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UHD Journal of Science and Technology | Jan 2020 | Vol 4 | Issue 1 9

1. INTRODUCTION

The image processing techniques have been developed in the 
various of  areas such as pattern recognition, biometrics, image 
inpainting, medical image processing, image compression, 
information hiding [1], and multimedia security [2]. Medical 
image processing is a collection of  techniques that help the 
clinician in the diagnosis of  different diseases from medical 
images such as X-ray, magnetic resonance imaging, computed 
tomography, and ultrasound and microscopic images. Thus, 
various types of  cancer can be detected based on medical image 

processing from medical images such as breast cancer, brain 
tumor, lung cancer, skin cancer, and blood cancer (leukemia). 
The advantage of  creating a system based on medical image 
processing techniques is extracting the targeted diseases in 
higher accuracy, with reducing time consumption as well as 
decreasing cost, otherwise, the manual processing is taken a 
lot of  time and it also needs a professional staff  for detecting 
of  diseases [3]. In general, processing medical images include 
four main steps which are: Pre-processing, segmentation, 
feature extraction, and classification. This paper is mainly 
focused on the segmentation step in processing microscopic 
blood images which help the clinician in identifying various 
diseases in human’s blood such as blood cancer (leukemia), and 
anemia. Segmentation of  white blood cells (WBCs) is the most 
important step in identifying leukemia. Leukemia is a type of  
cancer that affects blood, lymphocyte system, and bone marrow. 
Thus, the correct segmentation of  WBCs has an impact on 
further steps, such as feature extraction and classification, to 

Access this article online

DOI: 10.21928/uhdjst.v4n1y2020.pp9-17 E-ISSN: 2521-4217
P-ISSN: 2521-4209

Copyright © 2020 Mohammed and Abdulla. This is an open access 
article distributed under the Creative Commons Attribution Non-
Commercial No Derivatives License 4.0 (CC BY-NC-ND 4.0)

Thresholding-based White Blood Cells 
Segmentation from Microscopic Blood Images
Zhana Fidakar Mohammed1, Alan Anwer Abdulla2,3
1Department of Information Technology, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, Iraq, 
2Department of Information Technology, College of Commerce, University of Sulaimani, Sulaimani, Iraq, 3Department of 
Information Technology, University College of Goizha, Sulaimani, Iraq

O R I G I N A L  A RT I C L E UHD JOURNAL OF SCIENCE AND TECHNOLOGY

A B S T R A C T
Digital image processing has a significant role in different research areas, including medical image processing, object 
detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important 
steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic 
images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) 
need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic 
blood images based on thresholding segmentation technique and it compares with the most commonly used segmentation 
technique which is known as color-k-means clustering. The comparison is done based on three well-known measurements, 
used for evaluating segmentation techniques which are probability random index, variance of information, and global 
consistency error. Experimental results demonstrate that the proposed thresholding-based segmentation technique provides 
better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of 
the proposed technique is less than the color-k-means which are 70.8144 ms and 204.7188 ms, respectively.

Index Terms: Medical image processing, Segmentation techniques, Thresholding, White blood cells

Corresponding author’s e-mail: Alan Anwer Abdulla, Department of Information Technology, College of Commerce, University of Sulaimani 
and University college of Goizha, Sulaimani, Iraq. E-mail: Alan.abdulla@univsul.edu.iq

Received: 12-01-2020 Accepted: 09-02-2020 Published: 13-02-2020



Mohammed and Abdulla: White Blood Cells Segmentation

10 UHD Journal of Science and Technology | Jan 2020 | Vol 4 | Issue 1

obtain results more accurately [3]-[5]. In general, the main 
components of  blood are four types as follows:
• Red blood cells (RBCs): Are responsible for delivering 

oxygen from the lungs to all the parts of  the human’s 
body [6]

• WBCs: Are responsible for body’s immune system. The 
WBCs are larger in size and fewer in number compared 
to RBCs [6]

• Platelets: Are responsible for blood clotting [6]
• Plasma: Is responsible for carrying nutrients that are 

required by the cells [3]. It makes up about half  of  the 
blood volume.

Most blood cells are produced in the bone marrow, and 
there are cells known as stem cells (lymphoid and myeloid) 
that are responsible for producing different blood cells [7] 
in general, there are different types of  WBCs which are 
Basophil, eosinophil, neutrophil, lymphocyte, and monocyte, 
as illustrated in Fig. 1.

