J. Software Engineering & Applications, 2010, 3, 756-760
doi:10.4236/jsea.2010.38087 Published Online August 2010 (http://www.SciRP.org/journal/jsea)
A Novel Regression Based Model for Detecting
Anemia Using Color Microscopic Blood Images
Saif AlZahir, Han Donker
University of N. British Columbia, Prince George, Canada.
Received May 7th 2010; revised June 27th 2010; accepted July 1st 2010.
ABSTRACT
Modeling human blood components and disorders is a complicated task. Few researchers have attempted to automate
the process of detecting anemia in human blood. These attempts have produced satisfactory but not highly accurate
results. In this paper, we present an efficient method to estimate hemoglobin value in human blood and detect anemia
using microscopic color image data. We have developed a logit regression model using one thousand (1000) blood
samples that were collected from Prince George Hospital laboratory. The output results of our model are compared
with the results of the same sample set using CELL-DYN 3200 System in Prince George Hospital laboratory, and found
to be near identical. These results exceed those reported in the literature. Moreover, the proposed method can be im-
plemented in hardware with minimal circuitry and nominal cost.
Keywords: Regression Models, Logit Regression Model, Anemia, Blood Components, CBC
1. Introduction
Color image analysis techniques are employed in a wide
range of medical applications including human blood
testing. Blood testing refers to laboratory analysis of
blood. A variety of blood tests are accessible to provide
information about the condition and status of the human
body. The most regular test is the complete blood count
(CBC). CBC is a series of tests used to appraise the
composition and concentration of the cellular compo-
nents of blood [1]. For example, anemia is a disease
caused by the reduction of red blood cells (RBC) count
and/or the hemoglobin (Hgb) level in human blood [2].
In general, a microscopic color image is a multi-spec-
tral image with one band for each of the three primary
colors (red, green and blue), assuming that we are work-
ing with RGB color model. Based on this understanding,
color images are produced by a weighted combination of
these three primary colors for each pixel. The color of an
image also depends on the light source illuminating the
image object and on the color of the surrounding region
of that object or simply the ambient. At present, color
images are extensively used in a wide range of applica-
tions and are exploited by researchers and developers in
almost every aspect of real life applications.
In this paper, we present a new method for estimating
hemoglobin level in human blood to detecting anemia
using microscopic color images.
The remaining part of this paper is organized as fol-
lows: In Section 2, we introduce the current methods for
testing CBC and present previous work related to cell
count, cell analysis, and hemoglobin estimation to pro-
vide the necessary information and appropriate back-
ground for this research. In Section 3 we present the
proposed model, and finally, in Section 4 we provide our
results and conclusions.
2. Previous Related Work
In this section, we briefly describe the main methods used
to calculate or estimate human blood components. Table
1 depicts male and female hemoglobin components and
their normal ranges as analyzed by CELL-DYN 3200
System. The CELL-DYN 3200 system is popular and is
being used by Prince George Regional Hospital (PGRH)
Laboratory where we obtained the blood samples and
with which we will compared our output results.
There are two main types of procedures to compute
CBC and hemoglobin level: 1) manual; and 2) auto-
Table 1. Hgb reference intervals for CELL-DYN 3200 Sys-
tem
Parameter Normal range
Hemoglobin 132–159 g/L for male *
123–151 g/L for female
* The content is taken from Reference [3]

A Novel Regression Based Model for Detecting Anemia Using Color Microscopic Blood Images 757
mated. The following are some examples on both proce-
dures.
2.1 Manual Procedures for Determining
Hemoglobin in Blood
A manual also called spectrometric procedure is straight-
forward and requires the use of spectrophotometer. The
spectrophotometer is a device that measures the mono-
chromatic light transmitted through a solution to deter-
mine the concentration of the light absorbing substance
in that solution. The light from the lamp passes through
the prism, which allows light of only a predetermined
wavelength to pass through the cuvette. The transmitted
light strikes a detector, where it is converted into electri-
cal energy and presented to the readout device [4]. This
procedure is satisfactory but requires the presence of an
attendant and consumes a significant amount of time to
perform. In short, this method is acceptable but not effi-
cient nor economic.
