50120140504007

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 57-64 © IAEME
57
STUDY OF THE EFFECTS OF ILLUMINATION AND THE CAMERA
PARAMETERS IN THE HAEMOGLOBIN ESTIMATION USING A
DIGITAL CAMERA
Sindhu Priyadarshini1
, Dr. Chandrashekar2
, Dr. Manjunath3
1, 2
(Centre for Emerging Technologies(CET), Jain University)
3
(Philips India Limited, Bangalore)
ABSTRACT
Estimation of hemoglobin is important for screening and categorization of the degree of the
severity of anemia in pregnant women. Based on the outcome of the screening, it would be possible
to provide the much required medical care in time and save precious lives. In order to reach tens of
thousands of households in developing and under developed countries under resource constrained set
up, it is required to keep the cost of the device minimal without compromising the accuracy. In
addition, the device is expected to be used by less skilled personnel. In this paper, a camera based
low cost method has been explored for the estimation of hemoglobin making use of image
processing algorithms and machine learning.
Keywords: Medical Instrumentation, Automation, Imaging.
1. INTRODUCTION
Human blood is mainly made up of Red Blood Cells (RBCs), White Blood Cells (WBCs),
platelets and plasma. The RBCs contain a metalloprotien called Hemoglobin (Hb or Hgb) that is
responsible for the gaseous exchange in human body. The red color of the RBCs is due to the
presence of iron in Hb. Each Hb molecule contains four ferrous iron molecules which can combine
with eight oxygen atoms or four oxygen molecules. Hb transports oxygen from the lungs to all the
tissues in the human body and it transports back the carbon-dioxide from all the tissues to the lungs.
1.1. Haemoglobin and Anaemia
The decrease in Hb levels in human blood or the number of RBCs less than the normal
quantity (which is according to the age and gender) leads to “Anemia”. According to the World
Health Organization, "Anemia is said to exist when the level of circulating hemoglobin in a patient is
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 4, April (2014), pp. 57-64
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2014): 8.5328 (Calculated by GISI)
www.jifactor.com
IJCET
© I A E M E

58
lower than that of healthy persons of the same age group and sex in the same environment".
According to WHO (World Health Organization), around 2 billion people or over 30% of world’s
population are anemic. WHO also states that anemia reduces the work capacity of the individuals [1]
and the entire population, bringing serious economic consequences and obstacles to the national
development. Hence it is necessary and critically important to diagnose anemia and treat it suitably.
1.2. Overview on the existing clinical methods in estimating Hemoglobin
There are many techniques for estimating the amount of Hb present in human blood [2].
These techniques can be broadly classified as invasive and non-invasive. Invasive techniques mean
estimating Hb by phlebotomy. Here the subject or the person will be pricked by a needle on a finger
[3] and a drop of blood will be taken or a few drops of venous blood will be taken for Hb test. In the
non-invasive techniques, there is no phlebotomy involved. The devices used for the Hb test can also
be classified based on the accuracy of the clinical device. Such clinical devices with accuracy of
≤ ±0.5g/dl can be considered as the diagnosis devices and that with accuracy of ≥ ±0.5g/dl as the
screening devices.
2. OBJECTIVES OF THE STUDY
TABLE 1: WHO'S HAEMOGLOBIN THRESHOLDS USED TO DEFINE ANEMIA (1 G/DL = 0.6206 MMOL/L)
Figure 1: EM Spectrum
Age or gender group Hb threshold (g/dl) Hb threshold (mmol/l)
Children (0.5–5.0 years) 11.0 6.8
Children (5–12 years) 11.5 7.1
Teens (12–15 years) 12.0 7.4
Women, non-pregnant (>15yrs) 12.0 7.4
Women, pregnant 11.0 6.8
Men (>15yrs) 13.0 8.1

