Suetens P, Bellon E, Vandermeulen D, Smet M, Marchal G, Nuyts J, Mortelmans L
Department of Electrical Engineering, ESAT, Heverlee, Belgium.
Eur J Radiol. 1993 Jun;17(1):14-21. doi: 10.1016/0720-048x(93)90023-g.
We review and discuss different classes of image segmentation methods. The usefulness of these methods is illustrated by a number of clinical cases. Segmentation is the process of assigning labels to pixels in 2D images or voxels in 3D images. Typically the effect is that the image is split up into segments, also called regions or areas. In medical imaging it is essential for quantification of outlined structures and for 3D visualization of relevant image data. Based on the level of implemented model knowledge we have classified these methods into (1) manual delineation, (2) low-level segmentation, and (3) model-based segmentation. Pure manual delineation of structures in a series of images is time-consuming and user-dependent and should therefore be restricted to quick experiments. Low-level segmentation analyzes the image locally at each pixel in the image and is practically limited to high-contrast images. Model-based segmentation uses knowledge of object structure such as global shape or semantic context. It typically requires an initialization, for example in the form of a rough approximation of the contour to be found. In practice it turns out that the use of high-level knowledge, e.g. anatomical knowledge, in the segmentation algorithm is quite complicated. Generally, the number of clinical applications decreases with the level and extent of prior knowledge needed by the segmentation algorithm. Most problems of segmentation inaccuracies can be overcome by human interaction. Promising segmentation methods for complex images are therefore user-guided and thus semi-automatic. They require manual intervention and guidance and consist of fast and accurate refinement techniques to assist the human operator.
我们回顾并讨论了不同类别的图像分割方法。一些临床病例说明了这些方法的实用性。分割是为二维图像中的像素或三维图像中的体素分配标签的过程。通常，其效果是将图像分割成片段，也称为区域或面积。在医学成像中，分割对于勾勒结构的量化以及相关图像数据的三维可视化至关重要。基于所实现的模型知识水平，我们将这些方法分为：（1）手动勾勒；（2）低级分割；（3）基于模型的分割。在一系列图像中纯手动勾勒结构既耗时又依赖用户，因此应仅限于快速实验。低级分割在图像的每个像素处进行局部分析，实际上仅限于高对比度图像。基于模型的分割使用对象结构的知识，如全局形状或语义上下文。它通常需要初始化，例如以要找到的轮廓的粗略近似形式。实际上，在分割算法中使用高级知识，例如解剖学知识，是相当复杂的。一般来说，分割算法所需的先验知识的水平和范围越高，临床应用的数量就越少。分割不准确的大多数问题可以通过人工交互来克服。因此，对于复杂图像，有前景的分割方法是用户引导的，即半自动的。它们需要人工干预和指导，并由快速准确的细化技术组成，以协助人工操作员。
