Conference proceeding
Automated detection of small-size pulmonary nodules based on helical CT images
Information processing in medical imaging : proceedings of the ... conference, Vol.19, pp.664-676
2005
PMID: 17354734
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.
Details
- Title: Subtitle
- Automated detection of small-size pulmonary nodules based on helical CT images
- Creators
- Xiangwei Zhang - University of IowaGeoffrey McLennanEric A HoffmanMilan Sonka
- Resource Type
- Conference proceeding
- Publication Details
- Information processing in medical imaging : proceedings of the ... conference, Vol.19, pp.664-676
- PMID
- 17354734
- NLM abbreviation
- Inf Process Med Imaging
- ISSN
- 1011-2499
- Language
- English
- Date published
- 2005
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Internal Medicine; Ophthalmology and Visual Sciences
- Record Identifier
- 9984186691402771
Metrics
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