J. Biomedical Science and Engineering, 2009, 2, 543-549
doi: 10.4236/jbise.2009.27079 Published Online November 2009 (http://www.SciRP.org/journal/jbise/
JBiSE
).
Published Online November 2009 in SciRes. http://www.scirp.org/journal/jbise
Retinal vasculature enhancement using independent
component analysis
Ahmad Fadzil M. Hani1*, Hanung Adi Nugroho1,2**
1Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Tronoh, Perak Darul
Ridzuan, Malaysia; 2Department of Electrical Engineering, Univeristas Gadjah Mada, Jl. Grafika 2, Kampus UGM, Jogjakarta, Indo-
nesia.
Received 22 June 2009; revised 16 July 2009; accepted 24 July 2009.
ABSTRACT
Retinal vasculature is a network of vessels in the
retinal layer. In ophthalmology, information of reti-
nal vasculature in analyzing fundus images is impor-
tant for early detection of diseases related to the ret-
ina, e.g. diabetic retinopathy. However, in fundus
images the contrast between retinal vasculature and
the background is very low. As a result, analyzing or
visualizing tiny retinal vasculature is difficult. There-
fore, enhancement of retinal vasculature in digital
fundus image is important to provide better visuali-
zation of retinal blood vessels as well as to increase
accuracy of retinal vasculature segmentation. Fluo-
rescein angiogram overcomes this imaging problem
but it is an invasive procedure that leads to other
physiological problems. In this research work, the
low contrast problem of retinal fundus images ob-
tained from fundus camera is addressed. We develop
a fundus image model based on probability distribu-
tion function of melanin, haemoglobin and macular
pigment to represent melanin, retinal vasculature
and macular region, respectively. We determine reti-
nal pigments makeup, namely macular pigment,
melanin and haemoglobin using independent com-
ponent analysis. Independent component image due
to haemoglobin obtained is used since it exhibits
higher contrast retinal vasculature. Contrast of reti-
nal vasculature from independent component image
due to haemoglobin is compared to those from other
enhancement methods. Results show that this ap-
proach outperforms other non-invasive enhancement
methods, such as contrast stretching, histogram eq-
ualization and CLAHE and can be beneficial for
retinal vasculature segmentation. Contrast enhance-
ment factor up to 2.62 for a digital retinal fundus
image model is achieved. This improvement in con-
trast reduces the need of applying contrasting agent
on patients.
Keywords: Contrast Enhancement; Independent Comp-
onent Analysis; Medical Image Processing; Retinal Fundus
Image
1. INTRODUCTION
Analyzing retinal fundus image is important for early
detection of several diseases related to the retina, e.g.
diabetic retinopathy. In diabetic retinopathy, retinal capi-
llary occlusion occurs and accordingly causes enlarge-
ment of foveal avascular zone. Foveal avascular zone is
the fovea where there is no blood vessels and located in
the very centre of macula. Information of retinal vascu-
lature is important to accurately determine the foveal av-
ascular zone. However, digital color fundus images ob-
tained from fundus camera suffer from several problems
as can be seen from Figure 1. Figure 1(a) illustrates the
problems of very low contrast and non-uniform illumi-
nation which can be seen at the area towards the edge of
the image. Figure 1(b) shows the occurrence of noise
which consists of impulse and Gaussian noises. Detec-
tion of the foveal avascular zone is even difficult due to
very low image contrast of retinal vasculature against the
background in the macular region.
A number of enhancement methods focused in the im-
age spatial domain [2,3,4,5]. Histogram equalization wi-
th its modification is commonly used to enhance the im-
age contrast [6]. However, histogram equalization tends
to over-enhance the image and results in noisy appear-
ance of the output image. One of the adaptive methods
called contrast limited adaptive histogram equalization
(CLAHE) worked well on the enhancement of retinal
vasculature [7]. Iznita found that the contrast improve-
ment using contrast limited adaptive histogram equaliza-
tion on an image model ranges between 1.7 and 3 [8].
