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Proceedings Paper

A novel spherical shell filter for reducing false positives in automatic detection of pulmonary nodules in thoracic CT scans
Author(s): Sil van de Leemput; Frank Dorssers; Babak Ehteshami Bejnordi
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Paper Abstract

Early detection of pulmonary nodules is crucial for improving prognosis of patients with lung cancer. Computer-aided detection of lung nodules in thoracic computed tomography (CT) scans has a great potential to enhance the performance of the radiologist in detecting nodules. In this paper we present a computer-aided lung nodule detection system for computed tomography (CT) scans that works in three steps. The system first segments the lung using thresholding and hole filling. From this segmentation the system extracts candidate nodules using Laplacian of Gaussian. To reject false positives among the detected candidate nodules, multiple established features are calculated. We propose a novel feature based on a spherical shell filter, which is specifically designed to distinguish between vascular structures and nodular structures. The performance of the proposed CAD system was evaluated by partaking in the ANODE09 challenge, which presents a platform for comparing automatic nodule detection programs. The results from the challenge show that our CAD system ranks third among the submitted works, demonstrating the efficacy of our proposed CAD system. The results also show that our proposed spherical shell filter in combination with conventional features can significantly reduce the number of false positives from the detected candidate nodules.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94142P (20 March 2015); doi: 10.1117/12.2082298
Show Author Affiliations
Sil van de Leemput, Radboud Univ. Nijmegen (Netherlands)
Frank Dorssers, Radboud Univ. Nijmegen (Netherlands)
Babak Ehteshami Bejnordi, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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