59
It can be observed that there is a tradeoff between the accuracy and the cost of the Hb
estimation technique. When there is cost constraint, then the accuracy of the test also reduces with
the cost. The devices hence can be classified as diagnosis and screening devices where the cost and
the accuracy of the diagnosis devices being higher than that of the screening devices. Our objective
is to come up with a screening device with an accuracy of ≤ ±1g/dl and should be cheaper than the
existing clinical invasive Hb estimating tests. The prototype should also be portable so that it can be
used by the ASHA (Accredited Social Healthcare Activist), where the ASHA worker will take the
device to remote places which are deprived of clinical laboratories to test for anemic cases.
A commercially available camera will be used and with the help of image processing and
pattern recognition, the Hb value will be estimated.
2.1. Characteristics of Light
Visible light, which occupies a small band in the Electro-Magnetic (EM) Spectrum, is the
reason why we can see objects. Visible Light has bandwidth of 380 to 780nm. The colors that we
perceive is nothing but the different wavelengths in the visible light spectrum shown in figure1 [4].
Color is the way we distinguish different wavelengths of light. The subject of color is a rather
complicated one, as it involves both the spectral characteristics of the light itself, the spectral
reflectance of the illuminated surface as well as the
Figure 2: CIE chromaticity space
perception of the observer [4]. For example, a red ball will appear red when seen in sunlight or red
light but when the same red ball is seen under the blue light, it will appear grey or black in color.
White light can be obtained by the various combinations of intensity ratios of wavelengths in
the visible spectrum. These white lights although looks the same, the objects when seen under them
will look differently colored. Hence it is important to characterize white lights to know the intensity
ratios of its composite wavelengths. “Color Temperature” is used for the same. Color temperature is
the temperature of the black body radiator which gives the similar color impression as that of the
light under consideration. Its unit is Kelvin (K).
A lower color temperature let us say 2000K means that it is a warm light and the ratio of red
is more in it whereas a higher color temperature let us say 7000K means that is a cool light and the
ratio of blue is more in it.
Fig. 2 shows the CIE chromaticity space, also showing the chromaticities of black body light
sources of various temperatures [4].

60
The four important photometric to be considered are [4] 1. Luminous flux (It is used to
indicate the total amount of light radiated per second by a light source. The unit is Lumen) 2.
Luminous intensity (It is used to indicate the amount of flux emitted in a certain direction. The unit is
Candela) 3. Illuminance (It gives the amount of light falling on a unit area of surface. The unit is
Lux) 4. Luminance (It gives the amount of light emitted in a certain direction from a unit area).
3. CAMERA PARAMETERS
In order to get excellent color rendering properties i.e., the image captured by the digital
camera to look very similar to that seen by our naked eye, the lighting system, the digital camera and
its parameters should be set suitably. Usually when an image is captured by the digital camera under
artificial lighting system, the image will appear to have different color composition compared to
what we see with our naked eyes. For example, when the image is taken under the incandescent bulb,
the image appears to have a little orange tint because the color temperature of the incandescent bulb
is around 2000K and the ratio of red is more than the other constituent wavelengths., The white
balance setting in the camera allows the user to adjust the gains of the red, green and blue
components of the image so that it looks more similar to the natural scene. Many digital cameras
have a few pre-set white balance adjusting modes such as sunny, cloudy, fluorescent, incandescent
etc., where that particular setting will be selected according to the situation of the scene. There is also
a custom mode in the white balance setting where the user will point at a white object first to make it
as the reference and then capture an image in which the camera will change the gains of RGB
components suitably.
An image captured by a digital camera should be well or optimally exposed so that it conveys
maximum information. The aperture, shutter speed and the ISO are the important camera parameters
that decide the exposure of the image captured by the camera. The ISO is used to indicate the
sensitivity of the camera sensors towards light. The ISO is indicated in numbers typically such as
80,100,200, etc. A high ISO number means high camera sensitivity and vice versa. But as the ISO is
increased, the image becomes noisier. Hence it is important to choose the correct ISO according to the
scene. The camera shutter speed is used to control the duration of time in which the camera sensors
will be exposed to the light by opening and closing the camera shutter in a period of time. If the
shutter speed is slow then more amount light will be recorded by the sensors and if the shutter speed
fast then the image will be under exposed making it look darker. Shutter speed becomes very critical
when capturing a moving object. Another important parameter is the aperture of the camera which is
the lens area through which the light will pass through. If the aperture is small, it means that less light
can enter the camera sensor, the depth of field is large and vice versa. Hence the exposure of an image
is due to the contribution of the above three parameters. In most of the commercially available mid -
range digital cameras, the user does not have the control over the shutter speed and the aperture but is
given the control over the exposure compensation where it can typically range from -3 to +3. An
exposure compensation which is more negative means a darker image compared to an exposure
compensation which is more positive. The camera sets the aperture and the shutter speed according to
the exposure compensation selected.
3.1. Goal of the experiments
As mentioned earlier the objective is to come up with a screening device that is used to
estimate the Hb value with a precision of ± 1g/dl and of low cost. The image of a blood drop on the
test strip in the appropriate lighting environment will be captured by a commercially available digital
camera. This image will be next processed to get the required features and then machine learning
algorithm will be used to predict the Hb value. Before the study on blood is done, it is very important
to arrive at the optimal lighting conditions, camera parameters, geometry of all the components of

