Watershed merge tree classification for electron microscopy image segmentation
Watershed merge tree classification for electron microscopy image segmentation
复制标题
DOI：
--
复制
发表时间：
2012-11
期刊：
影响因子：
--
通讯作者：
Ting Liu;E. Jurrus;Mojtaba Seyedhosseini;Mark Ellisman;T. Tasdizen
中科院分区：
文献类型：
--
作者：
Ting Liu;E. Jurrus;Mojtaba Seyedhosseini;Mark Ellisman;T. Tasdizen
电子显微镜图像的自动分割是一个具有挑战性的问题。在本文中，我们提出了一种新的方法，利用层次结构和边界分类的二维神经元分割。利用膜检测概率图，建立分水岭合并树，用于表示分水岭算法的分层区域合并。一个边界分类器的学习与非本地图像特征预测每个潜在的合并树，合并决策与一致性约束，以获得最终的分割。独立的分类器和决策策略，我们的方法提出了一个通用的框架，有效的分层分割与统计学习。我们证明，我们的方法导致分割精度的大幅提高。
Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier is learned with non-local image features to predict each potential merge in the tree, upon which merge decisions are made with consistency constraints to acquire the final segmentation. Independent of classifiers and decision strategies, our approach proposes a general framework for efficient hierarchical segmentation with statistical learning. We demonstrate that our method leads to a substantial improvement in segmentation accuracy.