Sebastian Kurtek1, Eric Klassen2, Anuj Srivastava1, Zhaohua Ding3,4, Sandra W. Jacobson5, Joseph L. Jacobson5, Malcolm J. Avison3,4
1Department of Statistics, Florida State University, Tallahassee, FL, United States; 2Department of Mathematics, Florida State University, Tallahassee, FL, United States; 3Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States; 4Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States; 5Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
Shape analysis of anatomical structures is central to medical diagnosis, especially when using MRI data. We propose a novel Riemannian framework for analyzing shapes of 3D brain substructures (e.g. putamen). This framework provides metrics that are invariant to rigid motion, scaling and most importantly parameterizations of surfaces (placements of meshes). The metric is evaluated by a gradient-based alignment of meshes for the surfaces being compared. Consequently, the distance between identical surfaces with different meshes is zero. We present results of this methodology applied to comparisons of left putamens across subjects and to classification of subjects with prenatal exposure to alcohol.