AN AUTOMATED APPROACH FOR PREPROCESSING

RETINOGRAPHIES

Silvia Alayón, José Luis Sánchez, José Sigut, Jorge Marrero

Department of Ingeniería de Sistemas y Automática y Arquitectura y Tecnología de Computadores

University of La Laguna, Spain

Manuel González

Department of Cirugía, Oftalmología y Otorrinolaringología, University of La Laguna, Spain

Keywords: Retinography, Glaucoma, Image preprocessing, Color spaces, Illumination correction.

Abstract: A retinography is a retinal photography useful for the precise tracking of any retinal pathology, especially

the glaucoma. Although there are sophisticated procedures for studying the evolution of the optic nerve,

sometimes it is not feasible the development of a rigorous tracking due to the high number of patients, the

high cost of the procedure and the need of high qualified staff. The design of an automated method for

detecting this pathology in its first stages through the automated analysis of retinographies could reduce the

cost of the process and the number of required specialists. Inspired by this objective, an automated

preprocessing method for retinographies is presented in this paper. The proposed methodology combines

information of different color spaces for achieving illumination and contrast enhancement.

1 INTRODUCTION

A retinography is a retinal photography useful for

the precise tracking of any retinal pathology,

especially the glaucoma. Glaucoma refers to a group

of diseases that affect the optic nerve and involves a

loss of retinal ganglion cells in a characteristic

pattern. Glaucoma without treatment leads to an

irreversible damaged of the optic nerve and to a loss

of the visual field that could originate partial or total

blindness. Therefore, one of the regions of interest in

the study of retinographies is the optic nerve head

(ONH).

Glaucoma is the most frequent cause of

blindness in the industrialized countries. The best

prevention method to delay its evolution and avoid

the vision loss is the regular tracking of the medical

treatment. The design of an automated method for

detecting this pathology in its first stages through the

automated analysis of retinographies could reduce

the cost of the process and the number of required

specialists.

The application of image processing techniques

on the retinography analysis is an increasing

research field. These techniques are not automated

and they normally require the user intervention. The

computer techniques for processing eye fundus

images usually involve the manual or semiautomatic

draw of the papilla contour and other structures of

the ONH. However, the objective of minimizing the

human intervention in these systems is appearing in

related research works (Cox et al., 1991), (Iqbal et

al., 2006), (Teng et al., 2002).

The work presented in this paper is part of a

research project developed in collaboration with the

Hospital Universitario de Canarias. The main

objective of the project is the design of an automated

software system for the delimitation of the ONH and

for the differentiation of superimposed structures

(arteries, vessels). Due to the irregularities presented

in the analyzed images it is essential to carry out a

preprocessing stage, in order to improve the image

before developing segmentation of the different

regions of interest. This enhancement process is the

core of this paper.

The problem of preprocessing retinographies has

been tackled by several researchers (Himaga et al.,

2002), (Fang et al., 2003) , (Echevarría et al., 2004),

(Chaudhuri et al., 1989). The preprocessing method

proposed in this paper pursues the improvement of

the illumination and contrast characteristics of the

images by using information of different color

381

Alayón S., Luis Sánchez J., Sigut J., Marrero J. and González M. (2010).

AN AUTOMATED APPROACH FOR PREPROCESSING RETINOGRAPHIES.

In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing, pages 381-384

DOI: 10.5220/0002690803810384

 SciTePress

spaces. In the next sections the proposed method is

explained, the experimental results are shown and

our main conclusions are offered.

2 DESCRIPTION OF THE

MEDICAL PROBLEM

The data base used in this research work consists of

28 high resolution images. These images have been

acquired in the Hospital Universitario de Canarias

with a Canon D5 camera.

A retinography of our data base is shown in

figure 1. Its different structures and parts are

marked.

Figure 1: Parts of a retinography: (a) Papilla or optic

Nerve Head (ONH), (b) Empty zone, (c) Blood vessels,

(d) Retina, (e) Macula.

