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Proceedings Article

Affine invariant surface evolutions for 3D image segmentation

[+] Author Affiliations
Yogesh Rathi, Allen Tannenbaum

Georgia Institute of Technology

Peter Olver, Guillermo Sapiro

Univ. of Minnesota

Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 606401 (February 16, 2006); doi:10.1117/12.640282
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From Conference Volume 6064

  • Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
  • Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian; Nasser M. Nasrabadi; Syed A. Rizvi
  • San Jose, CA | January 15, 2006

abstract

In this paper we present an algorithm for 3D medical image segmentation based on an affine invariant flow. The algorithm is simple to implement and semi-automatic. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The surface flow is obtained by minimizing a global energy with respect to an affine invariant metric. Affine invariant edge detectors for 3-dimensional objects are also computed which have the same qualitative behavior as the Euclidean edge detectors. Results on artificial and real MRI images show that the algorithm performs well, both in terms of accuracy and robustness to noise.

© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Yogesh Rathi ; Peter Olver ; Guillermo Sapiro and Allen Tannenbaum
"Affine invariant surface evolutions for 3D image segmentation", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 606401 (February 16, 2006); doi:10.1117/12.640282; http://dx.doi.org/10.1117/12.640282


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