Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 136921, 10 pages
http://dx.doi.org/10.1155/2015/136921
Research Article

Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features

1Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Nijenborgh 9, 9747 AG Groningen, Netherlands
2Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, Netherlands
3Neuroimaging Center, University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 2, 9713 AW Groningen, Netherlands

Received 28 December 2014; Revised 9 March 2015; Accepted 14 March 2015

Academic Editor: Luca Faes

Copyright © 2015 D. Mudali et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. I. Litvan, K. P. Bhatia, D. J. Burn et al., “SIC task force appraisal of clinical diagnostic criteria for parkinsonian disorders,” Movement Disorders, vol. 18, no. 5, pp. 467–486, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. J. R. Moeller, S. C. Strother, J. J. Sidtis, and D. A. Rottenberg, “Scaled subprofile model: a statistical approach to the analysis of functional patterns in positron emission tomographic data,” Journal of Cerebral Blood Flow & Metabolism, vol. 7, no. 5, pp. 649–658, 1987. View at Publisher · View at Google Scholar · View at Scopus
  3. J. R. Moeller and S. C. Strother, “A regional covariance approach to the analysis of functional patterns in positron emission tomographic data,” Journal of Cerebral Blood Flow and Metabolism, vol. 11, no. 2, pp. A121–A135, 1991. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Ma, C. Tang, P. G. Spetsieris, V. Dhawan, and D. Eidelberg, “Abnormal metabolic network activity in Parkinson's disease: test-retest reproducibility,” Journal of Cerebral Blood Flow & Metabolism, vol. 27, no. 3, pp. 597–605, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. T. Eckert, C. Tang, Y. Ma et al., “Abnormal metabolic networks in atypical parkinsonism,” Movement Disorders, vol. 23, no. 5, pp. 727–733, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Eidelberg, “Metabolic brain networks in neurodegenerative disorders: a functional imaging approach,” Trends in Neurosciences, vol. 32, no. 10, pp. 548–557, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. L. K. Teune, R. J. Renken, D. Mudali et al., “Validation of parkinsonian disease-related metabolic brain patterns,” Movement Disorders, vol. 28, no. 4, pp. 547–551, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. C. C. Tang, K. L. Poston, T. Eckert et al., “Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis,” The Lancet Neurology, vol. 9, no. 2, pp. 149–158, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Hellwig, F. Amtage, A. Kreft et al., “[18F]FDG-PET is superior to [123I]IBZM-SPECT for the differential diagnosis of parkinsonism,” Neurology, vol. 79, no. 13, pp. 1314–1322, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, Calif, USA, 1993.
  11. M. E. Cintra, M. C. Monard, and H. A. Camargo, “FuzzyDT—a fuzzy decision tree algorithm based on C4.5,” in Proceedings of the Brazilian Congress on Fuzzy Systems, pp. 199–211, 2012.
  12. L. K. Teune, A. L. Bartels, B. M. de Jong et al., “Typical cerebral metabolic patterns in neurodegenerative brain diseases,” Movement Disorders, vol. 25, no. 14, pp. 2395–2404, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Gilman, G. K. Wenning, P. A. Low et al., “Second consensus statement on the diagnosis of multiple system atrophy,” Neurology, vol. 71, no. 9, pp. 670–676, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. I. Litvan, Y. Agid, D. Calne et al., “Clinical research criteria for the diagnosis of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome): report of the NINDS-SPSP International Workshop,” Neurology, vol. 47, no. 1, pp. 1–9, 1996. View at Publisher · View at Google Scholar · View at Scopus
  15. P. G. Spetsieris, V. Dhawan, and D. Eidelberg, “Three-fold cross-validation of parkinsonian brain patterns,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '10), pp. 2906–2909, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. P. G. Spetsieris and D. Eidelberg, “Scaled subprofile modeling of resting state imaging data in Parkinson's disease: methodological issues,” NeuroImage, vol. 54, no. 4, pp. 2899–2914, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. P. G. Spetsieris, Y. Ma, V. Dhawan, and D. Eidelberg, “Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features,” NeuroImage, vol. 45, no. 4, pp. 1241–1252, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Ma, C. Tang, J. R. Moeller, and D. Eidelberg, “Abnormal regional brain function in Parkinson's disease: truth or fiction?” NeuroImage, vol. 45, no. 2, pp. 260–266, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. M. A. Westenberg and J. B. T. M. Roerdink, “Mixed-method identifications,” in Automatic Diatom Identification, J. M. H. du Buf and M. M. Bayer, Eds., vol. 51 of Series in Machine Perception and Artificial Intelligence, chapter 12, pp. 245–257, World Scientific, Singapore, 2002. View at Google Scholar
  20. J. R. Quinlan, “Learning decision tree classifiers,” ACM Computing Surveys, vol. 28, no. 1, pp. 71–72, 1996. View at Google Scholar · View at Scopus
  21. H. du Buf and M. M. Bayer, Eds., Automatic Diatom Identification, World Scientific Publishing, Singapore, 2002.
  22. A. P. Muniyandi, R. Rajeswari, and R. Rajaram, “Network anomaly detection by cascading K-means clustering and C4.5 decision tree algorithm,” in Proceedings of the International Conference on Communication Technology and System Design, vol. 30, pp. 174–182, Procedia Engineering, 2012.
  23. K. Polat and S. Güneş, “A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems,” Expert Systems with Applications, vol. 36, no. 2, pp. 1587–1592, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Ture, F. Tokatli, and I. Kurt, “Using Kaplan-Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) in determining recurrence-free survival of breast cancer patients,” Expert Systems with Applications, vol. 36, no. 2, part 1, pp. 2017–2026, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. P. Perner, “Improving the accuracy of decision tree induction by feature pre-selection,” Applied Artificial Intelligence, vol. 15, no. 8, pp. 747–760, 2001. View at Publisher · View at Google Scholar · View at Scopus
  26. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, San Diego, Calif, USA, 2nd edition, 1990. View at MathSciNet
  27. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley-Interscience, New York, NY, USA, 2nd edition, 2000. View at MathSciNet
  28. M. S. Al-Rawi and J. P. S. Cunha, “Using permutation tests to study how the dimensionality, the number of classes, and the number of samples affect classification analysis,” in Image Analysis and Recognition, pp. 34–42, Springer, New York, NY, USA, 2012. View at Google Scholar
  29. P. Golland and B. Fischl, “Permutation tests for classification: towards statistical significance in image-based studies,” in Information Processing in Medical Imaging, vol. 2732 of Lecture Notes in Computer Science, pp. 330–341, Springer, Berlin, Germany, 2003. View at Publisher · View at Google Scholar
  30. H. Akaike, “A new look at the statistical model identification,” IEEE Transactions on Automatic Control, vol. 19, pp. 716–723, 1974. View at Google Scholar · View at MathSciNet
  31. F. Pedregosa, G. Varoquaux, A. Gramfort et al., “Scikit-learn: machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. View at Google Scholar · View at MathSciNet
  32. J. R. Quinlan, “Improved use of continuous attributes in C4.5,” Journal of Artificial Intelligence Research, vol. 4, pp. 77–90, 1996. View at Google Scholar · View at Scopus
  33. G. Garraux, C. Phillips, J. Schrouff et al., “Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes,” NeuroImage: Clinical, vol. 2, pp. 883–893, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. L. K. Teune, D. Mudali, R. J. Renken et al., “Glucose imaging in parkinsonisms,” in Proceedings of the 16th International Congress of Parkinson's Disease and Movement Disorders, Abstract # 783, Dublin, Ireland, June 2012.