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Automated detection of lung nodules in computed tomography images: a review
journal contribution
posted on 2012-05-14, 00:00 authored by S Lee, Abbas KouzaniAbbas Kouzani, Eric HuLung nodules refer to a range of lung abnormalities the detection of which can facilitate early treatment for lung patients. Lung nodules can be detected by radiologists through examining lung images. Automated detection systems that locate nodules of various sizes within lung images can assist radiologists in their decision making. This paper presents a study of the existing methods on automated lung nodule detection. It introduces a generic structure for lung nodule detection that can be used to represent and describe the existing methods. The structure consists of a number of components including: acquisition, pre-processing, lung segmentation, nodule detection, and false positives reduction. The paper describes the algorithms used to realise each component in different systems. It also provides a comparison of the performance of the existing approaches.
History
Journal
Machine vision and applicationsVolume
23Issue
1Pagination
151 - 163Publisher
SpringerLocation
Heidelberg, GermanyPublisher DOI
ISSN
0932-8092eISSN
1432-1769Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2010, Springer-VerlagUsage metrics
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Keywords
computed tomographylung imagespulmonary nodulesautomated detectionperformance evaluationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, CyberneticsEngineering, Electrical & ElectronicComputer ScienceEngineeringHIGH-RESOLUTION CTTHIN-SECTION CTFALSE-POSITIVE REDUCTIONCELLULAR NEURAL-NETWORKSSMALL PULMONARY NODULESGROUND GLASS OPACITIESLOW-DOSE CTAIDED DIAGNOSISTHORACIC CTCHEST CTArtificial Intelligence and Image ProcessingComputation Theory and Mathematics
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