A New Active Contour Model Based on Distance-Weighted Potential Field

Abstract

Snakes, or active contours, have been widely used in various image processing applications. Typical problems of snakes, including limited capture range, poor convergence to concavities, noise sensitivity, and initialization sensitivity, have limited their applications. For solving these problems, we propose a new potential for the active contour model. In this proposed potential field, each location's potential is computed by integrating the feature information from all the pixels in the image with the distances as the weights. The external forces are computed as the gradients of this proposed potential field, and the computed external forces are static and have global capture range. Experiments and also the comparisons with the snake using gradient vector flow (GVF) as external forces are conducted to examine the performances of this proposed snake. The results show that the proposed snake has a large capture range and an excellent convergence to boundary concavities, and also the proposed snake is more robust to noise, more time efficient, and less sensitive to the initialization, compared with the GVF snake.