Image Segmentation in the Presence of Intensity in Homogeneities by Using Level Set Method with MRI and Satellite Images

This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. Here we can take both mri images and also satellite images. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results.