Affine invariant surface evolutions for 3D image segmentation

Yogesh Rathi; Peter Olver; Guillermo Sapiro; Allen Tannenbaum

doi:10.1117/12.640282

16 February 2006 Affine invariant surface evolutions for 3D image segmentation

Yogesh Rathi, Peter Olver, Guillermo Sapiro, Allen Tannenbaum

Proceedings Volume 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning; 606401 (2006) https://doi.org/10.1117/12.640282
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

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.

Citation Download 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 (16 February 2006); https://doi.org/10.1117/12.640282

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