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MICCAI
2007
Springer

Deformable Density Matching for 3D Non-rigid Registration of Shapes

9 years 9 months ago
Deformable Density Matching for 3D Non-rigid Registration of Shapes
There exists a large body of literature on shape matching and registration in medical image analysis. However, most of the previous work is focused on matching particular sets of features--point-sets, lines, curves and surfaces. In this work, we forsake specific geometric shape representations and instead seek probabilistic representations-specifically Gaussian mixture models--of shapes. We evaluate a closedform distance between two probabilistic shape representations for the general case where the mixture models differ in variance and the number of components. We then cast non-rigid registration as a deformable density matching problem. In our approach, we take one mixture density onto another by deforming the component centroids via a thin-plate spline (TPS) and also minimizing the distance with respect to the variance parameters. We validate our approach on synthetic and 3D arterial tree data and evaluate it on 3D hippocampal shapes.
Arunabha S. Roy, Ajay Gopinath, Anand Rangarajan
Added 14 Nov 2009
Updated 14 Nov 2009
Type Conference
Year 2007
Where MICCAI
Authors Arunabha S. Roy, Ajay Gopinath, Anand Rangarajan
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