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Brain Tumor Segmentation: A Comparative Analysis

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) (AICV 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1377))

Abstract

Brain tumor is an abnormal cell population that occurs in the human brain. Nowadays, medical imaging techniques play an essential role in tumor diagnosis. Magnetic resonance imaging (MRI) is a medical imaging technique that uses radio waves and a magnetic field as sound waves are created to produce detailed images of tissues and organs in the human body by computer. In this study, three different methods were reviewed and compared to the tumor’s extraction from a set of MRI brain images. These methods are seeded region growing, k-means, and global thresholding. The images used in this study are obtained from the Cancer Imaging Archive (TCIA) and Kaggle. All images are grayscale and in JPEG format. The images from TCIA dataset are 100 images that contain abnormal (with a tumor) brain MRI images while there are 35 images in the Kaggle dataset. The Kaggle dataset contains 20 normal images and 15 abnormal images. The results show that the k-means segmentation algorithm performed better than the others on TCIA dataset according to the Root Mean Square Error (RMSE), the Peak to Signal Noise Ration (PSNR), and Segmentation Accuracy while global thresholding is the best on Kaggle dataset.

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Acknowledgment

I would like to thank my supervisor Prof. Dr. Maher Shedid, for his valuable guidance, support, and motivation throughout this research.

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Authors and Affiliations

  1. Faculty of Science, Benha University, Banha, Egypt

    Eman Mohammed & Mosab Hassaan

  2. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Safaa Amin & Hala M. Ebied

Authors
  1. Eman Mohammed
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  2. Mosab Hassaan
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  3. Safaa Amin
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  4. Hala M. Ebied
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Editor information

Editors and Affiliations

  1. Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. School of Medicine, University of Missouri, Columbia, MO, USA

    Peter J. Tonellato

  4. ISAE-ENSMA, Futuroscope Chasseneuil Cedex, France

    Ladjel Bellatreche

  5. British University Vietnam, Hung Yen, Vietnam

    Sam Goundar

  6. Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmad Taher Azar

  7. NEST Research Group, ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Essaid Sabir

  8. High National School for Computer Science and Systems Analysis (ENSIAS), Mohammed V University, Rabat, Morocco

    Driss Bouzidi

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Mohammed, E., Hassaan, M., Amin, S., Ebied, H.M. (2021). Brain Tumor Segmentation: A Comparative Analysis. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_46

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