Paper Info
Title
Application of single-image camera calibration for ultrasound augmented laparoscopic visualization
Abstract
Accurate calibration of laparoscopic cameras is essential for enabling many surgical visualization and navigation technologies such as the ultrasound-augmented visualization system that we have developed for laparoscopic surgery. In addition to accuracy and robustness, there is a practical need for a fast and easy camera calibration method that can be performed on demand in the operating room (OR). Conventional camera calibration methods are not suitable for the OR use because they are lengthy and tedious. They require acquisition of multiple images of a target pattern in its entirety to produce satisfactory result. In this work, we evaluated the performance of a single-image camera calibration tool (rdCalib; Percieve3D, Coimbra, Portugal) featuring automatic detection of corner points in the image, whether partial or complete, of a custom target pattern. Intrinsic camera parameters of a 5-mm and a 10-mm standard Stryker laparoscopes obtained using rdCalib and the well-accepted OpenCV camera calibration method were compared. Target registration error (TRE) as a measure of camera calibration accuracy for our optical tracking-based AR system was also compared between the two calibration methods. Based on our experiments, the single-image camera calibration yields consistent and accurate results (mean TRE = 1.18 +/- 0.35 mm for the 5-mm scope and mean TRE = 1.13 +/- 0.32 mm for the 10-mm scope), which are comparable to the results obtained using the OpenCV method with 30 images. The new single-image camera calibration method is promising to be applied to our augmented reality visualization system for laparoscopic surgery.
Year
DOI
Venue
2015
10.1117/12.2082194
Proceedings of SPIE
Keywords
DocType
Volume
camera calibration,intrinsic parameters,augmented reality,optical tracking,laparoscopic surgery
Conference
9415
ISSN
Citations 
PageRank 
0277-786X
3
0.40
References 
Authors
3
5
Name
Order
Citations
PageRank
xinyang liu171.58
he su231.07
Sukryool Kang3162.62
Timothy D. Kane4132.74
Raj Shekhar528232.08