Automatic Smoothing Parameter Adjustment of Region-Based Active Contour Using Edge Information

Abstract

Active contour model is one of the most popular image segmentation frameworks. Conventional active contour model requires the empirical adjustment of smoothing parameter in energy functional and the smoothing parameter for active contour is a challenging problem. In this paper, we propose an automated adjustment method for the smoothing parameter in region-based active contour models and thus a full automated segmentation method is obtained. In proposed active contour model, the region term is the same as that in traditional region-based active contour model, whereas the prior term in traditional region-based active contour model is substituted by gray statistic of edge image on the contour. Thus the evolution of contour has the same iteration form as that in traditional region-based active contour model. But a driving force from the edge information takes the role of smoothing term and the adjustment of smoothing parameter is avoided. Experimental results show the proposed model can obtain segmentation quality comparable to the those obtained by traditional region-based active contour model, without the cumbersome trial of smoothing parameter.