摘要：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.