In this paper, a segmentation technique based on thresholding 
is proposed to segment WBCs from the microscopic blood 
images using public and well-known dataset acute lymphoblastic 
leukemia-image database (ALL-IDB). The proposed technique 
includes the following steps: Pre-processing step to enhance the 

image quality, segmentation step to separate WBCs from other 
blood components, and image cleaning step to remove the 
unwanted objects inside the segmented image. Consequently, 
the proposed segmentation technique is compared with other 
technique, which is known as color-k-means clustering, in 
terms of  time consumption as well as performance of  the 
segmentation technique based on probability random index 
(PRI), variance of  information (VOI), and global consistency 
error (GCE) which are measurements that using for evaluating 
a segmentation techniques.

1.1. Problem Statement
The manual processing of  medical images is time consuming 
and it requires a professional staff  which mainly depends 
on personal skills, and sometimes it may produce inaccurate 
results.

1.2. Objective of the Research
The main objective of  the research is to segment WBCs from 
microscopic blood images accurately and quickly because 
accurate segmentation of  WBCs makes other processes 
(such as feature extraction) easer consequently producing 
an accurate result of  classification. Thus, the overall process 
could help the clinician in identifying different diseases 
accurately in a faster way, which further helps them to provide 
treatment for the patients sooner.

The rest of  the paper is organized as follows: In Section 2, 
the literature review is presented. Section 3 presents the 
proposed approach. The experimental results are illustrated 
in Section 4. Finally, our conclusions are given in Section 5.

2. LITERATURE REVIEW

Nowadays, processing medical images have a crucial role 
for early identification of  diseases. Thus, segmenting WBCs 
from microscopic blood images are the most important step 
in identifying leukemia. This section focuses on reviewing 
the most important existing works on segmenting WBCs.

In 2009, Sadeghian et al. [8] proposed a segmentation 
technique to segment WBCs as well as their nuclei and a 
segmentation technique to separate the cytoplasm of  the 
cell. The red, green, and blue (RGB) image is converted into 
gray image; then canny edge detection is applied followed 
by gradient vector flow to connect the boundary of  the 
nucleus. Consequently, the hole filling technique is applied 
to get the nucleus. Furthermore, Zack algorithm is applied 
into the gray image to get the binary image to extract the Fig. 1. Bone marrow and blood components.



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UHD Journal of Science and Technology | Jan 2020 | Vol 4 | Issue 1 11

cytoplasm of  the cell by subtracting the binary image from 
the gray image. Finally, their proposed work is obtained 92% 
accuracy for nucleus segmentation and 70% accuracy for 
cytoplasm segmentation.

In 2015, Marzukia et al. [9] proposed a system to segment the 
nuclei of  WBCs based on the active contour technique. The 
RGB image is converted to a gray image and then the active 
contour is applied to the resulted image to get the segmented 
nucleus. Continuously, the obtained image is converted to a 
binary image to find the roundness value to determine the 
grouped and individual WBCs. As they claimed, their proposed 
system can accurately extract the boundary of  WBC nuclei.

Continuously, in 2015 Madhloom et al. [10] proposed an 
approach to segment WBCs and their nucleus. First, it 
segments the WBCs based on the color transformation as well 
as mathematical morphology. Moreover, the marker-control 
watershed technique is applied to separate overlapped cells. 
Furthermore, the seeded region growing technique is used 
to segment the nucleus of  the cells. Finally, the performance 
of  their approach is evaluated using relative ultimate 
measurement accuracy and misclassification error to measure 
the accuracy and it achieves 96% for WBCs segmentation 
and 94% for nucleus segmentation.

In 2016, Sobhy et al. [11] proposed two segmentation 
techniques for segmenting WBCs. First, color correction is 
applied to extract the mean intensity from the histogram of  
each RGB channel of  the original image. Moreover, the image 
is converted to hue saturation intensity color space, and then 
the two tested segmentation techniques are applied to the 
S component of  the image. The first technique was Otsu’s 
thresholding and the second technique was marker-control 
watershed segmentation. Furthermore, the exoskeleton 
algorithm is used to separate the adjusted WBCs. Finally, they 
compared their work with another study which is manually 
counted the WBCs based on 30 images. As they claimed, 
their proposed is achieved an accuracy of  93.3% for Otsu’s 
segmentation and 99.3% for marker-control watershed 
segmentation.

