Gao L, Heath D G, Fishman E K
The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland 21287, USA.
Invest Radiol. 1998 Jun;33(6):348-55. doi: 10.1097/00004424-199806000-00006.
The authors develop a three-dimensional (3-D) deformable surface model-based segmentation scheme for abdominal computed tomography (CT) image segmentation.
A parameterized 3-D surface model was developed to represent the human abdominal organs. An energy function defined on the direction of the image gradient and the surface normal of the deformable model was introduced to measure the match between the model and image data. A conjugate gradient algorithm was adapted to the minimization of the energy function.
Test results for synthetic images showed that the incorporation of surface directional information improved the results over those using only the magnitude of the image gradient. The algorithm was tested on 21 CT datasets. Of the 21 cases tested, 11 were evaluated visually by a radiologist and the results were judged to be without noticeable error. The other 10 were evaluated over a distance function. The average distance was less than 1 voxel.
The deformable model-based segmentation scheme produces robust and acceptable outputs on abdominal CT images.
作者开发了一种基于三维（3-D）可变形表面模型的分割方案，用于腹部计算机断层扫描（CT）图像分割。
开发了一个参数化的3-D表面模型来表示人体腹部器官。引入了一个基于图像梯度方向和可变形模型表面法线定义的能量函数，以衡量模型与图像数据之间的匹配度。采用共轭梯度算法对能量函数进行最小化。
合成图像的测试结果表明，与仅使用图像梯度幅值的方法相比，纳入表面方向信息可改善分割结果。该算法在21个CT数据集上进行了测试。在测试的21个病例中，11个由放射科医生进行了视觉评估，结果被判定无明显误差。另外10个通过距离函数进行评估。平均距离小于1个体素。
基于可变形模型的分割方案在腹部CT图像上产生了稳健且可接受的输出。
