Segmentation of blood vessels in retinal fundus images

TL;DR

This paper evaluates various blood vessel segmentation methods in retinal images, focusing on B-COSFIRE, and discusses their performance, parameter effects, and potential applications beyond medical imaging.

Contribution

The study provides a detailed analysis of B-COSFIRE's performance on the IOSTAR dataset and compares it with other segmentation methods, highlighting when it is most effective.

Findings

Achieved a segmentation accuracy of 0.9419 with B-COSFIRE.

Parameter tuning significantly affects segmentation performance.

B-COSFIRE is suitable for elongated structure segmentation beyond retinal vessels.

Abstract

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.