@article {46224,
	title = {A context-aware delayed agglomeration framework for EM segmentation.},
	journal = {arXiv},
	year = {2014},
	month = {2014 Jun 5},
	pages = {arXiv:1406.1476 [cs.CV]},
	abstract = {<p>This paper proposes a novel agglomerative framework for Electron Microscopy (EM) image (or volume) segmentation. For the overall segmentation methodology, we propose a context-aware algorithm that clusters the over-segmented regions of different sub-classes (representing different biological entities) in different stages. Furthermore, a delayed scheme for agglomerative clustering, which postpones the merge of newly formed bodies, is also proposed to generate a more confident boundary prediction. We report significant improvements in both segmentation accuracy and speed attained by the proposed approaches over existing standard methods on both 2D and 3D datasets.</p>
},
	url = {http://arxiv.org/abs/1406.1476},
	author = {Parag, Toufiq and Chakraborty, Anirban and Plaza, Stephen M}
}
