Bronchoscope tracking based on image registration using multiple initial starting points estimated by motion prediction

Authors:
Kensaku Mori;Daisuke Deguchi;Takayuki Kitasaka;Yasuhito Suenaga;Hirotsugu Takabatake;Masaki Mori;Hiroshi Natori;Calvin R. Maurer, Jr.
Affiliations:
Graduate School of Information Science, Nagoya University, Nagoya, Aichi, Japan;Graduate School of Information Science, Nagoya University, Nagoya, Aichi, Japan;Graduate School of Information Science, Nagoya University, Nagoya, Aichi, Japan;Graduate School of Information Science, Nagoya University, Nagoya, Aichi, Japan;Sapporo Minami-Sanjo Hospital;Sapporo Kosei-General Hospital;Keiwakai Nishioka Hospital;Dept. of Neurosurgery, Stanford University
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

Citing 2
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Abstract

This paper presents a method for tracking a bronchoscope based on motion prediction and image registration from multiple initial starting points as a function of a bronchoscope navigation system. We try to improve performance of bronchoscope tracking based on image registration using multiple initial guesses estimated using motion prediction. This method basically tracks a bronchoscopic camera by image registration between real bronchoscopic images and virtual ones derived from CT images taken prior to the bronchoscopic examinations. As an initial guess for image registration, we use multiple starting points to avoid falling into local minima. These initial guesses are computed using the motion prediction results obtained from the Kalman filter’s output. We applied the proposed method to nine pairs of X-ray CT images and real bronchoscopic video images. The experimental results showed significant performance in continuous tracking without using any positional sensors.