Non-local Shape Descriptor: A New Similarity Metric for Deformable Multi-modal Registration

  • Mattias P. Heinrich
  • Mark Jenkinson
  • Manav Bhushan
  • Tahreema Matin
  • Fergus V. Gleeson
  • J. Michael Brady
  • Julia A. Schnabel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

Deformable registration of images obtained from different modalities remains a challenging task in medical image analysis. This paper addresses this problem and proposes a new similarity metric for multi-modal registration, the non-local shape descriptor. It aims to extract the shape of anatomical features in a non-local region. By utilizing the dense evaluation of shape descriptors, this new measure bridges the gap between intensity-based and geometric feature-based similarity criteria. Our new metric allows for accurate and reliable registration of clinical multi-modal datasets and is robust against the most considerable differences between modalities, such as non-functional intensity relations, different amounts of noise and non-uniform bias fields. The measure has been implemented in a non-rigid diffusion-regularized registration framework. It has been applied to synthetic test images and challenging clinical MRI and CT chest scans. Experimental results demonstrate its advantages over the most commonly used similarity metric - mutual information, and show improved alignment of anatomical landmarks.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mattias P. Heinrich
    • 1
    • 2
  • Mark Jenkinson
    • 2
  • Manav Bhushan
    • 1
    • 2
  • Tahreema Matin
    • 3
  • Fergus V. Gleeson
    • 3
  • J. Michael Brady
    • 4
  • Julia A. Schnabel
    • 1
  1. 1.Institute of Biomedical EngineeringUniversity of OxfordUK
  2. 2.Oxford University Centre for Functional MRI of the BrainUK
  3. 3.Department of Radiology Churchill HospitalOxfordUK
  4. 4.Department of Radiation Oncology and BiologyUniversity of OxfordUK

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