Towards semantically steered navigation in shape spaces exemplified by rodent skull morphology in correlation to external attributes

Applicant:

Prof. Dr. Reinhard Klein, Bonn
Rheinische Friedrich-Wilhelms-Universität Bonn
Institut für Informatik
Bonn

Dr. Anja Schunke, Plön
Max-Planck-Institut für Evolutionsbiologie
Plön

Project:

Towards semantically steered navigation in shape spaces exemplified by rodent skull morphology in correlation to external attributes (Publications)

Summary:

A major task of morphometrics in biology is to discover correlations between extrinsic attributes like genetic, geographic and ecological data and the specific shape of a class of organisms. This requires techniques for mapping data analysis results from attribute space to shape space and vice versa allowing the user to refine the data analysis in shape space as well as in attribute space within an interactive feedback loop. This project builds on an already existing research cooperation between Computer Science and Biology in which we established a linear shape space for CT data of rodent mandibles. Together with state-of-the-art volume visualization techniques the mapping of analysis results in attribute space to shape space allows the interactive navigation along meaningful directions (traits) in shape space. In this project we want to extend this research towards four aspects. First of all shape variations detected using the navigation techniques of the current system will be quantified for the purpose of genetic mapping enabling correlation analysis of shape and genetic factors. Second we aim for a hierarchical shape model that allows to separate coarse and fine scale shape variations. Third, to enable the user to incorporate human knowledge into the analysis process we plan to integrate interactive deformation techniques of individual shapes. This will allow navigating along corresponding directions in shape space and using the results to refine the analysis in attribute space. In addition, the shape space can be extended purposefully by the edited shape. Last but not least we plan to enhance the shape space analysis from rigid to deformable shapes. For this aim we include rodent tails as additional important data source for correlation analysis and plan to apply spectral embedding methods to come up with an isometric invariant shape representation.