Multi-modal Medical Images Registration Using Differential Geometry and the Hausdorff Distance

Abstract

Image Guided Interventions (IGI) has the potential to replace conventional open and invasive procedures. Such interventions also minimize damage to healthy tissue.Real-time 3D visualization of the problem area is now becoming a necessity for successful IGI. Real-time intra-operative imaging devices like multi-slice CT or 3D ultrasound are noisy and have low resolution, hence they must be compared with pre-operative high resolution images like MRI. Registering and comparing multi-modal images is inherently difficult due to the differences in visualization and the large number of missing features. A new deformable rigid body technique is presented that uses differential geometry based features and the Hausdorff similarity measure for rigid body sub-division registration. The differential geometry based features and the Hausdorff similarity measure are chosen because of their robustness to missing features and intensity variations. A translation deformation field is obtained that maps each pixel to a new location, followed by a cubic interpolator to obtain the registered image. Experimental results show that the new registration technique offers better accuracy and quality and is computationally less intensive compared with previous methods. INTRODUCTIONImage guidance and planning is well established in neuro, otolaryngology, maxillo-facial, and orthopedic surgery. A 3D pre-operative plan is first made by surgeons based on images acquired before surgery. This surgical plan is then used as a framework during the surgery. The success and accuracy of such pre-operative