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Recent Advancements in Fuzzy C-means Based Techniques for Brain MRI Segmentation
Ghazanfar Latif1, Jaafar Alghazo1, Fadi N Sibai1
1College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia.
Current Medical Imaging
|January 5, 2021
Summary
This review summarizes Fuzzy C-means (FCM) segmentation techniques for brain MRI analysis. These methods aid in the early diagnosis of brain tumors, a challenging but crucial medical task.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Biomedical Engineering
Background:
- Fuzzy C-means (FCM) segmentation techniques offer a range of complexities for brain MRI analysis.
- Variations span from basic standard FCM to enhanced algorithms.
Purpose of the Study:
- To comprehensively review thirteen variations of FCM segmentation techniques.
- Focus on the application of FCM for brain tumor segmentation in MRI scans.
- Highlight the importance of early brain tumor diagnosis.
Main Methods:
- Critical review and summarization of FCM-based techniques.
- Analysis of FCM applications in brain MRI segmentation.
- Evaluation of challenges in brain tumor segmentation due to anatomical variations and low contrast.
Main Results:
- FCM segmentation is compatible with MRI data, requiring minimal hospital integration.
- The techniques are applicable to images from standard MRI scanners.
- Early tumor diagnosis is facilitated by these segmentation methods.
Conclusions:
- FCM-based techniques provide a valuable approach for brain MRI segmentation.
- This review consolidates knowledge on FCM variations for brain tumor detection.
- The methods support improved diagnostic capabilities in clinical settings.
