Niessen W J, ter Haar Romeny B M, Viergever M A
Image Sciences Institute, University Hospital Utrecht, The Netherlands.
IEEE Trans Med Imaging. 1998 Aug;17(4):634-41. doi: 10.1109/42.730407.
In this paper implicit representations of deformable models for medical image enhancement and segmentation are considered. The advantage of implicit models over classical explicit models is that their topology can be naturally adapted to objects in the scene. A geodesic formulation of implicit deformable models is especially attractive since it has the energy minimizing properties of classical models. The aim of this paper is twofold. First, a modification to the customary geodesic deformable model approach is introduced by considering all the level sets in the image as energy minimizing contours. This approach is used to segment multiple objects simultaneously and for enhancing and segmenting cardiac computed tomography (CT) and magnetic resonance images. Second, the approach is used to effectively compare implicit and explicit models for specific tasks. This shows the complementary character of implicit models since in case of poor contrast boundaries or gaps in boundaries e.g. due to partial volume effects, noise, or motion artifacts, they do not perform well, since the approach is completely data-driven.
本文考虑了用于医学图像增强和分割的可变形模型的隐式表示。隐式模型相对于经典显式模型的优势在于其拓扑结构能够自然地适应场景中的物体。隐式可变形模型的测地线公式特别具有吸引力，因为它具有经典模型的能量最小化特性。本文的目的有两个。首先，通过将图像中的所有水平集视为能量最小化轮廓，对传统的测地线可变形模型方法进行了修改。该方法用于同时分割多个物体，并用于增强和分割心脏计算机断层扫描（CT）和磁共振图像。其次，该方法用于针对特定任务有效比较隐式模型和显式模型。这显示了隐式模型的互补特性，因为在对比度边界较差或边界存在间隙（例如由于部分容积效应、噪声或运动伪影）的情况下，它们表现不佳，因为该方法完全由数据驱动。
