Nempont Olivier, Atif Jamal, Angelini Elsa, Bloch Isabelle
Ecole Nationale Supérieure des Télécommunications (GET - Télécom Paris) CNRS UMR 5141 LTCI, Paris, France.
Inf Process Med Imaging. 2007;20:283-95. doi: 10.1007/978-3-540-73273-0_24.
Segmentation of anatomical structures via minimal surface extraction using gradient-based metrics is a popular approach, but exhibits some limits in the case of weak or missing contour information. We propose a new framework to define metrics, robust to missing image information. Given an object of interest we combine gray-level information and knowledge about the spatial organization of cerebral structures, into a fuzzy set which is guaranteed to include the object's boundaries. From this set we derive a metric which is used in a minimal surface segmentation framework. We show how this metric leads to improved segmentation of subcortical gray matter structures. Quantitative results on the segmentation of the caudate nucleus in T1 MRI are reported on 18 normal subjects and 6 pathological cases.
通过使用基于梯度的度量进行最小表面提取来分割解剖结构是一种流行的方法，但在轮廓信息薄弱或缺失的情况下存在一些局限性。我们提出了一个新的框架来定义对缺失图像信息具有鲁棒性的度量。给定一个感兴趣的对象，我们将灰度信息和关于脑结构空间组织的知识组合成一个模糊集，该模糊集保证包含对象的边界。从这个集合中我们导出一个度量，该度量用于最小表面分割框架。我们展示了这个度量如何导致对皮质下灰质结构的分割得到改善。报告了18名正常受试者和6例病理病例的T1加权磁共振成像中尾状核分割的定量结果。
