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Geodesic Distances to Landmarks for Dense Correspondence on Ensembles of Complex Shapes

Institution:
1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
2Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
3Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
4CARMA Center, University of Utah, Salt Lake City, UT, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2013
Publication Date:
Sep-2013
Volume Number:
16
Issue Number:
Pt 2
Pages:
19-26
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 2):19-26.
PubMed ID:
24579119
PMCID:
PMC4156012
Appears in Collections:
NA-MIC
Sponsors:
P41 RR012553/RR/NCRR NIH HHS/United States
P41 GM103545/GM/NIGMS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
Generated Citation:
Datar M., Lyu I., Kim S., Cates J., Styner M., Whitaker R. Geodesic Distances to Landmarks for Dense Correspondence on Ensembles of Complex Shapes. Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 2):19-26. PMID: 24579119. PMCID: PMC4156012.
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Establishing correspondence points across a set of biomedical shapes is an important technology for a variety of applications that rely on statistical analysis of individual subjects and populations. The inherent complexity (e.g. cortical surface shapes) and variability (e.g. cardiac chambers) evident in many biomedical shapes introduce significant challenges in finding a useful set of dense correspondences. Application specific strategies, such as registration of simplified (e.g. inflated or smoothed) surfaces or relying on manually placed landmarks, provide some improvement but suffer from limitations including increased computational complexity and ambiguity in landmark placement. This paper proposes a method for dense point correspondence on shape ensembles using geodesic distances to a priori landmarks as features. A novel set of numerical techniques for fast computation of geodesic distances to point sets is used to extract these features. The proposed method minimizes the ensemble entropy based on these features, resulting in isometry invariant correspondences in a very general, flexible framework.

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