In 2017, Gowda and Kumar [12] proposed a system to 
segment WBCs based on k-means clustering and Gram-
Schmidt orthogonalization. The proposed system is started by 
pre-processing step which includes: (1) Converting image from 
RGB to gray image, (2) median filter is implemented to remove 
noise, and (3) image normalization is applied for contrast 
stretching. Consequently, k-means clustering segmentation 
technique is applied to segment WBC and its subtypes such as: 

Basophil, eosinophil, neutrophil, lymphocyte, and monocyte. 
Furthermore, Gram-Schmidt orthogonalization scheme is 
applied to segment the nucleus from the cell.

Finally, in 2017, Nain et al. [13] proposed a system to 
differentiate individual and overlapped WBCs based on the 
watershed segmentation scheme to segment the cells. The 
edge map extraction technique, which is a circle fitted on 
each cell, is used to identify both individual and overlapped 
cells. To evaluate the performance of  the proposed work, 
two evaluation measurements which are detection rate (DR) 
and false alarm rate (FAR), are used. As they claimed, the 
proposed system is achieved 97.10% and 7.80%, respectively, 
for DR and FAR, the lower value of  FAR and higher value 
of  DR means better segmentation of  WBCs.

3. PROPOSED APPROACH

A microscopic image of  the blood sample contains three 
components, as discussed before, which are RBCs, WBCs, and 
background (platelets and plasma). The proposed approach 
focuses on segmenting WBCs from other components (which 
are RBCs and background). The segmentation is done based 
on the thresholding technique. This section concentrates to 
explain the steps of  the proposed work, as illustrated in Fig. 2.

3.1. Pre-processing
The first step of  the proposed work is pre-processing, which 
is an important step to provide a better quality of  the input 
image and make the next step (segmentation step) easier. In 
this step, the three channels of  the input image, which are 
RGB are separated, and then the median filter (to remove 
noise) and histogram equalization (to contrast enhancement) 
are applied on the only green channel of  the input image to 
enhance the image quality, Fig. 3.

The median filter is a non-liner filter, which is used for 
removing noise. While, the histogram equalization is a 
technique that uses image’s histogram to improve low or 
high contrast of  the image which makes better distribution 
of  intensities of  the image’s histogram [14].

3.2. Segmentation
To segment the WBCs from the other components of  
the microscopic image, i.e., RBCs and background, the 
thresholding segmentation technique is applied to the image.

The thresholding-based segmentation technique is the simplest 
and useful segmentation technique that segments an image 
based on the intensity of  the pixels value [15]. This technique is 



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Fig. 2. Steps of the proposed approach.



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UHD Journal of Science and Technology | Jan 2020 | Vol 4 | Issue 1 13

produce a binary image from a gray image (the value of  0, which 
represents background and it is black and the value of  one which 
represents object inside the image and it is white color). This 
process has the advantage of  reducing complex information 
and simplifies the classification and recognition processes [16]. 
The threshold value of  this technique can be selected manually 
or automatically based on the information from the features 
of  the image [17]. There are types of  thresholding which are:

3.2.1. Global thresholding (single thresholding)
This technique use only one threshold (T) to segment the 
whole image into objects and background; this technique 
is more appropriate for those images that have bimodal 
histogram. The global thresholding can be defined as 
equation (S1) [16], [17]: Suppose a sample image of  f (x, y) 
has the following diagram (Fig. 4):

Then, the binary image g (x, y) of  the f (x, y) can be defined 
as following equation:

g (x, y) = 1 if  f (x, y) >T otherwise 0 if  f (x, y) ≤T (1)

Where T is the threshold value.

3.2.2. Local thresholding (multiple thresholding)
In this thresholding technique, the image segments based 
on multiple threshold values and the image partitions into 

multiple region of  interests (ROIs) and backgrounds [15]. 
Moreover, it segments an image by partitioning an image into 
(n × n) sub-images and then selects a threshold Tij for every 
sub-image [16], as illustrated in Fig. 5.

This technique is more appropriate for images that are 
contained disparate illuminations, this technique can be 
defined as follows [16]:

 g (x, y) = 0 if  f (x, y) <T (x, y) otherwise  
  1 if  f (x, y) ≥T (x, y) (2)

Where f (x, y) is an input image and g (x, y) is a binary image 
produces depending on multiple threshold value T (x, y).

