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Proceedings Paper

Fuzzy pulmonary vessel segmentation in contrast enhanced CT data
Author(s): Jens N. Kaftan; Atilla P. Kiraly; Annemarie Bakai; Marco Das; Carol L. Novak; Til Aach
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Paper Abstract

Pulmonary vascular tree segmentation has numerous applications in medical imaging and computer-aided diagnosis (CAD), including detection and visualization of pulmonary emboli (PE), improved lung nodule detection, and quantitative vessel analysis. We present a novel approach to pulmonary vessel segmentation based on a fuzzy segmentation concept, combining the strengths of both threshold and seed point based methods. The lungs of the original image are first segmented and a threshold-based approach identifies core vessel components with a high specificity. These components are then used to automatically identify reliable seed points for a fuzzy seed point based segmentation method, namely fuzzy connectedness. The output of the method consists of the probability of each voxel belonging to the vascular tree. Hence, our method provides the possibility to adjust the sensitivity/specificity of the segmentation result a posteriori according to application-specific requirements, through definition of a minimum vessel-probability required to classify a voxel as belonging to the vascular tree. The method has been evaluated on contrast-enhanced thoracic CT scans from clinical PE cases and demonstrates overall promising results. For quantitative validation we compare the segmentation results to randomly selected, semi-automatically segmented sub-volumes and present the resulting receiver operating characteristic (ROC) curves. Although we focus on contrast enhanced chest CT data, the method can be generalized to other regions of the body as well as to different imaging modalities.

Paper Details

Date Published: 26 March 2008
PDF: 12 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69141Q (26 March 2008); doi: 10.1117/12.768795
Show Author Affiliations
Jens N. Kaftan, RWTH Aachen Univ. (Germany)
Siemens Medical Solutions (Germany)
Atilla P. Kiraly, Siemens Corporate Research (United States)
Annemarie Bakai, Siemens Medical Solutions (Germany)
Marco Das, RWTH Aachen Univ. (Germany)
Carol L. Novak, Siemens Corporate Research (United States)
Til Aach, RWTH Aachen Univ. (Germany)

Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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