About: Neuromuscular fiber segmentation through particle filtering and discrete optimization     Goto   Sponge   NotDistinct   Permalink

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  • ftp://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Dietenbeck-SPIEMI2014.pdf
Description
  • We present an algorithm to segment a set of parallel, intertwined and bifurcatin g fibers from 3D images, targeted for the identification of neuronal fibers in very large sets of 3D confocal microscopy images. The method consists of preprocessing, local calcula tion of fiber probabilities, seed detection, tracking by particle filtering, global superv ised seed clustering and final voxel segmentation.
  • We present an algorithm to segment a set of parallel, intertwined and bifurcatin g fibers from 3D images, targeted for the identification of neuronal fibers in very large sets of 3D confocal microscopy images. The method consists of preprocessing, local calcula tion of fiber probabilities, seed detection, tracking by particle filtering, global superv ised seed clustering and final voxel segmentation. (en)
Title
  • Neuromuscular fiber segmentation through particle filtering and discrete optimization
  • Neuromuscular fiber segmentation through particle filtering and discrete optimization (en)
skos:prefLabel
  • Neuromuscular fiber segmentation through particle filtering and discrete optimization
  • Neuromuscular fiber segmentation through particle filtering and discrete optimization (en)
skos:notation
  • RIV/68407700:21230/14:00218860!RIV15-GA0-21230___
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  • P(GAP202/11/0111)
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  • 32265
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  • RIV/68407700:21230/14:00218860
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  • segmentation; fibers; tracking (en)
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  • [B484081880CC]
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  • San Diego
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  • SPIE Medical Imaging
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  • Kybic, Jan
  • Basset, O.
  • Cachard, C.
  • Varray, F.
  • Dietenbeck, T.
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  • 000338543300010
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issn
  • 1605-7422
number of pages
http://bibframe.org/vocab/doi
  • 10.1117/12.2043257
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  • SPIE
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  • 978-0-8194-9827-4
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  • 21230
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