61
the prototype etc. In order to achieve this, the aid of WHO HCS is taken. The idea is to come up with
the optimal setting, in which all the Hb bands of the WHO HCS are most separated in the cluster
space so that the blood samples of different Hb values can also be separated in the same cluster
space. The experiments start with sunlight as the source of illumination as it was recommended by
the WHO in order to use the WHO HCS.
3.2. Metrics for deciding the optimal settins
A digital image is made up of building blocks called as the pixels. In a digital colour image,
each pixel will have three components called the red, green and blue (RGB) where each component
is represented by eight bits. When the color image is converted to a gray scale image, all the three
components of the pixel will be combined to get a single value which is an approximate to the
brightness of the pixel.
1) Bhattacharya co-efficient
The brightness information of the entire image can be viewed with the help of histogram of
that image. A histogram is the plot of the frequency of occurrence of a particular brightness level in
that image. If an eight bit image is considered then the histogram can have a maximum of 256 bins.
Here, a set of Region Of Interest (ROI) is selected from each of the Hb bands of the WHO
HCS which are of a fixed size. The features selected from the image are the RGB components of the
image. Individual histograms are plotted for each of the RGB components and for all the selected
ROIs. The idea is plot a set of histograms individually corresponding to each of RGB components of
all the ROIs corresponding to different Hb bands in the WHO HCS to see the degree of overlap
between the histograms. If any two histograms are having less overlap then it means that they can be
easily separated in the cluster domain. Hence it is important to quantify the degree of overlap
between any two histograms. Bhattacharya co-efficient is one such metric that fulfils the
requirement.
Before using the Bhattacharya co-efficient [5], the histograms have to be normalized to get a
p.d.f (probability distribution function) format.
)1(
)(
)(
)(
∑
=
i
ia
ka
kA
Where a (k) is the histogram and gives the total sum of histogram values of all the
bins. A (k) is the normalized histogram.
After the histogram normalization, the degree of overlap between each of them with respect to
the other remaining ones is calculated as follows:
)2())()(( 2/1
∑=−
k
kBkAeffBhatCo
Where ‘A(k)’, ‘B(k)’ is the p.d.f of any two histograms, ‘k’ is the bin number and BhatCo-eff
is the Bhattacharya Co-efficient.
If the BhatCo-eff is one it means that the histograms are completely overlapping where as if
BhatCo-eff is zero then it means that the histograms are completely non-overlapping.
Ideally all the clusters corresponding to different Hb bands should be non-overlapping i.e., the
BhatCo-eff should be zero.

