Automatic classification of retinal blood vessels based on multilevel thresholding and graph propagation

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Beatriz Remeseiro (Author) Other; Ana Maria Mendonça (Author) FEUP; Aurélio Campilho (Author) FEUP

Journal

Title: Visual Computer

Vol. 37

Pages: 1247-1261

ISSN: 0178-2789

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00S-913

DOI: 10.1007/s00371-020-01863-z

Abstract (EN): Several systemic diseases affect the retinal blood vessels, and thus, their assessment allows an accurate clinical diagnosis. This assessment entails the estimation of the arteriolar-to-venular ratio (AVR), a predictive biomarker of cerebral atrophy and cardiovascular events in adults. In this context, different automatic and semiautomatic image-based approaches for artery/vein (A/V) classification and AVR estimation have been proposed in the literature, to the point of having become a hot research topic in the last decades. Most of these approaches use a wide variety of image properties, often redundant and/or irrelevant, requiring a training process that limits their generalization ability when applied to other datasets. This paper presents a new automatic method for A/V classification that just uses the local contrast between blood vessels and their surrounding background, computes a graph that represents the vascular structure, and applies a multilevel thresholding to obtain a preliminary classification. Next, a novel graph propagation approach was developed to obtain the final A/V classification and to compute the AVR. Our approach has been tested on two public datasets (INSPIRE and DRIVE), obtaining high classification accuracy rates, especially in the main vessels, and AVR ratios very similar to those provided by human experts. Therefore, our fully automatic method provides the reliable results without any training step, which makes it suitable for use with different retinal image datasets and as part of any clinical routine.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15