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Proceedings Paper

Improvement in automated detection of pulmonary nodules on helical x-ray CT images
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Paper Abstract

We previously developed a scheme to automatically detect pulmonary nodules on CT images, as a part of computer-aided diagnosis (CAD) system. The proposed method consisted of two template-matching approaches based on simple models that simulate real nodules. One was a new template-matching technique based on a genetic algorithm (GA) template matching (GATM) for detecting nodules within the lung area. The other one was a conventional template matching along the lung wall [lung wall template matching (LWTM)] for detecting nodules on the lung wall. After the two template matchings, thirteen feature values were calculated and used for eliminating false positives. Twenty clinical cases involving a total of 557 sectional images were applied; 71 nodules out of 98 were correctly detected with the number of false positives at approximately 30.8/case by applying two template matchings (GATM and LWTM) and elimination process of false positives. In this study, five features were newly added, and threshold-values of our previous features were reconsidered for further eliminating false positives. As the result, the number of false positives was decreased to 5.5/case without elimination of true positives.

Paper Details

Date Published: 12 May 2004
PDF: 9 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.536162
Show Author Affiliations
Yongbum Lee, Niigata Univ. School of Health Sciences (Japan)
Du-Yih Tsai, Niigata Univ. School of Health Sciences (Japan)
Takeshi Hara, Gifu Univ. School of Medicine (Japan)
Hiroshi Fujita, Gifu Univ. School of Medicine (Japan)
Shigeki Itoh, Nagoya Univ. School of Health Sciences (Japan)
Takeo Ishigaki, Nagoya Univ. School of Medicine (Japan)

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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