AUTOMATIC DETECTION OF HARD EXUDATES AND OPTIC DISC

IN DIGITAL FUNDUS IMAGES

Elizabeth Chavez-Hernandez

and M. Elena Martinez-Perez

Postgraduate Program in Computer Science and Engineering, Insituto de Investigaciones en Matem

aticas Aplicadas y en

Sistemas, Universidad Nacional Aut

onoma de M

exico, 04510 M

exico City, M

exico

Department of Computer Science, Instituto de Investigaciones en Matem

aticas Aplicadas y en Sistemas

Universidad Nacional Aut

onoma de M

exico, Apartado Postal 20-726, 04510 M

exico City, M

exico

Keywords:

Hard exudates, Optic disc, Segmentation, Retinal images.

Abstract:

Automatic detection of characteristic patterns of diabetic retinopathy such as hard exudates may help to an

early diagnosis. Methods for automatic detection of hard exudates and optic disc are presented. Exudates

detection involves a preprocessing stage, threshold selection and region growing. For optic disc detection

a Bayes classiﬁer is applied followed by mathematical morphology techniques in order to improve the ﬁnal

result. The methods here presented were evaluated using the IMAGERET database, which contains fundus

images evaluated by qualiﬁed experts. In average, the area of exudates automatically detected overlaped

with 60.75% and 63.91% areas deﬁned by each of the two experts. For optic disc detection, sensitivity and

speciﬁcity were 72.12% and 95.56% respectively.

1 INTRODUCTION

Diabetes Mellitus (DM) has became a public health

problem worldwide. Diabetic retino- pathy (DR) is

an illness that affects blood vessels in the retina,

and it is directly correlated with evolution time of

DM and if it is not early detected may cause par-

tial or even total blindness (Pereira Delgado, 2005).

One of the observed patterns in fundus images of

RD patients are the hard exudates, shinny and yel-

lowish intraretinal protein deposits of irregular shape.

The optic disc (OD) is observed with similar inten-

sities as the exudates but its shape is more regular

and it does not represent a pathological sign. De-

tection of OD is an important step in developing sys-

tems for automatic diagnosis of various serious oph-

thalmic pathologies. There are previous works re-

lated with the detection of exudates such as those

based on a combination of local and global threshold-

ing (Phillips et al., 1993); color normalization, local

contrast enhancement followed by a fuzzy C-means

clustering and neural networks (Osareh et al., 2001);

ﬁnally those based on recursive region growing thech-

nique (Sinthanayothin et al., 1999). For OD detec-

tion methods can be grouped into three types: i) re-

lated with location, generally representative of its cen-

ter (Foracchia et al., 2004); ii) templated-based meth-

ods to obtein OD boundary approximation (Wong

et al., 2008); and iii) based on deformable models or

snakes (Xu et al., 2007). In this work, we present

an alternative methodology for hard exudates and op-

tic disc detection. Segmentation preliminary results

are evaluated using the public database IMAGERET-

DIARETDB1 V2.1 (Kauppi et al., 2009).

2 HARD EXUDATES

RECOGNITION

In order to reduce the shade effect of the non uni-

form lightening, a process of contrast correction is

needed (Fleming et al., 2006). First, a 3 × 3 pixels

median ﬁlter is applied to the green component of the

RGB image, followed by a gaussian ﬁlter with σ = 2,

the resulting image is called I. Second, a 35 × 35 pix-

els median ﬁlter is applied to the green component of

the original RGB image, calling G

to this image. Im-

age S

is calculated by S

= I/G

− 1, and ﬁnally, con-

trast corrected image is obtained after normalizing S

by its standard deviation, S = S

/std(S

) (Figure 1b).

2.1 Threshold Selection and Region

Growing

The objective of the ﬁrst stage of our method is to

543

Chavez-Hernandez E. and Elena Martinez-Perez M..

AUTOMATIC DETECTION OF HARD EXUDATES AND OPTIC DISC IN DIGITAL FUNDUS IMAGES.

DOI: 10.5220/0003890905430547

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (MIAD-2012), pages 543-547

ISBN: 978-989-8425-89-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

(a) (b) (c) (d)

Figure 1: (a) Original images (S). (b) Contrast correction of image. (c) Binary image resulted by the threshold method (BW1).

(d) Final recognition of hard exudates.

ﬁnd an optimal threshold t that discriminates between

two classes of pixels, the background, and exudates.

For this purpose the Otsu method (Otsu, 1979) is ap-

plied to identify a threshold t

. In order to minimize

false positives this step is repeated a second time over

the region with intensities higher than t

, resulting in

a threshold value t. The binary image with thresh-

old t is called BW 1 (Figure 1c). To improve exudates

detection and correct for the underselection, BW 1 im-

age is considered as seed regions for exudates, BW1

is then dilated by 15 pixels, and the followed criterion

is applied for every pixel p(i, j) in the dilated region,

(i, j)







exudate if p ≥ (t − 2s)

background if p < (t − 2s)

(1)

where t is threshold found in the ﬁrst stage and s,

the standard deviation of seed pixels deﬁned by BW1.

