REVISTA AUS 26-2 / Saif S Al-jaboriy et al.,/ DOI:10.4206/aus.2019.n26.2.60/ www.ausrevista.com/ [email protected]
Saif S Al-jaboriy1,2, Nilam Nur Amir Sjarif1, Suriayati Chuprat1
Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Malaysia. 1,2Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Malaysia; and also with Al-Musiab Technical College, Al-Furat Al-awsat Technical University, Babylon, Iraq. (e-mail: [email protected] ; [email protected])
ABSTRACT/ Leukemia is a blood cancer that affects the white blood cells in the bone marrow. The detection of acute leukemia
is dependent on counting the white blood cells percentages in the peripheral blood. This kind of cancer can be a fatal disease
if left without early detection. In practice, the manual microscopic examination methods are used for detection of acute leukemia. But these manual methods are inaccurate, prone to errors, and time-consuming due to human factors such as
fatigue, stress, and lack of experience. Therefore, several of image processing techniques have been proposed to replace with
the manual methods. The results of this study: various computer-aided systems for acute leukemia diagnoses are reviewed; these systems are including image acquisition, pre-processing, segmentation, extraction of feature, and classification. Also this
paper provides the pros and cons of existing computer-aided methods with their accuracy. Key Words: Leukemia, image Acquisition, pre-processing, segmentation techniques, feature extraction Classification.
RESUMEN / La leucemia es un cáncer de sangre que afecta los glóbulos blancos en la médula ósea. La detección de leucemia aguda
depende de contar los porcentajes de glóbulos blancos en la sangre periférica. Este tipo de cáncer puede ser una enfermedad mortal si se deja sin detección temprana. En la práctica, los métodos de examen microscópico manual se utilizan para la detección de leucemia aguda. Pero estos métodos manuales son inexactos, propensos a errores y requieren mucho tiempo debido a factores humanos como la fatiga, el estrés y la falta de experiencia. Por lo tanto, se han propuesto varias técnicas de procesamiento de imágenes para reemplazar con los métodos manuales. Los resultados de este estudio: se revisan varios sistemas asistidos por computadora para el diagnóstico de leucemia aguda; Estos sistemas incluyen adquisición de imágenes, preprocesamiento, segmentación, extracción de características y clasificación. Además, este documento proporciona las ventajas y desventajas de los métodos asistidos por computadora existentes con su precisión.
Palabras clave: leucemia, adquisición de imágenes, preprocesamiento, técnicas de segmentación, extracción de características Clasificación
1. Introduction
The human body contains several parts that
complement each other in the performance of
life functions. Blood is one of the most critical
parts of the human body that helps to pass
minerals and oxygen to all parts of the body.
It also maintains body temperature; as well as
protects the body through antibody
production. There are three essential
components in blood, namely white blood cells
(WBC), red blood cells (RBC), and platelets
[1]. Blood can be infected with various
diseases that lead to body dysfunction and
thus affect the life and health of humans.
Some of these diseases are not very serious
and are treatable such as anemia, and others
are very serious and not easy to treat such as
leukemia. Leukemia is a type of blood cancer
that attacks the bone marrow, which is
characterized by an increase in the number of
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Recepción/ 27 junio 2019
Aceptación/ 25 agosto 2019
Segmentation and Detection of Acute Leukemia Using
Image Processing and Machine Learning Techniques: A
Review
Segmentación y detección de leucemia aguda mediante
técnicas de procesamiento de imágenes y aprendizaje
automático: una revisión
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665
immature WBCs named ‘blasts cells’.
Leukemia can be classified into two types
which are acute leukemia and chronic
leukemia [2]. This research focuses on the
acute type of leukemia. In acute leukemia, the
abnormal WBCs called immature blasts cells
malfunction, where it grows very quickly, and
the condition of the patient becomes very
dangerous if it not controlled quickly. Acute
leukemia infects the bone marrow and
progresses very fast. This kind of cancer can
infect children and adults too; as this disease
can develop many abnormal WBCs in their
body [3,4]. Acute leukemia can be classified
into two types that include acute
lymphoblastic leukemia (ALL) and acute
myeloid leukemia (AML) based on a French–
American–British model. ALL is one of the
acute leukemia types that negatively affect
the lymphocytes (WBCs that fight infection).
