Cortex Segmentation: a Fast Variational Geometric Approach

Roman Goldenberg, Ron Kimmel, Ehud Rivlin, and Michael Rudzsky.
Cortex Segmentation: A Fast Variational Geometric Approach.
MedImg, 21(12):1544-1551, 2002

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Abstract

An automatic cortical gray matter segmentation from a three-dimensional (3-D) brain images [magnetic resonance (MR) or computed tomography] is a well known problem in medical image processing. In this paper, we first formulate it as a geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is then used to implement the geodesic active surface model. Experimental results of cortex segmentation on real 3-D MR data are provided.

Co-authors

Bibtex Entry

@article{GoldenbergKRR02a,
  title = {Cortex Segmentation: A Fast Variational Geometric Approach},
  author = {Roman Goldenberg and Ron Kimmel and Ehud Rivlin and Michael Rudzsky},
  year = {2002},
  month = {December},
  journal = {MedImg},
  volume = {21},
  number = {12},
  pages = {1544-1551},
  abstract = {An automatic cortical gray matter segmentation from a three-dimensional (3-D) brain images [magnetic resonance (MR) or computed tomography] is a well known problem in medical image processing. In this paper, we first formulate it as a geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is then used to implement the geodesic active surface model. Experimental results of cortex segmentation on real 3-D MR data are provided.}
}