Szczegóły publikacji
Opis bibliograficzny
Advanced medical image segmentation enhancement: a particle-swarm-optimization-based histogram equalization approach / Shoffan SAIFULLAH, Rafał DREŻEWSKI // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2024 — vol. 14 iss. 2 art. no. 923, s. 1–26. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 22–26, Abstr. — Publikacja dostępna online od: 2024-01-22. — S. Saifullah - dod. afiliacja: Universitas Pembangunan Nasional Veteran Yogyakarta, Yogyakarta, Indonesia. – R. Dreżewski - dod. afiliacja: Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 151582 |
|---|---|
| Data dodania do BaDAP | 2024-01-30 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app14020923 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
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.