The bone marrow produces a number of blasts
cells that develop quickly to be lymphocytes
[5]. This condition is fatal if left untreated
because it quickly spreads; the early diagnosis
of ALL is essential for saving patients’ life and
for recovery [6-8]. ALL occurs when the white
blood cells have yet to completely mature.
While in the case of AML, the immature white
blood cells are unable to fight infections
[11,12]. The detection of acute leukemia in
different types is essential for early diagnosis
and drug discovery [13,14]. The diagnosis of
acute leukemia is dependent on counting the
percentages of blasts cells in the blood.
Manual microscopic examination of the blood
smear is less accurate and time-consuming
[9,15,16]. Acute leukemia diagnosis requires
tools and automated solutions, and the ability
for early detection [17]. Therefore, the image
processing and computer vision systems can
play an important role in diagnosis of such
types of these medical problems. In this case,
the system uses to examine the blood sample
automatically in order to overcome the
limitations of manual examination. Can be
built these type of systems with use the
microscopic images to recognize types of
acute leukemia cells. Using these types of
systems will reduce the time and effort and
increase the accuracy of the diagnosis [10].
The detection and classification of leukemia
depend on color, texture, size, and shape of
WBCs image [18].
Medical images provide useful information
about normal and abnormal blood cells.
Presently, the microscope images are most
widely used for diagnosing the existence of
acute leukemia. Essentially, the microscope
images involve both normal and abnormal
blood cells images. Despite extensive
research, the accurate detection and
classification of acute leukemia by using
image-processing techniques remains
challenging. The reasons are due to the
variation of size, shapes, locations, and image
intensities for various kinds of acute leukemia
[19,20]. The detecting of leukemia using
image processing and computer vision
techniques consists of five stages, which
include image acquisition, pre-processing
image, WBCs segmentation, feature
extraction, and classification of leukemia cells.
Fig 1 shows the stages of detection of
leukemia [21].
Fig1. Illustrate the stages of detection
leukemia
This aim of this study is to presents a detailed
review of computer systems that used to
detect and diagnosis acute leukemia (AL). This
paper is organized as follows: describes the
related work that is divided into five steps with
description each method followed by the
literature survey about leukemia. Then, the
techniques used for segmenting and detecting
acute leukemia will also be discussed. 2. Related research
Various researchers have proposed many
techniques in the field of medical image
processing. The majority of the researchers'
work is to find an optimal technique to help
the hematologists to detect acute leukemia
early. The major steps for diagnosis of acute
leukemia using image processing and machine
learning techniques involves pre-processing of
the medical images, segmentation (extract
the blast cells from the rest of image), feature
extraction, and then classification. These
steps are interrelated to each other, and the
success of the step will reflect on other steps.
In this section, we provide a survey of various
methods that have proposed for develop an
automatic system that used to detection of
acute leukemia cell by image processing. 2.1 Image acquisition
The detection of acute leukemia disease
through microscopic medical blood image
begins with the peripheral blood images
acquisition phase. At this stage, the medical
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blood images samples are captured from the
bone marrow by a camera to obtain digital
data that is used to test a proposed technique.
The light is one of the factors that has an effect
on the blood images, where the light
determines the quality and clarity of the
images. Thus, the pre-processing of the
images consists of the variety of techniques
that are used to change the domain of images
to another domain (this will be described in
detail in the next section) to be more easily
handled by the proposed systems [18,19].
