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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Hybrid Multilevel Thresholding Image Segmentation Approach for Brain MRI.

Suvita Rani Sharma1, Samah Alshathri2, Birmohan Singh1

  • 1Department of Computer Science and Engineering, Sant Longowal Institute of Technology and Engineering, Longowal, Sangrur 148106, Punjab, India.

Diagnostics (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

A new Dynamic Opposite Bald Eagle Search (DOBES) algorithm improves brain MRI segmentation for tumor detection. This method enhances accuracy and efficiency over traditional techniques, aiding neurological diagnosis.

Keywords:
brain tumormultilevel thresholdingoptimization algorithmsegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Brain tumors pose significant health risks, necessitating accurate detection methods.
  • Magnetic Resonance Imaging (MRI) is crucial for brain tumor diagnosis.
  • Accurate segmentation of brain MRI is vital for clinical applications.

Purpose of the Study:

  • To develop an improved metaheuristic algorithm for brain MRI segmentation.
  • To enhance the accuracy and efficiency of multilevel thresholding for tumor detection.
  • To address limitations of existing algorithms, such as local optima and slow convergence.

Main Methods:

  • A novel Dynamic Opposite Bald Eagle Search (DOBES) algorithm was proposed, incorporating Dynamic Opposition Learning (DOL).
  • A hybrid multilevel thresholding image segmentation approach was developed using DOBES for MRI.
  • The approach combines DOBES-based thresholding with morphological operations for refined segmentation.

Main Results:

  • The DOBES algorithm demonstrated superior performance compared to the original Bald Eagle Search (BES) algorithm.
  • The proposed method achieved higher Peak Signal-to-Noise Ratio (PSNR) and Structured Similarity Index Measure (SSIM) values.
  • Comparative analysis confirmed the proposed hybrid segmentation approach's effectiveness for brain tumor segmentation in MRI.

Conclusions:

  • The DOBES algorithm effectively overcomes the limitations of traditional metaheuristic approaches.
  • The hybrid segmentation method significantly improves the accuracy of brain tumor detection in MRI.
  • This advancement offers a more robust tool for neurological quantitative analysis and diagnosis.