Interactive extraction of neural structures with user-guided morphological diffusion

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Publication Type pre-print
School or College College of Engineering
Department Computing, School of
Creator Wan, Yong
Other Author Otsuna, Hideo; Chien, Chi-Bin; Hansen, Charles
Title Interactive extraction of neural structures with user-guided morphological diffusion
Date 2012-01-01
Description Extracting neural structures with their fine details fromconfocal volumes is essential to quantitative analysis in neurobiology research. Despite the abundance of various segmentation methods and tools, for complex neural structures, both manual and semi-automatic methods are ineffective either in full 3D or when user interactions are restricted to 2D slices. Novel interaction techniques and fast algorithms are demanded by neurobiologists to interactively and intuitively extract neural structures from confocal data. In this paper, we present such an algorithm-technique combination, which lets users interactively select desired structures from visualization results instead of 2D slices. By integrating the segmentation functions with a confocal visualization tool neurobiologists can easily extract complex neural structures within their typical visualization workflow.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Issue 6378577
First Page 1
Last Page 8
Dissertation Institution University of Utah
Language eng
Bibliographic Citation Wan, Y., Otsuna, H., Chien, C.-B., & Hansen, C. (2012). Interactive extraction of neural structures with user-guided morphological diffusion. IEEE Symposium on Biological Data Visualization 2012, BioVis 2012 - Proceedings, no. 6378577, 1-8.
Rights Management (c)2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 1,709,380 bytes
Identifier uspace,18172
ARK ark:/87278/s6m33djd
Setname ir_uspace
ID 708308
Reference URL https://collections.lib.utah.edu/ark:/87278/s6m33djd
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