Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach
Source abstract
Abstract
Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. Best-cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation and pave the way for further research and method integration to enhance this critical healthcare application.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Image Enhancement Techniques · Retinal Imaging and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Computer Science, AGH University of Krakow, 30-059 Krakow, Poland
Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta, Yogyakarta 55281, Indonesia
Artificial Intelligence Research Group (AIRG), Informatics Department, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta 55166, Indonesia
Funding
Polish Ministry of Education and Science · PLGrid Infrastructure
References (100+)
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