Interactive Segmentation of Clustered Cells via Geodesic Commute Distance and Constrained Density Weighted Nystrom Method
Interactive Segmentation of Clustered Cells via Geodesic Commute Distance and Constrained Density Weighted Nystrom Method
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DOI：
10.1002/cyto.a.20993
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发表时间：
2010-12-01
期刊：
影响因子：
3.7
通讯作者：
Bretschneider, Till
中科院分区：
文献类型：
--
作者：
Du, Cheng-Jin;Marcello, Marco;Bretschneider, Till
提出了一种用于复杂细胞分割的交互式方法，特别是聚类细胞。本文的主要贡献有两个：首先，我们探索了随机游走和基于测地线图的图像分割方法的混合组合，并提出了测地线通勤距离的新概念来对像素进行分类。测地线交换距离的计算需要从要分割的图像构造的图的加权拉普拉斯矩阵的特征向量分解。其次，通过将种子的成对约束引入算法，我们提出了一种新的特征向量分解方法，即约束密度加权Nystrom方法。与其他半自动算法，包括基于Voronoi分割，生长割，图割，随机游走，测地线方法的视觉和定量比较，给出了所提出的方法的性能，这是一个强大的工具，定量分析的聚类细胞图像的活细胞成像。(C)2010年国际细胞计数促进学会
An interactive method is proposed for complex cell segmentation, in particular of clustered cells. This article has two main contributions: First, we explore a hybrid combination of the random walk and the geodesic graph based methods for image segmentation and propose the novel concept of geodesic commute distance to classify pixels. The computation of geodesic commute distance requires an eigenvector decomposition of the weighted Laplacian matrix of a graph constructed from the image to be segmented. Second, by incorporating pairwise constraints from seeds into the algorithm, we present a novel method for eigenvector decomposition, namely a constrained density weighted Nystrom method. Both visual and quantitative comparison with other semiautomatic algorithms including Voronoi-based segmentation, grow cut, graph cuts, random walk, and geodesic method are given to evaluate the performance of the proposed method, which is a powerful tool for quantitative analysis of clustered cell images in live cell imaging. (C) 2010 International Society for Advancement of Cytometry