Automated Detection of Small-Size Pulmonary Nodules Based on Helical CT Images

Xiangwei Zhang; Geoffrey McLennan; Eric A Hoffman; Milan Sonka

doi:10.1007/11505730_55

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Automated Detection of Small-Size Pulmonary Nodules Based on Helical CT Images

Book chapter

Peer reviewed

Automated Detection of Small-Size Pulmonary Nodules Based on Helical CT Images

Xiangwei Zhang, Geoffrey McLennan, Eric A Hoffman and Milan Sonka

Information Processing in Medical Imaging, pp.664-676

Lecture Notes in Computer Science, Springer Berlin Heidelberg

2005

DOI: 10.1007/11505730_55

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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.

Cellular Neural Network

Linear Discriminant Analysis

Nodule Detection

Pulmonary Nodule

Shape Index

Details

Title: Subtitle: Automated Detection of Small-Size Pulmonary Nodules Based on Helical CT Images
Creators: Xiangwei Zhang - University of Iowa
Geoffrey McLennan - University of Iowa
Eric A Hoffman - University of Iowa
Milan Sonka - University of Iowa
Resource Type: Book chapter
Publication Details: Information Processing in Medical Imaging, pp.664-676
Series: Lecture Notes in Computer Science
DOI: 10.1007/11505730_55
eISSN: 1611-3349
ISSN: 0302-9743
Publisher: Springer Berlin Heidelberg; Berlin, Heidelberg
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: 9984186692502771

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