Adaptive Energy Functionals for Image Segmentation
Speaker: Ghassan Hamarneh , Simon Fraser University
Date: May 9 2008
Time: 2:00PM to 3:00PM
Location: 32-D507
Host: Polina Golland, CSAIL
Contact: Polina Golland, x38005, polina@csail.mit.edu
Relevant URL: Energy functional minimization is an increasingly popular technique
for image segmentation. However, it is far too commonly applied with
hand-tuned parameters and initializations that have only been
validated for a few images. Fixing these parameters over a set of
images assumes the same parameters are ideal for each image. We
highlight the effects of varying the parameters and initialization
on segmentation accuracy and propose a framework for attaining
improved results using image adaptive parameters and
initializations. We provide an analytical definition of optimal
weights for functional terms through an examination of segmentation
in the context of image manifolds, where nearby images on the
manifold require similar parameters and similar initializations. Our
results validate that fixed parameters are insufficient in
addressing the variability in real clinical data, that similar
images require similar parameters, and demonstrate how these
parameters correlate with the image manifold. We present
significantly improved segmentations for synthetic images and a set
of 470 clinical examples.
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