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Proceedings Paper

Analysis of a three-dimensional lung nodule detection method for thoracic CT scans
Author(s): Samuel G. Armato; Maryellen Lissak Giger; Heber MacMahon
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Paper Abstract

We are developing an automated method to analyze the three- dimensional nature of structures within CT scans and identify those structures that represent lung nodules. The set of segmented lung regions from all sections of a CT scan forms a segmented lung volume within which multiple gray-level thresholds are applied. Contiguous three-dimensional structures are identified within each thresholded lung volume, and structures that satisfy a volume criterion constitute an initial set of nodule candidates. A feature vector is then computed for each nodule candidate. A rule-based scheme is applied to the initial candidate set to reduce the number of nodule candidates that correspond to normal anatomy. Feature vectors for the remaining candidates are merged through an automated classifier to further distinguish between candidates that correspond to nodules and candidates that correspond to normal structures. This automated method demonstrates promising performance in its ability to detect lung nodules in CT images. Such a technique may assist radiologists evaluate, for example, images from low-dose, screening thoracic CT examinations.

Paper Details

Date Published: 6 June 2000
PDF: 7 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387742
Show Author Affiliations
Samuel G. Armato, Univ. of Chicago (United States)
Maryellen Lissak Giger, Univ. of Chicago (United States)
Heber MacMahon, Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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