Image segmentation based on active contours using discrete time cellular neural networks

We present a new proposal for image segmentation using deformable models, as an application of discrete-time cellular neural networks (DTCNN). This approach is based on active contours (also called snakes) which evolve until reaching a final desired location. The contours are guided by both external information from the image under consideration which attracts them towards salient characteristics of the scene, and internal energy from the contour image which tries to maintain the smoothness in the curve shape. The massively parallel processing in DTCNN and the use of local information permit a VLSI implementation, suitable for real time applications.