Data Mining for Biomarker Discovery pp 91-116

Part of the Springer Optimization and Its Applications book series (SOIA, volume 65) | Cite as

A Framework for Multimodal Imaging Biomarker Extraction with Application to Brain MRI

Chapter

Abstract

The crucial role of imaging biomarkers is sparsely mentioned in the literature due to the complex nature of medical images, the interpretation variability and the multidisciplinary approach needed to extract, validate, and translate such biomarkers to the clinical setting. In the case of cancer, imaging biomarkers can play an important role in understanding the stage of the disease as well as the response (or not) to initial treatment as early as possible. In neurodegenerative diseases, imaging biomarkers can assist the early detection and diagnosis, before substantial symptoms appear. In this chapter, we describe the clinical importance of establishing robust imaging biomarkers as well as the limitations that need to be addressed. Then, we propose a clinically driven/ assisted image-analysis-based framework for extracting and assessing temporal image biomarkers comprising of geometrical normalization and image-information extraction. The proposed biomarker image discovery framework including a number of clinically useful tools developed by our group has been integrated in a platform called ‘DoctorEye’, a novel, open access and easy to use clinical multimodal image analysis environment. Based on this clinical platform, we describe three examples of imaging biomarker discovery involving our recent work for the case of brain MRI.

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References

  1. 1.
    Contracancrum project.Google Scholar
  2. 2.
    R. Cárdenes, M. Bach, Y. Chi, I. Marras, R. De Luis, M. Anderson, P. Cashman, and M. Bultelle. Multimodal evaluation for medical image segmentation. In Computer Analysis of Images and Patterns, pages 229–236. Springer, 2007.Google Scholar
  3. 3.
    Y. Chen, E.R. Dougherty, and M.L. Bittner. Ratio-based decisions and the quantitative analysis of cdna microarray images. Journal of Biomedical optics, 2(4):364–374, 1997.CrossRefGoogle Scholar
  4. 4.
    G. Clapworthy, M. Viceconti, PV Coveney, and P. Kohl. The virtual physiological human: building a framework for computational biomedicine. Phil. Trans. R. Soc, 366:2975–2978, 2008.Google Scholar
  5. 5.
    L.D. Cohen and I. Cohen. Finite-element methods for active contour models and balloons for 2-d and 3-d images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1131–1147, 1993.CrossRefGoogle Scholar
  6. 6.
    B.C. Dickerson and R.A. Sperling. Neuroimaging biomarkers for clinical trials of disease-modifying therapies in Alzheimer’s disease. NeuroRx, 2(2):348–360, 2005.CrossRefGoogle Scholar
  7. 7.
    C. Farmaki, K. Mavrigiannakis, K. Marias, M. Zervakis, and V. Sakkalis. Assessment of automated brain structures segmentation based on the mean-shift algorithm: Application in brain tumor. In ITAB2010, Corfu, Greece, November 2–5, 2010.Google Scholar
  8. 8.
    Cristina Farmaki, Konstantinos Marias, Vangelis Sakkalis, and Norbert Graf. Spatially adaptive active contours: a semi-automatic tumor segmentation framework. International Journal of Computer Assisted Radiology and Surgery, 5:369–384, 2010. 10.1007/s11548-010-0477-9.Google Scholar
  9. 9.
    C. Harris and M. Stephens. A combined corner and edge detector. In Alvey vision conference, volume 15, page 50. Manchester, UK, 1988.Google Scholar
  10. 10.
    R. Highnam and M. Brady. Mammographic image analysis, volume 14. Springer Netherlands, 1999.Google Scholar
  11. 11.
    M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International journal of computer vision, 1(4):321–331, 1988.CrossRefGoogle Scholar
  12. 12.
    G. Manikis, D. Emmanouilidou, V. Sakkalis, N. Graf, and K. Marias. A fully automated image analysis framework for quantitative assessment of temporal tumor changes. International Conference on e-Health and Bioengineering (EHB 2011), Iaşi, Romania, November 24–26, 2011.Google Scholar
  13. 13.
    K. Marias, J. Ripoll, H. Meyer, V. Ntziachristos, and S. Orphanoudakis. Image analysis for assessing molecular activity changes in time-dependent geometries. IEEE Transactions on Medical Imaging, 24(7):894–900, 2005.CrossRefGoogle Scholar
  14. 14.
