Paper
26 February 2008 Fast approximate curve evolution
James Malcolm, Yogesh Rathi, Anthony Yezzi, Allen Tannenbaum
Author Affiliations +
Proceedings Volume 6811, Real-Time Image Processing 2008; 68110L (2008) https://doi.org/10.1117/12.768546
Event: Electronic Imaging, 2008, San Jose, California, United States
Abstract
The level set method for curve evolution is a popular technique used in image processing applications. However, the numerics involved make its use in high performance systems computationally prohibitive. This paper proposes an approximate level set scheme that removes much of the computational burden while maintaining accuracy. Abandoning a floating point representation for the signed distance function, we use the integral values to represent the interior, zero level set, and exterior. We detail rules governing the evolution and maintenance of these three regions. Arbitrary energies can be implemented with the definition of three operations: initialize iteration, move points in, move points out. This scheme has several nice properties. First, computations are only performed along the zero level set. Second, this approximate distance function representation requires only a few simple integer comparisons for maintenance. Third, smoothness regularization involves only a few integer calculations and may be handled apart from the energy itself. Fourth, the zero level set is represented exactly removing the need for interpolation off the interface. Lastly, evolution proceeds on the order of milliseconds per iteration using conventional uniprocessor workstations. To highlight its accuracy, flexibility and speed, we demonstrate the technique on standard intensity tracking and stand alone segmentation.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James Malcolm, Yogesh Rathi, Anthony Yezzi, and Allen Tannenbaum "Fast approximate curve evolution", Proc. SPIE 6811, Real-Time Image Processing 2008, 68110L (26 February 2008); https://doi.org/10.1117/12.768546
Lens.org Logo
CITATIONS
Cited by 1 patent.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Interfaces

Image segmentation

Gaussian filters

Image processing

Computing systems

Mechanics

Binary data

Back to Top