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Related Experiment Video

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An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean

Surjeet Dalal1, Umesh Kumar Lilhore2, Poongodi Manoharan3

  • 1Department of Computer Science and Engineering, Amity University Gurugram, Gurugram 122412, Haryana, India.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

This study introduces an efficient Adaptive Moving Self-Organizing Map and Fuzzy k-means clustering (AMSOM-FKM) method for brain tumor segmentation in MRI scans. The AMSOM-FKM technique significantly improves tumor detection accuracy compared to existing methods.

Keywords:
K-meansadaptive self-organizing mapbrain tumorgray level co gray level co-occurrence matrixmedical imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Brain tumor segmentation in Magnetic Resonance Imaging (MRI) is a complex challenge.
  • Machine learning has shown promise in improving tumor detection and segmentation accuracy.
  • Existing methods often struggle with precise tumor boundary delineation.

Purpose of the Study:

  • To develop an efficient technique for brain tumor detection and segmentation using MRI data.
  • To improve the accuracy and efficiency of tumor region extraction.
  • To evaluate the performance of the proposed method against established techniques.

Main Methods:

  • Utilized the Kaggle Brats-18 brain tumor dataset (1691 images).
  • Employed Adaptive Moving Self-Organizing Map (AMSOM) for unsupervised feature learning and classification.
  • Applied Fuzzy k-means (FKM) clustering for precise tumor region segmentation.
  • Incorporated Wiener filtering for noise removal and Gray Level Co-occurrence Matrix (GLCM) for feature extraction.

Main Results:

  • The proposed AMSOM-FKM technique demonstrated superior performance in brain tumor segmentation.
  • Achieved over 10% improvement in accuracy, sensitivity, precision, and similarity index compared to Fuzzy-C-means and K-means methods.
  • Successfully segmented tumor regions by distinguishing them from surrounding tissues.

Conclusions:

  • The AMSOM-FKM method offers an efficient and accurate approach for brain tumor segmentation in MRI.
  • This technique holds potential for clinical applications in neuro-oncology.
  • Further research can explore its application on diverse brain tumor datasets.