Department of Radiology, Harvard Medical School, Boston, MA 02115, USA.
J Med Syst. 2012 Oct;36(5):2829-39. doi: 10.1007/s10916-011-9761-7. Epub 2011 Aug 9.
The basic principle of graph-based approaches for image segmentation is to interpret an image as a graph, where the nodes of the graph represent 2D pixels or 3D voxels of the image. The weighted edges of the graph are obtained by intensity differences in the image. Once the graph is constructed, the minimal cost closed set on the graph can be computed via a polynomial time s-t cut, dividing the graph into two parts: the object and the background. However, no segmentation method provides perfect results, so additional manual editing is required, especially in the sensitive field of medical image processing. In this study, we present a manual refinement method that takes advantage of the basic design of graph-based image segmentation algorithms. Our approach restricts a graph-cut by using additional user-defined seed points to set up fixed nodes in the graph. The advantage is that manual edits can be integrated intuitively and quickly into the segmentation result of a graph-based approach. The method can be applied to both 2D and 3D objects that have to be segmented. Experimental results for synthetic and real images are presented to demonstrate the feasibility of our approach.
基于图的图像分割方法的基本原理是将图像解释为一个图，其中图的节点表示图像的 2D 像素或 3D 体素。图的加权边由图像中的强度差异获得。一旦构建了图，就可以通过多项式时间的 s-t 割计算图上的最小代价闭集，将图分为两部分：目标和背景。然而，没有一种分割方法能提供完美的结果，因此需要进行额外的手动编辑，特别是在医学图像处理这一敏感领域。在本研究中，我们提出了一种手动细化方法，利用基于图的图像分割算法的基本设计。我们的方法通过使用附加的用户定义的种子点来限制图割，从而在图中设置固定节点。其优点是可以直观、快速地将手动编辑集成到基于图的方法的分割结果中。该方法可应用于需要分割的二维和三维物体。通过合成和真实图像的实验结果证明了我们方法的可行性。
