ICTMI 2017 pp 137-149 | Cite as

Brain Tumor Detection and Classification of MRI Brain Images Using Morphological Operations

  • Mavis Gezimati
  • Munyaradzi C. Rushambwa
  • J. B. JeevaEmail author
Conference paper


Purpose Image processing is a vital aspect of medical science which enables visualization of various anatomical structures of human body. Planar imaging can be used for detecting and visualizing hidden abnormal structures which are not use to visualize using simple imaging. Magnetic resonance imaging (MRI) modality is one of the techniques which enables scan and capture of internal body soft tissues. This work describes the process implemented for detection and extraction of brain tumor from patient’s MRI scan images of the brain. Procedure The process includes some contrast enhancement, noise removal functions, segmentation, and morphological operations which are the basic terms of image processing. By using MATLAB software, we detected and extracted tumor from 24 MRI scan images of the brain. We calculated the tumor properties including area, perimeter, and eccentricity. Using those properties, we then used k-medoid clustering for classification. Results Detection of tumor was performed on 24 MRI brain images, and their properties were calculated. The images that have maximum similarity and show the characteristics of a benign tumor type and few of the tumors have malignant characteristics. Conclusion The work finds significant application in diagnosis of epilepsy, cancer, radiotherapy, etc.


Brain tumor Image processing MRI images 


  1. 1.
    National Cancer Institute (2014) Defining Cancer, Retrieved 10 June 2014Google Scholar
  2. 2.
    Gonzalez W (2014) Digital image processing, 3rd edn. Prentice Hall, Year of PublicationGoogle Scholar
  3. 3.
    Jayaraman S (2009) Digital image processing, Year of PublicationGoogle Scholar
  4. 4.
    Yaghini M (2010) Data mining. SpringGoogle Scholar
  5. 5.
    Ali, AH (2014) Segmentation of brain tumor using Enhanced Thresholding Algorithm and Calculate the area of the tumor. IOSR 2014Google Scholar
  6. 6.
    Nandi A (2015) Detection of human brain tumor using MRI image segmentation and morphological operators, IEEE 2015Google Scholar
  7. 7.
    Murthy TSD (2014) Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor, ICAECC 2014Google Scholar
  8. 8.
    Gavhande SS (2015) Image segmentation and identification of brain tumor from MRI image, IRJET 2015Google Scholar
  9. 9.
    Mancas M, Gosselin B, Benq, B Segmentation using a region growing thresholdingGoogle Scholar
  10. 10.
    Deng W, Xiao W, Pan C, Liu Key J (2009) MRI brain tumor segmentation based on improved fuzzy c-means method. In: Laboratory of education ministry for image processing and intelligence control institute for pattern recognition and artificial intelligence SPIE vol 7497, p 74972 NGoogle Scholar
  11. 11.
    Sujji E, Lakshmi YVS, Wiselin Jiji G MRI Brain image segmentation based on thresholding. Int J Adv Comput ResGoogle Scholar
  12. 12.
    Bandhyopadhya SK, Paul TU (2012) Segmentation of brain MRI image–a review. Int J Adv Res Comput Sci Software Eng 2(3):2277–128XGoogle Scholar
  13. 13.
    Subashini M (2013) M and Sarat Kumar Sahoo: brain MR image segmentation for tumor detection using artificial neural networks, ISSN: 0975–4024 5(2)Google Scholar
  14. 14.
    Patil RC, Bhalchandra AS: Brain tumour extraction from MRI images using MATLAB. Int J Electron Commun Soft Comput Sci Eng 2(1) ISSN: 2277–9477Google Scholar
  15. 15.
    National Neurosurgery Quality and Outcomes Database,
  16. 16.
    The Cancer imaging archieve,
  17. 17.
  18. 18.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Mavis Gezimati
    • 1
  • Munyaradzi C. Rushambwa
    • 1
  • J. B. Jeeva
    • 1
    Email author
  1. 1.Department of Sensors and Biomedical EngineeringVIT UniversityKatpadi VelloreIndia

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