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Image segmentation plays a key role in the field of biomedical image processing. This provides a path for better and early diagnosis of diseases. The brain tissue segmentation is a major problem to diagnose the abnormalities present in the internal organs in the brain anatomy. In this article, a Self-Organizing Map (SOM)-based clustering technique is discussed for brain tissue segmentation from Magnetic Resonance Imaging representations. The abnormal growth of cells in the brain is segmented and provides a segmented boundary of the brain. The obtained results of SOM clustering technique are compared with a fuzzy clustering method and the outputs are illustrated. The evaluation metrics show an accuracy of 93.9% which is 1.3% greater than the existing method.

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An Efficient Method in Brain Tissue Segmentation Using Self-Organizing Map Clustering Technique

  • 会议论文

  • 发表日期：

    2026年

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  • R. Remya,
  • O. Jeba Singh,
  • R. Rajesh Sharma

摘要

Image segmentation plays a key role in the field of biomedical image processing. This provides a path for better and early diagnosis of diseases. The brain tissue segmentation is a major problem to diagnose the abnormalities present in the internal organs in the brain anatomy. In this article, a Self-Organizing Map (SOM)-based clustering technique is discussed for brain tissue segmentation from Magnetic Resonance Imaging representations. The abnormal growth of cells in the brain is segmented and provides a segmented boundary of the brain. The obtained results of SOM clustering technique are compared with a fuzzy clustering method and the outputs are illustrated. The evaluation metrics show an accuracy of 93.9% which is 1.3% greater than the existing method.