A Visual Analytics Approach to Study Anatomic Covariation

Gaining insight into anatomic co variation helps the understanding of organismic shape variability in general and is of particular interest for delimiting morphological modules. Generation of hypotheses on structural co variation 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 co variation at different levels of detail, stimulating rapid formation and (qualitative) evaluation of hypotheses. The effectiveness of the presented approach is demonstrated on a μCT dataset of mouse mandibles for which results from the literature are successfully reproduced, while providing a more detailed representation of co variation compared to state-of-the-art methods.