Active contours with local and global energy based-on fuzzy clustering and maximum a posterior probability for retinal vessel detection

The performance of active contour model is limited on retinal vessel segmentation as vessel images are usually corrupted with intensity inhomogeneity, low contrast, and weak boundary, which severely affect the segmentation results of retinal vessels. A new active contour model combining the local and global information is proposed in this paper to facilitate the vessel segmentation. In our model, the fuzzy conception is firstly introduced as fuzzy methods generally provide more accurate and robust clustering and the concept of fuzziness in fuzzy clustering, which is represented by membership, can reflect the intensity distribution of the image. Then, we define local energy based on Maximum a Posterior Probability and use spatially varying parameters, mean and stand deviation, to describe the local Gaussian distribution in order to better deal with intensity inhomogeneity. Furthermore, we combine local and global energy based on fuzzy clustering, with a weight coefficient. The coefficient is computed by a weight function according to contrast ratio of the image. Experiments on synthetic and real images and comparisons with other state-of-the-art active contour models show that the proposed model can detect objects more accurate and robust, especially for vessels on retinal angiogram.