However, contrast limited adaptive histogram equaliza-
tion creates artefacts in the enhanced image and the se-
lection of contrast gain limit is image-dependent.
Other related works used the information of color
taken from digital color images [9,10]. Colors observed

A. F. M. H et al. / J. Biomedical Science and Engineering 2 (2009) 543-549
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544
Figure 1. Digital fundus images obtained from fundus camera [1].
in the retinal image correspond to the architecture of re-
tinal layer and the optical properties of the pigments
[11,12]. Styles et al. developed a model using the con-
centrations of the five main absorbers found in the fun-
dus layers, namely retinal haemoglobin, choroidal hae-
moglobin, choroidal melanin, retinal pigment epithet-
lium melanin and macular pigment [13]. This approach
focuses more towards reconstructing rather than improv-
ing the contrast. Tsumura et al. showed that spatial dis-
tributions of melanin and haemoglobin from a skin color
image can be separated using independent component
analysis [9,14]. Nugroho et al. successfully applied a
technique based on principal component analysis and
independent component analysis to convert the RGB
skin image into a skin image that represents skin areas
due to melanin and haemoglobin only [10]. The above
efforts focus on using independent component analysis
to transform digital color image (RGB) into independent
components that correspond to the biological makeup of
the skin.
The objective of this work is to address the low con-
trast problem of retinal fundus images obtained from
fundus camera when no contrasting agent is injected. A
novel approach is presented to enhance the contrast of
retinal vasculature by determining the retinal pigments,
namely macular pigment, haemoglobin and melanin
from fundus images. Distribution of haemoglobin is ex-
tracted from a fundus image to reveal retinal vasculature,
which is a network of vessels in the retinal layer. Con-
trast of retinal vasculature obtained using this approach
is compared to those from other enhancement methods,
such as contrast stretching, histogram equalization and
contrast limited adaptive histogram equalization to test
the performance of this approach.
2. APPROACH
The approach taken in this research is as follows. First, a
model of ocular fundus based on the light interaction is
developed to describe the reflectance of the fundus. Se-
cond, a model of spectral absorbance of the retinal image
is developed to show the components composing the ob-
served colours in a digital fundus image. Third, inde-
pendent component analysis based on the spectral ab-
sorbance of the model is applied to determine retinal
pigments from fundus images. Finally, two fundus image
models are developed to test performance of the pro-
posed algorithm.
2.1. Ocular Fundus Model
Ocular fundus represents the structure of the back of the
eyes that consists of multiple layers of tissue [13]. The
ocular fundus image obtained from a fundus camera sh-
ows different intensity of reflectance. The reflectance
depends on the wavelength, the structure of fundus’ lay-
ers, the optical properties and quantities of retinal pig-
ments in the ocular fundus. The incident light from a
fundus camera can be reflected, absorbed, scattered or
transmitted by the retinal tissues.
Generally, the structure of the eye can be classified
into two main groups, namely ocular media and ocular
fundus [15]. Ocular media consists of cornea, lens and
vitreous. It is located between the ocular fundus and the
observer. The ocular fundus consists of the retina, the
retinal pigment epithelium, the choroid and the sclera.
The reflectance of the fundus can be described in the
terms of these layers [16]. Figure 2 depicts a model of
ocular fundus showing possible pathways of the re-
flected light.
2.2. Ocular Fundus Spectral Absorbance Model
The spectral absorbance image provides useful informa-
tion to identify the absorbance components [14]. In this
work, we focus on the distribution of retinal pigments,
namely haemoglobin, melanin, and macular pigment,
rather than on the fundus layers, to model spectral ab-
sorbance of the ocular fundus [17].
Basis of linear combination of the absorption coeffi-
cients of melanin, haemoglobin and macular pigment is
modelled from three absorbances μa(λ1), μa(λ2) and
Figure 2. A model of ocular fundus showing pathways of re-
flected light.

