@PhDThesis{Barva-TR-2007-12,
  IS = { zkontrolovano 15 Jan 2008 },
  UPDATE  = { 2007-11-28 },
  author =         {Barva, Martin},
  supervisor =     {Kybic, Jan and Cachard, Christian and 
                    Hlav{\'a}{\v c}, V{\'a}clav},
  title =          {Localization of Surgical Instruments in 3D Ultrasound
                    Images},
  school =         {Center for Machine Perception, K13133 FEE Czech Technical
                    University},
  address =        {Prague, Czech Republic},
  year =           {2007},
  month =          {June},
  day =            {1},
  type =           {{PhD Thesis CTU--CMP--2007--12}},
  issn =           {1213-2365},
  pages =          {153},
  figures =        {92},
  authorship =     {100},
  psurl =          {<a
href="ftp://cmp.felk.cvut.cz/pub/cmp/articles/barva/Barva-TR-2007-12.pdf">[Barva-TR-2007-12.pdf]</a>},
  project =        {GAAS 1ET101050403, 2005-06-007-1, French Embassy doctoral
                    studies under co-supervision},
  annote = {The medical interventions involving surgical instruments
    are frequently combined with ultrasound based imaging systems to
    safely navigate instruments into a pre-defined place inside the
    body. To facilitate visual tracking of the instrument in acquired
    images, we propose robust techniques that automatically determine
    the position of an electrode-like object in a 3D ultrasound
    image. The task is decomposed into the localization of the
    electrode axis and the detection of its tip. We show that the axis
    can be found by maximizing the parallel projection which we
    describe by a Parallel Integral Projection transform. The
    maximization is accelerated by a hierarchical mesh-grid method. A
    second algorithm based on the model fitting paradigm is introduced
    to identify the axis of a curved electrode. Three distinct models
    are suggested to describe the geometrical shape and the prior
    information about the intensity distribution. Their parameters are
    robustly estimated by the R-RANSAC algorithm. The electrode
    position is optimized using the simplex method. The electrode tip
    is detected by analyzing the intensity along the axis with a
    priori calculated parameters. 3D ultrasound images were simulated
    to test the influence of various conditions such as the electrode
    orientation, depth of penetration, curvature and the effect of
    changing the signal-to-noise ratio on the localization
    accuracy. The algorithms were also experimentally verified on real
    ultrasound images acquired by a 3D scanner scanning a portion of a
    cryogel phantom that contained a thin metallic electrode. The
    experiments show that the algorithms are capable of detecting a
    curved electrode with the accuracy of 0.2 mm. The computational
    time of the order of seconds permit fast electrode localization.},
  keywords =     {ultrasound, 3D, localization, segmentation, electrode,
                  needle, projection, ransac},
}