Nanyang Technological University, Singapore, Singapore.
Comput Med Imaging Graph. 2010 Jul;34(5):354-61. doi: 10.1016/j.compmedimag.2009.12.006. Epub 2010 Jan 18.
A new joint parametric and nonparametric curve evolution algorithm is proposed for medical image segmentation. In this algorithm, both the nonlinear space of level set function (nonparametric model) and the linear subspace of level set function spanned by the principle components (parametric model) are employed in the evolution procedure. The nonparametric curve evolution can drive the curve precisely to object boundaries while the parametric model acts as a statistical constraint based on the Bayesian framework in order to match object shape more robustly. As a result, our new algorithm is as robust as the parametric curve evolution algorithms and at the same time, yields more accurate segmentation results by using the shape prior information. Comparative results on segmenting ventricle frontal horns and putamen shapes in MR brain images confirm the advantages of the proposed joint curve evolution algorithm.
提出了一种新的用于医学图像分割的联合参数和非参数曲线演化算法。在该算法中，水平集函数的非线性空间（非参数模型）和由主分量张成的水平集函数的线性子空间（参数模型）都被用于演化过程中。非参数曲线演化可以精确地将曲线驱动到目标边界，而参数模型则作为基于贝叶斯框架的统计约束，以更稳健地匹配目标形状。因此，我们的新算法与参数曲线演化算法一样稳健，同时通过使用形状先验信息，获得更准确的分割结果。在 MR 脑图像中分割脑室额角和豆状核形状的比较结果证实了所提出的联合曲线演化算法的优势。
