PROCEEDINGS ARTICLE | April 12, 2005
Proc. SPIE. 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display
KEYWORDS: Image segmentation, Image processing, C++, Tissues, Blood, Lung, Image processing algorithms and systems, Heart, Medical imaging, Algorithm development
For a clinical application of the inverse problem of
electrocardiography, a flexible and fast generation of a patient's
volume conductor model is essential. The volume conductor model
includes compartments like chest, lungs, ventricles, atria and the
associated blood masses. It is a challenging task to create an
automatic or semi-automatic segmentation procedure for each
compartment. For the extraction of the lungs, as one example, a
region growing algorithm can be used, to extract the blood masses
of the ventricles Active Appearance Models may succeed, and to
construct the atrial myocardium a multiplicity of operations are
necessary. These examples illustrate that there is no common
method that will succeed for all compartments like a least common
denominator. Another problem is the automatization of combining
different methods and the origination of a segmentation pipeline
in order to extract a compartment and, accordingly, the desired
model - in our case the complete volume conductor model for
estimating the spread of electrical excitation in the patient's
heart. On account of this, we developed a C++ framework and a
special application with the goal of creating tissue-specific
segmentation pipelines. The C++ framework uses different standard
frameworks like DCMTK for handling medical images
(http://dicom.offis.de/dcmtk.php.en), ITK (http://www.itk.org/)
for some segmentation methods, and Qt (http://www.trolltech.com/) for creating user interfaces. Our Medical Segmentation Toolkit (MST) enables to combine different segmentation techniques for each compartment. In addition, the framework enables to create user-defined compartment pipelines.
