Transactions on Machine Intelligence

Transactions on Machine Intelligence

Review and Comparison of Three Clustering Methods: FCM, HCM, and C-Means for Brain Tumor Image Detection

Document Type : Original Article

Author
J. Emamipour
Payame Noor University, Department of Computer Engineering and Information Technology
10.47176/TMI.2018.146
Abstract
The application of computers in medical fields has significantly increased compared to the past, revolutionizing how healthcare professionals process, analyze, and interpret clinical data. Today, we observe extensive use of advanced computational techniques, including machine learning and data mining, in analyzing complex medical datasets and extracting meaningful insights. One of the most critical and impactful applications of computer methods in the medical sciences is the segmentation and analysis of medical images, which plays a vital role in supporting diagnosis, treatment planning, and disease monitoring. Among various medical imaging challenges, brain tumor image segmentation has garnered considerable research attention in recent years, as accurate delineation of tumor boundaries is essential for effective clinical decision-making and improving patient outcomes. Numerous segmentation techniques have been proposed, among which clustering algorithm-based solutions stand out due to their flexibility and effectiveness in handling medical image data. In particular, fuzzy clustering methods have emerged as powerful tools because they account for the inherent ambiguity and uncertainty present in medical images. This paper focuses on evaluating and comparing three prominent clustering algorithms Hard C-Means (HCM), Fuzzy C-Means (FCM), and traditional C-Means with the aim of examining their relative strengths, limitations, and suitability for the specific task of brain tumor image segmentation. The results offer valuable insights into their comparative performance and practical applications.
Keywords
Image Segmentation
Brain Tumor Image
Clustering Algorithm
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Transactions on Machine Intelligence
Volume 1, Issue 3
Summer 2018
Pages 146-154

  • Receive Date 05 June 2018
  • Revise Date 13 August 2018
  • Accept Date 27 September 2018