Self Similarity Image Registration Based on Reorientation of the Hessian

Abstract. The modality independent neighbourhood descriptor (MIND) is a local registration metric that is based on the principle of self-similarity. However, the metric requires recalculation of the self similarity during registration as this inherently changes during image deformation. We propose a self similarity registration method based on the Hessian (HE) that efficiently deals with the recalculation issue. The representation of the local self-similarity via the Hessian enables keeping it up to date dur-ing deformation. As such, the registration procedure is efficient and not prone to fall in local minima. We have shown that reorienting the hessian gives a significant improvement (p<0.05) over leaving the reorientation out. Our technique also has a better performance over the existing MIND method on the DIR-Lab dataset as well as on abdominal MRI datasets albeit not significant. Ultimately, we will use the technique to quantify Crohn’s disease severity based on the relative contrast enhancement in registered images