Active contours, or snakes, have a wide range of applications in object segmentation, which use an energy minimizing spline to extract objectsâ€™ borders. Classical snakes have several drawbacks, such as the initial contour sensitivity and convergence ability to local minima. Many approaches based on active contours are put forward to addressing these problems. However, these approaches have limitation that they all depend too much on the amplitude of edge gradient and abandon directional information. This can lead to poor convergence toward the object boundary in the presence of strong background edges and cluttered noises. To deal with these issues, we first propose a novel external force, called adaptive edge preserving generalized gradient vector flow based on component-based normalization (CN-AEGGVF), which can adaptively adjust the process of diffusion according to the local characteristics of an image and preserve weak edges by adding the gradient information of an image. The experimental results show that the new model provides much better results than other approaches in terms of noise robustness, weak edge preserving, and convergence. Secondly, an improved multi-step decision model based on CN-AEGGVF is presented, which added new effective weighting function to attenuate the magnitudes of unwanted edges and adopted narrow band method to reduce time complexity. The novel method is analyzed visually and qualitatively on nature image dataset. Experimental results and comparisons against other methods show that the proposed method has better segmentation accuracy than other comparative approaches. 
A novel snake model using new multi-step decision model for complex image segmentation