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@ARTICLE{Dammers:16171,
      author       = {Dammers, J. and Breuer, L. and Axer, M. and Kleiner, M. and
                      Eiben, B. and Gräßel, D. and Dickscheid, T. and Zilles, K.
                      and Amunts, K. and Shah, N.J. and Pietrzyk, U.},
      title        = {{A}utomatic identification of gray and white matter
                      components in polarized light imaging},
      journal      = {NeuroImage},
      volume       = {59},
      number       = {2},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {PreJuSER-16171},
      pages        = {1338–1347},
      year         = {2012},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {Polarized light imaging (PLI) enables the visualization of
                      fiber tracts with high spatial resolution in microtome
                      sections of postmortem brains. Vectors of the fiber
                      orientation defined by inclination and direction angles can
                      directly be derived from the optical signals employed by PLI
                      analysis. The polarization state of light propagating
                      through a rotating polarimeter is varied in such a way that
                      the detected signal of each spatial unit describes a
                      sinusoidal signal. Noise, light scatter and filter
                      inhomogeneities, however, interfere with the original
                      sinusoidal PLI signals, which in turn have direct impact on
                      the accuracy of subsequent fiber tracking. Recently we
                      showed that the primary sinusoidal signals can effectively
                      be restored after noise and artifact rejection utilizing
                      independent component analysis (ICA). In particular, regions
                      with weak intensities are greatly enhanced after ICA based
                      artifact rejection and signal restoration. Here, we propose
                      a user independent way of identifying the components of
                      interest after decomposition; i.e., components that are
                      related to gray and white matter. Depending on the size of
                      the postmortem brain and the section thickness, the number
                      of independent component maps can easily be in the range of
                      a few ten thousand components for one brain. Therefore, we
                      developed an automatic and, more importantly, user
                      independent way of extracting the signal of interest. The
                      automatic identification of gray and white matter components
                      is based on the evaluation of the statistical properties of
                      the so-called feature vectors of each individual component
                      map, which, in the ideal case, shows a sinusoidal waveform.
                      Our method enables large-scale analysis (i.e., the analysis
                      of thousands of whole brain sections) of nerve fiber
                      orientations in the human brain using polarized light
                      imaging.},
      keywords     = {Algorithms / Artificial Intelligence / Brain: cytology /
                      Humans / Image Enhancement: methods / Image Interpretation,
                      Computer-Assisted: methods / Lighting: methods / Microscopy,
                      Polarization: methods / Nerve Fibers, Myelinated:
                      ultrastructure / Neurons: cytology / Pattern Recognition,
                      Automated: methods / Reproducibility of Results /
                      Sensitivity and Specificity},
      cin          = {INM-1 / INM-2 / INM-4},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406 / I:(DE-Juel1)INM-2-20090406 /
                      I:(DE-Juel1)INM-4-20090406},
      pnm          = {Funktion und Dysfunktion des Nervensystems (FUEK409) /
                      89574 - Theory, modelling and simulation (POF2-89574)},
      pid          = {G:(DE-Juel1)FUEK409 / G:(DE-HGF)POF2-89574},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:21875673},
      UT           = {WOS:000298210600054},
      doi          = {10.1016/j.neuroimage.2011.08.030},
      url          = {https://juser.fz-juelich.de/record/16171},
}