When strong CPU power consumption constraints must be met, and high computation speed is mandatory (real-time processing), it can be preferable to adopt custom hardware for some computationally intensive image processing tasks. An alternative approach to the conventional ones is provided by the cellular neural network (CNN) paradigm. CNNs have been extensively used in image processing applications in the past, we developed a still image segmentation technique based on an active contour obtained via single-layer CNNs. This technique suffered from sensitivity to noise as most of edge-based methods noise may create meaningless false edges or determine ’edge fragmentation’. The aim of this paper is to reformulate the algorithm previously proposed in order to step-over the cited weakness. The new formulation is introduced and motivated and experimental results are presented. Finally, a …