McInerney T, Terzopoulos D
Department of Computer Science, University of Toronto, ON, Canada.
Med Image Anal. 1996 Jun;1(2):91-108. doi: 10.1016/s1361-8415(96)80007-7.
This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics and approximation theory. They have proven to be effective in segmenting, matching and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching and motion tracking.
本文综述了可变形模型,这是一种很有前景且得到大力研究的计算机辅助医学图像分析技术。在基于模型的技术中,可变形模型为图像分析提供了一种独特且强大的方法,它将几何、物理和逼近理论结合在一起。事实证明,通过利用从图像数据中得出的(自下而上的)约束以及关于这些结构的位置、大小和形状的(自上而下的)先验知识,它们在分割、匹配和跟踪解剖结构方面是有效的。可变形模型能够适应生物结构随时间和不同个体的显著变化。此外,它们支持高度直观的交互机制,必要时,这些机制允许医学科学家和从业者将他们的专业知识应用于基于模型的图像解释任务。本文回顾了关于可变形模型的开发及其在医学图像分析中具有根本重要性的问题(包括分割、形状表示、匹配和运动跟踪)上的应用的迅速扩展的工作成果。