Bayesian differentiation of multi-scale line-structures for model-free instrument segmentation in thoracoscopic images

Abstract

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. © Springer-Verlag Berlin Heidelberg 2005.