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

Matús Straka, Alexandra La Cruz, Arnold Köchl, Milos Srámek, Meister Eduard Gröller, Dominik Fleischmann
3D Watershed Transform Combined with a Probabilistic Atlas for Medical Image Segmentation
In MIT 2003. 2003.
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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

@inproceedings{Straka-2003-3DW,
  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 Milos Sr\'{a}mek and Meister Eduard Gr\"{o}ller and
               Dominik Fleischmann",
  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.",
  booktitle =  "MIT 2003",
  journal =    "Journal of Medical Informatics & Technologies",
  keywords =   "Thin-Plate-Spline, Knowledge Based Segmentation Probabilist,
               CT Angiography, Histogram Classification",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2003/Straka-2003-3DW/",
}