Izvorni znanstveni članak
https://doi.org/10.32985/ijeces.15.4.4
Optimizing Enhanced Extended Topological Active Nets Model Using Parallel Processing
Ranjita Akash Asati
; Department of Computer Technology, Yeshwantrao Chavan College of Engineering Nagpur, India
*
M. M. Raghuwanshi
K. R. Singh
; Department of Computer Technology, Yeshwantrao Chavan College of Engineering Nagpur, India
* Dopisni autor.
Sažetak
In numerous clinical applications that support the diagnosis and treatment planning of a broad variety of disorders, medical image segmentation is essential. Medical picture segmentation using the Enhanced Extended Topological Active Net (EETAN) model has proven to be successful in correctly identifying structures. This study suggests a novel way to combine the best clustering techniques and parallel processing approaches to maximize the segmentation performance of the EETAN model. The Probabilistic Depth Search Optimization (PDSO) Algorithm, which makes the parallel searching technique to find the ideal contour set, is responsible for this. This work implements parallel processing and ideal clustering to improve the EETAN model's performance in medical image segmentation. Performance metrics like accuracy, precision, recall, dice similarity, and computational time are used for a comparison study. The results demonstrate the notable enhancements attained by employing parallel processing and effective clustering.
Ključne riječi
Image Segmentation; Parallel Computing; Probabilistic Depth Search Optimization (PDSO); Enhanced Extended Topological Active Net (EETAN);
Hrčak ID:
315579
URI
Datum izdavanja:
28.3.2024.
Posjeta: 441 *