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Title: Watershed Merge Tree Classification for Electron Microscopy Image Segmentation

Abstract

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 in the sense of optimization 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.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1111240
Report Number(s):
PNNL-SA-88717
WN0219080
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: 21st International Conference on Pattern Recognition (ICPR 2012), November 11-15, 2012, Tsukuba, Japan, 133-137
Country of Publication:
United States
Language:
English

Citation Formats

Liu, TIng, Jurrus, Elizabeth R., Seyedhosseini, Mojtaba, Ellisman, Mark, and Tasdizen, Tolga. Watershed Merge Tree Classification for Electron Microscopy Image Segmentation. United States: N. p., 2012. Web.
Liu, TIng, Jurrus, Elizabeth R., Seyedhosseini, Mojtaba, Ellisman, Mark, & Tasdizen, Tolga. Watershed Merge Tree Classification for Electron Microscopy Image Segmentation. United States.
Liu, TIng, Jurrus, Elizabeth R., Seyedhosseini, Mojtaba, Ellisman, Mark, and Tasdizen, Tolga. Sun . "Watershed Merge Tree Classification for Electron Microscopy Image Segmentation". United States.
@article{osti_1111240,
title = {Watershed Merge Tree Classification for Electron Microscopy Image Segmentation},
author = {Liu, TIng and Jurrus, Elizabeth R. and Seyedhosseini, Mojtaba and Ellisman, Mark and Tasdizen, Tolga},
abstractNote = {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 in the sense of optimization 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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2012},
month = {11}
}

Conference:
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