Automatic Classification and Segmentation of Low-Grade Gliomas in Magnetic Resonance Imaging

  • Marta Barbosa
  • Pedro Moreira
  • Rogério Ribeiro
  • Luis CoelhoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 942)


In this article a new methodology is proposed to tackle the problem of automatic segmentation of low-grade gliomas. The possibility of knowing the limits of this type of tumor is crucial for effectively characterizing the neoplasm, enabling, in certain cases, to obtain useful information about how to treat the patient in a more effective way. Using a database of magnetic resonance images, containing several occurrences of this type of tumors, and through a carefully designed image processing pipeline, the purpose of this work is to accurately locate, isolate and thus facilitate the classification of the pathology. The proposed methodology, described in detail, was able to achieve an accuracy of 87.5% for a binary classification task. The quality of the identified regions had an accuracy of 81.6%. These are promising results that may point the effectiveness of the approach. The low contrast of the images, as a result of the acquisition process, and the detection of very small tumors are still challenges that bring motivation to further pursue additional results.


Low-grade glioma Image segmentation MRI 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marta Barbosa
    • 1
  • Pedro Moreira
    • 1
  • Rogério Ribeiro
    • 1
  • Luis Coelho
    • 1
    • 2
    Email author
  1. 1.Polytechnic Institute of Porto, ISEP-IPPPortoPortugal
  2. 2.CIETI - Center for Innovation in Engineering and Industrial TechnologyPortoPortugal

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