用于灰度不均图像分割的自适应灰度拟合模型

针对灰度不均图像的分割问题，提出了一个结合全局信息的局部区域自适应灰度拟合模型。首先，分别利用图像的局部和全局信息构造了局部拟合项和全局拟合项；其次，利用像素点邻域内灰度的极差反映该点邻域内灰度的偏差程度，并以此定义了一个自适应权值函数；最后，利用定义的权值函数为局部项和全局项自适应赋权值，得到所提模型的能量泛函，并使用变分法推导出模型的水平集函数迭代方程。数值实现采用有限差分法。实验结果表明，与区域可变灰度拟合（RSF）模型和局部和全局灰度拟合（LGIF）模型相比，所提模型不仅能够稳定、准确地分割多种灰度不均图像，而且对演化曲线初始轮廓的位置、大小和形状具有更强的鲁棒性。

Adaptive intensity fitting model for segmentation of images with intensity inhomogeneity

For the segmentation of images with intensity inhomogeneity, a region-adaptive intensity fitting model combining global information was proposed. Firstly, the local and global terms were constructed based on local and global image information respectively. Secondly, an adaptive weight function was defined to indicate the deviation degree of the gray scale of a pixel neighborhood by utilizing the extreme difference level in the pixel neighborhood. Finally, the defined weighting function was used to assign weights to local and global terms adaptively to obtain the energy functional of the proposed model and the iterative equation of the model's level set function was deduced by the variational method. The experimental results show that the proposed model can segment various inhomogeneous images stably and accurately in comparison with Region-Scalable Fitting (RSF) model and Local and Global Intensity Fitting (LGIF) model, which is more robust in the position, size and shape of initial contour of evolution curve.