Affine invariant surface evolutions for 3D image segmentation

Yogesh Rathi, Peter Olver, Guillermo Sapiro, Allen Tannenbaum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationImage Processing
Subtitle of host publicationAlgorithms and Systems, Neural Networks, and Machine Learning - Proceedings of SPIE-IS and T Electronic Imaging
DOIs
StatePublished - 2006
EventImage Processing: Algorithms and Systems, Neural Networks, and Machine Learning - San Jose, CA, United States
Duration: Jan 16 2006Jan 18 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6064
ISSN (Print)0277-786X

Conference

ConferenceImage Processing: Algorithms and Systems, Neural Networks, and Machine Learning
CountryUnited States
CitySan Jose, CA
Period1/16/061/18/06

Keywords

  • 3D Surface Evolution
  • Geometric Active Contours
  • Level Sets
  • Segmentation

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