A variational approach to vesicle membrane reconstruction from fluorescence imaging
A variational approach to vesicle membrane reconstruction from fluorescence imaging
复制标题
通过荧光成像重建囊泡膜的变体方法
DOI：
10.1016/j.patcog.2011.04.019
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
D. Cremers
中科院分区：
文献类型：
--
作者：
K. Kolev;N. Kirchgessner;Sebastian Houben;Á. Csiszár;W. Rubner;C. Palm;B. Eiben;R. Merkel;D. Cremers
生物学应用，如囊泡膜分析，涉及在嘈杂的体积数据中精确分割3D结构，这些数据是通过磁共振成像（MRI）或激光扫描显微镜（LSM）等技术获得的。处理这些数据是一项具有挑战性的任务，需要强大而准确的分割方法。在这篇文章中，我们提出了一种新的能量模型的3D分割融合各种线索，如区域强度细分，边缘对齐和方向信息。该方法的独特之处在于定义了一个新的各向异性正则化子，该正则化子考虑了测量体积数据的不平衡切片，以及基于线性化和定点迭代的有效数值方案的推广，用于解决所产生的最小化问题。我们展示了如何利用最近的连续凸松弛技术，提出的能量模型可以在全球范围内进行优化。通过在多个真实的数据集上进行评估，并将其与基于水平集的其他分割方法进行比较，证明了该方法的准确性和鲁棒性。虽然所提出的模型的设计重点放在手头的特定应用程序，它是足够的一般适用于各种不同的分割任务。
Biological applications like vesicle membrane analysis involve the precise segmentation of 3D structures in noisy volumetric data, obtained by techniques like magnetic resonance imaging (MRI) or laser scanning microscopy (LSM). Dealing with such data is a challenging task and requires robust and accurate segmentation methods. In this article, we propose a novel energy model for 3D segmentation fusing various cues like regional intensity subdivision, edge alignment and orientation information. The uniqueness of the approach consists in the definition of a new anisotropic regularizer, which accounts for the unbalanced slicing of the measured volume data, and the generalization of an efficient numerical scheme for solving the arising minimization problem, based on linearization and fixed-point iteration. We show how the proposed energy model can be optimized globally by making use of recent continuous convex relaxation techniques. The accuracy and robustness of the presented approach are demonstrated by evaluating it on multiple real data sets and comparing it to alternative segmentation methods based on level sets. Although the proposed model is designed with focus on the particular application at hand, it is general enough to be applied to a variety of different segmentation tasks.
DOI：
10.1115/1.1865197
发表时间：
2005-04-01
影响因子：
1.7
作者：
Kosawada, T;Inoue, K;Schmid-Schönbein, GW
通讯作者：
Schmid-Schönbein, GW