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.
CITATION STYLE
Duggan, N., Bae, E., Shen, S., Hsu, W., Bui, A., Jones, E., … Vese, L. (2015). A technique for lung nodule candidate detection in ct using global minimization methods. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8932, pp. 478–491). Springer Verlag. https://doi.org/10.1007/978-3-319-14612-6_35
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