Share Email Print
cover

Proceedings Paper

Branch identification method for CT-guided bronchoscopy based on eigenspace image matching between real and virtual bronchoscopic images
Author(s): Riyoko Shinohara; Kensaku Mori; Daisuke Deguchi; Takayuki Kitasaka; Yasuhito Suenaga; Hirotsugu Takabatake; Masaki Mori; Hiroshi Natori
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

This paper presents a method for identifying branches for CT-guided bronchoscopy based on eigenspace image matching. This method outputs the current location of a real bronchoscope (RB) by displaying branches where a bronchoscope is currently observing or by presenting anatomical names of branches currently being observed. In the previous method of bronchoscope navigation, the motion of a real bronchoscope is tracked by image registration between RB and virtual bronchoscopic (VB) images. Although bronchoscope tracking based on image registration gives us very accurate tracking results, it requires a lot of computation time and it is difficult to perform real-time tracking. If we focus only on navigation to a target branch, it is enough to identify a branch where a bronchoscope is currently located. This paper presents a method for identifying branches in which a bronchoscope is currently observing and presenting their anatomical names. Branch identification is done by image matching between RB images and pre-generated VB images. VB images are pre-generated at each bifurcation point based on structural analysis results of bronchi regions extracted from CT images. For each frame of an RB video, we find the most similar VB image to the input one from a training dataset (pre-generated VB image) and output the branch levels associated with the found image by using the eigenspace method. We have applied the proposed method to a pair of comprising a 3D CT image and real bronchoscopic video footage. The experimental results showed that the proposed method can identify branches for about 77.7% of the input frames.

Paper Details

Date Published: 13 March 2006
PDF: 12 pages
Proc. SPIE 6143, Medical Imaging 2006: Physiology, Function, and Structure from Medical Images, 614314 (13 March 2006); doi: 10.1117/12.654451
Show Author Affiliations
Riyoko Shinohara, Nagoya Univ. (Japan)
Kensaku Mori, Nagoya Univ. (Japan)
Daisuke Deguchi, Nagoya Univ. (Japan)
Takayuki Kitasaka, Nagoya Univ. (Japan)
Yasuhito Suenaga, Nagoya Univ. (Japan)
Hirotsugu Takabatake, Minami Sanjyo Hospital (Japan)
Masaki Mori, Sapporo Kosei General Hospital (Japan)
Hiroshi Natori, Sapporo Medical Univ. (Japan)


Published in SPIE Proceedings Vol. 6143:
Medical Imaging 2006: Physiology, Function, and Structure from Medical Images
Armando Manduca; Amir A. Amini, Editor(s)

© SPIE. Terms of Use
Back to Top