Han Qiong, Merck Derek, Levy Josh, Villarruel Christina, Damon James N, Chaney Edward L, Pizer Stephen M
Medical Image Display & Analysis Group, University of North Carolina, Chapel Hill, North Carolina 27599, USA.
Inf Process Med Imaging. 2007;20:751-62. doi: 10.1007/978-3-540-73273-0_62.
In deformable model segmentation, the geometric training process plays a crucial role in providing shape statistical priors and appearance statistics that are used as likelihoods. Also, the geometric training process plays a crucial role in providing shape probability distributions in methods finding significant differences between classes. The quality of the training seriously affects the final results of segmentation or of significant difference finding between classes. However, the lack of shape priors in the training stage itself makes it difficult to enforce shape legality, i.e., making the model free of local self-intersection or creases. Shape legality not only yields proper shape statistics but also increases the consistency of parameterization of the object volume and thus proper appearance statistics. In this paper we propose a method incorporating explicit legality constraints in training process. The method is mathematically sound and has proved in practice to lead to shape probability distributions over only proper objects and most importantly to better segmentation results.
在可变形模型分割中，几何训练过程在提供用作似然性的形状统计先验和外观统计方面起着至关重要的作用。此外，几何训练过程在寻找类间显著差异的方法中提供形状概率分布方面也起着至关重要的作用。训练的质量严重影响分割的最终结果或类间显著差异的发现。然而，训练阶段本身缺乏形状先验使得难以强制形状合法性，即使模型没有局部自相交或褶皱。形状合法性不仅能产生适当的形状统计，还能增加对象体积参数化的一致性，从而得到适当的外观统计。在本文中，我们提出了一种在训练过程中纳入显式合法性约束的方法。该方法在数学上是合理的，并且在实践中已证明能导致仅在适当对象上的形状概率分布，最重要的是能带来更好的分割结果。
