Statistical 2D and 3D shape analysis using Non-Euclidean Metrics |
Rasmus Larsen, Klaus Baggesen Hilger, Mark Christoph Wrobel
|
| Abstract | We address the problem of extracting meaningful, uncorrelated biological
modes of variation from
tangent space shape coordinates in 2D and 3D using non-Euclidean metrics. We
adapt the maximum autocorrelation factor analysis and the minimum noise
fraction transform to shape decomposition. Furthermore, we study metrics based
on repated annotations of a training set. We define a way of assessing
the correlation between landmarks contrary to landmark coordinates. Finally,
we apply the proposed methods to a 2D data set consisting of outlines of
lungs and a 3D/(4D) data set consisting of sets of
mandible surfaces. In the latter
case the end goal is to construct a model for growth prediction and simulation. |
| Keywords | maximum autocorrelation factors, maximum noise fractions, shape analysis, growth modelling |
| Type | Conference paper [With referee] |
| Conference | Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan |
| Year | 2002 |
| Publisher | Springer |
| Series | Lecture Notes in Computer Science |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics |