Cortex Segmentation: a Fast Variational Geometric Approach
.
Cortex Segmentation: A Fast Variational Geometric Approach.
MedImg, 21(12):1544-1551, 2002
Online Version
A pdf version is available for download.
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.}
}