Fast 3d brain segmentation using dual-front active contours with optional user-interaction

  • Authors:

  • Hua Li;Anthony Yezzi;Laurent D. Cohen

  • Affiliations:

  • School of ECE, Georgia Institute of Technology, Atlanta, GA;School of ECE, Georgia Institute of Technology, Atlanta, GA;CEREMADE, UMR 7534 University Paris-Dauphine, Paris, France

  • Venue:
  • CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
  • Year:
  • 2005

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Abstract

Important attributes of 3D brain segmentation algorithms include robustness, accuracy, computational efficiency, and facilitation of user interaction, yet few algorithms incorporate all of these traits. Manual segmentation is highly accurate but tedious and laborious. Most automatic techniques, while less demanding on the user, are much less accurate. It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result. We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours, which minimize image-based energies in a manner that yields more global minimizers compared to standard active contours. The resulting scheme is not only more robust but much faster and allows the user to guide the final segmentation through simple mouse clicks which add extra seed points. Due to the global nature of the evolution model, single mouse clicks yield corrections to the segmentation that extend far beyond their initial locations, thus minimizing the user effort. Results on 15 simulated and 20 real 3D brain images demonstrate the robustness, accuracy, and speed of our scheme compared with other methods.