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Adaptive Regularization for Image Segmentation Using Local Image Curvature Cues

Summary: Adaptive Regularization for Image Segmentation
Using Local Image Curvature Cues
Josna Rao1
, Rafeef Abugharbieh1
, and Ghassan Hamarneh2
Biomedical Image & Signal Computing Lab,
University of British Columbia, Canada
Medical Image Analysis Lab, Simon Fraser University, Canada
{josnar,rafeef}@ece.ubc.ca, hamarneh@cs.sfu.ca
Abstract. Image segmentation techniques typically require proper
weighting of competing data fidelity and regularization terms. Conven-
tionally, the associated parameters are set through tedious trial and error
procedures and kept constant over the image. However, spatially vary-
ing structural characteristics, such as object curvature, combined with
varying noise and imaging artifacts, significantly complicate the selec-
tion 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.


Source: Abugharbieh, Rafeef - Department of Electrical and Computer Engineering, University of British Columbia


Collections: Biology and Medicine; Computer Technologies and Information Sciences