基于Random-Walk算法的DR图像分割方法

本文算法对原图像进行快速Mallat小波分解得到骨干图后，利用其高频子带梯度信息优化边的权重，并在概率阈值的准则下对争议区域做进一步划分，最后把最大到达概率所在类的标签赋予未标定顶点，并扩展到原图像，得到分割边界。用微软GrabCut分割数据库图像和实际DR图像对该算法进行了验证，该算法能快速而有效地分割出特定的图像，适用于DR图像的分割，为进行组织增强和进一步提高DR图像质量打下基础。

DR Image Segmentation Algorithm Based on Random-Walk

A novel method which is based on the Random-Walk algorithm is proposed for DR image segmentation and solves the key problem of tissue enhancement to improve the quality of DR image further. Firstly,the original image is decomposed to build the backbone graph by using Mallat＇s fast wavelet transform. And the edge weight is optimized by using gradient information from high-frequency subband. Then a further division of the ambiguous area is done under the probability threshold criteria. Finally, the label with the greatest probability is assigned to each unlabeled vertex,and image segmentation boundaries are obtained by expanding the labeled backbone graph to the original image. The experimental results on Microsoft GrabCut segmentation database images and real DR images demonstrated that this algorithm above is able to segment out the expectable part from DR image effectively and fast.