62
2) Euclidean Distance
It is also important to know the inter-cluster distance and hence the Euclidean distance
between the medians of the two clusters are calculated. The greater the Euclidean distance and lesser
the BhatCo-eff, better the separation between the two clusters.
)3())()()((_ 2/1222
bbggrr NMNMNMdistEuclid −+−+−=
Where M,N are the medians of the two clusters, Mr, Nr are their corresponding red
component. Mg, Ng are their corresponding green component. Mb, Nb are their corresponding blue
component.
4. EXPERIMENTS UNDER NATURAL LIGHT
The experiments were conducted in the sunlight to study the effect of using sunlight as the
source of illumination. Images were captured using a mid range Sony DSC-W730 digital camera.
Visually all the bands in the WHO HCS appeared to be distinct for the naked eye. A final metric[6]
is derived from the individual Bhattacharya co-efficients of the RGB components.
Results and inference
Figure 3: results of image taken under sunlight during the time t1

63
Figure 4: results of image taken under sunlight during the time t2
The above two figures shows the results of the image taken under sunlight during time
different times t1 and t2. It can be seen that there is good separation between Hb 14 and Hb 12; Hb
12 and Hb 10 in the histogram space for the red components. One very important observation to be
noted is that the clusters are different during different times of the same day. Hence we cannot rely
on sunlight as the source of illumination as it is not reliable or consistent. Hence we move towards
the controlled illumination settings i.e., use the artificial lighting system.
5. CONCLUSIONS
Hemoglobin estimation and accurate categorization of anemia in pregnant women plays a
crucial role in preventing maternal death. The color scale suggested by World health organization for
the categorization has widely penetrated the low resource settings. The accuracy of estimation using
the scale can be enhanced by substituting the manual judgment with automated image processing
algorithms for the color match. Towards this end, the right choice of the camera parameters and light
settings are very important. The results are very sensitive to these parameters.

64
ACKNOWLEDGMENT
The authors would like to thank Philips India limited and Jain University for all the support
received during the research.
REFERENCES
[1] A. Kalantri, M. Karambelkar, R. Joshi, S. Kalantri, and U. Jajoo, “Accuracy and reliability of
pallor for detecting anaemia: a hospital-based diagnostic accuracy study,” PloS One, vol. 5,
no. 1, p. e8545, 2010.
[2] “Anemia Detection Methods in Low-Resource Settings: A Manual for Health Workers -
PATH.” [Online]. Available: http://www.path.org/publications/detail.php?i=730. [Accessed:
10-Aprt-2014].
[3] S. AlZahir and H. Donker, “A Novel Regression Based Model for Detecting Anemia Using
Color Microscopic Blood Images,” J Softw. Eng. Appl., no. 3, pp. 756–760, 2010.
[4] Philips, Basics of light and lighting, 2008.
[5] Nilufar, S. ; Ray, N. ; Hong Zhang, Automatic blood cell classification based on joint
histogram based feature and bhattacharya kernel, 42nd Asilomar Conference on Signals,
Systems and Computers, 2008, pp, 1915 – 1918.
[6] Rajendra Kumar et al, Digital WHO Hemoglobin Color Scale: Analysis and Performance,
eTELEMED 2014 : The Sixth International Conference on eHealth, Telemedicine, and Social
Medicine, 2014.
[7] Bharati. S R, Parvathi. C S and P. Bhaskar, “Measurement of Human Blood Clotting Time
using LabVIEW”, International Journal of Electronics and Communication Engineering &
Technology (IJECET), Volume 5, Issue 2, 2014, pp. 83 - 92, ISSN Print: 0976-6464,
ISSN Online: 0976-6472.
[8] Minal D. Joshi, Prof. A.H.Karode and Prof. S.R.Suralkar, “Detection of Acute Leukemia using
White Blood Cells Segmentation Based on Blood Samples”, International Journal of
Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 3,
2013, pp. 148 - 153, ISSN Print: 0976-6464, ISSN Online: 0976-6472.

50120140504007

Recomendados

Recomendados

Mais conteúdo relacionado

Semelhante a 50120140504007

Semelhante a 50120140504007 (20)

Mais de IAEME Publication

Mais de IAEME Publication (20)

Último

Último (20)

50120140504007