Many researchers tested and evaluated their
systems with a few samples of blood images,
which are not available publicly. Therefore,
they could not compare their findings with
other researchers’ results. Hence, many
researchers used a public dataset that is
available online such as ALL-IDB1 (These kind
of samples were captured and collected by
experts at the M. Tettamanti Research Centre
in Monza, Italy).This is an example of section. 2.2 Pre-processing image
Enhancing medical images is an important and
complex step in computer vision, machine
learning and image processing techniques
because it used to improve the quality of an
image. Many medical images types (e.g. MRI,
microscope image, ultrasound Image, Nuclear
medicine, and others) are used to develop
diagnostic systems [20]. Many authors have
enhanced the blood image by converting to
another domain such as convert RGB to HSV
or to HSL domain to highlight the features of
objects for efficiently detecting an interesting
area [21]. Many factors can affect the quality
of images such as low contrast, false
background, and pepper noise. These kinds of
noises may occur because of the camera and
light condition during the capture of the
images. Currently, various techniques have
been proposed and suggested by Authors to
detect and enhance these kinds of factors in
order to make the blood images suitable for
segmenting region of interest (ROI) [22].
Histogram equalization is one of the
techniques that used for adjust the images
contrast. This technique is suitable to improve
and enhance the dark background and blood
image contrast [6,73]. Another technique is
linear contrast that also used to improve and
enhance the quality of the blood images by
increasing the contrast; this technique also
known as normalization of an image [7, 8]. In
addition, many proposed techniques are used
the minimum filter to highlight the lighter
object, which can easily recognize during the
segmentation process [10,72]. All the
technique that have been mentioned contain
disadvantages such as vulnerable to noise,
useful with image has low contrast, cannot
perform with blood images that having high
percentage of noise, i.e. led to remove some
detail from the image, and so forth. Therefore,
researchers need to find and propose new
techniques for enhancing image quality. 2.3 Segmentation image
Understanding the digital images or extracting
the information from some regions in the
image is one of the most crucial tasks in image
processing techniques. The first step in
detecting an object in the digital image is
image segmentation. In practice, image
segmentation is one of the important bases for
the recognition of the region of interest in the
image, and it is considered a hotspot in the
image processing, computer vision and
machine learning techniques [5,20,71].
Fig 2: WBCs segmentation methods.
The segmentation stage has divided an image
into a number of objects that have the same
features based on certain criteria in order to
extract the area of interest. Many authors
have been proposed different techniques to
segment blast cells (AML and ALL) from the
rest of the blood image as depicted in Figure
2; but the results of these techniques could
not produce ideal segmentation for the
complex blood cell images. In addition, many
factors made the segmentation of WBCs from
the rest of image a challenging process (e.g.,
the light, contrast, and quality of medical
images) [22,70]. Generally, different features
in the medical blood image can be used at
segmentation stage such as color, shape, and
texture and level intensity.
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2.3.1 Thresholding
Threshold segmentation is one of the simplest
and most common segmentation algorithms
that divide an image based on the gray-scale
value to get the target objects. It can be
classified this methods into two techniques
that include the local threshold and global
threshold techniques such as Otsu's method,
maximum entropy method, and others. The
local threshold technique divides an image
into multiple objects and background by
implementing multiple thresholds. While in the
global threshold technique, an image can be
divided into two target regions and the
background by implementing a single
threshold [23,24]. This technique works with
a threshold value; if the image intensity values
are less than the threshold values, the all
values will be zero (black color). While if the
intensity level values are greater, then the
intensity values will be one (white color).
Thus, these operations will generate the image
in binary mode and will help to get a better
image understanding [25]. Mathematically,
the technique can be written as follows:
………… (1)
Where the 𝐼 is the original image in grey mode
and the 𝑖 is the image pixel value. And the 𝐼' is conceder as binary image.
The advantage of this method is the operation
speed is faster, and the calculation is simple;
especially, when the target objects have high
contrast. Nevertheless, the disadvantage of
this method is it is difficult getting the accurate
results; especially when there is a large
overlap of the gray-scale or no significant
gray-scale difference in the image. It is also
sensitive to gray-scale unevenness and noise
in the image. Therefore, it needed to be
combined with other methods to get better
performance [26].
