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@MASTERSTHESIS{Westhoff:138037,
      author       = {Westhoff, Anna Maria},
      title        = {{GPU}-accelerated {S}egmentation of high-resolution {H}uman
                      {B}rain {I}mages acquired with {P}olarized {L}ight
                      {I}maging},
      volume       = {4365},
      school       = {FH Aachen-Jülich},
      type         = {MS},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2013-04310, Juel-4365},
      series       = {Berichte des Forschungszentrums Jülich},
      pages        = {84 p.},
      year         = {2013},
      note         = {FH Aachen-Jülich, Masterarbeit, 2013},
      abstract     = {High-resolution three-dimensional polarized light imaging
                      (PLI) is an approach pursued by the Institute of
                      Neuroscience and Medicine at Forschungszentrum Jülich to
                      map nerve fibers and their pathways in human brains.
                      Sections of cut post-mortem brains are imaged with a
                      microscopic device. A section is moved during the
                      imagingwithin the microscope so that a mosaic of about 30x30
                      image tiles is created for a gross histological human brain
                      section. These up to 900 tiles per section and about 1500
                      sections in total have to be handled in the 3D
                      reconstruction of the brain. A way to accelerate this
                      process is a previous segmentation of the image tiles which
                      leads to black-and-white masks marking the brain and
                      background pixels of the original tiles. Hence, all
                      non-brain parts of the tiles can be ignored during the
                      reconstruction.A region growing segmentation is developed
                      and implemented for the PLI data. The challenge to adapt
                      this algorithm to the given dataset is to automatize the
                      choice of seeds needed as starting points for the growing
                      process. Therefore, an automated method of seed
                      determination has to be developed. It uses statistics of the
                      whole brain based on the joint intensity histogram. This
                      approach leads to a minimal fixed amount of required manual
                      input which is independent of the number of image tiles to
                      be segmented. The software is parallelized for the GPU
                      cluster JUDGE, i.e. it combines two levels of parallelism,
                      namely a multicore implementation and the data parallel
                      execution of appropriate subtasks on a GPU. This leads to a
                      well-scaling application that achieves the expected
                      segmentation results.},
      keywords     = {Unveröffentlichte Hochschulschrift (GND)},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {411 - Computational Science and Mathematical Methods
                      (POF2-411)},
      pid          = {G:(DE-HGF)POF2-411},
      typ          = {PUB:(DE-HGF)19 / PUB:(DE-HGF)15},
      url          = {https://juser.fz-juelich.de/record/138037},
}