The automated detection of lung nodules in CT scans is an important problem in computer-aided diagnosis. In this paper an approach to nodule candidate detection is presented which utilises the local image features of shape index and curvedness. False-positive candidates are removed by means of a two-step approach using kNN classification. The kNN classifiers are trained using features of the image intensity gradients and grey-values in addition to further measures of shape index and curvedness profiles in the candidate regions. The training set consisted of data from 698 scans while the independent test set comprised a further 142 images. At 84% sensitivity an average of 8.2 false-positive detections per scan were observed.

Date Published: 29 March 2007
PDF: 12 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 651410 (29 March 2007); doi: 10.1117/12.713370

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