Adaptive regularization for image segmentation using local image curvature cues

Authors:
Josna Rao;Rafeef Abugharbieh;Ghassan Hamarneh
Affiliations:
Biomedical Image & Signal Computing Lab, University of British Columbia, Canada;Biomedical Image & Signal Computing Lab, University of British Columbia, Canada;Medical Image Analysis Lab, Simon Fraser University, Canada
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Year:
2010

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Abstract

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.