A TWO-PHASE PRE-FILTERING APPROACH TO THE

AUTOMATIC SCREENING OF DIGITAL FUNDUS IMAGES

alint Antal, Andr

as Hajdu

University of Debrecen, Faculty of Informatics, POB 12, 4010 Debrecen, Hungary

Adrienne Csutak

University of Debrecen, Medical and Health Science Centre, Nagyerdei Krt. 98, 4032 Debrecen, Hungary

unde Pet

Moorﬁelds Eye Hospital, London, U.K.

Keywords:

Biomedical image processing, Medical decision-making, Quality assurance, Medical expert systems.

Abstract:

In this paper, we present an approach to decrease the computational burden of an automatic screening system

designed for diabetic retinopathy. The proposed method consists of two steps. First, a pre-screening algorithm

is considered to classify the input digital fundus images based on their abnormality. If an image is found to

be abnormal, it will not be analyzed further with robust lesion detector algorithms. As an improvement, we

introduce a novel feature extraction approach based on clinical observations. The second step of the proposed

method detects regions which contain possible lesions for images that have been passed pre-screening. These

regions will serve as inputs to lesion detectors later on, which can achieve better computational performance

by operating on speciﬁc regions only instead of the entire image. Experimental results show that both two

steps of the proposed approach are valid to efﬁciently exclude a large amount of data from further processing

to improve the performance of an automatic screening system.

1 INTRODUCTION

Retinal fundus photographs are widely used in the di-

agnosis and consequent treatment of various eye dis-

eases, such as diabetic retinopathy (DR), age related

macular degeneration (AMD) and glaucoma. DR is

one of the most frequent causes of visual impairment

in developed countries and is the leading cause of new

cases of legal blindness among those in the working

age. DR can be prevented and its progression slowed

down if diagnosed and treated early. Screening for

DR is the mainstay of identifying patients at risk. The

result of screening is determined by the recognizable

lesions of the retina.

Nowadays, automated detection systems have be-

come very popular in medical imaging, including DR

screening (Abramoff et al., 2008). Our current inter-

est is to develop an automatic system to detect abnor-

malities caused by DR. We also consider the insertion

of a pre-ﬁltering phase before the detailed analysis,

which is, to the best of our knowledge, is not a part of

other systems. Our approach is realized in two steps:

pre-screening and pre-ﬁltering. During pre-screening,

we classify the images as severely diseased (highly

abnormal) or to be forwarded to further processing.

The aim of this step is twofold. On the one hand,

we minimize the risk that an abnormal image pass the

screening without a warning, since it is immediately

spotted by the automatic system before detailed anal-

ysis. On the other hand, we save computational time,

since only the not abnormal fundus images are ana-

lyzed in details. Figure 1 gives an impression about

these two classes. In the case of fundus images, ma-

chine learning algorithms are often applied to classi-

ﬁcation based on feature vectors. We extract features

based on clinical observations about the inhomogene-

ity of the diseased retina.

As a second – pre-ﬁltering – step of our approach,

we extract those candidate subregions of fundus im-

ages that are expected to contain speciﬁc lesions. The

155

Antal B., Hajdu A., Csutak A. and Petõ T. (2010).

A TWO-PHASE PRE-FILTERING APPROACH TO THE AUTOMATIC SCREENING OF DIGITAL FUNDUS IMAGES.

In Proceedings of the International Conference on Signal Processing and Multimedia Applications, pages 155-158

DOI: 10.5220/0002988101550158

 SciTePress

most common lesion on the fundus is the microa-

neurysm (see Figure 2a), which is an early sign of

diabetic retinopathy. A microaneurysm appears as a

small red spot on the retina.

(a) (b)

Figure 1: Samples from the image set (both taken from the

DRIVE database (Staal et al., 2004)); (a) abnormal fundus,

(b) fundus image that needs detailed analysis.

The detection of DR related bright lesions (exu-

dates) has a rich literature, as well. Exudates appear at

an advanced stage of diabetic retinopathy (see Figure

2c). The retinal pigment epihelium (RPE) is usually

caused by age-related macular degeneration. The sign

of RPE is the inhomogeneous surface of the retina, as

it is shown in Figure 2c.

