Automated blood vessel segmentation in fundus image based on integral channel features and random forests

Automated detection of blood vessel structures is becoming a crucial interest for better management of vascular disease. In this paper, we propose an algorithm for vessel segmentation in digital retinal images based on integral channel features and random forests. In the first stage, preprocessing is performed to obtain the candidate pixels of vessels, then a host of simple features are extracted for each candidate pixels based on integral channels. Furthermore random forests is used to classify the candidate pixels as vessels or not. Finally, postprocessing is applied to fill pixel gaps in classified blood vessels. The proposed algorithm achieves an average accuracy of 0.9614, 0.9588, sensitivity of 0.7191, 0.6996 and specificity of 0.9849, 0.9787 on two public databases DRIVE and STARE respectively.