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Volumn , Issue , 2008, Pages 497-502

Automated identification of lung nodules

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL ORGANS; CLASSIFIERS; DATA STORAGE EQUIPMENT; DECISION THEORY; DECISION TREES; ELECTRIC INSTRUMENT TRANSFORMERS; LEARNING SYSTEMS; MATHEMATICAL MODELS; SIGNAL PROCESSING; TECHNICAL PRESENTATIONS; TURBULENT FLOW;

EID: 58049101388     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MMSP.2008.4665129     Document Type: Conference Paper
Times cited : (12)

References (14)
  • 1
    • 58049107734 scopus 로고    scopus 로고
    • J. H. Austin, N. L. Mueller, and P. J. Friedman, Glossary of terms for CT of the lungs: recommendations of the nomenclature, Committee of the Fleischner Society. Radiology, 331, pp. 200:327, 1996.
    • J. H. Austin, N. L. Mueller, and P. J. Friedman, "Glossary of terms for CT of the lungs: recommendations of the nomenclature," Committee of the Fleischner Society. Radiology, vol. 331, pp. 200:327, 1996.
  • 2
    • 4143056820 scopus 로고    scopus 로고
    • Lung Image Database Consortium Developing a Resource for the Medical Imaging Research Community
    • S. G. Armato III, G. McLennan, M. F. McNitt-Gray, C. R. Meyer, D. Yankelevitz, and e. al., "Lung Image Database Consortium Developing a Resource for the Medical Imaging Research Community," Radiology, vol. 232, pp. 739-748, 2004.
    • (2004) Radiology , vol.232 , pp. 739-748
    • Armato III, S.G.1    McLennan, G.2    McNitt-Gray, M.F.3    Meyer, C.R.4    Yankelevitz, D.5    al, E.6
  • 3
    • 0035478854 scopus 로고    scopus 로고
    • Random Forests
    • L. Breiman, "Random Forests," Machine Learning, vol. 45, pp. 5-32, 2001.
    • (2001) Machine Learning , vol.45 , pp. 5-32
    • Breiman, L.1
  • 4
    • 33750965256 scopus 로고    scopus 로고
    • Improved Classification of Pulmonary Nodules by Automated Detection of Benign Subpleural Lymph Nodes
    • M. A. J. Klik, E. M. v. Rikxoort, J. F. Peters, H. A. Gietema, M. Prokop, and B. v. Ginneken, "Improved Classification of Pulmonary Nodules by Automated Detection of Benign Subpleural Lymph Nodes." in ISBI, 2006.
    • (2006) ISBI
    • Klik, M.A.J.1    Rikxoort, E.M.V.2    Peters, J.F.3    Gietema, H.A.4    Prokop, M.5    Ginneken, B.V.6
  • 6
    • 33646457262 scopus 로고    scopus 로고
    • False positive reduction for lung nodule CAD using support vector machines and genetic algorithms
    • L. Zhao, L. Boroczky, and K. P. Lee, "False positive reduction for lung nodule CAD using support vector machines and genetic algorithms," International Congress Series vol. 1281, pp. 1109-1114, 2005.
    • (2005) International Congress Series , vol.1281 , pp. 1109-1114
    • Zhao, L.1    Boroczky, L.2    Lee, K.P.3
  • 9
    • 48349084576 scopus 로고    scopus 로고
    • Accuracy Improvement of Pulmonary Nodule Detection Based on Spatial Statistical Analysis of Thoracic CT Scans
    • H. Takizawa, S. Yamamoto, and T. Shiina, "Accuracy Improvement of Pulmonary Nodule Detection Based on Spatial Statistical Analysis of Thoracic CT Scans," IEICE TRANS. INF. & SYST., vol. 90-D, pp. 1168-1174, 2007.
    • (2007) IEICE TRANS. INF. & SYST , vol.90-D , pp. 1168-1174
    • Takizawa, H.1    Yamamoto, S.2    Shiina, T.3
  • 11
    • 0345040873 scopus 로고    scopus 로고
    • Classification and regression by randomForest
    • A. Liaw and M. Wiener, "Classification and regression by randomForest," R News, vol. 2, pp. 18-20, 2002.
    • (2002) R News , vol.2 , pp. 18-20
    • Liaw, A.1    Wiener, M.2
  • 13
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman, "Bagging predictors," Machine Learning, vol. 24, 1996.
    • (1996) Machine Learning , vol.24
    • Breiman, L.1
  • 14
    • 58049099058 scopus 로고    scopus 로고
    • Lung Imaging Database Consortium LIDC, Online, Available
    • "Lung Imaging Database Consortium (LIDC)." [Online]. Available: http://imaging.cancer.gov/programsandresources/InformationSystems/LIDC.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.