Abstract:
:We consider 3-D brain structures as continuous parameterized surfaces and present a metric for their comparisons that is invariant to the way they are parameterized. Past comparisons of such surfaces involve either volume deformations or nonrigid matching under fixed parameterizations of surfaces. We propose a new mathematical representation of surfaces, called q-maps, such that L² distances between such maps are invariant to re-parameterizations. This property allows for removing the parameterization variability by optimizing over the re-parameterization group, resulting in a proper parameterization-invariant distance between shapes of surfaces. We demonstrate this method in shape analysis of multiple brain structures, for 34 subjects in the Detroit Fetal Alcohol and Drug Exposure Cohort study, which results in a 91% classification rate for attention deficit hyperactivity disorder cases and controls. This method outperforms some existing techniques such as spherical harmonic point distribution model (SPHARM-PDM) or iterative closest point (ICP).
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Kurtek S,Klassen E,Ding Z,Jacobson SW,Jacobson JL,Avison MJ,Srivastava Adoi
10.1109/TMI.2010.2099130subject
Has Abstractpub_date
2011-03-01 00:00:00pages
849-58issue
3eissn
0278-0062issn
1558-254Xjournal_volume
30pub_type
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