Landmark optimization using local curvature for point-based nonlinear rodent brain image registration

  • Authors:

  • Yutong Liu;Balasrinivasa R. Sajja;Mariano G. Uberti;Howard E. Gendelman;Tammy Kielian;Michael D. Boska

  • Affiliations:

  • Department of Radiology, University of Nebraska Medical Center, Omaha, NE;Department of Radiology, University of Nebraska Medical Center, Omaha, NE;Department of Radiology, University of Nebraska Medical Center, Omaha, NE;Department of Pharmacology and Experimental Neuroscience and Center for Neurodegenerative Disorders, University of Nebraska Medical Center, Omaha, NE;Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE;Department of Radiology, University of Nebraska Medical Center, Omaha, NE

  • Venue:
  • Journal of Biomedical Imaging - Special issue on MRI in Neurosciences
  • Year:
  • 2012

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Abstract

Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed) and target (reference image). Point landmarks are placed at regular intervals on contours of anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity and displacements of the homologous landmarks. The method was evaluated in two cases (n = 5 each). In one, MRI was registered to histological sections; in the second, geometric distortions in EPI MRI were corrected.Normalizedmutual information and target registration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks. Results. Statistical analyses demonstrated significant improvement (P P