A Kinect-based Medical Augmented Reality System for Craniofacial Applications Using Image-to-Patient Registration

Abstract

In this study, we proposed a kinect-based medical augmented reality (AR) system for craniofacial applications.By using a Kinect sensor to acquire the surface structure of the patient, image-to-patient registration is accomplished by an Enhanced Iterative Closest Point (EICP) algorithm automatically. Moreover, a pattern-free AR scheme is designed by integrating the Kanade-Lucas-Tomasi (KLT) feature tracking and RANdom Sample Consensus (RANSAC) correction, which is better than traditional pattern-based and sensor-based AR environment. The demonstrated system was evaluated with a plastic dummy head and a human subject. Result shows that the image-to-patient registration error is around 3~4 mm, and the pattern-free AR scheme can provide smooth and accurate AR camera localization as the commercial tracking device does.