Image Segmentation for Intensity Inhomogeneity in Presence of High Noise

Abstract

Automated segmentation of fine objects details in a given image is becoming of crucial interest in different imaging fields. In this paper, we propose a new variational level-set model for both global and interactive\selective segmentation tasks, which can deal with intensity inhomogeneity and the presence of noise. The proposed method maintains the same performance on clean and noisy vector-valued images. The model utilizes a combination of locally computed denoising constrained surface and a denoising fidelity term to ensure a fine segmentation of local and global features of a given image. A two-phase level-set formulation has been extended to a multi-phase formulation to successfully segment medical images of the human brain. Comparative experiments with state-of-the-art models show the advantages of the proposed method.