@TechReport{Borovec-TR-2013-03,
  IS = { zkontrolovano 16 Jan 2013, by Borovec },
  UPDATE  = { 2013-01-09 },
  author =       {Borovec, Ji{\v r}{\'i} and Kybic, Jan and Bu{\v s}ta, Michal
                  and Ortiz-de-Solorzano, Carlos and Mu{\~ n}oz-Barrutia,
                  Arrate},
  title =        {Registration of multiple stained histological sections},
  institution =  {Center for Machine Perception, K13133 FEE Czech Technical
                  University},
  address =      {Prague, Czech Republic},
  year =         {2013},
  month =        {January},
  type =         {Research Report},
  number =       {CTU--CMP--2013--03},
  issn =         {1213-2365},
  pages =        {10},
  figures =      {4},
  psurl =        {[Borovec-TR-2013-03.pdf]},
  project =      {SGS12/190/OHK3/3T/13, GACR P202/11/0111, DPI2009-14115-C03-03,
                  DPI2012-38090-C03-02},
  annote =       {The analysis of protein-level multigene expression signature
                  maps computed from the fusion of differently stained
                  immunohistochemistry images is an emerging tool in cancer
                  management. Creating these maps requires registering sets of
                  histological images, a challenging task due to their large
                  size, the non-linear distortions existing between
                  consecutive sections and to the fact that the images
                  correspond to different histological stains and thus, may
                  have very different appearance. In this manuscript, we
                  present a novel segmentation-based registration algorithm
                  that exploits a multi-class pyramid and optimizes a fuzzy
                  class assignment specially designed for this task. Compared
                  to a standard non-rigid registration, the proposed method
                  achieves an improved matching on both synthetic as well as
                  real histological images of cancer lesions.},
  keywords =     {Nonrigid registration, superpixels, multiple-class matching,
                  light microscopy images, lung cancer lesions},
}