(a) (b)

Figure 2: (a) microaneurysms, (b) bright lesions (exudates),

To ﬁnd candidate regions containing lesions, our

approach is based on the fact that asides form its

anatomical parts, the intensity values of the normal

retina surface have small saliences (see Figure 2d).

If there is a connected set of salient values with a

given cardinality, we can assume that there is a le-

sion within the examined region. The goal is to pre-

serve those regions only, which possibly contain le-

sions. The rest of the paper is organized as follows.

In section 2 we present our approach for classifying

the images as abnormal or not (pre-screening). Sec-

tion 3 exhibits how candidate regions are pre-ﬁltered

on fundus images that have passed the pre-screening

phase. The datasets and corresponding experimental

results are shown in section 4. Finally, some conclu-

sions are drawn in section 5.

2 PRE-SCREENING

– CLASSIFYING THE INPUT

IMAGE

As the ﬁrst step of our approach, we check whether

the image has so severe abnormality that the patient

should be sent directly to a medical expert. In the case

of high-loaded automatic systems, skipping these im-

ages will enhance the performance, since detailed

analyses do not take place. The pre-screening is re-

alized based on machine learning algorithms. Next,

we summarize the components of pre-screening orga-

nized into consequent steps.

2.1 Pre-processing

As a pre-processing step, we convert the input

RGB images to grayscale ones as proposed e.g. in

(Sopharak et al., 2008), to get a suitable representa-

tion for possible disorders. Then, we apply adaptive

histogram equalization (AHE) as an intensity normal-

ization step proposed by (Youssif et al., 2006). Fi-

nally, we rescale the images to the size of 90 × 90

pixels.

2.2 Feature Vectors and Classiﬁers

We also take advantage of the clinical observation

that fundi with severe diabetic retinopathy often have

inhomogeneity caused by retinal pigment epithelium

(RPE) atrophy, which is the waste of the pigmented

cell layer of the retina. Composing feature vectors

based on this observation leads to more accurate re-

sults both in classiﬁcation and computational perfor-

mance, as will be presented in the results section.

To extract these features, we used the following ap-

proaches:

• Inhomogeneity. Let the image be split into dis-

joint subimages of size s×s, e.g. with s = 5. Then,

for each pixel within a subimage, we compute the

sum of intensity differences larger than a given

threshold t for every subsequent subimage pixels.

After this step, we divide this sum with the size of

the subimage. If this number is larger than zero,

the feature is set to 1, otherwise to 0.

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• Standard Deviation. For each subimage we cal-

culate the standard deviation. This approach is for

referential purposes.

• Combined. The combination of the inhomogene-

ity feature and the standard deviation.

3 PRE-FILTERING

– EXTRACTING REGIONS

WITH LESION CANDIDATES

As the second step of our approach, we extract re-

gions with lesions candidate in the images that passed

the pre-screening phase. Since these images must

undergo detailed image analyses to extract speciﬁc

lesions later on, this pre-ﬁltering is highly recom-

mended to restrict the input of the corresponding de-

tector algorithms. Now we summarize the steps how

the candidate regions are extracted.

3.1 Pre-processing

Similarly to the pre-processing steps discussed for the

pre-screening phase, we use the green plane of the im-

age by following literature recommendations (Youssif

et al., 2006). Then, we perform histogram equaliza-

tion on the image to reduce the vignetting effect (see

Figure 3a) and calculate the background image by ap-

plying a strong median ﬁlter of size A × A (e.g. with

A = 25).

(a) (b)

Figure 3: (a) the green plane after histogram equalization,

(b) the background image, (c) the pre-processed image for

candidate region extraction, (d) regions with lesion candi-

dates.

We use the background image shown in Figure

(see Figure 3b), to perform shade correction by sub-

tracting it from the original image.

To suppress noise, we apply a median ﬁlter of size

B × B (e.g. with B = 13) to the shade corrected im-

age. As the ﬁnal pre-processing step, we apply un-

sharp masking to increase the acutance (see Figure

3c).

3.2 Removal of Anatomical Parts

Detecting the anatomical parts of the fundus is an im-

portant step before lesion detection. For example, the

optic disc appears as the brightest circular patch on

the fundus, whose presence may disturb the detection

of exudates. Removing the vessel system is also rele-

vant, since a small portion of it appears basically the

same as haemorrhages. Besides these two anatomical

parts, we also remove the macula, because for cer-

tain region sizes, some parts of it can appear as a lo-

cally salient object. For these tasks, we use the vessel

detector published by (Staal et al., 2004), the mac-

ula detector of Petsatodis (Petsatodis et al., 2006) and

the optic disc detector described in (Sopharak et al.,

2008).

