New image similarity measures for bronchoscope tracking based on image registration between virtual and real bronchoscopic images
Kensaku Mori, Tsutomu Enjoji, Daisuke Deguchi, Takayuki Kitasaka, Yasuhito Suenaga, Jun‐ichiro Toriwaki, Hirotsugu Takabatake, Hiroshi Natori · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
This paper describes a new method for calculating image similarity between a real bronchoscopic (RB) image and a virtual endoscopic (VE) image for bronchoscope tracking based on image registration. Camera motion tracking is sequentially done by finding viewing parameters (camera position and orientation) that can render the most similar VE image to a currently processing RB frame based on image similarity, since it is difficult to attach a positional sensor at the tip of a bronchoscope. In the previous method, image similarity was calculated between real and virtual endoscopic images by summing gray-level differences up for all pixels of two images. This method could not estimate positions and orientations of a real bronchoscope camera properly, when image similarity changed only a little (but partly changed significantly) due to averaging of gray-level differences for the entire image. The proposed method divides the real and virtual endoscopic images into a set of subregions and selects the subregions that contain characteristic shapes such as the bifurcation and folding patterns of the bronchus. The proposed image similarity measure is implemented in the bronchoscope navigation system that equips the prediction function of the bronchoscope motion based on Kalman filtering. The predicted results are used as initial estimations of image registration. We applied the proposed method to eight pairs of bronchoscopic videos and three-dimensional (3-D) chest CT images. The experimental results showed that the proposed method improved the tracking performance by five orders of magnitude over the previous method. Computation time for one frame decreased to 20% of the previous method's.