2.3.2 Regional growth
The regional growth method is one of the
conventional region segmentation algorithms
that segment an image based on the similar
properties of the pixels in order to get the
target objects [27]. This method requires two
main steps that include selecting a seed pixel
and merging all the pixels that have similar
properties, and they are located around the
seed pixel. Its basic formula is as follows: a) 𝑈𝑛 𝑅𝑖 = 𝑅.
b) 𝑅𝑖 is a connected region, i = 1, 2, 3, . . . , n
c) 𝑅𝑖 ∩ 𝑅𝑗 = 𝜙 for all 𝑖 = 1, 2, . . . , 𝑛.
d) (𝑅𝑖) = TRUE for 𝑖 = 1, 2, . . . , 𝑛. e) (𝑅𝑖 ∪𝑅𝑗) = FALSE for regions of 𝑅𝑖 and 𝑅𝑗.
(a) Every pixel is part of the same region in
the image. While in (b) all the pixels should be
connected to each other in the region; (c) all
the region disjoint each other. The (d) are the
properties that are true or false with some
criteria. While in (e) all regions different with
predicate 𝑃 2.3.3 Clustering
In clustering method, there is a difference
between the pixel and the cluster center in the
blood image that depends on the density,
location, or other factors such as the K-means
clustering algorithm that is used to divide a
digital image according to the distance. The
process of implementing the K-means
clustering technique is expressed as follows:
(1) Randomly select the initial K clustering
centers value, (2) Calculate the distance from
each cluster center to each sample to return
the sample to the nearest center, (3) For each
cluster, the mean of the samples is connecting
with the new clustering centers [28]. The
advantage of this method is the algorithm is
simple and fast, and it is effective when used
with large datasets. In addition, it is close to
linear with the time, and thus it is suitable for
large-scale data sets. Mathematically it can be
defined as: Where each of the 𝑥𝑝 will be assigned to one
(𝑡).While the centroid of the calculate the new
mean in the cluster. It can be defined as
following:
The disadvantage of the clustering method is
it is difficult to estimate the K clustering
number; where there is has no explicit
selection criteria [28]. Secondly, this method
is currently very expensive because of the
iteration. Finally, the clustering algorithm is a
partitioning method that depends on the
distance [29].
2.3.4 Watershed Segmentation Watershed segmentation technique that starts from the initial pixel (marker) and then deluges all the neighboring pixels of that marker, which known as basins. These basins are partition the markers into different regions
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using the watershed technique [30].
Watershed technique used in the medical
image because of the objects (e.g. WBCs) in
the images are overlapping; therefore,
Authors need this kind of algorithms to
separate the overlapping objects and get more
accurate classification results. Although the
watershed segmentation technique is suitable
for parallel processing, it can produce over-
segmentation. Nevertheless, many studies
have been used the marker-controlled
watershed for segmenting the acute leukemia
cells [31]. In marker-controlled watershed
segmentation technique, the boundaries can
be defined between external that manually
defined according to ROI and internal markers
that automatically obtained. This method will
help to solve the over-segmentation problem
[41]. Mathematically it can be written as
Where the LS (𝑥) is lower slope of image 𝐼 in 𝑥 pixel and the (𝑥) are neighbor pixels. While the
(𝑥, 𝑦) is the watershed distance. If 𝑖 = 𝑗, then
the LS will be zero.
2.3.5 Morphology
Morphology is a technique used to extract
components (ROI) from the medical image for
better description of the regions details [32].
It is deals with the shape of the objects in an
image using specific operations. The basic idea
of morphology technique is move a small
shape that known as the structuring element
over the medical image so that can get the
desired result [33].
The morphological include four operators,
which are the dilation ⊕, erosion ⊖, opening,
and closing. Can define erosion ⊖ and dilation
⊕ of a binary image as the following:
Where the E is binary image and S is the
structuring element.
The opening of an image is erosion followed by
dilation of a binary image as the following:
The closing of an image is dilation followed by
erosion of a binary image as the following:
In acute leukemia detection, many
researchers have been used the morphological
operation for segmenting the blast cells.