The excavation of papilla of optic nerve is the

lightest zone inside the ONH. There are no nerve

fibers in this zone. A big excavation of papilla

involves a loss of nerve fibers and is sign of a

glaucomatous papilla. Therefore, the automatic

detection of this region can be helpful for the

medical diagnosis process.

3 DESCRIPTION OF THE

PROPOSED PREPROCESSING

METHOD

3.1 Step 1: Scaling of the RGB

Histogram

A narrow histogram produces low contrast images,

independently of the image luminosity. A simple

manner to develop contrast enhancement is to

increase the histogram’s dynamic range. The scaling

of the histogram preserves the shape of the

histogram and does not increase the background

noise.

The RGB histograms of the retinographies

(see figure 2, red) present a narrow distribution for

G and B components (with a low average) and a

wider distribution for the R.

Histograms’ bimodality is clearly appreciated.

The lower part represents the empty zone and the

upper part corresponds to the ocular globe.

Therefore, it is necessary the elimination of the

distribution corresponding to the empty zone before

expanding the histogram by the left side.

Once bimodality correction has been developed

on the original histogram, a new histogram is

obtained. We denote it as a. The scaling process is

carried out as shown in equation 1, where min is the

value n2, max is the percentile 99, (2

-1) is the

number of levels of the desired range (255 in our

case) and b is the resultant histogram after the

scaling procedure. Figure 2 shows the result of the

scaling of the RGB histograms. It can be observed

how each distribution is now wider.

(1)

Figure 2: Original histogram (red) and scaled histogram

(blue) of R, G and B components.

3.2 Step 2: Luminance Equalization in

YIQ Space

The aim of this step is the equalization of the image

luminance (to enhance the contrast and brightness of

the image) without loss of color information. As

result of this operation the blood vessels will be

better defined in the three RGB components.

In the YIQ color space all the information about

the image luminance is concentrated in Y

component Therefore, an initial conversion from

minmax

min],[

)12(],[

−

⋅−=

nma

nmb

(a)

(b)

(c)

(d)

(e)

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

382

RGB to YIQ is required and the Y component must

be extracted. The luminance equalization is carried

out through an adaptive equalization procedure.

Finally, the image is composed again in the YIQ

space and converted to the RGB color space.

3.3 Step 3: Illumination Correction

The studied retinographies present illumination

inhomogeneities. The illumination growths slowly

from the borders to the center producing dark areas

near the border.

For the tackled problem in this paper, images for

calibration purposes have not been acquired.

Therefore, the illumination correction must be done

through a posteriori estimation. According to

(Young et al., 1998), if I(x,y) is an image with

illumination inhomogeneities, LP is a low pass filter

and c a constant, the image after the illumination

correction I

(x,y) will be the result of applying

equation2.

cyxILPyxIyxI

+−= )},({),(),( (2)

Such formula can be implemented by separable

kernel filters (one for rows and the other for

columns). The low pass filter is calculated as the

mean of every row or column and the constant to be

added is the total average of the image (scalar) M, as

it is shown in equation 3.

(3)

where M represents the scalar average of a

component, CR is an image in which every column

is substituted by the mean of the column and CC an

image in which ever row is substituted by its mean.

Although order is not important, we have done in

first place the correction by columns and then by

rows for each component RGB.

A modification to the application of equation 3

on each image component must be done because

some results have shown an illumination

overcorrection. There are outstanding brighter zones

in the retinographies (the ONH zone, as example).

For these zones the row and column averages will be

considerably higher than for the rest of zones of the

image. The final illumination correction method

develops a double process on the image, as shown in

figure 3:

- Sub-process A: apply equation 3 on the image

previously prepared as explained in the last

subsection, to avoid overcorrection. A global

illumination correction is obtained without

considering the influence of the high illuminated

zones.

- Sub-process B: apply equation 3 on the image

without previous preparation. Therefore, an image

with illumination overcorrection is obtained.

The last operation is the combination of the

obtained images in both sub-processes: pixels of the

high illuminated zones are extracted from the image

of sub-process B; the other pixels are extracted from

the image of sub-process A.

