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Proceedings Paper

Classification of lung nodules in diagnostic CT: an approach based on 3D vascular features, nodule density distribution, and shape features
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Paper Abstract

We have developed various segmentation and analysis methods for the quantification of lung nodules in thoracic CT. Our methods include the enhancement of lung structures followed by a series of segmentation methods to extract the nodule and to form 3D configuration at an area of interest. The vascular index, aspect ratio, circularity, irregularity, extent, compactness, and convexity were also computed as shape features for quantifying the nodule boundary. The density distribution of the nodule was modeled based on its internal homogeneity and/or heterogeneity. We also used several density related features including entropy, difference entropy as well as other first and second order moments. We have collected 48 cases of lung nodules scanned by thin-slice diagnostic CT. Of these cases, 24 are benign and 24 are malignant. A jackknife experiment was performed using a standard back-propagation neural network as the classifier. The LABROC result showed that the Az of this preliminary study is 0.89.

Paper Details

Date Published: 15 May 2003
PDF: 7 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481878
Show Author Affiliations
Shih-Chung Benedict Lo, Georgetown Univ. Medical Ctr. (United States)
Li-Yueh Hsu, National Institutes of Health (United States)
Matthew T. Freedman, Georgetown Univ. Medical Ctr. (United States)
Yuan Ming Fleming Lure, Deus Technologies, Inc. (United States)
Hui Zhao, Georgetown Univ. Medical Ctr. (United States)
Catholic Univ. of America (United States)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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