Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
Sci Rep. 2012;2:420. doi: 10.1038/srep00420. Epub 2012 May 24.
We present a scale-invariant, template-based segmentation paradigm that sets up a graph and performs a graph cut to separate an object from the background. Typically graph-based schemes distribute the nodes of the graph uniformly and equidistantly on the image, and use a regularizer to bias the cut towards a particular shape. The strategy of uniform and equidistant nodes does not allow the cut to prefer more complex structures, especially when areas of the object are indistinguishable from the background. We propose a solution by introducing the concept of a "template shape" of the target object in which the nodes are sampled non-uniformly and non-equidistantly on the image. We evaluate it on 2D-images where the object's textures and backgrounds are similar, and large areas of the object have the same gray level appearance as the background. We also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning purposes.
我们提出了一种基于不变量和模板的分割范例，它构建了一个图并执行图割操作，以将目标从背景中分离出来。通常，基于图的方案将图的节点均匀且等距地分布在图像上，并使用正则化项将分割偏向于特定形状。均匀等距节点的策略不允许分割偏向更复杂的结构，特别是当目标区域与背景难以区分时。我们通过引入目标对象的“模板形状”的概念来解决这个问题，其中节点在图像上是非均匀和非等距地采样的。我们在二维图像上对其进行了评估，其中目标的纹理和背景相似，并且目标的大部分区域与背景具有相同的灰度外观。我们还在 3D 上对 60 个用于神经外科规划目的的脑肿瘤数据集进行了评估。
