Illustration of width measurement via the retinal image segment.

<p>The blue centreline is rotated counterclockwise around the red central point, to the green solid line, overlapping with some of the white segment pixels.</p

Quantitative evaluation of vessel segmentation algorithms related to the second class of the images.

<p>Comparison of performance between the recent studies according to the second class of the images, including 1th, 5th, 11th, 15th, 16th, 19th test images from the DRIVE database.</p

Extraction of the error image in the region that the error image is overlapped with the straight line based image of Fig. 15(H).

<p>Extraction of the error image in the region that the error image is overlapped with the straight line based image of Fig. 15(H).</p

The steps are involved to process three class members of retina images.

<p>The numbered steps are illustrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095943#pone-0095943-g001" target="_blank">Fig. 1</a>.</p

Outputs of interim processing steps.

<p>(A) Illustration of binarisation with threshold selected to maximise entropy. (B) Illustration of the final segmentation, where the effect of central light reflex, indicated by green arrows in Fig. 5(B) has been removed in the resultant image.</p

Multiplication of images with the original image named 02_test from the DRIVE database.

<p>(A) Illustration of the resultant mask used for extraction of the enhanced retinal vessels via entropy based binarisation. (B) A global thresholded image after combining (A) and Fig. 4.</p

A further example showing the source of noise effects.

<p>(A) The green channel of original image named 08_test. (B) Colour coded mapping. (C) Final segmentation with noise effect partly from pathological tissue, indicated by yellow dash line, and partly from optic disk, indicated by green dash-dot line. (D) The superposition of the segmentation produced by our algorithm and manual segmentation, the yellow part of which represents the misclassified pixels of retinal blood vessels.</p

Overview of the main steps taken by our algorithm when processing a fundus image.

<p>(A) Illustration of globally thresholded image after multiplication between Fig. 4 and Fig. 5(A). (B) and (C) Illustration of two partitions of segmentation of (A) according to color coded mapping in Fig. 3(B). (D) Illustration of good overlapping (blue) between the resultant segment (yellow) and gold standard segment (green).</p

Quantitative evaluation of vessel segmentation algorithms related to the first class of the images.

<p>Comparison of performance between the recent studies according to the first class of the images, including 6th, 9th, 12th, 17th, 18th, 20th test images from the DRIVE database.</p

The green channel only image of a fundus photograph.

<p>The image is 02_test from DRIVE database.</p