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Diffeomorphic Point Set Registration using Non-stationary Mixture Models

1Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston MA, USA.
2Laboratory of Mathematical Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
ISBI 2013
Publication Date:
Proc IEEE Int Symp Biomed Imaging.
Proc IEEE Int Symp Biomed Imaging. 2013 Apr;
PubMed ID:
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
R01 HL116931/HL/NHLBI NIH HHS/United States
R01 MH092862/MH/NIMH NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
K25 HL104085/HL/NHLBI NIH HHS/United States
K23 HL089353/HL/NHLBI NIH HHS/United States
Generated Citation:
Wassermann D., Ross J., Washko G., Westin C-F., San Jose Estepar R. Diffeomorphic Point Set Registration using Non-stationary Mixture Models. Proc IEEE Int Symp Biomed Imaging. 2013 Apr; PMID: 24419463. PMCID: PMC3886289.
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This paper investigates a diffeomorphic point-set registration based on non-stationary mixture models. The goal is to improve the non-linear registration of anatomical structures by representing each point as a general non-stationary kernel that provides information about the shape of that point. Our framework generalizes work done by others that use stationary models. We achieve this by integrating the shape at each point when calculating the point-set similarity and transforming it according to the calculated deformation. We also restrict the non-rigid transform to the space of symmetric diffeomorphisms. Our algorithm is validated in synthetic and human datasets in two different applications: fiber bundle and lung airways registration. Our results shows that non-stationary mixture models are superior to Gaussian mixture models and methods that do not take into account the shape of each point.

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