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μa(λ3) at three wavelengths λ1, λ2 and λ3. These wave-
lengths λ1, λ2 and λ3 represent the red (R), green (G) and
blue (B) color channels. Fundus spectral absorbance
image shows spectral characteristics of the absorbance
components in the ocular fundus. Two conditions are
assumed when analyzing fundus spectral absorbances.
First, the color observed in the fundus image is due to
the distributions of melanin, haemoglobin and macular
pigment. Second, the quantities of these components are
spatially independent of each other. The spectral absorb-
ance in the fundus image represents the linear combina-
tion of the absorption coefficients of melanin, haemo-
globin and macular pigment.
Let sx,y and vx,y designate a three-dimensional (3-D)
quantity vector and composite color vector on an image
coordinate (x, y) of a digital color image. A mixing ma-
trix A with a1, a2 and a3 represents pure color vectors of
the three components (haemoglobin, melanin, macular
pigment) per unit quantity. It is assumed that a linear
combination of mutually independent pure color vectors
with the quantities of s1x,y, s2x,y and s3x,y result in the
composite color vectors of v1x,y, v2x, y and v3x,y on the im-
age coordinate (x, y). The following equation illustrates
the transformation matrix, where T denotes the trans-
pose.
vx,y = A sx,y (1)
sx,y = [s1x,y, s2x,y, s3x,y]T (2)
The pixel value of each channel corresponds to each
element of the color vector. Figure 3 depicts the spectral
absorbances of the ocular fundus which consist of pure
spectral vectors of melanin, haemoglobin and macular
pigment.
2.3. ICA of Fundus Spectral Absorbance Image
Independent component analysis (ICA) is a technique to
determine the original signals from mixtures of several
independent sources [18,19]. The independent compo-
Figure 3. Model of spectral absorbance of the ocular fundus.
Macular pigment
Hemoglobins
Melanin
Red channel
Green channel
Blue channel
Macular pigment
Hemoglobins
Melanin
mixture
(A) separatio n
(W)
original sourcesestimated source
s
Ocular fundus im ag e
(observed ima g e)
Macular pigment
Hemoglobins
Melanin
Red channel
Green channel
Blue channel
Macular pigment
Hemoglobins
Melanin
mixture
(A) separatio n
(W)
original sourcesestimated source
s
Ocular fundus im ag e
(observed ima g e)
Figure 4. The problem of ICA in ocular fundus image.
nent analysis is modelled as
v = As (3)
with mixing matrix A and random vector v, denoting the
mixtures v1, v2, …, vn. Similarly, s random vector denotes
the elements of s1, s2, …, sn. This model shows how the
observed data vn is generated by a process of mixing the
components si. The independent components cannot be
directly observed and neither can the mixing matrix.
Only the random vector v is being observed. Mixing
matrix A and random vector s are estimated using v.
Subsequently, separating matrix W is used to find the
independent component simply by
ŝ = Wv, (4)
with ŝ is defined as estimated sources. The objective of
independent component analysis is then to get ŝ as close
as possible to s, which is determined as original sources,
by determining the optimum separating matrix W. Mu-
tually independent components are determined as ele-
ments of vector s from the mixture of vectors in the im-
age. A diagram is shown in Figure 4 to illustrate the idea
of using independent component analysis in separating
the spatial distributions of melanin, haemoglobin, and
macular pigment in the ocular fundus. Three color cha-
nnels, namely red, green and blue channels, represent
random vector v and are used to determine these inde-
pendent components [17]. By applying the independent
component analysis to the composite colour vectors in
the image, the relative quantity and pure colour vectors
of each independent component are determined with no
prior information on the quantity as well as colour vector.
In this case, the independent components represent the
retinal pigments, i.e. melanin, haemoglobin and macular
pigment. The quantities of the retinal pigments are pre-
sumed to be mutually independent for the image coordi-
nate. The separating matrix W is defined to separate
vector ŝx,y using the following equations.
