LAUSR

Ultrasound-based navigation, as a non-invasive and non-radiation image guiding system, is becoming a research focus in minimally invasive navigation surgery, and calibration between an ultrasonic probe and a 3D vision device is one of the key technologies for ultrasonic registration-based navigation. In this paper, a phantom model was designed as a benchmark for calibration. Both iterative closet point (ICP) and coherent point drift (CPD) algorithms are chosen as point cloud registration methods to implement calibration between ultrasonic scanned points and original phantom points to set up the relationship between the ultrasonic probe and the 3D vision device. Because of large topological difference between the ultrasound scanned points and the points from model, ICP algorithm cannot complete the registration, but the CPD algorithm could implement the registration automatically. The average errors of the center point position for each cavity were 1.50, 1.31, and 1.19 mm, respectively, and the average errors of the axis for each cavity were 0.85°, 0.61°, and 0.99°, respectively. Experiment results showed that the average error of calibration by this method satisfies acquirements of most orthopedic surgeries, and the fully automatic implementation of ultrasonic image processing and subsequent calculation is suitable for on-line calibration and verification in surgery.