    K. Marias, V. Sakkalis, A. Roniotis, C. Farmaki, G. Stamatakos, D. Dionysiou, S. Giatili, N. Uzunoglou, N. Graf, R. Bohle, et al. Clinically oriented translational cancer multilevel modeling: The contracancrum project. In World Congress on Medical Physics and Biomedical Engineering, September 7–12, 2009, Munich, Germany, pages 2124–2127. Springer, 2009.Google Scholar
  15. 15.
    T. McInerney and D. Terzopoulos. Deformable models in medical image analysis: a survey. Medical image analysis, 1(2):91–108, 1996.CrossRefGoogle Scholar
  16. 16.
    S. Mussurakis, DL Buckley, AM Coady, LW Turnbull, and A. Horsman. Observer variability in the interpretation of contrast enhanced mri of the breast. British journal of radiology, 69(827):1009, 1996.Google Scholar
  17. 17.
    G.P. Penney, J. Weese, J.A. Little, P. Desmedt, and D.L.G. Hill. A comparison of similarity measures for use in 2-d-3-d medical image registration. IEEE Transactions on Medical Imaging, 17(4):586–595, 1998.CrossRefGoogle Scholar
  18. 18.
    P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629–639, 1990.CrossRefGoogle Scholar
  19. 19.
    E.M. Reiman, K. Chen, G.E. Alexander, R.J. Caselli, D. Bandy, D. Osborne, A.M. Saunders, and J. Hardy. Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer’s dementia. Proceedings of the National Academy of Sciences of the United States of America, 101(1):284, 2004.Google Scholar
  20. 20.
    A. Rizk-Jackson, D. Stoffers, S. Sheldon, J. Kuperman, A. Dale, J. Goldstein, J. Corey-Bloom, R.A. Poldrack, and A.R. Aron. Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington’s disease using machine learning techniques. NeuroImage, 2010.Google Scholar
  21. 21.
    T. Rohlfing, N.M. Zahr, E.V. Sullivan, and A. Pfefferbaum. The sri24 multichannel atlas of normal adult human brain structure. Hum Brain Mapp, 31:798–819, 2010.CrossRefGoogle Scholar
  22. 22.
    A. Roniotis, G. Manikis, V. Sakkalis, M. Zervakis, I. Karatzanis, and K. Marias. High grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from atlases. IEEE Transactions on Information Technology, (available online: doi:10.1109/TITB.2011.2171190).Google Scholar
  23. 23.
    D. Rueckert, L.I. Sonoda, C. Hayes, D.L.G. Hill, M.O. Leach, and D.J. Hawkes. Nonrigid registration using free-form deformations: application to breast mr images. IEEE Transactions on Medical Imaging, 18(8):712 –721, aug. 1999.Google Scholar
  24. 24.
    V. Sakkalis, A. Roniotis, C. Farmaki, I. Karatzanis, and K. Marias. Evaluation framework for the multilevel macroscopic models of solid tumor growth in the glioma case. In Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, pages 6809–6812. IEEE, 2010.Google Scholar
  25. 25.
    E. Skounakis, C. Farmaki, V. Sakkalis, A. Roniotis, K. Banitsas, N. Graf, and K. Marias. Doctoreye: a clinically driven multifunctional platform, for accurate processing of tumors in medical images. Open Medical Informatics Journal, 4:105–115, 2010.Google Scholar
  26. 26.
    J.H. Thrall. Biomarkers in Imaging: Realizing Radiologys Future. Radiology, 227:633–638, 2003.CrossRefGoogle Scholar
  27. 27.
    P. Viola and W.M. Wells III. Alignment by maximization of mutual information. In Computer Vision, 1995. Proceedings., Fifth International Conference on, pages 16–23. IEEE, 1998.Google Scholar
  28. 28.
    C. Xu and J.L. Prince. Gradient vector flow: A new external force for snakes. In cvpr, page 66. Published by the IEEE Computer Society, 1997.Google Scholar
  29. 29.
    Jonathan Zepp, Norbert Graf, Emmanouil Skounakis, Rainer Bohle, Eckart Meese, Holger Stenzhorn, Yoo-Jin Kim, Christina Farmaki, Vangelis Sakkalis, Wolfgang Reith, Georgios Stamatakos, and Konstantinos Marias. Tumor segmentation: The impact of standardized signal intensity histograms in glioblastoma. In 4th International Advanced Research Workshop on In Silico Oncology and Cancer Investigation, Athens, Greece, September 8–9, 2010.Google Scholar
  30. 30.
    K.H. Zou, S.K. Warfield, A. Bharatha, C. Tempany, M.R. Kaus, S.J. Haker, W.M. Wells III, F.A. Jolesz, and R. Kikinis. Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Academic Radiology, 11(2):178–189, 2004.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Kostas Marias
    • 1
  • Vangelis Sakkalis
    • 1
  • Norbert Graf
    • 2
  1. 1.Institute of Computer Science, FORTHHeraklionGreece
  2. 2.Department of Paediatric OncologyUSAARHomburgGermany

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