Active Contour Model Driven by Non-Local Feature Fitting Energy Function With Scalable Normalization

It is challenging for active contour models (ACMs) to segment weak-edge and noisy images efficiently and accurately. To solve this problem, a novel ACM is proposed in this work. The proposed ACM achieves high-precision segmentation for weak-edge and noisy images using a non-local feature fitting energy function and a scalable normalization method. The non-local feature fitting energy function is constructed based on the distances calculated by Jeffreys divergence between non-local weighted fitting images and the image processed by the non-local means (NLM) algorithm. The non-local weighted fitting images include the fitting foreground and background with image edge features. The images processed by the NLM algorithm is used to reduce the influence of noise. The data-driven term, obtained by minimizing the non-local feature fitting energy function, is computed before the level set iteration, which improves the computation speed. In addition, a scalable normalization method is proposed to normalize the data-driven term. The ability to distinguish the targets from the background for different types of images is enhanced by adjusting a scaling factor, improving the robustness and accuracy of the proposed model. Experimental results demonstrate the advantages of the proposed model.