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Proceedings Paper

Automatic detection of lung nodules from multislice low-dose CT images
Author(s): Li Fan; Carol L. Novak; JianZhong Qian; Gerhard Kohl; David Naidich M.D.
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Paper Abstract

We describe in this paper a novel, efficient method to automatically detect lung nodules from low-dose, high- resolution CT (HRCT) images taken with a multi-slice scanner. First, the program identifies initial anatomical seeds, including lung nodule candidates, airways, vessels, and other features that appear as bright opacities in CT images. Next, a 3D region growing method is applied to each seed. The thresholds for segmentation are adaptively adjusted based upon automatic analysis of the local histogram. Once an object has been examined, vessels and other non-nodule objects are quickly excluded from future study, thus saving computation time. Finally, extracted 3D objects are classified a nodule candidates or non-nodule structures. Anatomical knowledge and multiple measurements, such as volume and sphericity, are used to categorize each object. The detected nodules are presented to the user for examination and verification. The proposed method was applied to 14 low dose HRCT patient studies. Since the CT images were taken with a multi-slice scanner, the average number of slices per study was 292. In every case the x-ray exposure was about 20 mAs, a suitable dosage for screening. In our preliminary results, the method detected an average of 8 nodules per study, with an average size of 3.3 mm in diameter.

Paper Details

Date Published: 3 July 2001
PDF: 8 pages
Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); doi: 10.1117/12.431073
Show Author Affiliations
Li Fan, Siemens Corporate Research, Inc. (United States)
Carol L. Novak, Siemens Corporate Research, Inc. (United States)
JianZhong Qian, Siemens Corporate Research, Inc. (United States)
Gerhard Kohl, Siemens AG (Germany)
David Naidich M.D., New York Univ. Medical Ctr. (United States)

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

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