A Visual Analytics Approach to Study Anatomic Covariation

Max Hermann, Anja C. Schunke, Thomas Schultz, and Reinhard Klein
In proceedings of IEEE PacificVis 2014, Mar. 2014
 

Abstract

Gaining insight into anatomic covariation helps the understanding of organismic shape variability in general and is of particular interest for delimiting morphological modules. Generation of hypotheses on structural covariation is undoubtedly a highly creative process, and as such, requires an exploratory approach. In this work we propose a new local anatomic covariance tensor which enables interactive visualizations to explore covariation at different levels of detail, stimulating rapid formation and (qualitative) evaluation of hypotheses. The effectiveness of the presented approach is demonstrated on a muCT dataset of mouse mandibles for which results from the literature are successfully reproduced, while providing a more detailed representation of covariation compared to state-of-the-art methods.

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Bibtex

@INPROCEEDINGS{hermann2014-covariance,
     author = {Hermann, Max and Schunke, Anja C. and Schultz, Thomas and Klein, Reinhard},
      title = {A Visual Analytics Approach to Study Anatomic Covariation},
  booktitle = {IEEE PacificVis 2014},
       year = {2014},
      month = mar,
   abstract = {Gaining insight into anatomic covariation helps the understanding of organismic shape variability in
               general and is of particular interest for delimiting morphological modules. Generation of hypotheses
               on structural covariation is undoubtedly a highly creative process, and as such, requires an
               exploratory approach. In this work we propose a new local anatomic covariance tensor which enables
               interactive visualizations to explore covariation at different levels of detail, stimulating rapid
               formation and (qualitative) evaluation of hypotheses. The effectiveness of the presented approach is
               demonstrated on a muCT dataset of mouse mandibles for which results from the literature are
               successfully reproduced, while providing a more detailed representation of covariation compared to
               state-of-the-art methods.}
}