Abstract
Assessment of the growth of pleural thickenings is crucial for an early diagnosis of pleuramesothelioma. The presented automatic system supports the physician in comparing two temporally consecutive CT data-sets to determine this growth. The algorithms perform the determination of the pleural contours. After surface-based smoothing, anisotropic diffusion, a model-oriented probabilistic classification specifies the thickening’s tissue. The volume of each detected thickening is determined. While doctors still have the possibility to supervise the detection results, a full automatic registration carries out the matching of the same thickenings in two consecutive datasets to fulfill the change follow-up, where manual control is still possible thereafter. All algorithms were chosen and designed to meet runtime requirements, which allow an application in the daily routine.
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References
Hagemeyer O, Otten H, Kraus T. Asbestos consumption, asbestos exposure and asbestos-related occupational diseases in Germany. Int Arch Occup Environ Health. 2006;79:613–20.
Ochsmann E, Carl T, Brand P, et al. Inter-reader variability in chest radiography and HRCT for the early detection of asbestos-related lung and pleural abnormalities in a cohort of 636 asbestos-exposed subjects. Int Arch Occup Environ Health. 2010;83:39–46.
Lehmann T, Meinzer H, Tolxdorff T. Advances in biomedical image analysis past, present and future challenges. Methods Inf Med. 2004;43(4):308–14.
Sensakovic W, Armato III S, Straus C, et al. Computerized segmentation and measurement of malignant pleural mesothelioma. Med Phys. 2011;38(1):238–44.
Armato III S, Ogarek J, Starkey A, et al. Variability in mesothelioma tumor response classification. Am J Roentgenol. 2006;186:1000–6.
Rudrapatna M, Mai V, Sowmya A, et al. Knowledge-driven automated detection of pleural plaques and thickening in high resolution CT of the lung. Proc Int Conf Inf Process Med Imaging. 2005; p. 270–85.
B¨urger C, Chaisaowong K, Knepper A, et al. A topology-oriented and tissuespecific approach to detect pleural thickenings from 3D CT data. Proc SPIE. 2009;7259:72593D–1–11.
Faltin P, Chaisaowong K, Kraus T, et al. Markov-Gibbs model based registration of CT lung images using subsampling for the follow-up assessment of pleural thickenings. Proc ICIP. 2011;18:2229–32.
Ohm JR. Multimedia Communication Technology: Representation, Transmission and Identification of Multimedia Signals. Springer; 2004.
Wolf I, Vetter M, Wegner I, et al. The medical imaging interaction toolkit. Med Image Anal. 2005;9:594–604.
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© 2014 Springer-Verlag Berlin Heidelberg
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Chaisaowong, K., Faltin, P., Kraus, T. (2014). Automated Assessment of Pleural Thickening. In: Deserno, T., Handels, H., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2014. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54111-7_7
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DOI: https://doi.org/10.1007/978-3-642-54111-7_7
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