Automated detection of small-size pulmonary nodules based on helical CT images

Authors:
Xiangwei Zhang;Geoffrey McLennan;Eric A. Hoffman;Milan Sonka
Affiliations:
Dept. of Electrical Engineering, University of Iowa, Iowa City, IA;Department of Internal Medicine, University of Iowa, Iowa City, IA;Department of Radiology, University of Iowa, Iowa City, IA;Dept. of Electrical Engineering, University of Iowa, Iowa City, IA
Venue:
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Year:
2005

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Abstract

A computer-aided diagnosis (CAD) system to detect small-size (from 2 mm to around 10 mm) pulmonary nodules in helical CT scans is developed. This system uses different schemes to locate juxtapleural nodules and non-pleural nodules. For juxtapleural nodules, morphological closing, thresholding and labeling are performed to obtain volumetric nodule candidates; gray level and geometric features are extracted and analyzed using a linear discriminant analysis (LDA) classifier. To locate non-pleural nodules, a discrete-time cellular neural network (DTCNN) uses local shape features which successfully capture the differences between nodules and non-nodules, especially vessels. The DTCNN was trained using genetic algorithm (GA). Testing on 17 cases with 3979 slice images showed the effectiveness of the proposed system, yielding sensitivity of 85.6% with 9.5 FPs/case (0.04 FPs/image). Moreover, the CAD system detected many nodules missed by human visual reading. This showed that the proposed CAD system acted effectively as an assistant for human experts to detect small nodules and provided a “second opinion” to human observers.