3.3 Statistical Analysis of Regions

We split the image into disjoint regions of size s × s

(e.g. with s = 75). For each region, we compute

the local mean µ and the standard deviation σ of

its intensity values. Let d

sig

(x, y) = x − y. We

label the pixel P (x, y) having intensity I (x, y) as

high, if d

sig

(I (x, y) , µ) > σ, while P (x, y) is low, if

sig

(I (x, y) , µ) < −σ. Otherwise, P remains unla-

beled. After labeling, we select connected compo-

nents, which composed of pixels with identical labels

and with cardinality at least n. If a component sat-

isﬁed these conditions, we consider that as a lesion

candidate. We use the areas which possibly contain

lesions as input for speciﬁc lesion detectors, designed

for e.g. microaneurysms or exudates.

4 RESULTS

4.1 Results on Pre-screening

Our experimental dataset consisted of 34 training and

28 test images, classiﬁed by ophthalmologists. We

selected images from three databases: the publicly

available DRIVE (Staal et al., 2004), DIARETDB1

(Kauppi et al., 2007) and the database provided by

the Moorﬁelds Eye Hospital, London, UK for our re-

search purposes. We label the elements of the test

A TWO-PHASE PRE-FILTERING APPROACH TO THE AUTOMATIC SCREENING OF DIGITAL FUNDUS

IMAGES

157

database as images with serious disorder (ﬁrst class)

and images to be processed further (second class). We

used a Naive Bayes classiﬁer and trained for the com-

bined features extracted from all regions of the im-

ages as disclosed in section 2.2. With this approach,

we have successfully classiﬁed all elements of the test

dataset. To make the approach faster, we used back-

ward elimination for feature subset selection. That is,

we have selected the best 11 regions each image to be

extracted the features from for classiﬁcation. In this

case, our approach still provided no false predictions

with the computational time below milliseconds.

4.2 Results on Pre-ﬁltering

We have tested our approach on those images which

have been classiﬁed as ”to be processed further” by

the previous pre-screening phase and the positive

samples of the training set. The detector missed only

1 fundus image which contained lesions. Our results

are summarized in Table 1 in details containing the

value of the size parameters s, the number of correctly

/ incorrectly (true / false) identiﬁed regions, the num-

ber of misclassiﬁed images and the percentage of the

remaining pixels.

Table 1: Experimental results on pre-ﬁltering.

Size (s) True False Mis- Percentage

classiﬁed

10 24 10 4 0.05

25 26 10 4 0.34

50 25 9 5 1.28

75 27 3 1 2.5

100 16 7 5 3.47

200 4 4 5 4.82

With this regions candidate detection, we can re-

duce the total number of pixels of the database from

more than 6 millions to 168 750, which is nearly 2,5%

of the original data. To demonstrate how its reduction

affected consequent detailed image processing anal-

ysis, we tested a speciﬁc lesion detector. Namely,

the computational time of the state-of-the-art microa-

neurysm detection algorithm (Fleming et al., 2006)

reduced by 90% after this candidate selection step.

5 CONCLUSIONS

We have presented an automatic approach that can

separate fundus images with serious lesions from the

ones that should undergo detailed screening. This step

can direct patients with serious lesions immediately

to ophthalmologists by automatic screening systems.

With a use of a Naive Bayes classiﬁer, we were able to

classify all the test images correctly. As a secondary

pre-ﬁltering step for images passing pre-screening,

we have presented an approach which is eligible to

detect areas which possibly contain lesions. As a fair

trade off with accuracy, we gain high computational

performance with using only small regions to detect

the actual lesions within.

ACKNOWLEDGEMENTS

This work was supported in part by the J

anos Bolyai

grant of the Hungarian Academy of Sciences, and

by the TECH08-2 project DRSCREEN - Develop-

ing a computer based image processing system for

diabetic retinopathy screening of the National Of-

ﬁce for Research and Technology of Hungary (con-

tract no.: OM-00194/2008, OM-00195/2008, OM-

00196/2008).

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