【摘要】 为了进行眼底疾病辅助诊断,提出一种基于多特征融合和随机森林的视网膜血管分割方法.首先为彩色眼底图中的每个像素点提取一个23维特征向量(包括图像不变矩、灰度共生矩阵、LoG结合高斯二阶导、梯度法、相位一致性和Hessian矩阵特征);然后选取一定数量的像素点,提取其特征共同构造一个特征矩阵作为输入数据,并采用随机森林算法训练分类器;再用训练好的分类器对待分割图像中的像素点进行分类,判断其是否为血管点;最后在初步分割基础上进行基于连通区域补足血管的后处理,得到优化后的血管分割结果.在DRIVE公共数据库上进行实验的结果表明,该方法平均精确度达0.9606,平均灵敏度达0.7447,平均特异性达0.9838,比已有方法性能更优.更多还原

【Abstract】 For the ophthalmic disease computer-aided diagnosis, this paper presents a multiple feature fusion fundus retinal blood vessels segmentation algorithm based on Random Forest. For each pixel in the field of view, a 23-D feature vector is constructed(encoding information on the moment invariant, gray level co-occurrence matrix, LoG with Gaussian second derivative, gradient of the image, phase congruency and Hessian matrix).Then a matrix is constructed for pixel of the training set as the input of the Random Forest; as a result, a Random Forest classifier used for classifying the test images is obtained. Finally, the post-processing method based on the connected area is used to make up blood vessels. The experimental result testing on DRIVE database demonstrates that our method performance is better than other state-of-theart methods based on machine learning. Meanwhile, the average accuracy, sensitivity, specificity are 0.9606, 0.7447, 0.9838, respectively.更多还原