Figure 3: Complete illumination correction procedure

(step 3).

4 EXPERIMENTAL RESULTS

The experimental results obtained with the proposed

preprocessing method for retinographies are offered

in figure 4. The figure shows the results

corresponding to 8 retinographies of the data base of

the Hospital Universitario de Canarias.

5 CONCLUSIONS

The application of image processing techniques on

the retinography analysis is an increasing research

field. Different techniques have been studied and

developed, but these techniques are not automated

Sub-process A

Image after step 2

Final image

Extraction of high

illuminated pixels

Application of ecuation 3

Replacement of high

illuminated pixels’ values

Extraction of not high

illuminated pixels

Localization of high illuminated zones

Compose

Application of

equation 3

Sub-process B

MCCIRIC

MCRIIR

yxImeanM

+−=

= )),((

AN AUTOMATED APPROACH FOR PREPROCESSING RETINOGRAPHIES

383

and they normally require the user intervention. The

work presented in this paper is part of a research

project developed in collaboration with the Hospital

Universitario de Canarias. The main objective of the

project is the design of an automated software

system for the delimitation of the ONH and for the

differentiation of superimposed structures (arteries,

vessels, and so on).

Figure 4: Experimental results. From left to right: original

image, image after step 1, image after step 2, image after

step 3.

Due to the difficulties presented by the

retinographies it is essential to carry out a

preprocessing stage, in order to improve the image

before developing segmentation of the different

regions of interest. This enhancement process has

been the core of this paper.

The proposed preprocessing method comprises

three steps: 1) scaling of the RGB histogram

(improvement of the image information), 2)

luminance equalization in YIQ color space (contrast

enhancement without loss of color information), and

3) illumination correction. As shown in the

experimental results, the preprocessing method

develops an important enhancement of the main

structures contained in the image. This will

considerably allow designing efficient segmentation

algorithms.

Our future research work will be oriented to the

design of automated and efficient segmentation

methods to be applied on the preprocessed images

obtained in this work.

ACKNOWLEDGEMENTS

This research work has been partially financiated by

the project ULLAPD-08/01 of the Agencia Canaria

de Investigación, Innovación y Sociedad de la

Información.

REFERENCES

Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M.,

Goldbaum, M., 1989. Detection of blood vessels in

retinal images using two-dimensionalmatched filters.

IEEE Trans. Med. Imag; vol. 8(3), pp.263-269.

Cox, J., Wood I., 1991. Computer-assisted optic nerve

head assessment. Ophthal. Physiol. Opt.; 11, pp.27-35.

Echevarria, P., Miller, T., O'Meara, J., 2004. Blood Vessel

Segmentation in Retinal Images. Project P14: Blood

Vessel Segmentation in Retinal Images.

http://robots.stanford.edu/cs223b04/project14.html.

Fang, B., Hsu, W., Lee, M.L., 2003. On the Detection of

Retinal Vessels in Fundus Images. Singapore-MIT

Alliance National University of Singapore. Computer

Science (CS).

Himaga, M., Usher, D., Boyce, J.F., 2002. Retinal Blood

Vessel Extraction by using Multi-resolution Matched

Filtering and Directional Region Growing

Segmentation. IAPR Workshop on Machine Vision

Applications.

Iqbal, M.I., Aibinu, A.M., Gubbal, N.S., Khan, A., 2006.

Automatic Diagnosis of Diabetic Retinopathy using

Fundus Images” University essay from Blekinge

Tekniska Högskola/Sektionen för Teknik (TEK).

Tesis.

Teng, T., Lefley, M., Claremont, D., 2002. Progress

towards automated diabetic ocular screening: a review

of image analysis and intelligent systems for diabetic

retinopathy. Med. Biol. Eng. Comput.; 40(1:2-13.

Young, I.Y., Gerbrands, J.J., Van Vliet, L.J., 1998. Image

Processing Fundamentals, in The Digital Signal

Processing Handbook, V. K. Madisetti and D. B.

Williams, Eds. Boca Raton, Florida: CRC Press in

cooperation with IEEE Press.

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

384