Fast retinal vessel detection and measurement using wavelets and edge location refinement

• Verfasst von: Bankhead, Peter [VerfasserIn]
• Titel: Fast retinal vessel detection and measurement using wavelets and edge location refinement
• Verf.angabe: Peter Bankhead, C. Norman Scholfield, J. Graham McGeown, Tim M. Curtis
• E-Jahr: 2012
• Jahr: March 12, 2012
• Umfang: 12 S.
• Teil: volume:7
• Teil: year:2012
• Teil: number:3
• Teil: elocationid:e32435
• Teil: pages:1-12
• Teil: extent:12
• Fussnoten: Received October 21, 2011; Accepted January 31, 2012; Published March 12, 2012 ; Gesehen am 27.09.2018
• Titel Quelle: Enthalten in: PLOS ONE
• Ort Quelle: San Francisco, California, US : PLOS, 2006
• Jahr Quelle: 2012
• Band/Heft Quelle: 7(2012), 3, Artikel-ID e32435, Seite 1-12
• ISSN Quelle: 1932-6203
• DOI: doi:10.1371/journal.pone.0032435
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Algorithms
• Sach-SW: Blood vessels
• Sach-SW: Image processing
• Sach-SW: Imaging techniques
• Sach-SW: Matched filters
• Sach-SW: Reflexes
• Sach-SW: Retinal vessels
• Sach-SW: Wavelet transforms
• K10plus-PPN: 1581380976

Abstract

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available.