3D Watershed Transform Combined with a Probabilistic Atlas for Medical Image Segmentation

Information

• Publication Type: Technical Report
• Workgroup(s)/Project(s): not specified
• Date: November 2003
• Number: TR-186-2-03-13
• Keywords: Histogram Classification, Thin-Plate-Spline, Probabilistic Atlas, Knowledge Based Segmentation, CT Angiography

Abstract

Recent advances in medical imaging technology using multiple detector-row computed tomography (CT) provide volumetric datasets with unprecedented spatial resolution. This has allowed for CT to evolve into an excellent non-invasive vascular imaging technology, commonly referred to as CT-angiography. Visualization of vascular structures from CT datasets is demanding, however, and identification of anatomic objects in CT-datasets is highly desirable. Density and/or gradient operators have been used most commonly to classify CT data. In CT angiography, simple density/gradient operators do not allow precise and reliable classification of tissues due to the fact that different tissues (e.g. bones and vessels) possess the same density range and may lie in close spatial vicinity. We hypothesize, that anatomic classification can be achieved more accurately, if both spatial location and density properties of volume data are taken into account. We present a combination of two well-known methods for volume data processing to obtain accurate tissue classification. 3D watershed transform is used to partition the volume data in morphologically consistent blocks and a probabilistic anatomic atlas is used to distinguish between different kinds of tissues based on their density.

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BibTeX

@techreport{Straka-2003-CTA,
  title =      "3D Watershed Transform Combined with a Probabilistic Atlas
               for Medical Image Segmentation",
  author =     "Mat\'{u}s Straka and Alexandra La Cruz and Arnold K\"{o}chl
               and Milo\v{s} \v{S}r\'{a}mek and Dominik Fleischmann and
               Eduard Gr\"{o}ller",
  year =       "2003",
  abstract =   "Recent advances in medical imaging technology using multiple
               detector-row computed tomography (CT) provide volumetric
               datasets with unprecedented spatial resolution. This has
               allowed for CT to evolve into an excellent non-invasive
               vascular imaging technology, commonly referred to as
               CT-angiography. Visualization of vascular structures from CT
               datasets is demanding, however, and identification of
               anatomic objects in CT-datasets is highly desirable. Density
               and/or gradient operators have been used most commonly to
               classify CT data. In CT angiography, simple density/gradient
               operators do not allow precise and reliable classification
               of tissues due to the fact that different tissues (e.g.
               bones and vessels) possess the same density range and may
               lie in close spatial vicinity. We hypothesize, that anatomic
               classification can be achieved more accurately, if both
               spatial location and density properties of volume data are
               taken into account. We present a combination of two
               well-known methods for volume data processing to obtain
               accurate tissue classification. 3D watershed transform is
               used to partition the volume data in morphologically
               consistent blocks and a probabilistic anatomic atlas is used
               to distinguish between different kinds of tissues based on
               their density.",
  month =      nov,
  number =     "TR-186-2-03-13",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  institution = "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  note =       "human contact: technical-report@cg.tuwien.ac.at",
  keywords =   "Histogram Classification, Thin-Plate-Spline, Probabilistic
               Atlas, Knowledge Based Segmentation, CT Angiography",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2003/Straka-2003-CTA/",
}