@InProceedings{Svihlik-SPIEMI2014,
  IS = { zkontrolovano 08 Aug 2014 },
  UPDATE   = { 2014-05-26 },
  author =       {{\v S}vihl{\'\i}k, J. and Kybic, J. and Habart,  D. and Berkov{\'a}, Z. and Girman, P. and K{\v r}{\'\i}{\v z}, J. and Zacharovov{\' a}, K.},
  title =        {Classification of microscopy images of Langerhans islets},
booktitle = { SPIE Medical Imaging },
pages       = { 1-8 },
  year =         {2014},
volume      = { 9034 },
doi         = { 10.1117/12.2043621 },
  ut_isi =       {},
month    = { March },
  organization = {SPIE},
project  = { GACR P202/11/0111, GACR 14-10440S },
  authorship =   {45-25-10-5-5-5-5},
  publisher =    {SPIE},
isbn        = { 9780819498274 },
book_pages  = { 1250 },
  day =          {15--20},
  address =      {Bellingham, USA},
  annote = {Classification of images of Langerhans islet is crucial procedure for
optimization of diabetes treatment.
Hence, this paper deals with segmentation of microscopy images of
Langerhans islets and evaluation of islet parameters such as area,
islet diameter, islet equivalent (IE) etc.
For all the available images, the ground truth and the islet parameters were independently evaluated
by four medical experts in a blinded manner.
We utilized linear classifier (perceptron algorithm) and SVM (support vector machine) for image
segmentation. All the available image data were segmented and compared with corresponding ground truth.
The islet parameters were also evaluated and compared with parameters evaluated by medical experts.
The presented fully automatic algorithm analyzes the microscopy images as good as medical experts.},
  keywords =      {Langerhans islet, linear classifier, support vector machine, IE, segmentation},
  venue =         {San Diego, USA},
editor      = { Sebastien Ourselin, Martin A. Styner },
url         = { ftp://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Svihlik-SPIEMI2014.pdf },
}