical assistance systems. Here, Time-of-Flight (ToF) is a promising low-cost and real-time capable technique that exploits reflected near-infrared light to estimate the radial distances of points in a dense manner. This thesis covers the entire implementation pipeline of this new technology into a clinical setup, starting from calibration to data preprocessing up to medical applications. The first part of this work covers a novel automatic calibration scheme for hybrid data acquisition based on barcodes as recognizable feature points. The common checkerboard pattern is overlaid by a marker that includes unique 2-D barcodes. The prior knowledge about the barcode locations allows to detect only valid feature points for the calibration process. Based on detected feature points seen from different points of view a sensor data fusion for the complementary modalities is estimated. The proposed framework achieved subpixel reprojection errors and barcode identification rates above 90% for both the ToF and the RGB sensor. As range data of low-cost ToF sensors is typically error-prone due to different issues, e.g. specular reflections and low signal-to-noise ratio (SNR), preprocessing is a mandatory step after acquiring photometric and geometric information in a common ...