International Journal of Computer Vision
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Level Set Based Segmentation with Intensity and Curvature Priors
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Multiscale detection of curvilinear structures in 2-D and 3-D image data
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
RAGS: region-aided geometric snake
IEEE Transactions on Image Processing
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A reliable method to segment instruments in endoscope images is required as part of an enhanced reality system for minimally invasive surgery of the spine. Numerous characteristics of these images make typical intensity or model constraints for segmentation impractical. Rather, line-structure concepts are used to exploit the high length-to-diameter ratio expected of surgical instruments. A Bayesian selection scheme is proposed, and is shown to reliably differentiate these target objects from other line-like background structures.