Zheng Guoyan, Dong Xiao, Grutzner Paul Alfred, Nolte Lutz-Peter
MEM Research Center, University of Bern, CH-3014 Bern, Switzerland.
Comput Methods Programs Biomed. 2007 Jul;87(1):1-11. doi: 10.1016/j.cmpb.2007.03.002. Epub 2007 Apr 30.
Long bone fracture belongs to one of the most common injuries encountered in clinical routine trauma surgery. Automated identification, pose and size estimation, and contour extraction of diaphyseal bone fragments can greatly improve the usability of a computer-assisted, fluoroscopy-based navigation system for long bone fracture reduction. In this paper, a two-step solution is proposed. In the first step, the pose and size of a diaphyseal fragment are estimated through a three-dimensional (3D) morphable object-based fitting process using a parametric cylinder model. This fitting process is optimally solved by a hybrid optimization technique coupling a random sample consensus (RANSAC) paradigm and an iterative closest point (ICP) matching procedure. Monte Carlo simulation was used to determine the parameters for the RANSAC paradigm. The results of the fragment detection step are then fed to the second step, where a region information based active contour model is used to extract the fragment contours. We designed and conducted experiments to quantify the accuracy and robustness of the proposed approach. Our experimental results conducted on images of a plastic bone as well as on those of patients demonstrate a promising accuracy and robustness of the proposed approach.
长骨骨折是临床常规创伤手术中最常见的损伤之一。骨干骨碎片的自动识别、姿态和尺寸估计以及轮廓提取可以极大地提高基于荧光透视的计算机辅助长骨骨折复位导航系统的可用性。本文提出了一种两步解决方案。第一步，通过使用参数化圆柱模型的基于三维（3D）可变形物体的拟合过程来估计骨干碎片的姿态和尺寸。该拟合过程通过结合随机抽样一致性（RANSAC）范式和迭代最近点（ICP）匹配程序的混合优化技术得到最优解。使用蒙特卡罗模拟来确定RANSAC范式的参数。然后将碎片检测步骤的结果输入到第二步，在第二步中使用基于区域信息的主动轮廓模型来提取碎片轮廓。我们设计并进行了实验以量化所提方法的准确性和鲁棒性。我们在塑料骨图像以及患者图像上进行的实验结果表明所提方法具有良好的准确性和鲁棒性。
