@CONFERENCE\{IMM2002-0852,
    author       = "R. Larsen and K. B. Hilger and M. C. Wrobel",
    title        = "Statistical {2D} and {3D} shape analysis using Non-Euclidean Metrics",
    year         = "2002",
    keywords     = "maximum autocorrelation factors, maximum noise fractions, shape analysis, growth modelling",
    booktitle    = "Medical Image Computing and Computer-Assisted Intervention - {MICCAI} 2002, 5th Int. Conference, Tokyo, Japan",
    volume       = "",
    series       = "Lecture Notes in Computer Science",
    editor       = "",
    publisher    = "Springer",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/852-full.html",
    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."
}