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Proceedings Paper

False-positive elimination for computer-aided detection of pulmonary micronodules
Author(s): Sukmoon Chang; Jinghao Zhou; Dimitris N. Metaxas; Leon Axel
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Paper Abstract

Computed Tomography (CT) is generally accepted as the most sensitive way for lung cancer screening. Its high contrast resolution allows the detection of small nodules and, thus, lung cancer at a very early stage. Due to the amount of data it produces, however, automating the nodule detection process is viable. The challenging problem for any nodule detection system is to keep low false-positive detection rate while maintaining high sensitivity. In this paper, we first describe a 3D filter-based method for pulmonary micronodule detection from high-resolution 3D chest CT images. Then, we propose a false-positive elimination method based on a deformable model. Finally, we present promising results of applying our method to various clinical chest CT datasets with over 90% detection rate. The proposed method focuses on the automatic detection of both calcified (high-contrast) and noncalcified (low-contrast) granulomatous nodules less than 5mm in diameter.

Paper Details

Date Published: 15 March 2006
PDF: 8 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61445F (15 March 2006); doi: 10.1117/12.653674
Show Author Affiliations
Sukmoon Chang, Capital College, Pennsylvania State Univ. (United States)
Rutgers Univ. (United States)
Jinghao Zhou, Rutgers Univ. (United States)
Dimitris N. Metaxas, Rutgers Univ. (United States)
Leon Axel, New York Univ. (United States)


Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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