A Novel Fuzzy Level Set Approach for Image Contour Detection

The level set methods have provided powerful frameworks for image segmentation. However, to obtain accurate boundaries of the objects, especially when they have weak edges or inhomogeneous intensities, is still a very challenging task. Actually, we have studied the popular existing level set approaches and discovered that they failed to segment the images with weak edges or inhomogeneous intensities in many cases. The weak/blurry edges and inhomogeneous intensities cause uncertainty and fuzziness for segmentation. In this paper, a novel fuzzy level set approach is proposed. At first, S-function based on the maximum fuzzy entropy principle (MEP) is used to map the image from space domain to fuzzy domain. Then, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. A partial differential equation is derived for finding the minimum of the energy function. The proposed approach has been tested on both synthetic images and real images and evaluated by several popular metrics. The experimental results demonstrate that the proposed approach can locate the true object boundaries, even for objects with blurry boundaries, low contrast, and inhomogeneous intensities.