A Statistically Based Surface Evolution Method for Medical Image Segmentation: Presentation and Validation

Abstract

In this paper we present a new algorithm for 3D medical image segmentation. The algorithm is fast, relatively simple to implement, and semi-automatic. It is based on minimizing a global energy defined from a learned non-parametric estimation of the statistics of the region to be segmented. Implementation details are discussed and source code is freely available as part of the 3D Slicer project. In addition, a new unified set of validation metrics is proposed. Results on artificial and real MRI images show that the algorithm performs well on large brain structures both in terms of accuracy and robustness to noise.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA465665

Entities

People

  • Allen Tannenbaum
  • Eric Pichon
  • Ron Kikinis

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Boundaries
  • Brain
  • Computer Vision
  • Computers
  • Errors
  • Images
  • Personal Information Managers
  • Probability
  • Probability Density Functions
  • Random Variables
  • Segmented
  • Standards
  • Validation
  • Ventricles
  • Weighting Functions

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Image Processing and Computer Vision.
  • Software Engineering