Automatic identification of vessel crossovers in retinal images

Crossovers and bifurcations are interest points of the retinal vascular tree useful to diagnose diseases. Specifically, detecting these interest points and identifying which of them are crossings will give us the opportunity to search for arteriovenous nicking, this is, an alteration of the vessel tree where an artery is crossed by a vein and the former compresses the later. These formations are a clear indicative of hypertension, among other medical problems. There are several studies that have attempted to define an accurate and reliable method to detect and classify these relevant points. In this article, we propose a new method to identify crossovers. Our approach is based on segmenting the vascular tree and analyzing the surrounding area of each interest point. The minimal path between vessel points in this area is computed in order to identify the connected vessel segments and, as a result, to distinguish between bifurcations and crossovers. Our method was tested using retinographies from public databases DRIVE and VICAVR, obtaining an accuracy of 90%.

• Publication year

  2015

• Venue

  Other Conferences

• Publication date

  2015-02-12

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1117/12.2181376
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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