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Brain Tumor Segmentation with Optimized Random Forest

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Book cover Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10154))

Abstract

In this paper we propose and tune a discriminative model based on Random Forest (RF) to accomplish brain tumor segmentation in multimodal MR images. The objective of tuning is meant to establish the optimal parameter values and the most significant constraints of the discriminative model. During the building of the RF classifier, the algorithm evaluates the importance of variables, the proximities between data instances and the generalized error. These three properties of RF are employed to optimize the segmentation framework. At the beginning the RF is tuned for variable importance evaluation, and after that it is used to optimize the segmentation framework. The framework was tested on unseen test images from BRATS. The results obtained are similar to the best ones presented in previous BRATS Challenges.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Sapientia University Tîrgu-Mureş, Corunca, Romania

    László Lefkovits & László Szilágyi

  2. Department of Computer Science, “Petru Maior” University Tîrgu-Mureş, Târgu Mureş, Romania

    Szidónia Lefkovits

  3. Department of Control Engineering and Information Technology, University of Technology and Economics Budapest, Budapest, Hungary

    László Szilágyi

Authors
  1. László Lefkovits
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  2. Szidónia Lefkovits
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  3. László Szilágyi
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Corresponding author

Correspondence to László Lefkovits .

Editor information

Editors and Affiliations

  1. University of Zurich, Istituto Italiano di Tecnologia, Genoa, Italy

    Alessandro Crimi

  2. TU München, Computer Science, Munich, Germany

    Bjoern Menze

  3. Medical Informatics, University of Lübeck, Lübeck, Germany

    Oskar Maier

  4. Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland

    Mauricio Reyes

  5. Department of Medicine, University of Cambridge, Cambridge, United Kingdom

    Stefan Winzeck

  6. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany

    Heinz Handels

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Lefkovits, L., Lefkovits, S., Szilágyi, L. (2016). Brain Tumor Segmentation with Optimized Random Forest. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-55524-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55523-2

  • Online ISBN: 978-3-319-55524-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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