Pernozzoli A, Burghart C, Brief J, Hassfeld S, Raczkowsky J, Mühling J, Wörn H
Clinic of Cranio-Maxillo-Facial Surgery, University of Heidelberg.
Stud Health Technol Inform. 2000;70:246-52.
Planning, visualisation and intraoperative navigation in a robot assisted environment for craniofacial surgery require highly accurate methods for the segmentation of bone structures in CT data. Clinical systems are still based on time consuming interactive methods like the seed-point segmentation. Faster methods with no need for interactivity lacks in precision. In the following we will present an automatic and highly accurate algorithm for the segmentation of bone contours in CT data. It is based on an algorithm for the automatic calculation of a grey-value tissue relation model for CT and MRI data.
在机器人辅助颅面外科手术环境中进行规划、可视化和术中导航,需要用于CT数据中骨结构分割的高精度方法。临床系统仍基于耗时的交互式方法,如种子点分割。无需交互的更快方法在精度上有所欠缺。在本文中,我们将提出一种用于CT数据中骨轮廓分割的自动且高精度算法。它基于一种用于自动计算CT和MRI数据灰度值组织关系模型的算法。