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Advances in Bioinformatics
Volume 2015, Article ID 728164, 7 pages
http://dx.doi.org/10.1155/2015/728164
Research Article

High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features

Laboratory of Conception, Optimization and Modelling of Systems, University of Lorraine, 7 rue Marconie, Metz, 57070 Lorraine, France

Received 24 May 2015; Revised 28 September 2015; Accepted 1 October 2015

Academic Editor: Klaus Jung

Copyright © 2015 Ahmad Chaddad and Camel Tanougast. 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. R. Stupp, M. E. Hegi, M. J. van den Bent et al., “Changing paradigms—an update on the multidisciplinary management of malignant glioma,” The Oncologist, vol. 11, no. 2, pp. 165–180, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. B. J. McCarthy, C. Kruchko, and T. A. Dolecek, “The impact of the Benign Brain Tumor Cancer Registries Amendment Act (Public Law 107–260) on non-malignant brain and central nervous system tumor incidence trends,” Journal of Registry Management, vol. 40, no. 1, pp. 32–35, 2013. View at Google Scholar · View at Scopus
  3. E. D. Angelini, O. Clatz, E. Mandonnet, E. Konukoglu, L. Capelle, and H. Duffau, “Glioma dynamics and computational models: a review of segmentation, registration, and in silico growth algorithms and their clinical applications,” Current Medical Imaging Reviews, vol. 3, no. 4, pp. 262–276, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. W. B. Pope, J. R. Young, and B. M. Ellingson, “Advances in MRI assessment of gliomas and response to anti-VEGF therapy,” Current Neurology and Neuroscience Reports, vol. 11, no. 3, pp. 336–344, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Chaddad, C. Tanougast, A. Golato, and A. Dandache, “Carcinoma cell identification via optical microscopy and shape feature analysis,” Journal of Biomedical Science and Engineering, vol. 6, no. 11, pp. 1029–1033, 2013. View at Publisher · View at Google Scholar
  6. A. Chaddad, “Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models,” International Journal of Biomedical Imaging, vol. 2015, Article ID 868031, 11 pages, 2015. View at Publisher · View at Google Scholar
  7. A. Chaddad, C. Tanougast, A. Dandache, and A. Bouridane, “Extracted haralick's texture features and morphological parameters from segmented multispectrale texture bio-images for classification of colon cancer cells,” WSEAS Transactions on Biology and Biomedicine, vol. 8, no. 2, pp. 39–50, 2011. View at Google Scholar · View at Scopus
  8. W. P. Kegelmeyer Jr., “Computer detection of stellate lesions in mammograms,” in Biomedical Image Processing and Three-Dimensional Microscopy, vol. 1660 of Proceedings of SPIE, pp. 446–454, San Jose, Calif, USA, February 1992. View at Publisher · View at Google Scholar
  9. B. L. Wen, M. A. Brewer, O. Nadiarnykh et al., “Texture analysis applied to second harmonic generation image data for ovarian cancer classification,” Journal of Biomedical Optics, vol. 19, no. 9, Article ID 096007, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. M. F. Ahmad Fauzi, H. N. Gokozan, B. Elder, V. K. Puduvalli, J. J. Otero, and M. N. Gurcan, “Classification of glioblastoma and metastasis for neuropathology intraoperative diagnosis: a multi-resolution textural approach to model the background,” in Medical Imaging 2014: Digital Pathology, vol. 9041 of Proceedings of SPIE, p. 9, March 2014. View at Publisher · View at Google Scholar
  11. R. M. Palenichka, M. B. Zaremba, and R. Missaoui, “Multiscale model-based feature extraction in structural texture images,” Journal of Electronic Imaging, vol. 15, no. 2, Article ID 023013, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Chaddad and R. R. Colen, “Statistical feature selection for enhanced detection of brain tumor,” in Applications of Digital Image Processing XXXVII, vol. 9217 of Proceedings of SPIE, p. 8, 2014. View at Publisher · View at Google Scholar
  13. L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees, Chapman and Hall/CRC, New York, NY, USA, 1st edition, 1984.
  14. 3D Slicer, 2014, http://www.slicer.org/.
  15. K. Downey, S. F. Riches, V. A. Morgan et al., “Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images,” American Journal of Roentgenology, vol. 200, no. 2, pp. 314–320, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. A. B. Rosenkrantz, “Histogram-based apparent diffusion coefficient analysis: an emerging tool for cervical cancer characterization?” American Journal of Roentgenology, vol. 200, no. 2, pp. 311–313, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Cuevas, M. Febrero, and R. Fraiman, “An anova test for functional data,” Computational Statistics and Data Analysis, vol. 47, no. 1, pp. 111–122, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intelligent Systems and Their Applications, vol. 13, no. 4, pp. 18–28, 1998. View at Publisher · View at Google Scholar
  19. C. C. Aggarwal, Data Classification: Algorithms and Applications, CRC Press, New York, NY, USA, 2014. View at MathSciNet
  20. L. Rokach, Data Mining with Decision Trees: Theory and Applications, World Scientific, River Edge, NJ, USA, 2007.
  21. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, Calif, USA, 1993.
  22. R. A. Burrell, N. McGranahan, J. Bartek, and C. Swanton, “The causes and consequences of genetic heterogeneity in cancer evolution,” Nature, vol. 501, no. 7467, pp. 338–345, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Herlidou-Même, J. M. Constans, B. Carsin et al., “MRI texture analysis on texture test objects, normal brain and intracranial tumors,” Magnetic Resonance Imaging, vol. 21, no. 9, pp. 989–993, 2003. View at Publisher · View at Google Scholar · View at Scopus
  24. Cancer Genome Atlas Research Network, “Comprehensive genomic characterization defines human glioblastoma genes and core pathways,” Nature, vol. 455, no. 7216, pp. 1061–1068, 2008. View at Publisher · View at Google Scholar
  25. K. K. Holli, L. Harrison, P. Dastidar et al., “Texture analysis of MR images of patients with Mild Traumatic Brain Injury,” BMC Medical Imaging, vol. 10, no. 1, article 8, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. A. Chaddad, P. O. Zinn, and R. R. Colen, “Quantitative texture analysis for Glioblastoma phenotypes discrimination,” in Proceedings of the International Conference on Control, Decision and Information Technologies (CoDIT '14), pp. 605–608, Metz, France, November 2014. View at Publisher · View at Google Scholar · View at Scopus