In our proposed approach, we segment the microscopic 
images into ROI (which are WBCs) and background based 
on global threshold value with (T = 50) as clarified in the 
diagram of  the work (Fig. 2.). We apply this technique in the 
resulted image obtained after the pre-processing step is done 
(i.e., image d from Fig. 3). Moreover, we obtain a binary image 
that contains WBCs which are larger in size in the image and 
some remaining as unwanted objects (RBCs and platelets), 
as illustrated in Fig. 6.

Fig. 4. Histograms of a sample image.

Fig. 5. Segmentation based on local thresholding.

Fig. 3. Pre-processing: (a) Input image, (b) green channel, 
(c) resulted in image after the median filter is applied, (d) resulted 

image after histogram equalization is applied.

a b

c d



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3.3. Image Cleaning
The resulted image obtained in the previous step, as illustrated 
in Fig. 6, includes some unwanted objects such as RBCs or 
platelets. To overcome this drawback, the morphological 
opening operator is applied to the resulted image of  the 
segmentation step.

The opening operator is a morphological operation that 
uses to eliminating imperfections in the images that impact 
on texture and shape of  images. Thus, morphological 
operations have a crucial role in image processing especially 
image segmentation process because they treat with shape 
extraction within the image and they describe the structure of  
the image [18]. The most significant morphological operations 
are dilation and erosion. There are also two other operations 
which are opening and closing which work depending on the 
combination of  dilation and erosion [18], [19]. The opening 
operator is a combination of  both erosion and dilation. It first 
erodes an image depends on the structuring element and then 
dilated it by the same structural element. Opening smoothens 
the boundary of  an object and deletes small unwanted objects 
inside the image. It can be defined as the following equation 
[18], [19]:

  A o B = (A–B)+B (3)

Where A: Is an image

B: Is a structuring element.

Moreover, some of  the WBCs are located in the edge of  the 
image; thus, the border cleaning scheme is used to remove 
those cells because they have a negative impact on the 
accuracy rate in further steps such as feature extraction and 
classification. Thus, to make only complete WBCs remains 
in the image, the border cleaning is applied to the resulted 
image of  the opening operator, as illustrated in Fig. 7. The 
border cleaning is a morphological technique that used to 
remove an object that touches the edge of  the image [11].

3.4. WBCs Cropping
To crop each WBCs as an individual image, we used the 
bounding box technique. This technique can be defined 
as the smallest rectangle that soured an object of  interest, 
and it extracts the minimum area of  the box and it can be 
represented as follows [20]:

Area = Major axis length×Minor axis length (4)

Where the major axis represents the longest line that can be 
drawn between two points in the object, and the minor axis 

is the longest line between two points of  the object that can 
be drawn perpendicularly to the major axis, as illustrated in 
Fig. 8. [20].

We applied the bounding box technique on the original image 
(input image) based on the location of  WBCs in segmented and 
cleaned images obtained in Fig. 7, which further can be used for 
more processing such as feature extraction and classification. 

Fig. 6. The resulted image after thresholding segmentation 
is applied.

Fig. 8. The major axis and the minor axis.

Fig. 7. Image cleaning: (a) Resulted image after opening operator is 
applied, (b) resulted image after border cleaning is applied.

a b



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UHD Journal of Science and Technology | Jan 2020 | Vol 4 | Issue 1 15

The result of  the bounding box is represented in Fig. 9. And 
Fig. 10 illustrate cropped WBCs as individual images.

4. EXPERIMENTAL RESULTS

The main purpose of  the proposed approach is to 
segment the WBCs from microscopic blood images. In 
this section, to evaluate the performance of  the proposed 
approach, experiments are conducted on the dataset 
(explained in 4.1).

4.1. Dataset
The input images are taken from a public and commonly 
used dataset known as ALL-IDB, which consists of  two 
groups of  images:
• The first one is ALL-IDB1 which was designed for 

testing the segmentation techniques, and it contains 
108 microscopic blood images, including (49 abnormal 
images of  leukemia patients and 59 normal images) [21]

• The second one is called ALL-IDB2 which was 
designed for testing the classifier techniques and it 
consists of  260 cropped WBC images (50% abnormal 
cells and 50% normal cells) which are taken from ALL-
IDB1 [21].

