Share Email Print
cover

Proceedings Paper

Automatic detection of pulmonary nodules in low-dose screening thoracic CT examinations
Author(s): Martin Fiebich; Christian Wietholt; Bernhard C. Renger; Samuel G. Armato; Kenneth R. Hoffmann; Dag Wormanns; Stefan Diederich
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Computed tomography of the chest can be used as a screening method for lung cancer in a high-risk population. However, the detection of lung nodules is a difficult and time-consuming task for radiologists. The developed technique should improve the sensitivity of the detection of lung nodules without showing too many false positive nodules. In a study, which should evaluate the feasibility of screening lung cancer, about 1400 thoracic studies were acquired. Scanning parameters were 120 kVp, 5 mm collimation pitch of 2, and a reconstruction index of 5 mm. This results in a data set of about 60 to 70 images per exam. In the images the detection technique first eliminates all air outside the patient, then soft tissue and bony structures are removed. In the remaining lung fields a three-dimensional region detection is performed and rule-based analysis is used to detect possible lung nodules. This technique was applied to a small subset (n equals 17) of above studies. Computation time is about 5 min on an O2 workstation. The use of low-dose exams proved not be a hindrance in the detection of lung nodules. All of the nodules (n equals 23), except one with a size of 3 mm, were detected. The false positive rate was less than 0.3 per image. We have developed a technique, which might help the radiologist in the detection of pulmonary nodules in CT exams of the chest.

Paper Details

Date Published: 21 May 1999
PDF: 6 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348543
Show Author Affiliations
Martin Fiebich, Univ. of Muenster (Germany)
Christian Wietholt, Univ. of Muenster (United States)
Bernhard C. Renger, Univ. of Muenster (Germany)
Samuel G. Armato, Univ. of Chicago (United States)
Kenneth R. Hoffmann, Univ. of Chicago (United States)
Dag Wormanns, Univ. of Muenster (Germany)
Stefan Diederich, Univ. of Muenster (Germany)


Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

© SPIE. Terms of Use
Back to Top