2.2 Automated Procedures for Determining
Hemoglobin in Blood
The CELL-DYN 3200 System is a modern automated
CBC analyzer and hemoglobin estimator. It combines
spectrophotometry and modified cyanmethemoglobin
method for hemoglobin determination. In the cyan-
methemoglobin method, the whole blood is mixed with a
solution of potassium ferrycyanide to convert hemoglo-
bin in the ferrous state, to methemoglobin in the ferric
state, which then reacts with potassium cyanide to form
cyanmethemoglobin. This final product is also called
hemiglobincyanide (HiCN). HiCN is very stable and has
a wide absorption maximum of about 540 nm. The ab-
sorption of the solution at 540 nm is directly proportional
to the amount of Hemoglobin present in the blood [5].
The CELL-DYN 3200 system measures hemoglobin
within a sample in a hemoglobin Flow Cell. The hemo-
globin dilution that is analyzed in the Flow Cell is a
mixture of the sample plus reagent. The system takes five
reference readings. The lowest and highest readings are
discarded, and the remaining three readings are averaged
to produce the final hemoglobin reading. This is done to
eliminate the extreme values. The hemoglobin dilution is
analyzed at 555 nm in the hemoglobin Flow Cell; the
system receives, and then saves the results [6].
At present, both CELL-DYN 3200 and CELL-DYN
4000 systems are being used in hospital laboratories in
around the world. Both systems are considered to be re-
liable. As for the cost of the equipment, both systems are
considered to be expensive and require attendants pres-
ence compared to the proposed method which is highly
efficient and inexpensive.
3. The Proposed Model
In this section, we introduce the proposed method for
detecting anemia. A flow chart of this method is shown
in Figure 1. This method comprises of two main com-
ponents. The first is concerned with the development of
the logit regression model, (LRM) shown in the upper
rectangle of the figure and the second component is con-
cerned with testing of new blood samples, shown in the
lower rectangle of the figure. The major elements of the
first component are: 1) blood samples collection; 2) cap-
turing microscopic images of the blood samples; 3) im-
age preprocessing and color information gathering; and 4)
the development of the logit regression model. The sec-
ond component handles detecting anemia, if it exists, in
new blood samples. The process of doing that are similar
to those in the model development component except for
the last step which is the employment of the regression,
LRM, model. The following is a brief description of the
proposed method:
3.1 Blood Samples Collection
To build our regression model, we have collected one
thousand (1000) blood samples from Prince George Re-
gional Hospital Laboratory, British Columbia. These
samples are randomly chosen from the general public
and we arranged them in a specific numbering scheme so
as to protect their identity. The blood samples were
smeared on glass slides by the hospital-automated system.
At that point, we captured microscopic color images of
the samples using a digital camera mounted on a micro-
scope with 10 × magnification. Our original plan was to
use the whole size of sample image for processing but as
most of the sample images were distorted at the borders
due to the staining process and the nature of blood smea-
Develop
Regression
Model
Blood
Samples
Collection
Microscopic
Images
Capturing
RGB
Values
Calculation
Collect
New Blood
Sample
Capture
Microscopic
Ima
g
e
Perform
RGB
Calculation
L
R
M
Figure 1. Flow diagram of the proposed scheme

A Novel Regression Based Model for Detecting Anemia Using Color Microscopic Blood Images 758
ring, our choice was to select a segment that is uncor-
rupted of a window size (256 × 256 pixels) from each
image. The choice of the segments was done randomly.
The clipped images are saved in a separate file for further
processing. Figure 2 shows six (6) clipped images from
the blood samples. The samples shown in the figure are
of samples 1, 5, 6, 10 and 15.
