Self Similarity Image Registration Based on Reorientation of the Hessian

  • Zhang Li
  • Lucas J. van Vliet
  • Frans M. Vos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8198)


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 during 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.


Image registration hessian reorientation Crohn’s disease 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhang Li
    • 1
  • Lucas J. van Vliet
    • 1
  • Frans M. Vos
    • 1
    • 2
  1. 1.Quantitative Imaging Group, Department of Imaging Science and TechnologyDelft University of TechnologyDelftThe Netherlands
  2. 2.Department of RadiologyAcademic Medical CenterAmsterdamThe Netherlands

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