@inproceedings{71f7ed153dca4f3888bf683f2f6183ad,
title = "A fast projection method for connectivity constraints in image segmentation",
abstract = "We propose to solve an image segmentation problem with connectivity constraints via projection onto the constraint set. The constraints form a convex set and the convex image segmentation problem with a total variation regularizer can be solved to global optimality in a primal-dual framework. Efficiency is achieved by directly computing the update of the primal variable via a projection onto the constraint set, which results in a special quadratic programming problem similar to the problems studied as isotonic regression methods in statistics, which can be solved with O(n log n) complexity. We show that especially for segmentation problems with long range connections this method is by orders of magnitudes more efficient, both in iteration number and runtime, than solving the dual of the constrained optimization problem. Experiments validate the usefulness of connectivity constraints for segmenting thin structures such as veins and arteries in medical image analysis.",
author = "Jan St{\"u}hmer and Daniel Cremers",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2015 ; Conference date: 13-01-2015 Through 16-01-2015",
year = "2015",
doi = "10.1007/978-3-319-14612-6_14",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "183--196",
editor = "Xue-Cheng Tai and Egil Bae and Chan, {Tony F.} and Marius Lysaker",
booktitle = "Energy Minimization Methods in Computer Vision and Pattern Recognition - 10th International Conference,EMMCVPR 2015, Proceedings",
}