Abstract
We present a knowledge-based approach for the automatic segmentation of lungs in 3D thoracic CT images.
The segmentation of the dependent zones of the lungs with atelectasis poses a challenge because in CT images they have similar texture and gray level as the surrounding tissue and therefore there is no graphical information in this region of the lung that can be used to distinguish it from surrounding anatomical structures. Thus, finding the boundary of the lungs in the lower dorsal and juxta-diaphragm region may be very difficult even for an expert.
Anatomical information like shape and position of the ribs and airways, among others, which are independent of the degree of the disease, can be used to estimate the borders of the lungs.
In this paper we describe the features used to extract landmarks from the images as well as the algorithms that use them to segment the lungs.
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© 2009 Springer-Verlag Berlin Heidelberg
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Cuevas, L.M., Spieth, P.M., Carvalho, A.R., de Abreu, M.G., Koch, E. (2009). Automatic Lung Segmentation of Helical-CT Scans in Experimental Induced Lung Injury. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds) 4th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89208-3_183
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DOI: https://doi.org/10.1007/978-3-540-89208-3_183
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89207-6
Online ISBN: 978-3-540-89208-3
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