Improved local Gaussian distribution fitting energy model for image segmentation

Image segmentation is one of the most important parts of image processing. Several segmentation models have been proposed during study for recent decades. However noise, low contrast, and intensity inhomogeneity on images are still big challenges for image segmentation. Thus this paper presents an improved segmentation method based on well-known local Gaussian distribution fitting (LGDF) model. We first apply automatic initialization based on simple threshold segmentation to dealing with the drawback that LGDF model is sensitive to initialization position. Then we utilize result of effective and efficient Canny edge detector to get noteworthy edge information and after further processing we gain an edge field. The edge field is used to reduce the probability of local minima on regions far from true boundaries and to force evolving curve to snap to target boundaries. The experimental results demonstrate the advantages of our method on not only medical and synthetic images but also some natural images.