12 May 2004 Improvement in automated detection of pulmonary nodules on helical x-ray CT images
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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.
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Yongbum Lee, Yongbum Lee, Du-Yih Tsai, Du-Yih Tsai, Takeshi Hara, Takeshi Hara, Hiroshi Fujita, Hiroshi Fujita, Shigeki Itoh, Shigeki Itoh, Takeo Ishigaki, Takeo Ishigaki, } "Improvement in automated detection of pulmonary nodules on helical x-ray CT images", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.536162; https://doi.org/10.1117/12.536162
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