| Autor: |
Toufiq Parag, Anirban Chakraborty, Stephen Plaza, Louis Scheffer |
| Jazyk: |
angličtina |
| Rok vydání: |
2015 |
| Předmět: |
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| Zdroj: |
PLoS ONE, Vol 10, Iss 5, p e0125825 (2015) |
| Druh dokumentu: |
article |
| ISSN: |
1932-6203 |
| DOI: |
10.1371/journal.pone.0125825 |
| Popis: |
Electron Microscopy (EM) image (or volume) segmentation has become significantly important in recent years as an instrument for connectomics. This paper proposes a novel agglomerative framework for EM segmentation. In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron. Unlike existing agglomerative methods, the proposed context-aware algorithm divides superpixels (over-segmented regions) of different biological entities into different subsets and agglomerates them separately. In addition, this paper describes a "delayed" scheme for agglomerative clustering that postpones some of the merge decisions, pertaining to newly formed bodies, in order to generate a more confident boundary prediction. We report significant improvements attained by the proposed approach in segmentation accuracy over existing standard methods on 2D and 3D datasets. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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