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Generalised Coherent Point Drift for Group-Wise Registration of Multi-dimensional Point Sets

  • Nishant RavikumarEmail author
  • Ali Gooya
  • Alejandro F. Frangi
  • Zeike A. Taylor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

In this paper we propose a probabilistic approach to group-wise registration of unstructured high-dimensional point sets. We focus on registration of generalised point sets which encapsulate both the positions of points on surface boundaries and corresponding normal vectors describing local surface geometry. Richer descriptions of shape can be especially valuable in applications involving complex and intricate variations in geometry, where spatial position alone is an unreliable descriptor for shape registration. A hybrid mixture model combining Student’s t and Von-Mises-Fisher distributions is proposed to model position and orientation components of the point sets, respectively. A group-wise rigid and non-rigid registration framework is then formulated on this basis. Two clinical data sets, comprising 27 brain ventricle and 15 heart shapes, were used to assess registration accuracy. Significant improvement in accuracy and anatomical validity of the estimated correspondences was achieved using the proposed approach, relative to state-of-the-art point set registration approaches, which consider spatial positions alone.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nishant Ravikumar
    • 1
    • 2
    Email author
  • Ali Gooya
    • 1
    • 3
  • Alejandro F. Frangi
    • 1
    • 3
  • Zeike A. Taylor
    • 1
    • 2
  1. 1.CISTIB Centre for Computational Imaging and Simulation Technologies in BiomedicineINSIGNEO Institute for in silico MedicineSheffieldUK
  2. 2.Department of Mechanical EngineeringThe University of SheffieldSheffieldUK
  3. 3.Department of Electronic and Electrical EngineeringThe University of SheffieldSheffieldUK

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