A Visual Analytics Approach to Study Anatomic Covariation

Hermann, M.; Schunke, AC.; Schultz, T.; Klein, R.

doi:10.1109/PacificVis.2014.53

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A Visual Analytics Approach to Study Anatomic Covariation

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Schultz, T.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Hermann, M., Schunke, A., Schultz, T., & Klein, R. (2014). A Visual Analytics Approach to Study Anatomic Covariation. In Proceedings of IEEE Pacific Visualization 2014 (pp. 161-168). IEEE. doi:10.1109/PacificVis.2014.53.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0025-BD41-2

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.