Adaptive Regularization for Image Segmentation Using Local Image Curvature Cues

  • Josna Rao
  • Rafeef Abugharbieh
  • Ghassan Hamarneh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)


Image segmentation techniques typically require proper weighting of competing data fidelity and regularization terms. Conventionally, the associated parameters are set through tedious trial and error procedures and kept constant over the image. However, spatially varying structural characteristics, such as object curvature, combined with varying noise and imaging artifacts, significantly complicate the selection process of segmentation parameters. In this work, we propose a novel approach for automating the parameter selection by employing a robust structural cue to prevent excessive regularization of trusted (i.e. low noise) high curvature image regions. Our approach autonomously adapts local regularization weights by combining local measures of image curvature and edge evidence that are gated by a signal reliability measure. We demonstrate the utility and favorable performance of our approach within two major segmentation frameworks, graph cuts and active contours, and present quantitative and qualitative results on a variety of natural and medical images.


Active Contour Adaptive Weight Image Segmentation Technique Texture Edge Regularization Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

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