Anatomic Segmentation of Statistical Shape Models

Max Hermann, Anja C. Schunke und Reinhard Klein
Poster at Symposium on Statistical Shape Models & Applications (SHAPE2014) in Delémont, Switzerland, Juni 2014
 

Abstract

Shape segmentation is one of the fundamental tools in shape processing and provides the starting point for building part based shape models. A multitude of methods for segmenting static and dynamic shapes has been developed over the last decades based on various intrinsic and extrinsic geometric features. Interestingly, so far no method considering information from an available statistical shape model seems to exist. In this work a segmentation based on statistical covariance analysis is derived. Its results show meaningful parts in the sense that all points in a part share similar (co-)variability, i.e. behave similar according to the model, while different parts show distinct variation patterns. We show initial results in two application domains where this is a desired property: In morphometrics independent modules are sought relating to an underlying independent evolutionary development; in motion analysis correlated movement is relevant for motion understanding and compression.

SHAPE 2014 Best poster award

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Bibtex

@MISC{hermann2014-shape,
        author = {Hermann, Max and Schunke, Anja C. and Klein, Reinhard},
         title = {Anatomic Segmentation of Statistical Shape Models},
          year = {2014},
         month = jun,
  howpublished = {Poster at Symposium on Statistical Shape Models {\&} Applications (SHAPE2014) in Del{\'e}mont, Switzerland},
          note = {Best poster award},
      abstract = {Shape segmentation is one of the fundamental tools in shape processing and provides the starting
                  point for building part based shape models. A multitude of methods for segmenting static and dynamic
                  shapes has been developed over the last decades based on various intrinsic and extrinsic geometric
                  features. Interestingly, so far no method considering information from an available statistical
                  shape model seems to exist. In this work a segmentation based on statistical covariance analysis is
                  derived. Its results show meaningful parts in the sense that all points in a part share similar
                  (co-)variability, i.e. behave similar according to the model, while different parts show distinct
                  variation patterns. We show initial results in two application domains where this is a desired
                  property: In morphometrics independent modules are sought relating to an underlying independent
                  evolutionary development; in motion analysis correlated movement is relevant for motion
                  understanding and compression.}
}