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International Journal of Biomedical Imaging
Volume 2006 (2006), Article ID 86747, 6 pages

Brain Structure Segmentation from MRI by Geometric Surface Flow

1Department of Computer Science, College of Engineering, University of Missouri-Columbia, Columbia 65211-2060, USA
2Department of Computer Science, College of Liberal Arts and Sciences, Wayne State University, Detroit 48202, USA

Received 1 August 2005; Revised 22 September 2005; Accepted 28 September 2005

Academic Editor: Ming Jiang

Copyright © 2006 Greg Heckenberg et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present a method for semiautomatic segmentation of brain structures such as thalamus from MRI images based on the concept of geometric surface flow. Given an MRI image, the user can interactively initialize a seed model within region of interest. The model will then start to evolve by incorporating both boundary and region information following the principle of variational analysis. The deformation will stop when an equilibrium state is achieved. To overcome the low contrast of the original image data, a nonparametric kernel-based method is applied to simultaneously update the interior probability distribution during the model evolution. Our experiments on both 2D and 3D image data demonstrate that the new method is robust to image noise and inhomogeneity and will not leak from spurious edge gaps.