Active contour model driven by global and local intensity information for ultrasound image segmentation

Due to the abundant noise, blurry boundaries, and intensity inhomogeneities present in ultrasound (US) images, it is a difficult task to segment US images accurately. In this paper, we propose a novel active contour method that combines global and local region information to achieve this task. The global information can segment US images with noise and blurry boundaries; while the local information can settle the intensity homogeneities of images. The proposed method can be directly applied to synthetic, real, and US-images segmentation. Results demonstrate the superiority of the proposed method over other representative algorithms. Moreover, we also extend the proposed method to vector-valued images. Experiments are performed to testify the feasibility of the method, and the proposed vector-valued idea can be applied to the medical co-segmentation in the future.