ŝx,y = W vx,y (5)
ŝx,y = [ŝ1x,y, ŝ2x,y, ŝ3x,y]T (6)
The extracted independent components ŝ1x,y, ŝ2x,y and
ŝ3x,y may be similar to s1x,y, s2x,y and s3x,y, respectively.
The composite colour vector vx,y is determined based on
the logarithm transformation of the pixel intensities in

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546
the color channels of red, green and blue. Logarithmic
transformation is used to transfer reflectance spectra into
spectral absorbance since spectral absorbance image
provides useful information to identify the absorbance
components [14].
[μa(λ1),μa(λ2),μa(λ3)]=[-log(rx,y),-log(gx,y),-log(bx,y)] (7)
here, the values of rx,y, gx,y and bx,y correspond to
pixel intensity in the color channels of red, green and
blue respectively. The composite color vector is de-
noted as
vx,y = [μa(λ1), μa(λ2), μa(λ3)]T (8)
According to the model of spectral absorbance in the
ocular fundus from Figure 3, the color density vector of
the fundus can be stated as
vx,y = A sx,y + a4 (9)
where A = [a1, a2, a3] and sx,y = [s1x,y, s2x,y, s3x,y]T. Ele-
ments a1, a2 and a3 of the mixing matrix A represents
pure color vectors of the three components (haemoglo-
bin, melanin, macular pigment) per unit quantity. It is
assumed that a linear combination of mutually independ-
ent pure color vectors with the quantities of s1x,y, s2x, y
and s3x,y results in the composite color vectors of v1x,y,
v2x,y and v3x,y on the image coordinate (x, y). Additionally,
a4 is similar to noise in the ICA model. In this case, the
model is assumed to be noise-free, therefore a4 can be
neglected.
Several methods, such as fast fixed-point algorithm
(FastICA) [20], joint approximate diagonalization of
eigen-matrices (JADE) [21] and information- maximi-
zation (infomax) [22] have been proposed to solve the
problem of independent component analysis. In ICA, the
only assumption needed are: 1) the sources are statisti-
cally independent, 2) the probability densities of the
sources are non-Gaussian, 3) the mixing of the sources
into the observations is linear, and 4) the number of ob-
servations is larger than or equal to he number of sources
[19]. The FastICA algorithm with symmetrical orthogo-
nalization is used to get the estimated independent com-
ponents because of its good accuracy and high computa-
tional speed for high dimensional data [20].
2.4. Fundus Image Model
A model of fundus image is developed to test the per-
formance of independent component analysis in sepa-
rating the distribution of macular pigment, hemoglobin
and melanin. Mixture of three mutually independent
components, i.e. macular pigment, hemoglobin and
melanin is used to model a fundus image. As shown in
Table 1, the statistical intensity description of macular
pigment, haemoglobin and melanin in red, green and
blue channels are taken from the 44 test images from
FINDeRS [23]. A smaller region containing macular area
is sampled to get the probability density function of the
Table 1. Statistical intensity description of macular pigment,
haemoglobin and melanin in red (R), green (G) and blue (B)
channels.
Macular
pigment Haemoglobin Melanin
Mean R 97.14868 120.0417 156.5642
Standard
deviation R 28.39299 27.16076 23.98799
Skewness R 0.714194 0.74299 0.07827
Kurtosis R 0.522469 0.259348 0.215941
Minimum R 46.26375 73.32877 102.7158
Maximum R 174.1571 195.1525 219.6025
Mean G 48.29849 62.24773 96.08492
Standard
deviation G 13.82747 17.29835 19.95184
Skewness G 0.719616 0.376683 0.620011
Kurtosis G 1.577469 0.236853 1.59036
Minimum G 21.67789 32.06349 57.14166
Maximum G 95.15525 114.2881 164.2989
Mean B 7.971792 17.48195 35.1042
Standard
deviation B 5.483867 11.73761 18.85215
Skewness B 1.989686 1.596864 1.31945
Kurtosis B 6.239178 3.978853 2.216477
Minimum B 1.992481 2.412698 12.66622
Maximum B 31.36347 63.40678 100.4873
retinal pigments. In the macular region, retinal capillar-
ies usually show a very low contrast between retinal
blood vessels and the background. A clustering method
using k-means based on the intensity of red, green, and
blue channels of the macular pigment, haemoglobin and
melanin is performed to classify the samples due to large
value of standard deviation and intensity range of the
sample. Based on the experiment, two numbers of clus-
ters are found to be optimal to classify the samples.
