A patient-to-CT registration method based on spherical unwrapping and H-K curvature descriptors for surgical navigation system

Image-to-patient registration process is required to use actively pre-operative images such as CT and MRI during operation for surgical navigation system. One method to utilize scanning data of patients and 3D data from MRI or CT images is dealt with in this paper. After 3D surface measurement device measures the surface of patient's surgical site, this 3D data is registered to CT or MRI data using computer-based optimization algorithms like conventional ICP algorithms. However, general ICP algorithm has some disadvantages that it takes a long converging time if a proper initial location is not set up and also suffers from local minimum problem during the process. In this paper, we propose an automatic image-to-patient registration method that can accurately find a proper initial location without manual intervention of surgical operators. The proposed method finds and extracts the initial starting location for ICP by converting 3D data set of MRI or CT images and surface scanning data to 2D curvature images and by performing H-K curvature image matching between them automatically. It is based on the characteristics that curvature features are robust to the rotation, translation and even some deformation. Automatic image-to-patient registration is implemented by precisely 3D registration the extracted CT ROI and the patient's surface measurement data using ICP algorithm.