Ayvaci Alper, Freedman Daniel
Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5251-4. doi: 10.1109/IEMBS.2007.4353526.
In this paper, we present a novel method for the segmentation of the organs found in CT and MR images. The proposed algorithm utilizes the shape model of the target organ to gain robustness in the case where the objective organ is surrounded by other organs or tissue with the similar intensity profile. The algorithm labels the image based on the graph-cuts technique and incorporates the shape prior using a technique based on level-sets. The method requires proper registration of the shape template for an accurate segmentation, and we propose a unified registration-segmentation framework to solve this problem. Furthermore, to reduce the computational cost, the algorithm is designed to run on watershed regions instead of voxels. The accuracy of the algorithm is shown on the medical examples.
在本文中，我们提出了一种用于分割CT和MR图像中器官的新方法。所提出的算法利用目标器官的形状模型，以便在目标器官被其他具有相似强度轮廓的器官或组织包围的情况下获得鲁棒性。该算法基于图割技术对图像进行标记，并使用基于水平集的技术纳入形状先验。该方法需要对形状模板进行适当配准以实现准确分割，并且我们提出了一个统一的配准 - 分割框架来解决这个问题。此外，为了降低计算成本，该算法设计为在分水岭区域而不是体素上运行。在医学实例上展示了该算法的准确性。