By this, candidate pixels with intensities greater than

t −2s are classiﬁed as exudates and, pixels with inten-

sities below this, are classiﬁed as background (Figure

1d).

3 OPTIC DISC DETECTION

3.1 Bayes Classiﬁer

A Bayes classiﬁer is applied in order to obtain a bi-

nary image with only two classes of pixels, the so

called disc and background. Probabilities are calcu-

lated using training images on which the class for

each pixel is known, as deﬁned by experts. The prob-

ability of a pattern to belong to the class C

is obtained

as:

p(C

| x) =

p(x | C

)p(C

)

p(x)

(2)

Assigning a pattern x to the class with the highest a

posteriori probability minimises the error probability,

then, we assign a pattern x to a class if:

p(C

| x) > p(C

| x) ∀ j 6= k (3)

The ﬁrst step was then to choose a set of im-

ages to train the classiﬁer, for this purpose 10 im-

ages were randomly chosen from the database IM-

AGERET (Kauppi et al., 2009). The characteristics

considered for the training step were the following:

1. Red band of the original RGB image,called R.

2. Green band of the original RGB image, called G.

3. Variance image of G, this is calculated as

in (Sinthanayothin et al., 1999), a subimage

W (i, j) is deﬁned centered in the pixel (i, j) of di-

mensions M × M. Let

W (i, j)

be the mean inten-

sity in W (i, j). The variance image is given by:

V (i, j) =





− (

)

(4)

where f represents pixel intensity on image G.

4. Value of y axis of pixels on the optic disc, since

optic disc is showed in the central section of im-

ages.

Once the training step has ﬁnished, the classiﬁer

should get an image as input and return a binary im-

age, called BW 2 (Figure 2b). However, other tech-

niques are required to accurately deﬁne the OD.

3.2 Mathematical Morphology

The second stage on optic disc recognition involves

mathematical morphology in order to discriminate the

OD among the other regions showed as the result of

previous stage. First, a morphological aperture is ap-

plied, this operation removes those clusters with few

pixels. Next, a closure is applied using a disc as struc-

tural element in order to eliminate hypointense re-

gions within the larger clusters in the image. Finally

holes are ﬁlled in the clusters on the image. This dark

branches in the OD are due to the blood vessels that

appear as dark areas in the image.

After these operations, the image contains clus-

ters of different sizes, for each of these objects the

area and its roundness is calculated. Roundness is ob-

tained through,

roundness(k

) =

4πA

(5)

where A

is the area of object i and P

is the perime-

ter of the same object. The optic disc will be that

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

544

(a) (b) (c)

Figure 2: (a) Original image. (b) Result of Bayes classiﬁer (BW2). (c) Final recognition of optic disc.

(a) (b) (c)

(d) (e) (f)

Figure 3: (a) and (d) Original images. (b) and (e) Hard exudates found by Expert 1. (c) and (f) Hard exudates found by Expert

object whose roundness is greater than 0.8 and area

greater than 1.5% of the image (Figure 2c). All pa-

rameter values, constants and windows sizes given

along the description of the methodology refer to im-

ages of 1150 × 1500 size in pixels. For different im-

age resolutions, all of these values have to be scaled.

4 EVALUATION

For evaluation of exudates and OD recognition, the

image database IMAGERET (Kauppi et al., 2009)

was used, this database is open access and available

on the web. We used 25 fundus images from this

database where images have been evaluated by ex-

perts, each of them highlighting gross areas where

the experts recognize one of the following patterns:

hard exudates, soft exudates, haemorrhages and red

small dots. The database comes with a Matlab toolkit

that allows to explore annotations by experts. Figure

3 shows examples of two images with hard exudates

from the database and its annotations by experts.

Experts were asked to highlight the areas where

the pattern of interest is found using a circle, ellipse

or a polygon region. From Figure 3, it can be seen that

there are differences between the exudates selected by

different experts, in fact, in some cases there is no

coincidence in the areas detected as containing exu-

dates. As deﬁnition, methods of the experts are based

in gross detection and their results are not compara-

ble to the methods presented here, which deﬁne the

structures more precisely. Because of that, evaluation

was based on the percentage of the area of exudates

detected contained in the areas deﬁned by experts.

There are cases in which automatic detection of exu-

dates is evaluated as very good, for example in image

13 showed in Figure 4a, since exudates detected are

fully contained within the area deﬁned by the experts.

However, in image 27, showed in Figure 4e, one ex-

pert doesn’t reports exudates while the other reports

an area that only overlap with 1.63% of exudates re-

ported by our results.

