Year: 2025
Type: article
Abstract: This work focuses on using level set curves for medical image segmentation through the DRLSE (Distance Regularization Level Set Evolution) algorithm, recognized for its effectiveness and adaptability.Traditional systems face limitations in computation time and efficiency when implementing this algorithm.To overcome these challenges, FPGA (Field-Programmable Gate Arrays) are used for their parallelism and low resource consumption.The objective is to optimize medical image segmentation by implementing the DRLSE algorithm on FPGA while ensuring efficient resource and computation time management.The Algorithm was first simulated in MATLAB and tested on a database of brain, breast, and other medical images, demonstrating its robustness and flexibility.The results validate the effectiveness of the DRLSE algorithm and highlight the advantages of the FPGA in terms of speed and precision.Despite the limited documentation on implementing DRLSE on FPGA Our approach is distinguished by the use of DDR memory, which provides increased capacity to overcome the limitations of BRAM memory.Parameter optimization ensures better performance and efficient management of hardware resources.This work underscores the potential of FPGA-based implementations for accelerating computationally intensive tasks like medical image segmentation while maintaining high accuracy and efficiency.
Source: Traitement du signal
DOI: 10.18280/ts.420210
Authors: Fatma Z. Hamadi, Mohamed L. Hamidatou, Latifa Hamami-Mitiche, Sabrina Ait Belkacem, Bouchra Bouzid
Institution: Polytechnic School of Algiers
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409981908