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A divide et impera strategy for automatic
classification of retinal vessels into arteries and veins
Enrico Grisan, Alfredo Ruggeri
Abstract
The first pathologic alterations of the retina are seen in the vessel network. These modifications affect very differently arteries and
veins, and the appearance and entity of the modification differ as the retinopathy becomes milder or more severe. In order to
develop an automatic procedure for the diagnosis and grading of retinopathy, it is necessary to be able to discriminate arteries from
veins. The problem is complicated by the similarity in the descriptive features of these two structures and by the contrast and
luminosity variability of the retina. We developed a new algorithm for classifying the vessels, which exploits the peculiarities of
retinal images. By applying a divide et impera approach that partitioned a concentric zone around the optic disc into quadrants, we
were able to perform a more robust local classification analysis. A comparison with manual classification is reported.
Impera
Vessel Network Structure
Introduction
The first changes in the retina that point out the onset of a retinopathy, e.g. from a
systemic disease, appear in the vessels.
¾ Changes in vessel structure can affect very differently arteries and veins.
¾ A number of disease indicators need a distinction between arteries and veins
(e.g. focal arteriolar narrowing, venous beading, generalized arteriolar narrowing)
¾ Inter- and intra-image contrast, luminosity and color variability
¾ Fading of the differences between the two types of vessels in the periphery of
the retina
¾ Appearance variability of vessels of the same class (artery or vein ) when distant
but within the same image (see Fig. 1 and Fig. 2).
Specific structure for both arterial and venous network:
¾Main vessels emerge at the optic disc
¾Follow a double-parabolic path by branching and thinning
¾At a small distance from the optic disc border, vessels are distributed in a
balanced way. In Fig. 4 are shown the main vessel arcades.
Main supero-temporal vein
¾Vessel sample points pooled together, separately for each retina region.
¾For each vessel sample point, a circular neighborhood of radius dependent on the
sample vessel caliber is considered.
¾The mean of the hue values of the points belonging to this neighborhood, and the
variance of their red values are the features considered
¾In every region, a fuzzy c-mean classifier divides the pooled points into artery
points and vein points.
¾An empirical probability P for each vessel segment to be an artery (or vein) can
be determined
Main supero-nasal artery
Quadrant-wise classification is more robust against that obtained considering the
whole concentric zone about the optic disk (Fig. 6)
It would be even more confounding considering vessel points gathered from the
whole fundus image.
Main supero-nasal vein
Main supero-temporal artery
Main infero-nasal vein
Figure 1a: Two arteries and a vein from the same image
Main infero-temporal vein
Figure 1b: Features for manually sampled points of the vessels
Main infero-nasal artery
Main infero-temporal artery
Figure 4 Main vessel arcades around the optic disc: it is clear the quasi-radial layout
Figure 2a: Arteries from different position within
the same image
Figure 2b: Features for manually sampled points of the vessels
Conventional techniques trying a global classification will fail for the presence of
these intra-class features dissimilarities and inter-class features similarities. Even
high order nonlinear classifier are not able to handle this type of classification in a
simple way.
Methods
¾Retinal images have been acquired with a fundus camera, centered on the fovea
and with a 45° or 50° field of view.
¾The films digitized with a color depth of 24 bits and a resolution of 1360 dpi.
¾Image preproccesing to compensate for intra-image inhomogeneity [6], (Fig. 3)
¾Center and diameter of the optic disk were manually set.
¾Automatically extracted vessel segments by a sparse tracking algorithm [5]
Figure 6. Fetures distribution (red points for arteriey and blue points for vein) and classifier threshold (euclidean iso-distance from the
cluster centers) in the case of division of the concentric zone in four regions (left panel), and considering it as a whole (right panel)
Results and discussion
Divide
¾The algorithm was tested on 435 automatically-tracked vessel segments, coming from
¾Vessels may be classified reasonably well only in an area around the optic disc. In 35 different fundus images.
the periphery of the image (far from the optic disc) they become almost ¾Overall classification error of 12.4%
undistinguishable.
¾Classification error of 6.7% considering only the major vessels. These major vessels
¾Only vessels close to each other can be reliably recognized as arteries or veins by represent the 61% of the entire vessel set analized.
direct comparison, without any further semantic knowledge.
Fig. 7 shows an example of the classification, comparing it with the manual (ground truth)
and with that obtained by pooling the four regions together. The superiority of the
Local nature of this classification procedure Symmetry of the vessel network proposed method in this image is evident.
Partitioning of the retina into regions:
¾Similar number of veins and arteries
¾The two types of vessels have significant local differences in features
¾Only the major vessels in each retina regions are considered for the subsequent
classification.
Vessel set 2
Figure 7a Proposed classifcation: only
a minor vessel is misclassified
Figure 7b Manual classifcation
Figure 7c Classification pooling the
four region (global calssification): 3
major vessels are misclassified
Acknowledgements
Vessel set 1
This work was partly supported by a research grant from Nidek Technologies, Italy
Bibliography
Vessel set 3
Figure 3 Image appearance as aquired (left panel) and after luminosity and contrast inhomogeneity correction
Vessel set 4
Figure 5 Concentric zone about the optic disk and its subdivision into four regions (left panel), and selected vessels inside these
regions (right panel)
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[2] B. M. Ege et al., Comp. Meth. Progr. Biom., 62, 165-172, 2002
[3] A. Ruggeri et al., Eur. J. Opth., 13, 228, 2003
[4] A. Hoover et al., IEEE Trans. Med. Imag, 22, 951-958, 2003
[5] M. Foracchia et al., CAFIA 2001, 15, 2001
[6] E. Grisan et al., Eur. J. Opth., 13, 228-229, 2003