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Erschienen in:
2015 | OriginalPaper | Buchkapitel
A Technique for Lung Nodule Candidate Detection in CT Using Global Minimization Methods
verfasst von : Nóirín Duggan, Egil Bae, Shiwen Shen, William Hsu, Alex Bui, Edward Jones, Martin Glavin, Luminita Vese
Erschienen in: Energy Minimization Methods in Computer Vision and Pattern Recognition
Verlag: Springer International Publishing
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The first stage in computer aided pulmonary nodule detection schemes is a candidate detection step designed to provide a simplified representation of the lung anatomy, such that features like the lung wall, and large airways are removed leaving only data which has greater potential to be a nodule. Nodules which are connected to blood vessels tend to be characterized by irregular geometrical features which can result in their remaining undetected by rule-based classifiers relying only local image metrics. In the current paper a novel approach for lung nodule candidate detection is proposed based on the application of global segmentation methods combined with mean curvature minimization and simple rule-based filtering. Experimental results indicate that the proposed method can accurately detect nodules displaying a diverse range of geometrical features.