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Mathematical Problems in Engineering
Volume 2016, Article ID 3909645, 9 pages
Research Article

Unsupervised Joint Image Denoising and Active Contour Segmentation in Multidimensional Feature Space

1College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
3KTH Royal Institute of Technology, 10044 Stockholm, Sweden

Received 28 April 2016; Accepted 26 June 2016

Academic Editor: Giuseppina Colicchio

Copyright © 2016 Qi Ge et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We describe a new method for simultaneous image denoising and level set-based active contour segmentation using multidimensional features. We consider an image to be a surface embedded in a Riemannian manifold. By defining a metric in the embedded space, which in our case includes multidimensional image features as well as a level set-based active contour model, a minimization problem in the image space can be obtained through the Polyakov action framework. The resulting minimization problem is solved with a dual algorithm for efficiency. Benefits of this new method include the fact that it is independent of any artificial “running” parameters, and experiments using both synthetic and real images show that the method is robust with respect to noise and blurry object boundaries.