This incongruences doesn’t necessarily means

that automatic recognition has been poor, in the case

of image 27, it contains some patterns with intensities

very similar to those of exudates which are probably

due to a treatment that causes scars in the retina. In

average, for the 25 images evaluated, the area of ex-

udates detected by the methods here presented that

is within the area deﬁned by expert 1 is 60.75%, and

63.91% for expert 2, as shown on Table 1. For OD

evaluation, contingency tables were used, and so, sen-

sitivity and speciﬁcity were evaluated according to,

Sensitivity =

V P

V P + FN

Speci f icity =

V N

V N + FP

(6)

where V P represents the true positives, V N the true

negatives, FP the false positives and FN the false

AUTOMATIC DETECTION OF HARD EXUDATES AND OPTIC DISC IN DIGITAL FUNDUS IMAGES

545

(a) (b) (c) (d)

(e) (f) (g) (h)

Figure 4: (a) and (e) Original images. (b) and (f) Hard exudates found by Expert 1. (c) and (g) Hard exudates found by Expert

2. (d) and (h) Hard exudates found by our method.

Table 1: Results for evaluation of automatic detection of

exudates and optic disc. The average area of exudates over-

lapped with the areas highlighted by the experts is pre-

sented. Sensitivity and speciﬁcity is shown for optic disc

detection.

Hard exudates Optic Disc

Expert 1 Expert 2 Sensitivity Speciﬁcity

Mean 60.75% 63.91% 72.12% 95.56%

negatives number of pixels respectively . In Table 1

results for sensitivity and speciﬁcity are shown for

25 images from IMAGERET (Kauppi et al., 2009)

database. In average, our methods detect the OD with

sensitivity of 72.12% and speciﬁcity of 95.56%.

Ideally, the evaluation of exudates should be done

similar to that of the optic disc, however the database

we used does not allow us to make that assessment.

It is not possible to compare our preliminary results

with other studies since most of these works used their

own databases which are not public. It is necessary

therefore the generation of a public database of pat-

terns marked by experts accurately for a reliable eval-

uation and comparison.

5 CONCLUSIONS

Methods for automatic detection of hard exudates

and optic disc in fundus images were presented.

These methods preserved computational simplicity,

still achieving good results even for images with a

wide range of lightening conditions. Differences in

the selection of exudates by the experts and meth-

ods here presented complicated the deﬁnition of good

evaluation techniques. Selection of the optic disc

by the experts was more speciﬁc, allowing a better

evaluation of concordance. From our preliminary re-

sults we concluded that a more pattern speciﬁc public

database make by experts is needed in order to have a

more reliable evaluation. Work is being done in this

issue.

REFERENCES

Fleming, A., Philip, S., Goatman, K., Olson, J., and Sharp,

P. (2006). Automated microaneurysm detection us-

ing local contrast normalization and local vessel de-

tection. IEEE Transactions on Medical Imaging,

25(9):1223–1232.

Foracchia, M., Grisan, E., and Ruggeri, A. (2004). Detec-

tion of optic disc in retinal images by means of a ge-

ometrical model of vessel. IEEE Trans. Med. Imag.,

23(10):1189–1195.

Kauppi, T., Kalesnykiene, V., Kamarainen, J., Lensu, L.,

and et. al. (2009). Imageret-database. www2.it.lut.ﬁ/

project/imageret/.

Osareh, A., Mirmehdi, M., Thomas, B., and Markham, R.

(2001). Automatic recognition of exudative macu-

lopathy using fuzzy c-means clustering and neural net-

works. In Proc Medical Image Understanding and

Analysis, pages 49–52.

Otsu, N. (1979). A threshold selection method from gray-

level histograms. IEEE Trans. Syst., Man, Cybern.,

9:62–66.

Pereira Delgado, E. (2005). Nuevas perspectivas en oftal-

molog

ıa: Retinopat

ıa diab

etica. Glosa, Laboratorios

Esteve.

Phillips, R., Forrester, J., and Sharp, P. (1993). Auto-

mated detection and quantiﬁcation of retinal exudates.

Graefe’s Archive for Clinical and Experimental Oph-

thalmology, 231(2):90–94.

Sinthanayothin, C., Boyce, J., Cook, H., and Williamson,

T. (1999). Automated localization of the optic disc,

fovea, and retinal blood vessels from digital colour

fundus images. British Journal of Ophthalmology,

83(8):902–910.

Wong, D. W. K., Liu, J., Lim, J. H., Jia, X., Yin, F., Li, H.,

and Wong, T. Y. (2008). Level-set based automatic

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

546

cup-to-disc ratio determination using retinal fundus

images in ARGALI. In Proc. 30th Annu. Int. IEEE

EMBS Conf., pages 2266–2269.

Xu, J., Chutatape, O., Sung, E., Zheng, C., and Kuan, P.

C. T. (2007). Optic disk feature extraction via modi-

ﬁed deformable model technique for glaucoma analy-

sis. Pattern Recognition, 40(7):2063–2076.

AUTOMATIC DETECTION OF HARD EXUDATES AND OPTIC DISC IN DIGITAL FUNDUS IMAGES

547