title: Brain structure segmentation from MRI by geometric surface flow.
abstract: 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.
authors.affiliation: Department of Computer Science, College of Engineering, University of Missouri-Columbia, Columbia 65211-2060, USA.
authors.forename: Greg
authors.initials: G
authors.lastname: Heckenberg
authors.forename: Yongjian
authors.initials: Y
authors.lastname: Xi
authors.forename: Ye
authors.initials: Y
authors.lastname: Duan
authors.forename: Jing
authors.initials: J
authors.lastname: Hua