By Topic

Relaxation methods for supervised image segmentation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Hansen, M.W. ; David Sarnoff Res. Center, Princeton, NJ, USA ; Higgins, W.E.

We propose two methods for supervised image segmentation: supervised relaxation labelling and watershed-driven relaxation labelling. The methods are particularly well suited to problems in 3D medical image analysis, where the images are large, the regions are topologically complex, and the tolerance of errors is low. Each method uses predefined cues for supervision. The cues can be defined interactively or automatically, depending on the application. The cues provide statistical region information and region topological constraints. Supervised relaxation labeling exhibits strong noise resilience. Watershed-driven relaxation labeling combines the strengths of watershed analysis and supervised relaxation labeling to give a computationally efficient noise-resistant method. Extensive results for 2D and 3D images illustrate the effectiveness of the methods

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:19 ,  Issue: 9 )