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Comparison of Brain Tumor Segmentation Methods Based on Different Algorithms Using MRI Images
- 1 Glasgow College
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Abstract
Brain tumor is a serious disease for human beings. MRI is the most widely used method for its innocuousness since people do not need to be exposure to radioactivity. The segmentation on MRI images is a vital step in tumor detection. To improve efficiency and accuracy of the segmentation, scientists apply different algorithms in this process. This paper focuses on three particular algorithms including Connected component label algorithm (CCLA), Watershed algorithm (WSA) and Fuzzy C-means clustering algorithm (FCCA). The principles and applied procedures of these three algorithms are introduced. Basing on this background information, algorithms are compared from three aspects. Fuzzy C-means clustering algorithm is considered as more efficient and accurate among these three algorithms. All algorithms have good research prospects, and the segmentation result can be improved through the improvement on algorithms.
Keywords
connected component label algorithm, watershed algorithm, Fuzzy C-means (FCM) clustering algorithm, brain tumor segmentation, MRI images
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Cite this article
Yong,J. (2023). Comparison of Brain Tumor Segmentation Methods Based on Different Algorithms Using MRI Images. Applied and Computational Engineering,8,13-17.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning
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