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A Multidimensional Particle Swarm Optimization-Based Algorithm for Brain MRI Tumor Segmentation.

Zsombor Boga1, Csanád Sándor1, Péter Kovács2

  • 1Faculty of Mathematics and Computer Science, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced Particle Swarm Optimization (PSO) for brain tumor segmentation in MRI scans. The method automatically determines segmentation levels, improving accuracy with less training data.

Keywords:
adaptive number of segmentsbrain tumor segmentationclusteringimage segmentationmagnetic resonance imagingmultidimensional particle swarm optimizationrandom forest classifier

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Image segmentation is crucial for medical image analysis, particularly for identifying pathologies like brain tumors.
  • Traditional segmentation methods often require manual parameter tuning and predefined segment counts, limiting their adaptability.
  • Existing approaches may struggle with complex data like multi-modal MRI, necessitating more robust techniques.

Purpose of the Study:

  • To develop an automated, clustering-based brain tumor segmentation algorithm using a multidimensional Particle Swarm Optimization (PSO).
  • To enhance segmentation precision by integrating PSO with a Random Forest Classifier (RFC).
  • To reduce the dependency on large, labeled datasets for training accurate tumor segmentation models.

Main Methods:

  • Implementation of a multidimensional PSO variant for unsupervised clustering-based image segmentation.
  • Incorporation of grayscale intensity and spatial information from multi-modal MRI data.
  • Integration of initial segmentations with a Random Forest Classifier (RFC) for refined results.
  • Validation using the RSNA-ASNR-MICCAI brain tumor segmentation (BraTS) challenge dataset.

Main Results:

  • The proposed algorithm automatically determines optimal segmentation granularity without predefined segment numbers.
  • The method effectively isolates brain tumors by leveraging multi-modal MRI data and spatial information.
  • Integration with RFC significantly enhanced segmentation precision.
  • Achieved robust results on the BraTS dataset with reduced need for extensive labeled training data.

Conclusions:

  • The developed PSO-based approach offers an efficient and accurate method for brain tumor segmentation.
  • Automatic selection of segmentation granularity and multi-modal data integration improve clinical relevance.
  • This method presents a promising alternative for scenarios with limited labeled training data in medical image analysis.