Kronman A, Joskowicz Leo, Sosna J
School of Eng. and Computer Science, The Hebrew Univ. of Jerusalem, Israel.
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):363-70. doi: 10.1007/978-3-642-33418-4_45.
Fuzzy boundaries of anatomical structures in medical images make segmentation a challenging task. We present a new segmentation method that addresses the fuzzy boundaries problem. Our method maps the lengths of 3D rays cast from a seed point to the unit sphere, estimates the fuzzy boundaries location by thresholding the gradient magnitude of the rays lengths, and derives the true boundaries by Laplacian interpolation on the sphere. Its advantages are that it does not require a global shape prior or curvature based constraints, that it has an automatic stopping criteria, and that it is robust to anatomical variability, noise, and parameters values settings. Our experimental evaluation on 23 segmentations of kidneys and on 16 segmentations of abdominal aortic aneurysms (AAA) from CT scans yielded an average volume overlap error of 12.6% with respect to the ground-truth. These results are comparable to those of other segmentation methods without their underlying assumptions.
医学图像中解剖结构的模糊边界使得分割成为一项具有挑战性的任务。我们提出了一种新的分割方法来解决模糊边界问题。我们的方法将从种子点投射到单位球体的3D射线长度进行映射，通过对射线长度的梯度幅度进行阈值处理来估计模糊边界位置，并通过在球体上进行拉普拉斯插值来推导真实边界。其优点在于它不需要全局形状先验或基于曲率的约束，具有自动停止标准，并且对解剖变异、噪声和参数值设置具有鲁棒性。我们对来自CT扫描的23个肾脏分割和16个腹主动脉瘤（AAA）分割进行的实验评估表明，相对于真实情况，平均体积重叠误差为12.6%。这些结果与其他分割方法的结果相当，且无需其潜在假设。
