Jiang Yifeng, Xie Jun, Sun Deqing, Tsui Hungtat
Department of Electronic Engineering, The Chinese University of Hong Kong.
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):809-17. doi: 10.1007/978-3-540-75759-7_98.
This paper proposes a novel approach that achieves shape registration by optimizing shape representation and transformation simultaneously, which are modeled by a constrained Gaussian Mixture Model (GMM) and a regularized thin plate spline respectively. The problem is formulated within a Bayesian framework and solved by an expectation-maximum (EM) algorithm. Compared with the popular methods based on landmarks-sliding, its advantages include: (1) It can naturally deal with shapes of complex topologies and 3D dimension; (2) It is more robust against data noise; (3) The registration performance is better in terms of the generalization error of the resultant statistical shape model. These are demonstrated on both synthetic and biomedical shapes.
本文提出了一种新颖的方法，该方法通过同时优化形状表示和变换来实现形状配准，形状表示和变换分别由约束高斯混合模型（GMM）和正则化薄板样条进行建模。该问题在贝叶斯框架内进行公式化，并通过期望最大化（EM）算法求解。与基于地标滑动的流行方法相比，其优点包括：（1）它可以自然地处理复杂拓扑和三维尺寸的形状；（2）对数据噪声具有更强的鲁棒性；（3）就所得统计形状模型的泛化误差而言，配准性能更好。这些在合成形状和生物医学形状上均得到了验证。
