Retinal Vessel Segmentation Based on Multiple Feature Fusion and Random Forest

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.