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Performance Improvement in Brain Tumor Detection in MRI Images Using a Combination of Evolutionary Algorithms and

Mahtab Saeidifar1, Mehran Yazdi2, Alireza Zolghadrasli3

  • 1School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.

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|September 25, 2021
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Summary

This study introduces an automated algorithm for accurate brain tumor detection in MRI scans. Particle Swarm Optimization (PSO) demonstrated superior performance in segmenting tumors compared to other methods.

Keywords:
Active contourEvolutionary algorithmsK-meansMorphological operatorsOtsu thresholding algorithmTumor detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Brain tumor diagnosis relies heavily on physician expertise, leading to potential variability and errors.
  • Automated tumor detection algorithms are crucial for assisting radiologists and physicians in accurate brain tumor diagnosis.
  • Accurate localization of brain tumors is essential for effective treatment planning and patient outcomes.

Purpose of the Study:

  • To develop and evaluate an automated algorithm for high-accuracy brain tumor detection and localization in MRI images.
  • To compare the performance of various segmentation algorithms, including evolutionary approaches, for brain tumor identification.
  • To refine tumor boundary delineation using an active contour model initialized with optimized segmentation results.

Main Methods:

  • Skull separation from brain MRI images using morphological operators.
  • Image segmentation employing six evolutionary algorithms (PSO, ABC, GA, DE, HS, GWO) and traditional methods (K-means, Otsu thresholding).
  • Tumor area isolation based on extracted features, followed by precise boundary definition using an active contour model.

Main Results:

  • Particle Swarm Optimization (PSO) exhibited the best segmentation performance among the evaluated algorithms.
  • The proposed algorithm, utilizing PSO and active contours, accurately detected and delineated tumor boundaries in T1-weighted brain MRI images.
  • Experimental results demonstrated superior performance of the proposed method over other evolutionary algorithms, K-means, and Otsu thresholding.

Conclusions:

  • The developed automated algorithm effectively assists in brain tumor diagnosis by providing accurate tumor localization and boundary definition.
  • PSO-based segmentation followed by active contours offers a robust approach for brain tumor analysis in MRI.
  • This method holds potential for improving the consistency and accuracy of brain tumor diagnosis and treatment planning.