Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement

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.

Exploiting the Retinal Vascular Geometry in Identifying the Progression to Diabetic Retinopathy Using Penalized Logistic Regression and Random Forests

“…Although many state-of-the-art automated tools have been proposed in literature, utilizing many different methods e.g. wavelets and edge location refinement both to segment and measure retinal vessels using image profiles, computed across a spline fit of each detected centerline [5], an infinite active contour model, using an infinite perimeter regularizer and multiple region information [29] or using neighbourhood estimator before filling filter [2], still they cannot be used in large studies for evaluating the progression of the disease. Their consistency and accuracy/precision as well as the measurement errors across datasets with different image quality , do not allow us to find these subtle changes that occur inside the vasculature over time, and which we are trying to identify in the same retinas.…”

Automated Tracing of Retinal Blood Vessels Using Graphical Models

“…Precision Recall F-1 Accuracy Ricci [22] ---.9563 Mendonca [5] .7315 --.9463 Peter Bankhead [11] .7027 .7177 .7101 .9371…”

“…It is critical for us to retain the vessel pixels that keep the local vein and artery branches from being broken or entirely missing. To achieve this, based on two existing methods [9,11], our segmenter is formed by merging the results, while emphasizing on maintaining the network connectivity. Due to space concern, we discuss here only the quantitative analysis of the segmentation results.…”

“…The first is based on segmentation [5][6][7][8][9][10][11][12], while the second is vessel tracking based methods [13][14][15][16][17]. The first type classifies the pixels into vessel or non-vessel pixels and produces a binary image where foreground pixels correspond to vessel pixels and background pixels correspond to non-vessel pixels.…”

A Retinal Vessel Detection Approach Based on Shearlet Transform and Indeterminacy Filtering on Fundus Images

“…Hysteresis thresholding was then applied for extracting the retinal vessels. Another multi-resolution approach was proposed by Bankhead et al [8], where the authors used wavelets. The authors achieved the vessel segmentation by thresholding the wavelet coefficients.…”