3.2 Blood Color Image Analysis
For each sample image, we calculate the pixels’ color
information as a function of red, green, and blue (f(R, G,
B)) using Matlab 7 software. To do so we created three
planes that represent the three colors (red, green, blue) of
the pixels’ values of the images. Then, the average of all
red values, green values, and blue values of the three
planes were calculated to produce three single values for
each image: the red value, R, the green value, G, and the
blue value, B. These three values were stored in a matrix
of size 3 × N, where N is the number of images as shown
in Figure 2. In our case N is equal one thousand (1000)
and hence the size of our matrix is 3 × 1000. In the Fig-
ure 3 the values r1, g1, b1 belongs to sample one 1 and rl,
gl, and bl belongs to the last sample, which is sample
number 1000.
1 5 6
7 10 15
Figure 2. Six blood test samples
Figure 3. RGB analysis of each sample image
3.3 Building the Model
Prior to model the data, i.e. the RGB values, we have
represented them visually to acquaint ourselves with
some of their statistics and properties. Fore example, we
plotted their histogram as shown in Figure 4.
As expected, in most cases, the red color values (plot-
ted in dark blue) is the highest value, followed by green
color values (plotted in pink), and finally the blue color
values (plotted in yellow) is the smallest value of the
three components. Examining the statistics of the hemo-
globin values of the test samples supplied by the Prince
George Hospital Lab. including the maximum and
minimum values of the set, we found that the set maxi-
mum value is 185 g/L, and the minimum value is 57.30
g/L, and the average value is 132.51 g/L. This statistics
helps determine the range of the set, which facilitates
modeling the data. The hemoglobin values of 900 sam-
ples histogram is plotted and shown in Figure 5 and the
last 100 samples are plotted and shown in Figure 6 for
clarity and ease of tracking.
Figure 4. Histogram of the blood samples color ranges
Figure 5. Histogram of the hemoglobin values of the 900
blood samples

A Novel Regression Based Model for Detecting Anemia Using Color Microscopic Blood Images 759
HG
B
0
20
40
60
80
100
120
140
160
180
1815 22 29 36 43 50 57 6471 78 85 92 99
Sam
p
le's Nu
m
HG
B
Figure 6. Histogram of the last 100 blood samples
From the data and the graph in Figure 5, we found
that there are 10 samples outside the range (75-to-175
g/L) in the whole set which represent 1.0% of the total
sample set. Based on this observation, we introduced an
upper and a lower cut-off threshold values to eliminate
those extreme values on both end of the range. We did
that by equating any Hgb value greater than the upper
threshold value to 175 g/L and any value that is less than
the lower threshold to 75 g/L. Doing so does not alter
any sample value group affiliation (i.e. , from high hemo-
globin (healthy) to low hemoglobin (anemic). In addition
such action will create a range of 100 (i.e., 175-75g/L),
which is easy to use.
At this point, several modeling methods were consid-
ered. In this paper, we used Eviews-5 software to pro-
duce the Logit regression model for this sample. Using
Eviews software and the values of the R, G, and B, of the
samples and if the sample indicates anemia or not, we
produced the following Logit model:
L=(e–1.922 + 0.206R – 0.241G + 0.012B)/(1 + e–1.922 + 0.206R – 0.241G + 0.012B)
Where L is the hemoglobin level and R, G, and B are the
color values of the blood sample.
3.4 Detecting Anemia
To determine if a person is anemic, we take a few drops
of his or her blood, smear it on a glass and take its pic-
ture using a simple digital camera. Then we calculate the
R, G, and B values of the image. We plug the values of
the R, G, and B values in our LRM model and find the
result. This method is simple and cost effective
4. Experimental Results
CBC has been a target for several automations attempts
by researchers from different fields. None of the pub-
lished paper attempted to model Hgb via regression
models and images analysis. Zahir and Chowdhry [7]
have presented a combined method that is based on arti-
ficial neural network (ANN) in conjunction with color
image analysis. They presented results that are far better
than those reported in [8]. In this research, Hgb value is
sought to determine anemia. They reported that the net-
work is trained with fixed training rate of 0.4 and for
accuracies of 20% and 15%. They claim that accuracies
below 15% demand more computing time. They added
that the computing time is about 20 hours to realize an
accuracy of 5%. The authors did not include explanation
to support their claims nor that did they explain how can
they train the NN to improve the results by 10% to 15%
by increasing the computing time.
