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Proceedings Paper

Automatic two-step detection of pulmonary nodules
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Paper Abstract

We present a computer-aided diagnosis (CAD) system to detect small-size (from 2mm to around 10mm) pulmonary nodules from helical CT scans. A pulmonary nodule is a small, round (parenchymal nodule) or worm (juxta-pleural) shaped lesion in the lungs. Both have greater radio density than lungs parenchyma. Lung nodules may indicate a lung cancer and its detection in early stage improves survival rate of patients. CT is considered to be the most accurate imaging modality for detection of nodules. However, the large amount of data per examination makes the interpretation difficult. This leads to omission of nodules by human radiologist. CAD system presented is designed to help lower the number of omissions. Our system uses two different schemes to locate juxtapleural nodules and parenchymal nodules. For juxtapleural nodules, morphological closing and thresholding is used to find nodule candidates. To locate non-pleural nodule candidates, 3D blob detector uses multiscale filtration. Ellipsoid model is fitted on nodules. To define which of the nodule candidates are in fact nodules, an additional classification step is applied. Linear and multi-threshold classifiers are used. System was tested on 18 cases (4853 slices) with total sensitivity of 96%, with about 12 false positives/slice. The classification step reduces number of false positives to 9 per slice without significantly decreasing sensitivity (89,6%).

Paper Details

Date Published: 30 March 2007
PDF: 12 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65143J (30 March 2007); doi: 10.1117/12.709161
Show Author Affiliations
Martin Dolejší, Czech Technical Univ. in Prague (Czech Republic)
Jan Kybic, Czech Technical Univ. in Prague (Czech Republic)


Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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