BioVis 2011 Paper
To meet the first objective, we advocate 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. For this purpose, we provide an interactive dynamic visualization of the shape space based on a custom GPU raycaster. A special feature of our implementation is that it builds the SDM directly on dense registrations of the volumes and does thereby not rely on a specific non-rigid registration method.
View Paper in IEEE Digital Library
BioVis 2011 Papers and Abstracts