为了消除血管减影图像中的噪声，从复杂背景中提取血管，提出了一种基于图像像素状态平衡的血管提取方法。将图像看成一个由目标区域与背景区域构成的平衡系统，目标区域与背景区域由于某种作用力处于一个内在平衡状态，但噪声的引入破坏了这种平衡，该方法通过恢复平衡状态来消除噪声，分离目标与背景区域。在此基础上发展出一种新的灰度图像二值化算法，并将其应用在脑部血管DSA图像的血管提取上，该算法能从背景噪声很强的DSA剪影图像中分离出完整的血管网络，实验效果令人满意。

The model of state balance of image pixels from the view of pixels' correlative degree is proposed to extract vascular network from DSA images. The image is regarded as a balanced system that consists of object and background areas. Under some certain force, there is an inner balanced state between these two kinds of areas. But the introduction of noise breaks the balance, this disturbance will make the boundary of vascular and background indistinct. In the circumstances, extracting vascular network directly is very difficulty. If the balanced state can be gained, the segmentation becomes easy and accurate relatively. Therefore it's possible to remove the noise and separate the object and background areas by resuming such balanced state. Based on this theory, a new binarization algorithm for gray images can be developed and used in the vessel extraction from the DSA images of cerebral vessels. An overlap algorithm is presented to resumes the balance state in this paper. Then using this algorithm, the vascular network can be segmented from background perfectly. It can extract the whole vascular network from the DSA subtraction images with high level noises and the experimental results are very satisfying.