IEEE Trans Image Process. 2018 Aug;27(8):3729-3738. doi: 10.1109/TIP.2018.2825101.
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.
在不同的成像领域中，对给定图像中的精细物体细节进行自动分割正变得至关重要。在本文中，我们提出了一种新的变分水平集模型，用于全局和交互式\选择性分割任务，该模型可以处理强度不均匀和噪声的存在。所提出的方法在干净和嘈杂的向量值图像上保持相同的性能。该模型利用局部计算的去噪约束曲面和去噪保真项的组合来确保对给定图像的局部和全局特征进行精细分割。将两阶段水平集公式扩展到多阶段公式，以成功分割人脑的医学图像。与最先进模型的比较实验表明了所提出方法的优势。
