主动形状模型的一种加权策略
A weighting strategy for Active Shape Models
摘要：
主动形状模型（ASM）是一种迭代分割技术，用于找到对象的基于地标的轮廓。在每次迭代中，确定一个合理的形状到一些检测到的目标地标的最小二乘拟合。找到这些目标是一个关键步骤：一些地标比其他地标更可靠地检测到，一些地标可能不在其检测器的视野内。为了在保持简单性的同时增加鲁棒性，可以使用广义最小二乘方法，其中加权矩阵包含关于地标的可靠性信息。我们提出了一种策略来选择这个矩阵，根据经验确定的残差的拟合的协方差。我们执行进一步的步骤来确定目标地标是否在其检测器的范围内。我们评估我们的策略，荧光透视X射线图像分割股骨。我们表明，我们的技术优于标准的ASM以及其他更具启发式的加权最小二乘策略。
英文摘要：
Active Shape Models (ASM) are an iterative segmentation technique to find a landmark-based contour of an object. In each iteration, a least-squares fit of a plausible shape to some detected target landmarks is determined. Finding these targets is a critical step: some landmarks are more reliably detected than others, and some landmarks may not be within the field of view of their detectors. To add robustness while preserving simplicity at the same time, a generalized least-squares approach can be used, where a weighting matrix incorporates reliability information about the landmarks. We propose a strategy to choose this matrix, based on the covariance of empirically determined residuals of the fit. We perform a further step to determine whether the target landmarks are within the range of their detectors. We evaluate our strategy on fluoroscopic X-ray images to segment the femur. We show that our technique outperforms the standard ASM as well as other more heuristic weighted least-squares strategies.
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