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Detection of Brain Tumor in MRI Images, Using a Combination of Fuzzy C-Means and Thresholding

Author

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  • Yousif Ahmed Hamad

    (Siberian Federal University, Krasnoyarsk, Russian Federation)

  • Konstantin Vasilievich Simonov

    (Institute of Computational Modeling of the Siberian Branch of the Russian Academy of Sciences, Krasnoyarsk, Russian Federation)

  • Mohammad B. Naeem

    (Al-Maarif University College, Ramadi, Iraq)

Abstract

The identification, segmentation, and detection of the infected area in brain tumor is a tedious and a time-consuming task. The different structures of the human body can be visualized by an image processing concept, an MRI. It is very difficult to visualize abnormal structures of the human brain using simple imaging techniques. An MRI technique contains many imaging modalities that scan and capture the internal structure of the human brain. This article concentrates on a noise removal technique, followed by improvement of medical images for a correct diagnosis using a balance contrast enhancement technique (BCET). Then, image segmentation is used. Finally, the Canny edge detection method is applied to detect the fine edges. The experiment results achieved nearly 98% accuracy in detecting the area of the tumor and normal brain regions in MRI images demonstrating the effectiveness of the proposed technique.

Suggested Citation

  • Yousif Ahmed Hamad & Konstantin Vasilievich Simonov & Mohammad B. Naeem, 2019. "Detection of Brain Tumor in MRI Images, Using a Combination of Fuzzy C-Means and Thresholding," International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), IGI Global, vol. 11(1), pages 45-60, January.
    • Handle: RePEc:igg:japuc0:v:11:y:2019:i:1:p:45-60
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