IS = { zkontrolovano 05 Dec 2007 },
  UPDATE  = { 2007-06-11 },
  author =      {Dolej{\v s}{\' i}, Martin and Kybic, Jan},
  title =       {Automatic two-step detection of pulmonary nodules},
  year =        {2007},
  pages =       {3j-1 -- 3j-12},
  booktitle =   {SPIE 2007, Medical Imaging 2007: Computer-Aided Diagnosis},
  editor =	{Giger, Maryllen L. and Karssemeijer, Nico},
  publisher =   {SPIE},
  address =     {Bellingham, USA},
  ISBN =	{9780819466327},
  volume =      {6514},
  series =      {Medical Imaging 2007: Computer-Aided Diagnosis},
  book_pages =	{425},
  month =       {February},
  day =         {17 -- 22},
  venue =       {San Diego, USA},
  organization ={SPIE},
  annote = {We present a computer-aided diagnosis (CAD) system to
    detect small-size (from 2mm to around 10mm) pulmonary nodules from
    helical CT scans.  A pulmonary nodule is a small, round
    (parenchymal nodule) or worm (juxta-pleural) shaped lesion in the
    lungs. Both have greater radio density than lungs parenchyma. Lung
    nodules may indicate a lung cancer and its detection in early
    stage improves survival rate of patients. CT is considered to be
    the most accurate imaging modality for detection of
    nodules. However, the large amount of data per examination makes
    the interpretation difficult. This leads to omission of nodules by
    human radiologist. CAD system presented is designed to help lower
    the number of omissions.  Our system uses two different schemes to
    locate juxtapleural nodules and parenchymal nodules. For
    juxtapleural nodules, morphological closing and thresholding is
    used to find nodule candidates. To locate non-pleural nodule
    candidates, 3D blob detector uses multiscale filtration. Ellipsoid
    model is fitted on nodules. To define which of the nodule
    candidates are in fact nodules, an additional classification step
    is applied. Linear and multi-threshold classifiers are
    used. System was tested on 18 cases (4853 slices) with total
    sensitivity of 96%, with about 12 false positives/slice. The
    classification step reduces number of false positives to 9 per
    slice without significantly decreasing sensitivity (89,6%).},
  keywords =    {computer aided diagnostic, nodule detection, 
    BW morphology, 3D filtering, ellipsoid fitting, pattern recognition},
  authorship =  {50-50},
  project  = { NR83143/2005,1ET101050403 },
  www =		{http://cmp.felk.cvut.cz/~dolejm1/noduledetection/},
psurl       = { pdf },