Qian Zhen, Metaxas Dimitris N, Axel Leon
Center for Computational Biomedicine Imaging and Modeling, Rutgers University, New Brunswick, New Jersey, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):636-44. doi: 10.1007/11866565_78.
In this paper we present an accurate cardiac boundary tracking method for 2D tagged MRI time sequences. This method naturally integrates the motion and the static local appearance features and generates accurate boundary criteria via a boosting approach. We extend the conventional Adaboost classifier into a posterior probability form, which can be embedded in a particle filtering-based shape tracking framework. To make the tracking process more robust and faster, we use a PCA subspace shape representation to constrain the shape variation and lower the dimensionality. We also learn two shape-dynamic models for systole and diastole separately, to predict the shape evolution. Our tracking method incorporates the static appearance, the motion appearance, the shape constraints, and the dynamic prediction in a unified way. The proposed method has been implemented on 50 tagged MRI sequences. The experimental results show the accuracy and robustness of our approach.
在本文中，我们提出了一种用于二维标记磁共振成像（MRI）时间序列的精确心脏边界跟踪方法。该方法自然地整合了运动和静态局部外观特征，并通过增强方法生成精确的边界准则。我们将传统的Adaboost分类器扩展为后验概率形式，其可嵌入基于粒子滤波的形状跟踪框架中。为使跟踪过程更稳健、更快，我们使用主成分分析（PCA）子空间形状表示来约束形状变化并降低维度。我们还分别学习了收缩期和舒张期的两个形状动态模型，以预测形状演变。我们的跟踪方法以统一方式整合了静态外观、运动外观、形状约束和动态预测。所提出的方法已在50个标记MRI序列上实现。实验结果表明了我们方法的准确性和稳健性。
