Pitiot Alain, Toga Arthur W, Thompson Paul M
Reed Neurological Research Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA.
IEEE Trans Med Imaging. 2002 Aug;21(8):910-23. doi: 10.1109/TMI.2002.803124.
This paper presents a fully automated segmentation method for medical images. The goal is to localize and parameterize a variety of types of structure in these images for subsequent quantitative analysis. We propose a new hybrid strategy that combines a general elastic template matching approach and an evolutionary heuristic. The evolutionary algorithm uses prior statistical information about the shape of the target structure to control the behavior of a number of deformable templates. Each template, modeled in the form of a B-spline, is warped in a potential field which is itself dynamically adapted. Such a hybrid scheme proves to be promising: by maintaining a population of templates, we cover a large domain of the solution space under the global guidance of the evolutionary heuristic, and thoroughly explore interesting areas. We address key issues of automated image segmentation systems. The potential fields are initially designed based on the spatial features of the edges in the input image, and are subjected to spatially adaptive diffusion to guarantee the deformation of the template. This also improves its global consistency and convergence speed. The deformation algorithm can modify the internal structure of the templates to allow a better match. We investigate in detail the preprocessing phase that the images undergo before they can be used more effectively in the iterative elastic matching procedure: a texture classifier, trained via linear discriminant analysis of a learning set, is used to enhance the contrast of the target structure with respect to surrounding tissues. We show how these techniques interact within a statistically driven evolutionary scheme to achieve a better tradeoff between template flexibility and sensitivity to noise and outliers. We focus on understanding the features of template matching that are most beneficial in terms of the achieved match. Examples from simulated and real image data are discussed, with considerations of algorithmic efficiency.
本文提出了一种用于医学图像的全自动分割方法。目标是在这些图像中定位并参数化各种类型的结构，以便进行后续的定量分析。我们提出了一种新的混合策略，该策略结合了通用的弹性模板匹配方法和进化启发式算法。进化算法利用关于目标结构形状的先验统计信息来控制多个可变形模板的行为。每个以B样条形式建模的模板在一个自身动态自适应的势场中变形。这样的混合方案被证明是有前景的：通过维护一组模板，我们在进化启发式算法的全局引导下覆盖了解决方案空间的很大一部分，并深入探索了有趣的区域。我们解决了自动图像分割系统的关键问题。势场最初基于输入图像中边缘的空间特征进行设计，并进行空间自适应扩散以保证模板的变形。这也提高了其全局一致性和收敛速度。变形算法可以修改模板的内部结构以实现更好的匹配。我们详细研究了图像在能够更有效地用于迭代弹性匹配过程之前所经历的预处理阶段：通过对学习集进行线性判别分析训练的纹理分类器，用于增强目标结构相对于周围组织的对比度。我们展示了这些技术如何在统计驱动的进化方案中相互作用，以在模板灵活性与对噪声和异常值的敏感性之间实现更好的权衡。我们专注于理解在实现的匹配方面最有益的模板匹配特征。讨论了来自模拟和真实图像数据的示例，并考虑了算法效率。
