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Assessment of Brain Tumor in Flair MRI Slice with Joint Thresholding and Segmentation

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Mining Intelligence and Knowledge Exploration (MIKE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13119))

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Abstract

Medical image assessment plays a vital role in hospitals during the disease assessment and decision making. Proposed work aims to develop an image processing procedure to appraise the brain tumor fragment from Flair modality recorded MRI slice. The proposed technique employs joint thresholding and segmentation practice to extract the infected part from the chosen image. Initially, a tri-level thresholding based on Mayfly Algorithm and Kapur’s Entropy (MA + KE) is implemented to improve the tumor and then the tumor area is mined using the automated Watershed Segmentation Scheme (WSS). The merit of the employed procedure is verified on various 2D MRI planes, such as axial, coronal and sagittal and the experimental outcome confirmed that this technique helps to mine the tumor area with better accuracy. In this work, the necessary images are collected from BRATS2015 dataset and 30 patient’s information (10 slices per patient) is considered for the examination. The experimental investigation is implemented using MATLAB® and 300 images from every 2D plane are examined. The proposed technique helps to get better values of Jaccard-Index (>85%), Dice-coefficient (>91%) and Accuracy (98%) on the considered MRI slices.

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Author information

Authors and Affiliations

  1. Department of Applied Data Science, Noroff University College, Kristiansand, Norway

    Seifedine Kadry

  2. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Seifedine Kadry

  3. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Seifedine Kadry

  4. Faculty of Information Technology, Monash University, Monash, Australia

    David Taniar

  5. Al-Mustaqbal University College, Hillah, Babil, Iraq

    Maytham N. Meqdad

  6. Department of Mathematics and Computer Science, Brandon University, 270 18th Street, Brandon, R7A 6A9, Canada

    Gautam Srivastava

  7. Department of Electronics and Instrumentation, St. Joseph’s College of Engineering, Chennai, Tamilnadu, 600 119, India

    Venkatesan Rajinikanth

Authors
  1. Seifedine Kadry
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  2. David Taniar
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  3. Maytham N. Meqdad
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  4. Gautam Srivastava
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  5. Venkatesan Rajinikanth
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Corresponding author

Correspondence to Seifedine Kadry .

Editor information

Editors and Affiliations

  1. Université de Pau et des Pays de l’Adour, Anglet, France

    Richard Chbeir

  2. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  3. Indian Institute of Information Technology Sri City, Chittoor, Andhra Pradesh, India

    Rajendra Prasath

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Kadry, S., Taniar, D., Meqdad, M.N., Srivastava, G., Rajinikanth, V. (2022). Assessment of Brain Tumor in Flair MRI Slice with Joint Thresholding and Segmentation. In: Chbeir, R., Manolopoulos, Y., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2021. Lecture Notes in Computer Science(), vol 13119. Springer, Cham. https://doi.org/10.1007/978-3-031-21517-9_5

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  • DOI: https://doi.org/10.1007/978-3-031-21517-9_5

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  • Online ISBN: 978-3-031-21517-9

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