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Proceedings Paper

Improvement of method for computer-assisted detection of pulmonary nodules in CT of the chest
Author(s): Martin Fiebich; Dag Wormanns; Walter Heindel
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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 the first step the CAD technique for nodule detection in CT examinations of the lung 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. In a study, which should evaluate the feasibility of screening lung cancer, about 2000 thoracic examinations were performed. The CAD system was used for reporting in a consecutive subset (n=100) of those studies. Computation time is about 5 min on an Silicon Graphics O2 workstation. Of the total number of found nodules >= 5 mm (n=68) 26 were found by the CAD scheme, 59 were detected by the radiologist. The CAD workstation helped the radiologist to identify 9 additional nodules. The false positive rate was less than 0.1 per image. The nodules missed by the CAD scheme were analyzed and the reasons for failure categorized into the density of the nodule is too low, nodules is connected to chest wall, segmentation error, and misclassification. Possible solutions for those problems are presented. We have developed a technique, which increased the detection rate of the radiologist in the detection of pulmonary nodules in CT exams of the chest. Correction of the CAD scheme using the analysis of the missed nodules will further enhance the performance of this method.

Paper Details

Date Published: 3 July 2001
PDF: 8 pages
Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); doi: 10.1117/12.431147
Show Author Affiliations
Martin Fiebich, Univ. of Applied Sciences (Germany)
Dag Wormanns, Univ. of Muenster (Germany)
Walter Heindel, Univ. of Muenster (Germany)

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

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