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Brain Tumor Segmentation in 3D-MRI Based on Artificial Bee Colony and Level Set

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

Medical imaging technologies developed to satisfy the significant need for information on medical imaging by visualizing internal organs like tumors help the radiologists to extract critical clinical data accurately. Brain tumor segmentation is very needed for a contemporaneous planning system for brain surgery. This paper offers a quick and accurate level set segmentation using a Modified Artificial Bee Colony (ABC) clustering technique to extract the tumor. In ABC, the food source initial position is identified using k-means rather than random initialization. The suggested model calculates the centroids of clusters and utilizes level set segmentation to handle topological changes of contours as the brain tumors vary in their form, structure, and size. Our model consists of an initial, pre-processing step to extract the brain from the head and enhances contrast stretching. Second, two-step ABC is employed to extract tumor edges, which will be used as an initial contour of the Magnetic Resonance Images (MRI) sequence. In the last step, the level set segment is employed to extract the tumor region from all volume slices with a fewer number of iterations. The experimental results using the benchmark BraTs’2017 dataset confirm the accuracy of the suggested model.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El-Shatby, P.O. Box 832, 21526, Alexandria, Egypt

    Yasmine Mahmoud Ibrahim & Saad Darwish

  2. Department of Information Technology, Informatics Research Institute City of Scientific Research and Technology Applications, Borg EL Arab, Alexandria, Egypt

    Walaa Sheta

Authors
  1. Yasmine Mahmoud Ibrahim
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  2. Saad Darwish
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  3. Walaa Sheta
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Corresponding author

Correspondence to Yasmine Mahmoud Ibrahim .

Editor information

Editors and Affiliations

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

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

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

    Fahmy M. Tolba

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Ibrahim, Y.M., Darwish, S., Sheta, W. (2020). Brain Tumor Segmentation in 3D-MRI Based on Artificial Bee Colony and Level Set. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_19

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