Level set evolution driven by optimized area energy term for image segmentation

As a classic and famous active contour model for image segmentation, distance regularized level set evolution method avoids the process of re-initialization and can segment images flexibly, but it is easy to leak from objects with weak boundaries and fall into false boundaries. In this paper, an improved level set evolution model is proposed, in which an optimized area energy term combining a region growing matrix and an adaptive boundary indicator function is added to effectively detect boundaries for images with several adjacent targets and accelerate convergence at the same time. With an adaptive boundary indicator function involving a threshold defined by the standard deviation of images to be detected, this model can cross false boundaries and implement a correct segmentation for low contrast images. Meanwhile, the double-well potential function is optimized to make the model more stable. Experimental results on images of different objects have proved that the proposed model not only improves the precision of locating boundaries but also reduces the computational cost and works a stronger robustness than some other edge-based active contour models. (C) 2018 Elsevier GmbH. All rights reserved.