A A divide et imperadivide et impera strategy for automatic strategy for automatic classification of retinal vessels into arteries and veinsclassification of retinal vessels into arteries and veins Enrico Grisan, Alfredo RuggeriEnrico Grisan, Alfredo Ruggeri

AbstractAbstract 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.

IntroductionIntroduction

Results and discussionResults and discussion

Figure 1a: Two arteries and a vein from the same image Figure 1b: Features for manually sampled points of the vessels

Figure 2a: Arteries from different position within the same image

Figure 2b: Features for manually sampled points of the vessels

The first changes in the retina that point out the onset of a retinopathy, e.g. from a systemic disease, appear in the vessels. Changes in vessel structure can affect very differently arteries and veins. A number of disease indicators need a distinction between arteries and veins (e.g. focal arteriolar narrowing, venous beading, generalized arteriolar narrowing)

Inter- and intra-image contrast, luminosity and color variability Fading of the differences between the two types of vessels in the periphery of the retina Appearance variability of vessels of the same class (artery or vein ) when distant but within the same image (see Fig. 1 and Fig. 2).

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.

MethodsMethodsRetinal images have been acquired with a fundus camera, centered on the fovea and with a 45° or 50° field of view.The films digitized with a color depth of 24 bits and a resolution of 1360 dpi.

Image preproccesing to compensate for intra-image inhomogeneity [6], (Fig. 3)Center and diameter of the optic disk were manually set.Automatically extracted vessel segments by a sparse tracking algorithm [5]

Vessel Network StructureVessel Network Structure

DivideDivide

ImperaImpera

Specific structure for both arterial and venous network:

Main vessels emerge at the optic disc Follow a double-parabolic path by branching and thinningAt 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

Main infero-temporal vein

Main supero-nasal vein

Main infero-nasal vein

Main supero-temporal artery

Main infero-temporal artery

Main infero-nasal artery

Main supero-nasal artery

Vessels may be classified reasonably well only in an area around the optic disc. In the periphery of the image (far from the optic disc) they become almost undistinguishable. Only vessels close to each other can be reliably recognized as arteries or veins by direct comparison, without any further semantic knowledge.

Local nature of this classification procedure Symmetry of the vessel network

Partitioning of the retina into regions:

Similar number of veins and arteriesThe two types of vessels have significant local differences in featuresOnly the major vessels in each retina regions are considered for the subsequent classification.

Vessel sample points pooled together, separately for each retina region. For each vessel sample point, a circular neighborhood of radius dependent on the sample vessel caliber is considered. The mean of the hue values of the points belonging to this neighborhood, and the variance of their red values are the features consideredIn every region, a fuzzy c-mean classifier divides the pooled points into artery points and vein points.An empirical probability P for each vessel segment to be an artery (or vein) can be determined

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.

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)

The algorithm was tested on 435 automatically-tracked vessel segments, coming from 35 different fundus images.Overall classification error of 12.4% Classification error of 6.7% considering only the major vessels. These major vessels represent the 61% of the entire vessel set analized.

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 proposed method in this image is evident.

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

AcknowledgementsAcknowledgementsThis work was partly supported by a research grant from Nidek Technologies, Italy

BibliographyBibliography[1] M. Goldbaum et al., 1996 IEEE Int. COnf. Im. Proc., 695-698, 1996[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 Figure 3 Image appearance as aquired (left panel) and after luminosity and contrast inhomogeneity correction

Figure 4 Main vessel arcades around the optic disc: it is clear the quasi-radial layout

Figure 5 Concentric zone about the optic disk and its subdivision into four regions (left panel), and selected vessels inside these regions (right panel)

Vessel set 1Vessel set 2

Vessel set 3 Vessel set 4