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Proceedings Paper

Affine invariant surface evolutions for 3D image segmentation
Author(s): Yogesh Rathi; Peter Olver; Guillermo Sapiro; Allen Tannenbaum
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Paper 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.

Paper Details

Date Published: 16 February 2006
PDF: 5 pages
Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 606401 (16 February 2006); doi: 10.1117/12.640282
Show Author Affiliations
Yogesh Rathi, Georgia Institute of Technology (United States)
Peter Olver, Univ. of Minnesota (United States)
Guillermo Sapiro, Univ. of Minnesota (United States)
Allen Tannenbaum, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6064:
Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
Nasser M. Nasrabadi; Edward R. Dougherty; Jaakko T. Astola; Syed A. Rizvi; Karen O. Egiazarian, Editor(s)

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