自适应局部区域型水平集分割算法

灰度不均匀现象普遍存在于自然图像和医学图像中，因此使用传统的图像分割方法很难精准的分割出目标物，从而导致图像分割在模式识别和临床医学的应用中会出现很多问题.为了更好地改善分割效果，解决灰度不均匀现象所带来的问题，本文结合图像的自适应梯度权重信息和局部区域信息提出一种新型的水平集分割算法.由于图像的梯度信息具有稳定性，因此文中通过在局部区域中使用自适应梯度权重信息，达到结合图像边缘信息和区域信息的目的以提高算法鲁棒性.同时，文中使用的梯度权重滤波增加了图像对比度，因此分割的效果有了显著改善.最后，通过与LCV （local Chan-Vese）模型和LIC （local intensity clustering）模型的对比实验来验证本文分割方法的有效性和鲁棒性.在实验对比中，本文方法均得到比较令人满意的结果，充分展示其在处理灰度不均匀图像上的优势.

Adaptive Level Set Segmentation Algorithm Based on Local Region

Intensity inhomogeneity often occurs in natural and medical images, and it is hard to accurately segment intensity inhomogeneous images because most popular segmentation models are based on intensity homogeneous images. In this paper, we propose a novel level set-based segmentation model which integrates adaptive gradient weighted information (AGWI) and local region information to handle intensity inhomogeneous images. By employing AGWI in local regions, we combine the edge information and region information. Furthermore, the complementation of edge information and region information will enhance the robustness and effectiveness of our method. Finally, we compare our model with the local Chan-Vese (LCV) model and local intensity clustering (LIC) model. Some experiments on synthetic and nature images will be shown to demonstrate the efficiency and robustness of our method.