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Proceedings Paper

A multiscale Laplacian of Gaussian filtering approach to automated pulmonary nodule detection from whole-lung low-dose CT scans
Author(s): Sergei V. Fotin; Anthony P. Reeves; Alberto M. Biancardi; David F. Yankelevitz; Claudia I. Henschke
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Paper Abstract

The primary stage of a pulmonary nodule detection system is typically a candidate generator that efficiently provides the centroid location and size estimate of candidate nodules. A scale-normalized Laplacian of Gaussian (LOG) filtering method presented in this paper has been found to provide high sensitivity along with precise locality and size estimation. This approach involves a computationally efficient algorithm that is designed to identify all solid nodules in a whole lung anisotropic CT scan. This nodule candidate generator has been evaluated in conjunction with a set of discriminative features that target both isolated and attached nodules. The entire detection system was evaluated with respect to a sizeenriched dataset of 656 whole-lung low-dose CT scans containing 459 solid nodules with diameter greater than 4 mm. Using a soft margin SVM classifier, and setting false positive rate of 10 per scan, we obtained a sensitivity of 97% for isolated, 93% for attached, and 89% for both nodule types combined. Furthermore, the LOG filter was shown to have good agreement with the radiologist ground truth for size estimation.

Paper Details

Date Published: 3 March 2009
PDF: 8 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601Q (3 March 2009); doi: 10.1117/12.811420
Show Author Affiliations
Sergei V. Fotin, Cornell Univ. (United States)
Anthony P. Reeves, Cornell Univ. (United States)
Alberto M. Biancardi, Cornell Univ. (United States)
David F. Yankelevitz, New York Presbyterian Hospital, Weill Cornell Medical Ctr. (United States)
Claudia I. Henschke, New York Presbyterian Hospital, Weill Cornell Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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