Vol. 28 No. 3 (2025) Cover Image
Vol. 28 No. 3 (2025)

Published: September 30, 2025

Pages: 420-435

Articles

An Overview of Medical Image Segmentation Methods

Hussain A. Jaber 1 Basma A. Al-Ghali 1 Muna M. Kareem 1 Ilyas Çankaya 2 Oktay Algin 3
1 Dept. of Biomedical Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq.
2 Electrical and Electronics Eng. Dept., Graduate School of Natural Science, Ankara Yıldırım Beyazıt University, Ankara, Turkey.
3 Interventional MR Clinical R&D Institute, Ankara University, Ankara, Turkey; Department of Radiology, Medical Faculty, Ankara University, Ankara, Turkey; National MR Research Center (UMRAM), Bilkent University, Ankara, Turkey.
History
  • Received: January 10, 2025
  • Revised: January 21, 2025
  • Accepted: February 25, 2025
  • Available Online: September 29, 2025
DOI

https://doi.org/10.29194/NJES.28030420

Abstract

Medical image segmentation plays a crucial role in the realm of medical imaging. The process involves the division of an image to obtain a comprehensive view and ensure precise diagnostics. There are various methods that are employed, ranging from traditional approaches to the more advanced deep learning techniques. Both play a significant role in enhancing healthcare. With the continuous advancement in technology, there is a growing need for accurate segmentation. While traditional methods such as thresholding and region growing are effective, they may require human intervention for complex cases. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have significantly improved the process by learning intricate details and accurately segmenting the image. When these methods are combined, healthcare professionals can achieve high-quality, precise results. Furthermore, with the advancements in hardware and technology, real-time segmentation is now possible. Generally, the process of dividing medical images into segments is extremely important for the progress of healthcare with the help of artificial intelligence and the most recent advancements in the industry, such as explainable AI and multimodal learning. However, this meticulously detailed and in-depth review provides an all-encompassing and extensive analysis of the current methods utilized, their multitude of applications across various fields, and the promising emerging advancements that have the potential to pave the way for remarkable future improvements and innovations.

Keywords

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