A simple boundary reinforcement technique for segmentation without prior

• A method for segmentation of low contrast images is proposed.
• The global optimal output of the CV model is used to create the edge attraction force in the GAC method.
• Experimental results demonstrate improved performance over existing data driven methods.

Accurate boundary detection is a critical step of the image segmentation process. While most edge detectors rely on the presence of strong intensity gradients, this criteria can limit robustness in many real world cases. In this work we propose a scheme which makes use of a combination of both global and local segmentation methods to capture the boundary of target objects in low contrast images. This approach has several advantages: the globally convex segmentation scheme is immune from initial conditions and is easily adapted to the data. The addition of a segmentation scheme based on local curve evolution produces a solution which is shown to help preserve topology between the initial and target shape, a property lacking in globally convex segmentation schemes. Experimental results show that the proposed method achieves enhanced performance compared to classical data-driven segmentation schemes proposed in the literature.