In Figure 5, two fundus image models to represent
fair and dark fundus images with mixture of specified
sample intensity distribution of macular pigment, hae-
moglobin and melanin in red, green and blue channels
are shown. Using these models as the input, the inde-
pendent component analysis should be able to separate
these components into three outputs, namely macular
pigment, haemoglobin and melanin.
3. RESULTS AND DISCUSSIONS
A fundus image model is firstly tested using independ-
ent component analysis to see performance of the algo-
rithm. The inputs to the FastICA are three separate
channels (i.e. red, green and blue channels) of a color
fundus image model. As can be seen from Figure 6, the
proposed algorithm successfully separates the compo-

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547
(a). Fair image model
(b). Dark image model
Figure 5. Fundus image model.
a. Macular regionb. Melanin
c. Retinal vasculature
Figure 6. Independent component analysis of dark fundus
image model.
nents into three, namely macular pigment, haemoglobin
and melanin. These three independent components rep-
resent macular region, retinal vasculature and melanin,
respectively. In Figure 6(a), the brighter area in lower
part of the fundus image model is related to the macular
region. In Figure 6(b), the melanin is illustrated as the
brighter area in upper part of the fundus image model.
These two components can be clearly distinguished from
the other component, which is related to retinal vascula-
ture, since the retinal vasculature is almost invisible in
the appearance of these two components (i.e. macular re-
gion and melanin). Furthermore, the retinal vasculature
is clearly visualized in Figure 6(c). As a result, indepen-
dent component image due to haemoglobin obtained ex-
hibits higher contrast retinal vasculature compared to
that of the original image.
In this work, 44 retinal fundus images containing
macular region are taken from FINDeRS database to
model a retinal fundus image. The fundus image model
undergoes several enhancement methods, such as con-
trast stretching, histogram equalization, contrast limited
adaptive histogram equalization (CLAHE) to measure
contrast improvement factor of these methods and com-
pare to the proposed algorithm. A smaller region con-
taining the macular area is taken to see the enhancement
of retinal capillaries, which usually show a very low
contrast between retinal blood vessels and the back-
ground. Figure 7 shows green band of dark fundus im-
age model undergoing several enhancement methods, i.e.
contrast stretching, histogram equalization and CLAHE.
Qualitatively, haemoglobin related ICA shows better
enhancement because no artefacts is produced in the
process. Nevertheless, the other three enhancement me-
thods tend to increase the noise presence in the image as
well as to produce artefacts.
From the fundus image model, the green band image
shows the average contrast intensity of 24.60 and 16.80
for fair and dark image model, respectively. Using these
values as a reference, the proposed algorithm using ICA
a. Contrast stretchingb. Histogram equalization
c. Contrast limited AHEd. ICA (haemoglobin)
Figure 7. Dark fundus image model undergoes several en-
hancement methods.
with contrast enhancement factor of 1.47 and 2.62 for

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548
fair and dark fundus image models shows a better im-
provement for both fair and dark fundus image models
than that of the other three enhancement methods. Fur-
thermore, as is shown in Figure 8, CLAHE with en-
hancement factor of 1.37 and 1.98 for fair and dark fun-
dus image model, respectively, is still better than that of
contrast stretching and histogram equalization. However,
compared to that of CLAHE, the proposed algorithm
produces no artefacts in the process.
