Seghers D, Hermans J, Loeckx D, Maes F, Vandermeulen D, Suetens P
Katholieke Universiteit Leuven, Faculty of Medicine, Medical Image Computing (Radiology-ESAT/PSI), University Hospital Gasthuisberg, Herestraat 49, B-3000 Leuven, Belgium.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):393-400. doi: 10.1007/978-3-540-85988-8_47.
A generic supervised segmentation approach is presented. The object is described as a graph where the vertices correspond to landmarks points and the edges define the landmark relations. Instead of building one single global shape model, a priori shape information is represented as a concatenation of local shape models that consider only local dependencies between connected landmarks. The objective function is obtained from a maximum a posteriori criterion and is build up of localized energies of both shape and landmark intensity information. The optimization problem is discretized by searching candidates for each landmark using individual landmark intensity descriptors. The discrete optimization problem is then solved using mean field annealing or dynamic programming techniques. The algorithm is validated for hand bone segmentation from RX datasets and for 3D liver segmentation from contrast enhanced CT images.
提出了一种通用的监督分割方法。该对象被描述为一个图，其中顶点对应于地标点，边定义了地标关系。不是构建一个单一的全局形状模型，而是将先验形状信息表示为仅考虑相连地标之间局部依赖性的局部形状模型的串联。目标函数从最大后验准则获得，并由形状和地标强度信息的局部能量组成。通过使用单个地标强度描述符搜索每个地标候选来离散化优化问题。然后使用平均场退火或动态规划技术解决离散优化问题。该算法在RX数据集中的手部骨骼分割以及对比增强CT图像的3D肝脏分割中得到了验证。
