@ARTICLE{Quellec-IEEE2010,
  IS = { zkontrolovano 24 Jan 2011 },
  UPDATE  = { 2011-01-14 },
  AUTHOR =	{Quellec, Gwenole and Lee, Kyungmoo and Dolejsi, Martin and 
                 Garvin, Mona K. and Abr{\` a}moff, Michael D. and Sonka, Milan},
  TITLE =	{Three-Dimensional Analysis of Retinal Layer Texture: 
                 {I}dentification of Fluid-Filled Regions in {SD-OCT} of the Macula},
  PROJECT = 	{NIH NIBIB R01-EB004640, NIH NEI R01-EY018853, 
                 Research to Prevent Blindness and the U.S. Dept. of Veterans Affairs},
  JOURNAL =	{IEEE Transactions on Medical Imaging},
  PUBLISHER =	{The Institute of Electrical and Electronic Engineers},
  ADDRESS =	{3 Park Avenue, 17th Floor, New York, USA},
  ISSN =	{0278-0062},
  YEAR =	{2010},
  NUMBER =	{6},
  VOLUME =	{29},
  MONTH =	{June},
  PAGES =	{1321--1330},
  ANNOTE = {Optical coherence tomography (OCT) is becoming one of the
   most important modalities for the noninvasive assessment of retinal
   eye diseases. As the number of acquired OCT volumes increases,
   automating the OCT image analysis is becoming increasingly
   relevant. In this paper, a method for automated characterization of
   the normal macular appearance in spectral domain OCT (SD-OCT)
   volumes is reported together with a general approach for local
   retinal abnormality detection. Ten intraretinal layers are first
   automatically segmented and the 3-D image dataset flattened to
   remove motion-based artifacts. From the flattened OCT data, 23
   features are extracted in each layer locally to characterize
   texture and thickness properties across the macula. The normal
   ranges of layer-specific feature variations have been derived from
   13 SD-OCT volumes depicting normal retinas. Abnormalities are then
   detected by classifying the local differences between the normal
   appearance and the retinal measures in question. This approach was
   applied to determine footprints of fluid-filled regions-SEADs
   (Symptomatic Exudate-Associated Derangements)-in 78 SD-OCT volumes
   from 23 repeatedly imaged patients with choroidal
   neovascularization (CNV), intra-, and sub-retinal fluid and pigment
   epithelial detachment. The automated SEAD footprint detection
   method was validated against an independent standard obtained using
   an interactive 3-D SEAD segmentation approach. An area under the
   receiver-operating characteristic curve of 0.961 ? 0.012 was
   obtained for the classification of vertical, cross-layer, macular
   columns. A study performed on 12 pairs of OCT volumes obtained from
   the same eye on the same day shows that the repeatability of the
   automated method is comparable to that of the human experts. This
   work demonstrates that useful 3-D textural information can be
   extracted from SD-OCT scans and-together with an anatomical atlas
   of normal retinas-can be used for clinically important
   applications.},
  KEYWORDS =     {OCT, SEADS, AMD},
  authorship =   {30-20-20-10-10-10},
  my_note =      {Dolejsi was not CTU member (e-mail 30.1.2011)
                  No athors and no grants with relevance to CTU},
}