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A Multi-resolution T-Mixture Model Approach to Robust Group-Wise Alignment of Shapes

  • Nishant RavikumarEmail author
  • Ali Gooya
  • Serkan Çimen
  • Alejandro F. Frangi
  • Zeike A. Taylor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

A novel probabilistic, group-wise rigid registration framework is proposed in this study, to robustly align and establish correspondence across anatomical shapes represented as unstructured point sets. Student’s t-mixture model (TMM) is employed to exploit their inherent robustness to outliers. The primary application for such a framework is the automatic construction of statistical shape models (SSMs) of anatomical structures, from medical images. Tools used for automatic segmentation and landmarking of medical images often result in segmentations with varying proportions of outliers. The proposed approach is able to robustly align shapes and establish valid correspondences in the presence of considerable outliers and large variations in shape. A multi-resolution registration (mrTMM) framework is also formulated, to further improve the performance of the proposed TMM-based registration method. Comparisons with a state-of-the art approach using clinical data show that the mrTMM method in particular, achieves higher alignment accuracy and yields SSMs that generalise better to unseen shapes.

Notes

Acknowledgments

This study was funded by the European Unions Seventh Framework Programme (FP7/2007 2013) as part of the project VPH-DARE@IT (grant agreement no. 601055) and partly supported by the Marie Curie Individual Fellowship (625745, A. Gooya). The authors would like to thank Dr. Fabian Wenzel, Philips Research Laboratories, Hamburg, Germany, for providing access to their fully automated tool, to segment the caudate nuclei.

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© Springer International Publishing AG 2016

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Authors and Affiliations

  • Nishant Ravikumar
    • 1
    • 2
    Email author
  • Ali Gooya
    • 1
    • 3
  • Serkan Çimen
    • 1
    • 3
  • Alejandro F. Frangi
    • 1
    • 3
  • Zeike A. Taylor
    • 1
    • 2
  1. 1.CISTIB Centre for Computational Engineering 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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