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Updated: Aug 18, 2025
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Automated brain tumour segmentation from multi-modality magnetic resonance imaging data based on new particle swarm
Wafa Gtifa1, Fayçal Hamdaoui2, Anis Sakly1
1Laboratory of Automation and Electrical Systems and Environment, Monastir National School of Engineers (ENIM), University of Monastir, Monastir, Tunisia.
Background:
Segmentation of brain tumours is a complex problem in medical image processing and analysis. It is a time-consuming and error-prone task. Therefore, computer-aided detection systems need to be developed to decrease physicians' workload and improve the accuracy of segmentation.
Methods:
This paper proposes a level set method constrained by an intuitive artificial intelligence-based approach to perform brain tumour segmentation. By studying 3D brain tumour images, a new segmentation technique based on the Modified Particle Swarm Optimisation (MPSO), Darwin Particle Swarm Optimisation (DPSO), and Fractional Order Darwinian Particle Swarm Optimisation (FODPSO) algorithms were developed.
Results:
The introduced technique was verified according to the MICCAI RASTS 2013 database for high-grade glioma patients. The three algorithms were evaluated using different performance measures: accuracy, sensitivity, specificity, and Dice similarity coefficient to prove the performance and robustness of our 3D segmentation technique.
Conclusion:
The result is that the MPSO algorithm consistently outperforms the DPSO and FO DPSO.
