Digital Medical Image Segmentation Using Fuzzy C-Means Clustering

  • Bakhtyar Ahmed Mohammed University of Human Development, College of Science and Technology, Department of Computer Science, Sulaymaniyah, Iraq University of Sulaimani, College of Science, Department of Computer, Sulaymaniyah, KRG, Iraq
  • Muzhir Shaban Al-Ani Department of Information Technology, University of Human Development, College of Science and Technology, Sulaymaniyah, KRG, Iraq

Abstract

In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases. One of the major components of medical image analysis is medical image segmentation. Medical image segmentation participates in the diagnosis process, and it aids the processes of other medical image components to increase the accuracy. In unsupervised methods, fuzzy c-means (FCM) clustering is the most accurate method for image segmentation, and it can be smooth and bear desirable outcomes. The intention of this study is to establish a strong systematic way to segment complicate medical image cases depend on the proposed method to share in the decision-making process. This study mentions medical image modalities and illustrates the steps of the FCM clustering method mathematically with example. It segments magnetic resonance imaging (MRI) of the brain to separate tumor inside the brain MRI according to four statuses.

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Published
2020-02-27
How to Cite
MOHAMMED, Bakhtyar Ahmed; AL-ANI, Muzhir Shaban. Digital Medical Image Segmentation Using Fuzzy C-Means Clustering. UHD Journal of Science and Technology, [S.l.], v. 4, n. 1, p. 51-58, feb. 2020. ISSN 2521-4217. Available at: <http://journals.uhd.edu.iq/index.php/uhdjst/article/view/691>. Date accessed: 26 may 2020. doi: https://doi.org/10.21928/uhdjst.v4n1y2020.pp51-58.
Section
Articles