ArticleSensors (Basel, Switzerland)2025
A Multidimensional Particle Swarm Optimization-Based Algorithm for Brain MRI Tumor Segmentation.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Brain Tumor Classification Using Convolutional Neural Network and Bitterling Fish Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2026Article
- A Fixed-Ratio Hybrid ARO-ALO Algorithm for Multi-Level Thresholding of Histopathological Colon Cancer Images.Cancers · 2026Article
- An Adaptive Hybrid Metaheuristic Algorithm for Lung Cancer in Pathological Image Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- Computational Adaptive Optics for HAR Hybrid Trench Array Topography Measurement by Utilizing Coherence Scanning Interferometry.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
Particle Swarm Optimization (PSO) has been extensively applied to optimization tasks in various domains, including image segmentation. In this work, we present a clustering-based segmentation algorithm that employs a multidimensional variant of PSO. Unlike conventional methods that require a predefined number of segments, our approach automatically selects an optimal segmentation granularity based on specified similarity criteria. This strategy effectively isolates brain tumors by incorporating both grayscale intensity and spatial information across multiple MRI modalities, allowing the method to be reliably tuned using a limited amount of training data. We further demonstrate how integrating these initial segmentations with a random forest classifier (RFC) enhances segmentation precision. Using MRI data from the RSNA-ASNR-MICCAI brain tumor segmentation (BraTS) challenge, our method achieves robust results with reduced reliance on extensive labeled datasets, offering a more efficient path toward accurate, clinically relevant tumor segmentation.
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