Kaus Michael R, Pekar Vladimir, Lorenz Christian, Truyen Roel, Lobregt Steven, Weese Jürgen
Philips Research Laboratories, Sector Technical Systems, Hamburg, Germany.
IEEE Trans Med Imaging. 2003 Aug;22(8):1005-13. doi: 10.1109/TMI.2003.815864.
In recent years, several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a priori knowledge. Examples include principal component analysis of manually or semiautomatically placed corresponding landmarks on the learning shapes [point distribution models (PDMs)], which is time consuming and subjective. However, automatically establishing surface correspondences continues to be a difficult problem. This paper presents a novel method for the automated construction of three-dimensional PDM from segmented images. Corresponding surface landmarks are established by adapting a triangulated learning shape to segmented volumetric images of the remaining shapes. The adaptation is based on a novel deformable model technique. We illustrate our approach using computed tomography data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes.
近年来，已经提出了几种构建统计形状模型的方法，通过提供先验知识来辅助图像分析任务。例如，对学习形状上手动或半自动放置的对应地标进行主成分分析[点分布模型（PDM）]，这既耗时又主观。然而，自动建立表面对应关系仍然是一个难题。本文提出了一种从分割图像自动构建三维PDM的新方法。通过将三角剖分的学习形状适配到其余形状的分割体图像来建立相应的表面地标。这种适配基于一种新颖的可变形模型技术。我们使用椎骨和股骨的计算机断层扫描数据来说明我们的方法。我们证明了我们的方法能够准确地表示和预测形状。
