Segmentation of blood vessels in retinal fundus images

Abstract

The inspection of the blood vessel tree in the fundus, which is the interior surface of the eye opposite to the lens, is important in the determination of various cardiovascular diseases. This can be done manually by ophthalmoscopy, which is an effective method of analysing the retina. However, it has been suggested that using fundus photographs is more reliable than ophthalmoscopy. Additionally, these images can be used for automatic identification of the blood vessels, which can be a difficult task due to obstacles such as low contrast with the background, narrow blood vessels and various blood vessel anomalies. A segmentation method with high accuracy can serve as a significant aid in diagnosing cardiovascular diseases, as it highlights the blood vessel tree in the fundus. In recent years, several segmentation methods have been proposed for the automatic segmentation of blood vessels, ranging from using cheap and fast trainable filters to complicated neural networks and even deep learning. In this paper we discuss and evaluate several of these methods by examining the advantages and disadvantages of each. Subsequently, we take a closer look at a filter-based method called B-COSFIRE. We study the performance of the method on test datasets of fundus images and we examine how the parameter values affect the performance. The performance is measured by comparing the extracted blood vessel tree with a manually segmented blood vessel tree. One of the datasets we consider is the recently published IOSTAR dataset and, if researchers have used the dataset already, we compare our results, using the IOSTAR dataset, with findings about blood vessel segmentation methods on this dataset in the field. Based on our findings there we discuss when B-COSFIRE is the preferred method to use and in which circumstances could it be beneficial to use a more (computationally) complex segmentation method. We also shortly discuss areas beyond blood vessel segmentation where these methods can be used to segment elongated structures, such as rivers in satellite images or nerves of a leave.