Superpixel-based active contour model via a local similarity factor and saliency

The region-based active contour models could present difficulties because of undesired initial contour, noise distribution and image weak edges. In order to overcome the above problems, this paper proposes a superpixelbased via a local similarity factor and saliency (SLSFS). Firstly, the initial contour is generated by combining super-pixel and fuzzy c-means clustering. Secondly, the difference between local space and local intensity is used to improve the segmentation accuracy under noise. Finally, the weak edge information is protected by improved saliency detection. In addition, a gradient similarity constraint is used to remove the redundant regions. SLSFS model can generate adaptive initial contour around the target, and protect the weak edge information of the target on the premise of ensuring certain noise robustness. Experiments show that the average dice of SLSFS is 14% higher than that of the optimal comparison model and 19% on gray images.

Automated summary

The paper proposes a superpixel-based SLSFS method to address the issues in region-based active contour models. The method improves segmentation accuracy under noise and protects weak edge information by generating adaptive initial contour and using improved saliency detection.