To test the performance and effectiveness of the LRM
model, we have tested one thousand samples using this
Logit model and compared them with the results of the
hospital results and found to be almost identical. Table 2
depicts the results we obtained and the number of faulty
samples in each of the hemoglobin ranges shown. This
Table shows the number of total errors is 65. This pro-
duces a 93.5% efficiency of the model. Out of this total
38 samples were in the hemoglobin range of 90-100.
This range is a borderline level and in most cases doctors
recommend that patients redo the test at a later time and
recommend a specific diet. If these samples are not
counted, then the efficiency of our model will be in-
creased from 93.5% to 96.2 % accuracy. In addition, only
5 samples were at error when the person was anemic and
the results shows he is not. Although this is a serious
mistake but it rarely occurred and represent only 0.005%
of the total sample.
5. Conclusions
The literature is scarce when it comes to simple, eco-
nomical, and reliable automated methods for diagnosing
anemia. There are, however, few methods like cyan-
methemoglobin, which is reliable but very expensive and
the WHO Hemoglobin Color Scale (HCS) method,
which is inexpensive but not so reliable. In this paper, we
Table 2. Results of the logit regression model
HEMOGLOBIN MODEL RESULT
No. Hgb Range # of Samples # of Errors
1 0-80 8 1
2 80-90 29 4
3 90-100 66 38
4 100-110 81 15
5 110-120 104 4
6 120-130 134 2
7 130-140 186 2
8 140-175 392 0
Total 1000 65
Sample’ Number

A Novel Regression Based Model for Detecting Anemia Using Color Microscopic Blood Images 760
introduced a new logit regression based model that uses
image analysis data to detect anemia. The simulation
results of the proposed model are significantly higher
than the published results. For a set of 1000 sample, our
results show an accuracy of 96.2% if we do not consider
38 samples on the borderline between anemic and not
anemic which is a grey area for all testing methods. Con-
sidering all samples, our model accuracy is 93.5%. In
addition to its high accuracy, the proposed model is easy
to implement and inexpensive. This model can be build
in hardware at a cheep cost.
6. Acknowledgements
The authors would like to thank Northern Health Author-
ity of British Columbia, Prince George Regional Hospital
Laboratory for their continued support to this research.
REFERENCES
[1] J. B. Henry, “Clinical Diagnosis and Management by
Laboratory Methods,” W. B. Saunders, Philadelphia,
2001.
[2] http://www.nlm.nih.gov/medlineplus
[3] M. Rendell, M. Anderson, W. Schlueter, J. Mailliard, D.
Honigs and R. Rosenthal, “Determination of Hemoglobin
Levels in The Finger Using Near Infrared Spectroscopy”,
Journal of Clinical & Laboratory Haematology, Vol. 25,
No. 2, April 2003, pp. 93-97.
[4] J. P. Greer, J. Foerster, J. N. Lukens, G. M. Rodgers, F.
Paraskevas and B. Glader, “Wintrobe’s Clinical hema-
tology,” 11th Edition, Lippincott Williams & Wilkins,
Philadelphia, 2003.
[5] G. Ongun, U. Halici, K. Leblebicioglu and V. Atalay,
“Feature Extraction and Classification of Blood Cells for
an Automated Differential Blood Count System,” Inter-
national Joint INNS-IEEE Conference on Neural Net-
works, Washington DC, Vol. 4, 2001, pp. 2461-2466.
[6] CELL-DYN 3200 System Training Guide, 2004.
[7] S. Zahir, C. G. Rejaul and W. Payne, “Automated
Assessment of Erythrocyte Disorders Using Artificial
Neural Network,” IEEE International Symposium on
Signal Processing and Information Technology, Vancou-
ver, 2006, pp. 776-780.
[8] H. Ranganath and N. Gunasekaran, “Artificial Neural
Network Approach in Estimation of Hemoglobin in Hu-
man Blood,” International Computer Engineering Con-
ference on New Technologies for the Information Society,
ICENCO, Cairo, 2004, pp. 341-344.