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@ARTICLE{Strohmer:141504,
      author       = {Strohmer, Sven and Reckfort, Julia and Dohmen, Melanie and
                      Huynh, Anh Minh and Axer, Markus},
      title        = {{R}elating {P}olarized {L}ight {I}maging {D}ata {A}cross
                      {S}cales},
      journal      = {Frontiers in neuroinformatics},
      volume       = {7},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2013-06672},
      pages        = {3},
      year         = {2013},
      abstract     = {Polarized light imaging (PLI) (Axer et al. (2011a,b))
                      enables scanning of individual histological human brain
                      sections with two independent setups: a large-area
                      polarimeter (LAP, “object space resolution”, which is
                      referred to as “resolution” in the remainder of this
                      abstract: 64 × 64 μm²/px) and a polarizing microscope
                      (PM, resolution: 1.6 × 1.6 μm²/px). While PM images are
                      of high resolution (HR) containing complex information, the
                      LAP provides low resolution (LR) overview-like data. The
                      information contained in an LR image is a mixture of the
                      information of its HR counterpart (Koenderink (1984)). Each
                      resolution yields valuable information, which multiplies if
                      they are combined.Image registration algorithms, for
                      example, handle multiple resolutions (1) in case of several
                      modalities with special metrics, and (2) in multi-resolution
                      approaches (e.g. Trottenberg et al. (2001)) to increase the
                      stability of the optimization process of automatic image
                      registration. In the latter case, the data is coarsened
                      synthetically. Our goal is to directly relate measured HR to
                      LR data of the same object, avoiding artificial intermediate
                      steps.All images show the average light intensity, that is
                      transmitted through a thin brain slice (Axer et al.
                      (2011a,b)), and depict a region from the human occipital
                      pole. The images were manually segmented and smoothed by a
                      Gaussian kernel suitable for noise reduction and adapted to
                      each resolution.We selected octave 2 at LR and octave 7 at
                      HR for SURF extraction (Bay et al. (2006)), where one octave
                      denotes a decrease in resolution by a factor of 2. Features
                      with corresponding scales were matched with FLANN (Muja and
                      Lowe (2009)). Homography estimation from the resulting
                      feature point pairs used RANSAC (Fischler and Bolles
                      (1981)). The homography and a linear interpolation scheme
                      were applied to transfer information from LR to HR and vice
                      versa.Localization of the HR ROI in the LR ROI is plausible
                      (figure 1(B)), while localization in the LAP image fails,
                      because the matched feature point positions in HR and LR do
                      not correspond. Numerical and feature point matching
                      inaccuracies become evident in figure 1(C).The experiments
                      were performed with one HR ROI (figure 1(A)), one LAP ROI
                      (figure 1(B)) and one LAP image. We plan to improve the
                      algorithm and to obtain complete HR data sets for further
                      exploration of the method’s performance.Figure 1. This
                      figure shows input data and results of the experiment. The
                      arrows indicate the flow of information and the color by
                      which it is displayed at its destination. Subfigure (A)
                      shows the down-scaled PM ROI (original size: 20604 px ×
                      17157 px). (B) shows the up-scaled LAP ROI (original size:
                      916 px × 510 px) with estimated PM ROI location (green
                      frame). Note, that only part of the HR ROI is contained in
                      the LR ROI. Also, most of the fine white structures depicted
                      in (A) vanished due to the low resolution of (B). (C) shows
                      the down-scaled overlay image (original size: 20604 px ×
                      17157 px) of LR data (enclosed in the green frame in (B))
                      transferred to HR versus PM ROI data of (A), where HR data
                      is labeled green and transferred LR data is labeled red. HR
                      data and transferred LR data were normalized. Numerical and
                      feature point matching inaccuracies become evident. Also,
                      displacement and distortion compared to HR data is visible.},
      month         = {Sep},
      date          = {2013-09-02},
      organization  = {Imaging the brain at different scales:
                       How to integrate multi-scale structural
                       information?, Antwerp (Belgium), 2 Sep
                       2013 - 6 Sep 2013},
      cin          = {JSC / INM-1 / JARA-HPC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-1-20090406 /
                      $I:(DE-82)080012_20140620$},
      pnm          = {411 - Computational Science and Mathematical Methods
                      (POF2-411) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF2-411 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.3389/conf.fninf.2013.10.00029},
      url          = {https://juser.fz-juelich.de/record/141504},
}