  In recent years, several automatic segmentation methods have been proposed
for blood vessels in retinal fundus images, ranging from using cheap and fast
trainable filters to complicated neural networks and even deep learning. One
example of a filted-based segmentation method is B-COSFIRE. In this approach
the image filter is trained with example prototype patterns, to which the
filter becomes selective by finding points in a Difference of Gaussian response
on circles around the center with large intensity variation. In this paper we
discuss and evaluate several of these vessel segmentation methods. We take a
closer look at B-COSFIRE and study the performance of B-COSFIRE on the recently
published IOSTAR dataset by experiments and we examine how the parameter values
affect the performance. In the experiment we manage to reach a segmentation
accuracy of 0.9419. Based on our findings we discuss when B-COSFIRE is the
preferred method to use and in which circumstances it could 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 leaf.
