A three-dimensional surface registration method using a spherical unwrapping method and HK curvature descriptors for patient-to-CT registration of image guided surgery

In image guided surgery, image-to-patient registration process is required to use actively pre-operative images such as CT and MRI during operation. One method that utilizes 3D surface measurement data of patients among several image-to-patient registration methods is dealt with in this paper. After a hand held 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. 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. Though this problem can be avoided by manual set-up of the proper initial location before performing ICP, it has also critical disadvantages that an experienced user has to perform the method due to algorithms' sensitivity, and also takes another long time. In this paper, we propose an automatic method that can accurately find the proper initial location without manual intervention. The proposed method finds the proper initial location for ICP by converting 3D data to 2D curvature images and performing image matching automatically. It is based on the characteristics that curvature features are robust to the rotation, translation, and even some deformation.