As this study is mainly focused on the segmentation of  
WBCs, thus the first group of  the dataset is used (i.e., ALL-
IDB1). Because as mentioned above, this group of  the dataset 
is dedicated for testing segmentation techniques.

4.2. Results
As mentioned in Section 3, in the thresholding segmentation, 
only the green channel of  the input microscopic image was 
used for the segmentation process. Because the green channel 
gives better results in terms of  segmentation compared to the 
other two channels (red and blue), since it holds more contrast 
information regarding to WBC [22]. In the segmentation step, 
the thresholding technique used for segmenting WBCs with 
T = 50, in our experiments first different threshold values are 
tested on all three channels of  normal and abnormal images 
and the experimental results demonstrate that the best result 
is given by the green channel with T = 50. As illustrated in 
Fig. 11. the WBCs have higher contrast in the green channel 
of  the image compared to two other channels; thus, it makes 
the segmentation process more accurate.

Fig. 9. Applying bounding box technique on the original image 
based on the segmented image, as presented in Fig. 5.

Fig. 10. Example of cropped white blood cells.
Fig. 11. A blood microscopic image of leukemia patient: (a) Original 

image, (b) red channel, (c) green channel, (d) blue channel.

a b

c d



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16 UHD Journal of Science and Technology | Jan 2020 | Vol 4 | Issue 1

To evaluate the performance of  the proposed approach, we 
compare our proposed approach with the commonly used 
segmentation technique which is known as color-k-means 
clustering, which was used by many existing works such as 
Mishra et al., Kumar and Vasuki, Ferdosi et al., Sarrafzadeh 
and Dehnavi [23]-[26]. We also applied the color-k-means 
clustering on the same dataset (ALL-IDB1) to evaluate both 
segmentation technique, the comparison was done in terms 
of  time consumption as well as the performance of  the 
segmentation techniques based on the PRI, GCE, and VOI. 
The PRI is a nonparametric technique used to measure the 
performance of  the segmentation technique and it can be 
calculated as follows [22], [27]:

 

PRI S G

C P  1 C  1 P

test k i j  i  j

i j i j i j i j

,
/ , &

, , , ,

( ) = ∑

+ −( ) −(
∀ <

1
2N

))   
(5)

Moreover, the GCE is a region-based segmentation 
consistency, which measures to quantify the consistency 
between image segmentation of  differing granularities, and 
it can be calculated as follows [28]:

GCE S  S min X S S X S S1 2 i i 1 2 i i 1 2, { ( , ), ( , )}( ) = ∑ ∑
1
n  

(6)

While the VOI is calculated the distance between two 
segmented areas which provide information about the 
participation of  pixels in different clusters and measures the 
knowledge lost or gained in two clusters it can be calculated 
as follows [28]:

  VOI = H(X)=H(Y)–2I(X,Y) (7)

The experimental results are as follows:

4.2.1. Execution time
The time consumption has been calculated for both 
segmentation techniques which implemented on the 
ALL-IDB1, as illustrated in Table 1. The systems were 
implemented using MATLAB on Lenovo computer with 
8 GB of  RAM and 64-bit Operating System\Windows 10.

Table 1 demonstrates that the proposed thresholding-based 
technique needs less time compared to the color-k-means 
technique for segmenting the WBCs from microscopic 
images which applies to 49 abnormal images and 59 normal 
images.

4.2.2. Performance of the segmentation techniques
Three well-known segmentation measurements such as PRI, 
GCE, and VOI are calculated for both proposed technique 
and color-k-means clustering and Table 2 illustrates the 
performance of  each technique. The lower value of  PRI, 
GCE, and VOI means the better/higher performance of  
the segmentation technique [27].

Table 2 demonstrates that the proposed technique has a 
lower value of  PRI, GCE, and VOI. Thus, it is better for 
segmenting WBCs from microscopic images.

5. CONCLUSION

The progression of  using techniques of  image processing 
in medical fields provides better accuracy for identifying 
different diseases from medical images. This paper focuses 
on the segmentation step, which is the most important step 
in medical image processing for segmenting WBCs from 
microscopic blood images, depending on the threshold-
based technique. Experimental results prove that the 
proposed segmentation technique can extract the WBCs 
from images better than color-k-means clustering in terms 
of  segmentation evaluation as well as time consuming.

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TABLE 2: Segmentation evaluation.
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UHD Journal of Science and Technology | Jan 2020 | Vol 4 | Issue 1 17

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