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Proceedings Paper

A topology-oriented and tissue-specific approach to detect pleural thickenings from 3D CT data
Author(s): C. Buerger; K. Chaisaowong; A. Knepper; T. Kraus; T. Aach
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Paper Abstract

Pleural thickenings are caused by asbestos exposure and may evolve into malignant pleural mesothelioma. The detection of pleural thickenings is today mostly done by a visual inspection of CT data, which is time-consuming and underlies the physician's subjective judgment. We propose a new detection algorithm within our computer-assisted diagnosis (CAD) system to automatically detect pleural thickenings within CT data. First, pleura contours are identified by thresholding and contour relaxation with a probabilistic model. Subsequently, the approach to automatically detect pleural thickenings is proposed as a two-step procedure. Step one; since pleural thickenings appear as fine-scale occurrences on the rather large-scale pleura contour, a surface-based smoothing algorithm is developed. Pleural thickenings are initially detected as the difference between the original contours and the resulting "healthy" model of the pleura. Step two; as pleural thickenings can expand into the surrounding thoracic tissue, a subsequent tissue-specific segmentation for the initially detected pleural thickenings is performed in order to separate pleural thickenings from the surrounding thoracic tissue. For this purpose, a probabilistic Hounsfield model for pleural thickenings as a mixture of Gaussian distributions has been constructed. The parameters were estimated by applying the Expectation-Maximization (EM) algorithm. A model fitting technique in combination with the application of a Gibbs-Markov random field (GMRF) model then allows the tissuespecific segmentation of pleural thickenings with high precision. With these methods, a new approach is presented in order to assure a precise and reproducible detection of pleural mesothelioma in its early stage.

Paper Details

Date Published: 27 March 2009
PDF: 11 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72593D (27 March 2009); doi: 10.1117/12.811425
Show Author Affiliations
C. Buerger, RWTH Aachen Univ. (Germany)
K. Chaisaowong, RWTH Aachen Univ. (Germany)
King Mongkut's Univ. of Technology (Thailand)
A. Knepper, RWTH Aachen Univ. (Germany)
T. Kraus, Univ. Hospital Aachen (Germany)
T. Aach, RWTH Aachen Univ. (Germany)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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