The augmented reality (AR) guidance system have been developed for image guided intervention, very intuitive and states of art technique in the surgical navigation. The microscope is preferred equipment in ENT surgery, thus we tried to apply the AR system into microscope. To use the AR in the microscope, the intrinsic parameters and hand-eye parameters should be obtained, which are essential parameters to calculate the position and orientation of microscope from the patient. Particularly, those parameters are changed according to variation of the magnification levels. Moreover, it is hard to predict the variation of those parameters due to nonlinearity of zoom lens. Therefore, we developed photogrammetric feedback system to find camera parameters accurately during a surgery. In the proposed system, camera calibration and hand-eye calibration are only performed in the low magnification level. It did not use parameter table, thus there is no need to calibrate a camera in other magnification levels. In this system, several images of calibration board are required to estimate the camera parameters when the magnification level is changed. The proposed photogrammetric feedback technique was used to find the intrinsic and hand-eye parameters in certain magnification level from the images and initial parameters which are taken from the lowest magnification level. Through the experiments, performance of the proposed technique were tested, the results showed sufficient stability and accuracy. Particularly, the proposed system are suitable to the microscope that is hard to use the inner encoder system. ⓒ 2014 DGIST
Table Of Contents
Ⅰ. INTRODUCTION 1 -- 1.1 Introduction of Surgical Navigation system based on Augmented Reality 1 -- 1.2 Microscopic AR systems in ENT and neurosurgery 2 -- 1.3 The estimation of the variable parameters in the microscope 2 -- 1.4 The proposed method 4 -- Ⅱ. Methods 5 -- 2.1 The overview of surgical navigation system 5 -- 2.2 The basic AR system using external tracker 7 -- 2.3 Patient to image registration 8 -- 2.4 camera calibration 9 -- 2.4.1 Pin-hole camera model 9 -- 2.4.2 Camera calibration 11 -- 2.4.3 Camera calibration results 12 -- 2.5 Hand-eye calibration 14 -- 2.5.1 The concept of hand-eye calibration 14 -- 2.5.2 Quaternion 15 -- 2.5.3 Dual quaternion 15 -- 2.5.4 The screw transformation with dual quaternion 16 -- 2.5.5 Hand-eye calibration results 19 -- 2.6 Zoom-lens camera system 21 -- 2.6.1 The characteristics of the zoom lens system 21 -- 2.6.2 The relationship between intrinsic and hand-eye matrix in the zoom lens 18 -- 2.6.3 Initial parameters for the estimation system 23 -- 2.6.4 The focal length estimation method using photogrammetric feedback 23 -- 2.6.5 The optimization of the AR parameters 26 -- 2.7 The evaluation method for the proposed system 29 -- 2.7.1 Experimental setup for evaluation of proposed system 29 -- 2.7.2 Evaluation of the initial parameters 30 -- 2.7.3 Evaluation of feedback system and estimated AR parameters 30 -- 2.8 Rendering system 31 -- 2.8.1 The configuration of real and virtual space 31 -- 2.8.2 The graphic display of AR system 32 -- 2.9 Software system 34 -- Ⅲ. Results and discussion 36 -- 3.1 The evaluation of the initial parameters 36 -- 3.1.1 The vision area of the camera 36 -- 3.1.2 The re-projection errors of conventional method 37 -- 3.2 The results of the estimated AR parameters 38 -- 3.3 The phantom study 40 -- Ⅳ. Conclusions 41