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Brain Tumor Segmantation Using Random Forest Trained on Iteratively Selected Patients

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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

This paper extends a previously published brain tumor segmentation methods based on Random Decision Forest (RDF). An iterative approach is used in training the RDF in each iteration some patients are added to the training data using some heuristics approach instead of randomly selected training dataset. Feature extraction and selection were applied to select the most discriminative features for training our Random Decision forest on. The post-processing phase has a morphological filter to deal with misclassification errors. Our method is capable of detecting the tumor and segmenting the different tumorous tissues of the glioma achieving competitive results.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Alexandria University, Alexandria, Egypt

    Abdelrahman Ellwaa, Ahmed Hussein, Essam AlNaggar, Mahmoud Zidan, Michael Zaki, Mohamed A. Ismail & Nagia M. Ghanem

Authors
  1. Abdelrahman Ellwaa
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  2. Ahmed Hussein
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  3. Essam AlNaggar
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  4. Mahmoud Zidan
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  5. Michael Zaki
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  6. Mohamed A. Ismail
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  7. Nagia M. Ghanem
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Corresponding author

Correspondence to Abdelrahman Ellwaa .

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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© 2016 Springer International Publishing AG

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Ellwaa, A. et al. (2016). Brain Tumor Segmantation Using Random Forest Trained on Iteratively Selected Patients. 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_13

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  • DOI: https://doi.org/10.1007/978-3-319-55524-9_13

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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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