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Data Mining for Biomarker Discovery pp 91–116Cite as

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

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

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.

Keywords

  • Positron Emission Tomography
  • Gaussian Mixture Model
  • Active Contour
  • Active Contour Model
  • Binary Mask

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgments

This work is supported by the European Commission under the project “TUMOR: Transatlantic Tumour Model Repositories” (FP7-ICT-2009.5.4-247754). The authors would like to thank C. Farmaki, E. Skounakis, A. Roniotis and K. Mavrigiannakis for their scientific work contributions to the presented implemented methods and tools.

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Authors and Affiliations

  1. Institute of Computer Science, FORTH, Heraklion, Greece

    Kostas Marias & Vangelis Sakkalis

  2. Department of Paediatric Oncology, USAAR, Homburg, Germany

    Norbert Graf

Authors
  1. Kostas Marias
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  2. Vangelis Sakkalis
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  3. Norbert Graf
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Corresponding author

Correspondence to Kostas Marias .

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Editors and Affiliations

  1. , Department of Industrial & Systems Engin, University of Florida, Weil Hall 401, Gainesville, 32611-6595, Florida, USA

    Panos M. Pardalos

  2. , Department of Industrial & Systems Engin, University of Florida, Weil Hall 401, Gainesville, 32611, Florida, USA

    Petros Xanthopoulos

  3. Dept. Electronic & Computer, Engineering, Technical University of Crete, Chania, Crete, 731 00, Greece

    Michalis Zervakis

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Marias, K., Sakkalis, V., Graf, N. (2012). A Framework for Multimodal Imaging Biomarker Extraction with Application to Brain MRI. In: Pardalos, P., Xanthopoulos, P., Zervakis, M. (eds) Data Mining for Biomarker Discovery. Springer Optimization and Its Applications(), vol 65. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-2107-8_6

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