Hardware Implementation of the GVF Approach for Deformable Contour Detection on FPGA

Résumé

Active contours, or snakes, are widely used in medical image segmentation due to their ability to accurately delineate object boundaries. The Gradient Vector Flow (GVF) model enhances traditional snakes by improving convergence and effectively capturing concave shapes. This paper presents a hardware implementation of the GVF algorithm on a PYNQ-Z2 FPGA using a modular architecture designed to optimize computation and parallelism. The algorithm was first developed and validated in MATLAB to evaluate its accuracy and stability on medical images. This step enabled fine-tuning of key GVF parameters, such as the regularization coefficient and the number of iterations, to ensure reliable convergence and precise contour segmentation. It was then implemented in Vivado HLS and translated into an optimized hardware architecture that leverages FPGA parallelism and pipelining to minimize latency and enhance performance. Experimental results demonstrate real-time operation, high segmentation accuracy, and low latency, confirming the suitability of this approach for embedded medical imaging applications requiring both speed and precision.

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