A Local Active Contour Model for Image Segmentation with Intensity Inhomogeneity
This addresses image segmentation problems in fields like medical imaging or computer vision where intensity variations occur, though it appears incremental as it builds on existing active contour models.
The paper tackles image segmentation under intensity inhomogeneity by modeling objects as Gaussian distributions and using a moving window to transform the image for better separation, achieving superior results compared to state-of-the-art methods in experiments on synthetic and real images.
A novel locally statistical active contour model (ACM) for image segmentation in the presence of intensity inhomogeneity is presented in this paper. The inhomogeneous objects are modeled as Gaussian distributions of different means and variances, and a moving window is used to map the original image into another domain, where the intensity distributions of inhomogeneous objects are still Gaussian but are better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field with the original signal within the window. A statistical energy functional is then defined for each local region, which combines the bias field, the level set function, and the constant approximating the true signal of the corresponding object. Experiments on both synthetic and real images demonstrate the superiority of our proposed algorithm to state-of-the-art and representative methods.