Statistical Shape Model to 3D Ultrasound Registration for Spine Interventions Using Enhanced Local Phase Features

Accurate registration of ultrasound images to statistical shape models is a challenging problem in percutaneous spine injection procedures due to the typical imaging artifacts inherent to ultrasound. In this paper we propose a robust and accurate registration method that matches local phase bone features extracted from ultrasound images to a statistical shape model. The local phase information for enhancing the bone surfaces is obtained using a gradient energy tensor filter, which combines advantages of the monogenic scale-space and Gaussian scale-space filters, resulting in an improved simultaneous estimation of phase and orientation information. A novel statistical shape model was built by separating the pose statistics from the shape statistics. This model is then registered to the local phase bone surfaces using an iterative expectation maximization registration technique. Validation on 96 in vivo clinical scans obtained from eight patients resulted in a root mean square registration error of 2 mm (SD: 0.4 mm), which is below the clinically acceptable threshold of 3.5 mm. The improvement achieved in registration accuracy using the new features was also significant (p < 0.05) compared to state of the art local phase image processing methods.