Here an example of retinal image showing macular
region is taken to see enhancement of retinal vasculature
using the proposed algorithm. In a preliminary work
using the above algorithm, it is found that non-uniform
illumination in fundus images resulted in false detection
of the retinal pigments [17]. This is because the algo-
rithm responds to the spectral reflectance or absorbance
of the retinal pigments in the image. Therefore, homo-
morphic filtering is performed prior to independent com-
ponent analysis to reduce the problem of non-uniform
illumination. Homomorphic filtering is used to reduce
illumination which varies slowly in space and at the
same time [24].
Figure 9 shows an original color fundus image un-
dergoing homomorphic filtering and its independent
components estimated by the FastICA algorithm. The
components represent the distribution of the pigments,
namely macular pigment, haemoglobin and melanin. The
brighter area in the centre of the first independent com-
ponent (Figure 9(b)) represents the distribution of
macular pigment. The second independent component
(Figure 9(c)) shows the distribution of haemoglobin. It
is indicated by the enhancement of retinal vasculature.
The third independent component (Figure 9(d)) shows
brighter area related to the distribution of melanin. This
result is consistent with the location of melanin, which is
fairly distributed in the retinal pigment epithelium and
the choroid. Based on the assumption that the image is
noise-free, independent component analysis is able to
determine the retinal pigments. Moreover, as shown in
Figure 10, a green band image undergoing CLAHE is
0
0.5
1
1.5
2
2.5
3
Contrast
Stretching
Histogr am
Equalization
CLAHE ICA
Fair
Dark
Figure 8. Contrast enhancement factor of retinal vasculature in
fundus image model.
a. Fundus image after
homomorphic filtering
b. First component
c. Second componentd. Third component
a. Fundus image after
homomorphic filtering
b. First component
c. Second componentd. Third component
Figure 9. Independent component analysis of a retinal image
containing macular region.
Figure 10. Comparison of contrast enhancement of retinal
vasculature between CLAHE and ICA.
compared to the haemoglobin-related component image
after the intensity is being inverted to demonstrate that
contrast enhancement is also achieved. In this work,
CLAHE is also performed on the same images undergo-
ing the proposed algorithm to compare the contrast im-
provement between these two methods. Having meas-
ured the contrast improvement factor on the fundus im-
age model, the proposed algorithm consistently shows
better visualization and enhancement compared to that
of the CLAHE, which is commonly used as pre-proce-
ssing for segmentation of retinal vasculature in fundus
images. This improvement can be beneficial to improve
the accuracy of retinal vasculature segmentation and re-
duce the need for injecting contrasting agent to the pa-
tients.
4. CONCLUSIONS
Analyzing retinal fundus images is usually difficult as
they are of very low contrast. Low contrast between
blood vessels and the background makes it difficult to

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SciRes Copyright © 2009
549
accurately determine retinal vasculature. Retinal vascu-
lature can be used to determine existence of pathology,
macular area and foveal avascular zone. Typical contrast
enhancement methods usually create artefacts or intro-
duce noise. Even though fluorescein angiography pro-
duces better contrast enhancement, it is not preferable
due to its invasive nature of injecting contrasting agent.
JBiSE
In this work, the developed method based on the spec-
tral absorbance model and independent component ana-
lysis enables us to determine the retinal pigments, name-
ly haemoglobin, melanin and macular pigment. A fundus
image model has been developed to test the performance
of the proposed algorithm. As a result, retinal vascula-
ture, macular pigment and melanin distribution can be
determined from digital fundus image. Results show that
this approach outperforms other non-invasive enhance-
ment methods, such as contrast stretching, histogram
equalization and CLAHE and can be beneficial for ves-
sel segmentation. The algorithm produces no artefacts in
the process. Using the haemoglobin component, the con-
trast between retinal blood vessels and the background
can be enhanced with contrast enhancement factor up to
2.62 for a model of fundus image. This improvement in
contrast reduces the need of applying contrasting agent
on patients.
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