Analysis of numerical methods for level set based image segmentation

authored by: Björn Scheuermann, Bodo Rosenhahn
Abstract: In this paper we analyze numerical optimization procedures in the context of level set based image segmentation. The Chan-Vese functional for image segmentation is a general and popular variational model. Given the corresponding Euler-Lagrange equation to the Chan-Vese functional the region based segmentation is usually done by solving a differential equation as an initial value problem. While most works use the standard explicit Euler method, we analyze and compare this method with two higher order methods (second and third order Runge-Kutta methods). The segmentation accuracy and the dependence of these methods on the involved parameters are analyzed by numerous experiments on synthetic images as well as on real images. Furthermore, the performance of the approaches is evaluated in a segmentation benchmark containing 1023 images. It turns out, that our proposed higher order methods perform more robustly, more accurately and faster compared to the commonly used Euler method.
Organisation(s): Institute of Information Processing
Type: Conference contribution
Pages: 196-207
No. of pages: 12
Publication date: 2009
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Theoretical Computer Science, General Computer Science
Electronic version(s): https://doi.org/10.1007/978-3-642-10520-3_18 (Access: Unknown)

BibTeX

@inproceedings{9ef485572fd84ed482b461d275567e9a,
title = "Analysis of numerical methods for level set based image segmentation",
abstract = "In this paper we analyze numerical optimization procedures in the context of level set based image segmentation. The Chan-Vese functional for image segmentation is a general and popular variational model. Given the corresponding Euler-Lagrange equation to the Chan-Vese functional the region based segmentation is usually done by solving a differential equation as an initial value problem. While most works use the standard explicit Euler method, we analyze and compare this method with two higher order methods (second and third order Runge-Kutta methods). The segmentation accuracy and the dependence of these methods on the involved parameters are analyzed by numerous experiments on synthetic images as well as on real images. Furthermore, the performance of the approaches is evaluated in a segmentation benchmark containing 1023 images. It turns out, that our proposed higher order methods perform more robustly, more accurately and faster compared to the commonly used Euler method.",
author = "Bj{\"o}rn Scheuermann and Bodo Rosenhahn",
year = "2009",
doi = "10.1007/978-3-642-10520-3_18",
language = "English",
isbn = "364210519X",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "196--207",
booktitle = "Advances in Visual Computing",
edition = "PART 2",
note = "5th International Symposium on Advances in Visual Computing, ISVC 2009 ; Conference date: 30-11-2009 Through 02-12-2009",
}