Temporal shape analysis via the spectral signature

Authors:
Elena Bernardis;Ender Konukoglu;Yangming Ou;Dimitris N. Metaxas;Benoit Desjardins;Kilian M. Pohl
Affiliations:
Dept. of Radiology, University of Pennsylvania, Philadelphia, PA;Microsoft Research, Cambridge, UK;Dept. of Radiology, University of Pennsylvania, Philadelphia, PA;Dept. of Computer Science, Rutgers University, Piscataway, NJ;Dept. of Radiology, University of Pennsylvania, Philadelphia, PA;Dept. of Radiology, University of Pennsylvania, Philadelphia, PA
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2012

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Abstract

In this paper, we adapt spectral signatures for capturing morphological changes over time. Advanced techniques for capturing temporal shape changes frequently rely on first registering the sequence of shapes and then analyzing the corresponding set of high dimensional deformation maps. Instead, we propose a simple encoding motivated by the observation that small shape deformations lead to minor refinements in the spectral signature composed of the eigenvalues of the Laplace operator. The proposed encoding does not require registration, since spectral signatures are invariant to pose changes. We apply our representation to the shapes of the ventricles extracted from 22 cine MR scans of healthy controls and Tetralogy of Fallot patients. We then measure the accuracy score of our encoding by training a linear classifier, which outperforms the same classifier based on volumetric measurements.