Cobzas Dana, Sen Abhishek
Computing Science, University of Alberta, Canada.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):557-65. doi: 10.1007/978-3-642-23629-7_68.
We introduce a novel discrete optimization method for non-rigid image registration based on the random walker algorithm. We discretize the space of deformations and formulate registration using a Gaussian MRF where continuous labels correspond to the probability of a point having a certain discrete deformation. The interaction (regularization) term of the corresponding MRF energy is convex and image dependent, thus being able to accommodate different types of tissue elasticity. This formulation results in a fast algorithm that can easily accommodate a large number of displacement labels, has provable robustness to noise and a close to global solution. We experimentally demonstrate the validity of our formulation on synthetic and real medical data.
我们基于随机游走算法引入了一种用于非刚性图像配准的新型离散优化方法。我们对变形空间进行离散化,并使用高斯马尔可夫随机场(MRF)来制定配准,其中连续标签对应于一个点具有某种离散变形的概率。相应的MRF能量的相互作用(正则化)项是凸的且依赖于图像,因此能够适应不同类型的组织弹性。这种公式化产生了一种快速算法,该算法能够轻松容纳大量位移标签,对噪声具有可证明的鲁棒性并且接近全局解。我们通过实验证明了我们的公式在合成和真实医学数据上的有效性。