Morphometric Visualisation

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Semantically steered visual analysis of highly detailed morphometric shape spaces

This SDM is based on dense registrations of the input shapes. For a valuable exploration of the shape space in the setting of biological morphometrics two prominent objectives for visual investigation have been identified. The first objective is to detect possible shape variations between anatomically different groups of individuals. The second is to integrate and exploit expert knowledge about relevant regions on the shapes. The first objective can be achieved through the use of dimensionality reduction methods combined with a parameterization defined on user specified classifications. This idea was already successfully applied in data-driven reflectance models and also turns out to be valuable in the context of biological morphometry, as it allows for intuitive exploration of shape variations. The second objective can be achieved by an appropriate weighted linear analysis which delivers a better approximation of shape variations in local neighbourhoods of a user defined region of interest. The methods were applied to real-world biological datasets of rodent mandibles and validated in cooperation with the MPI for Evolutionary Biology. This interactive dynamic visualization of the shape space is based on a custom GPU raycaster.

Release Date:
Data type:
3D, Spatial representation
PCA technique, direct volume rendering techniques

Project development

Institution: Institute of Computer Science II University of Bonn, Max-Planck-Institute of Evolutionary Biology Plön

This work was supported in part by NRW State within the B-IT Research School.