Computerized Medical Imaging and Graphics
Volume 34, Issue 7 , Pages 535-542 , October 2010

Random forest based lung nodule classification aided by clustering

  • S.L.A. Lee

      Affiliations

    • School of Engineering, Deakin University, Geelong, VIC 3217, Australia
    • ,
  • A.Z. Kouzani

      Affiliations

    • School of Engineering, Deakin University, Geelong, VIC 3217, Australia
    • Corresponding Author InformationCorresponding author. Tel.: +61 3 5227 2818; fax: +61 3 5227 2167.
    • ,
  • E.J. Hu

      Affiliations

    • School of Mechanical Engineering, Adelaide University, North Terrace, Adelaide, SA 5005, Australia

Received 27 August 2009 ,Revised 20 November 2009 ,Accepted 23 March 2010.

References 

  1. Austin JH, Mueller NL, Friedman PJ. Glossary of terms for CT of the lungs: recommendations of the nomenclature. Radiology. 1996;200:327–331
  2. Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI. Three-dimensional segmentation & growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Transactions on Medical Imaging. 2003;22:1259–1274
  3. Armato SG, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, et al. Lung image database consortium developing a resource for the medical imaging research community. Radiology. 2004;232:739–748
  4. Wang P, DeNuzio A, Okunieff P, O’Dell WG. Lung metastases detection in CT images using 3D template matching. Medical Physics. 2007;34:915–922
  5. Pu J, Roos J, Yi CA, Napel S, Rubin GD, Pa DS. Adaptive border marching algorithm: automatic lung segmentation on chest CT images. Computerized Medical Imaging and Graphics. 2008;32:452–462
  6. Kawata Y, Niki N, Ohmatsu H, Kusumoto M, Kakinuma R, Mori K, et al. Hybrid classification approach of malignant and benign pulmonary nodules based on topological and histogram features. MICCAI, Lecture Notes in Computer Science. Springer Berlin/Heidelberg: Berlin; 2000;pp. 297–306
  7. Ochs RA, Goldin JG, Fereidoun A, Kim HJ, Brown K, Batra P, et al. Automated classification of lung bronchovascular anatomy in CT using adaboost. Medical Image Analysis. 2007;11:315–324
  8. Sluimer IC, van Waes PF, Vierever MA, van Ginneken B. Computer-aided diagnosis in high resolution CT of the lungs. Medical Physics. 2003;30:3081–3090
  9. Suzuki K, Li F, Sone S, Doi K. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Transactions on Medical Imaging. 2005;24:1138–1150
  10. Takizawa H, Yamamoto S, Shiina T. Accuracy improvement of pulmonary nodule detection based on spatial statistical analysis of thoracic CT scans. IEICE Transactions on Information and Systems. 2007;90–D:1168–1174
  11. Lu J, Plataniotis KN, Venetsanopoulos AN, Li SZ. Ensemble-based discriminant learning with boosting for face recognition. IEEE Transactions on Neural Networks. 2006;17:166–178
  12. Li Q. Recent progress in computer-aided diagnosis of lung nodules on thin-section CT. Computerized Medical Imaging and Graphics. 2007;31:248–257
  13. Sluimer IC, Schilham A, Prokop M, van Ginneken B. Computer analysis of computed tomography scans of the lung: a survey. IEEE Transactions on Medical Imaging. 2006;25:385–405
  14. Retico A, Delogu P, Fantacci ME, Gori I, Martinez AP. Lung nodule detection in low-dose and thin-slice CT. Computers in Biology and Medicine. 2008;38:525–534
  15. Antonelli M, Frosini G, Lazzerini B, Marcelloni F. A CAD system for lung nodule detection based on an anatomical model and a fuzzy neural network. North American Fuzzy Information Processing Society. 2006;448–453
  16. Breiman L. Random forests. Machine Learning. 2001;45:5–32
  17. Dietterich TG. Ensemble methods in machine learning. Multiple Classifier Systems. LNCS. Heidelberg: Springer Berlin; 2008;pp. 1–15
  18. Opitz D, Maclin R. Popular ensemble methods: an empirical study. Journal of Artificial Intelligence Research. 1999;11:169–198
  19. Hu ZH, Li WG, Cai YZ, Xu XM. An empirical comparison of ensemble classification algorithms with SVM. Proceedings of Machine Learning and Cybernetics. 2004;
  20. Ge G, Wong GW. Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles. BMC Bioinformatics. 2008;9:1–12
  21. Boinee P, De Angelis A, Foresti GL. Meta random forests. International Journal of Computational Intelligence. 2006;2:138–147
  22. Robnik-Sikonja M. Improving random forests. In: ECML. 2004;
  23. Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing. 2007;28:823–870
  24. Vapnik V. The nature of statistical learning theory. Springer-Verlag; 1999;
  25. Basak J. Online adaptive decision trees: pattern classification and function approximation. Neural Computing. 2006;18:2062–2101
  26. Lin J-S, Lo SCB. Reduction of false positives in lung nodule detection using a two-level neural classification. IEEE Transactions on Medical Imaging. 1996;15:206–216
  27. Zhao L, Boroczky L, Lee KP. False positive reduction for lung nodule CAD using SVM and genetic algorithms. International Congress Series. 2005;1281:1109–1114

PII: S0895-6111(10)00041-8

doi: 10.1016/j.compmedimag.2010.03.006

Computerized Medical Imaging and Graphics
Volume 34, Issue 7 , Pages 535-542 , October 2010

Article Tools

* Image Usage & Resolution