Paper
26 March 2008 Fuzzy pulmonary vessel segmentation in contrast enhanced CT data
Jens N. Kaftan, Atilla P. Kiraly, Annemarie Bakai, Marco Das, Carol L. Novak, Til Aach
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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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jens N. Kaftan, Atilla P. Kiraly, Annemarie Bakai, Marco Das, Carol L. Novak, and Til Aach "Fuzzy pulmonary vessel segmentation in contrast enhanced CT data", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69141Q (26 March 2008); https://doi.org/10.1117/12.768795
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CITATIONS
Cited by 33 scholarly publications and 4 patents.
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KEYWORDS
Image segmentation

Lung

Computed tomography

Photovoltaics

Fuzzy logic

Binary data

Computer aided diagnosis and therapy

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