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Abstract #1431

Improving Diffusion Tensor Imaging Segmentation Through an Adaptive Distance Learning Scheme

Paulo Reis Rodrigues1, Anna Vilanova1, Thorsten Twellmann2, Bart M. ter Haar Romeny1

1Biomedical Image Analysis, Technical University of Eindhoven, Eindhoven, Noord Brabant, Netherlands; 2MeVis Medical Solutions AG, Bremen, Germany

Similarity of diffusion tensors is a crucial point in several applications like segmentation, group statistical analysis, etc. The selection of the most suitable measure, for a given task, is not always clear and often done by trial and error. We present a proof of concept of an initially flexible learning scheme that infers the measure or combination of measures that achieves the best discrimination between a user selected Region of Interest and a representative set of tensors of the whole volume. The results demonstrate the methods potential to infer the ideal parameters for a task specific segmentation algorithm, region growing.