Adaptive Contextual Energy Parameterization for Automated Image Segmentation

  • Josna Rao
  • Ghassan Hamarneh
  • Rafeef Abugharbieh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5875)


Image segmentation techniques are predominately based on parameter-laden optimization processes. The segmentation objective function traditionally involves parameters (i.e. weights) that need to be tuned in order to balance the underlying competing cost terms of image data fidelity and contour regularization. In this paper, we propose a novel approach for automatic adaptive energy parameterization. In particular, our contributions are three-fold; 1) We spatially adapt fidelity and regularization weights to local image content in an autonomous manner. 2) We modulate the weight using a novel contextual measure of image quality based on the concept of spectral flatness. 3) We incorporate our proposed parameterization into a general segmentation framework and demonstrate its superiority to two alternative approaches: the best possible spatially-fixed parameterization and the globally optimal spatially-varying, but non- contextual, parameters. Our segmentation results are evaluated on real and synthetic data and produce a reduction in mean segmentation error when compared to alternative approaches.


Adaptive regularization contextual weights image segmentation energy minimization adapting energy functional spectral flatness noise estimation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Josna Rao
    • 1
  • Ghassan Hamarneh
    • 2
  • Rafeef Abugharbieh
    • 1
  1. 1.Biomedical Signal and Image Computing LabUniversity of British ColumbiaCanada
  2. 2.Medical Image Analysis LabSimon Fraser UniversityCanada

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