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Proceedings Paper

Computerized lung nodule detection: comparison of performance for low-dose and standard-dose helical CT scans
Author(s): Samuel G. Armato III; Maryellen Lissak Giger; Kunio Doi; Ulrich Bick; Heber MacMahon
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Paper Abstract

The vast amount of image data acquired during a computed tomography (CT) scan makes lung nodule detection a burdensome task. Moreover, the growing acceptance of low-dose CT for lung cancer screening promises to further impact radiologists' workloads. Therefore, we have developed a computerized method to automatically analyze structures within a CT scan and identify those structures that represent lung nodules. Gray-level thresholding is performed to segment the lungs in each section to produce a segmented lung volume, which is then iteratively thresholded. At each iteration, remaining voxels are grouped into contiguous three-dimensional structures. Structures that satisfy a volume criterion then become nodule candidates. The set of nodule candidates is subjected to feature analysis. To distinguish candidates representing nodule and non-nodule structures, a rule-based approach is combined with an automated classifier. This method was applied to 43 standard-dose (diagnostic) CT scans and 13 low-dose CT scans. The method achieved an overall detection sensitivity of 71% with 1.5 false-positive detections per section on the standard-dose database and 71% sensitivity with 1.2 false-positive detections per section on the low-dose database. This automated method demonstrates promising performance in its ability to accurately detect lung nodules in standard-dose and low-dose CT images.

Paper Details

Date Published: 3 July 2001
PDF: 6 pages
Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); doi: 10.1117/12.431026
Show Author Affiliations
Samuel G. Armato III, Univ. of Chicago (United States)
Maryellen Lissak Giger, Univ. of Chicago (United States)
Kunio Doi, Univ. of Chicago (United States)
Ulrich Bick, Univ. of Chicago (United States)
Heber MacMahon, Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 4322:
Medical Imaging 2001: Image Processing
Milan Sonka; Kenneth M. Hanson, Editor(s)

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