SYDESCO: a laser-video scanner for 3D scoliosis evaluations.

SYDESCO is a new 3D vision system developed for trunk surface topography. This structured light surface scanner uses the principle of triangulation-based range sensing to infer 3D shape. The complete trunk acquisition is fast (2 seconds). The accuracy of the metric data is ensured by a subpixel image detection and a calibration process, which rectifies image deformations. A preliminary study presents results on 50 children in a gymnastics school. These children, aged between eight to sixteen years, are particularly exposed to spinal deformities. An asymmetry index is calculated from the 3D data to detect the pathologic cases. These results have been compared to an independent medical diagnosis. The system results have been confirmed for 72,1% of the patients.

Augmented reality in spine surgery: critical appraisal and status of development.

In spinal surgery, computer assistance in the operating room is gaining fast-paced acceptance, but it would be simplistic to imagine that added technology is systematically beneficial to the patient. As surgeons are not experts in computer technology, there is a tendency to abandon the evaluation to the market and say the best will emerge. Based on our experience with the development of our own system and confronted with the emergence of new systems adopting solutions we had rejected, we propose a framework to compare various systems between them. Similar evaluation protocols are in use for surgical implants, the same should be done for computer assistance.

Camera virtual equivalent model 0.01 pixel detectors.

In computer vision, a standard approach to eliminate spatial distortion is to use a calibrated grid. We developed an original method using such a grid to establish an equivalence between any camera with inherent technical limitations (optic, solid-state sensor, digitizer) and a perfect virtual pinhole camera model. This bijection based on interpolation concepts takes all the deformations generated by the optical and electronical equipment into account (distortion, decentration, scale, and affinity ratios) without the need for their explicit determination. We propose a detection method of this grid image by the use of grey levels approximation surfaces particularly adapted to this problem. We obtained a good precision on real grid images with modelization errors standard deviation of about 0.007 pixels with an off-the-shelf 512 x 512 camera using 256 grey levels. Three interpolation methods are compared: bilinear, cross-ratio in projective coordinates and bicubic splines. We conclude from this study that, in distorted images, the use of the bicubic splines interpolation method is a good improvement.