Bogovic John A, Prince Jerry L, Bazin Pierre-Louis
Johns Hopkins University, Baltimore, MD, USA.
Comput Vis Image Underst. 2013 Feb 1;117(2):145-157. doi: 10.1016/j.cviu.2012.10.006.
Deformable models are widely used for image segmentation, most commonly to find single objects within an image. Although several methods have been proposed to segment multiple objects using deformable models, substantial limitations in their utility remain. This paper presents a multiple object segmentation method using a novel and efficient object representation for both two and three dimensions. The new framework guarantees object relationships and topology, prevents overlaps and gaps, enables boundary-specific speeds, and has a computationally efficient evolution scheme that is largely independent of the number of objects. Maintaining object relationships and straightforward use of object-specific and boundary-specific smoothing and advection forces enables the segmentation of objects with multiple compartments, a critical capability in the parcellation of organs in medical imaging. Comparing the new framework with previous approaches shows its superior performance and scalability.
可变形模型被广泛用于图像分割，最常见的是在图像中找到单个物体。尽管已经提出了几种使用可变形模型分割多个物体的方法，但其效用仍存在重大局限性。本文提出了一种用于二维和三维的多物体分割方法，该方法使用了一种新颖且高效的物体表示。新框架保证了物体关系和拓扑结构，防止了重叠和间隙，实现了边界特定速度，并且具有计算效率高的演化方案，该方案在很大程度上与物体数量无关。维持物体关系以及直接使用特定于物体和边界的平滑和对流力能够对具有多个隔室的物体进行分割，这是医学成像中器官分割的一项关键能力。将新框架与先前的方法进行比较显示了其卓越的性能和可扩展性。