Where it used to enhances the blast cells by
filling gaps and removing the pepper noise
from the cells [34]. 2.4 Feature Extraction
Feature extraction (FE) is one of the most
important and critical stages of the image
processing techniques. This stage used to
extract and identify the features derived from
the objects that were segmented from parts of
the image or from the whole image. In other
words, transforming the data that is obtained
from the image into the set of features for
pattern recognition is called feature
extraction. One of the important issues related
to pattern recognition is choosing the relevant
set of features extraction in order to extract
the relevant information to perform the task
and get accurate information. The features
extraction have been used in many
applications such as leukemia detection,
character recognition, reading bank deposit
slips, applications for credit cards, tax forms,
data entry, check sorting and others [35].
Many features can be extracted from the
objects in the image, such as the shape
features (e.g. area, perimeter, solidity and
others), Texture Features (e.g. homogeneity,
energy, angular second, entropy contrast, and
others), Statistical Features (e.g. mean,
skewness and, variance), Geometrical
features (e.g. perimeter, area, compactnes
and symmetry), color features and so forth
[36].
In acute leukemia detection, the features
extraction stage plays an important role in
determining the leukemia type because blast
cells (ROI) have a lot of information that
included characteristics of nucleus and
cytoplasm [53]. There are different features
have been extracted in the current studies as
shown in Figure 3. The results of feature
extraction stage will be useful for the
classification stage (next stage).
……… (6)
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Fig 3: Features extraction techniques.
Feature extraction and feature selection are
crucial steps for image processing and
machine learning techniques. How to extract
and select the ideal features is still a
challenging problem in image processing.
Hence, in this paper, we survey image feature
representation techniques that are used to
extract and select the features from the blast
cell image and then determine whether the
blast cells are cancerous or otherwise.
2.4.1 Color feature
Color feature is one of the most important
relevant information that fetching from the
objects in the image. It is defined based on a
particular model or color space. There are a
number of color spaces such as LUV, HSV, RGB
and others that have been used to make
extracting the features easier. Therefore,
Color features are useful for extraction the
information from the blood cells image for
better classification. [37,38].
2.4.2 Texture feature
Texture feature is a useful characterization for
an image, where pixel properties are used to
measure the color in the image while the
group of pixels is used to measure a texture.
Two techniques are used to extract the texture
features based on the domain, which include
the spectral texture feature extraction and
spatial texture feature extraction. In the
spectral texture feature extraction approach,
an input image is transformed into the
frequency domain, and the texture feature
from the transformed image is then
calculated. While in the spatial approach, the
extracting features have been accomplished
by computing the statistics of a pixel in the
original image domain[39,40]. In the blood
smear images, the texture is an important
feature that used to identify the blast cells by
analyzing that features to get the ROI, which
can help to obtain better classification.
2.4.3 Shape features
Shape feature extraction technique is one of
the key methods in feature extraction field. It
can be classified into two groups, which
include the region-based and contour-based
methods. In the contour-based method, the
shape features are calculating from the
boundary of the shape only, while in the
region-based method, the features are
extracting from the whole region in the image.
Shape features play an important role in acute
leukemia cell detection [41,42]. 2.5 Feature Selection
Features selection (FS) is one of most
important technique used to selecting the
more relevant features from the objects in the
image processing. In this case, the algorithms
will select a subset of relevant features, which
will help to increase the accuracy and thus this
process will reduce the cost of computational
[43,44].Therefore, feature selections
algorithms will compare all features that are
extracted from the blood smear images and
then selecting the most relevant features.
Different techniques are used for leukemia
cells detection such as PCA (Principal
Component Analysis), GA (Genetic Algorithm),
and PPCA (Probabilistic Principal Component
Analysis) techniques [45]. 2.6 Classification
The stage of classification is one of the most
important stages in image processing and
machine learning techniques, and it is an in-
demand field in this area. Classification is used
to assign and classify a set of unclassified
data. There are two types of classifiers that are
known as supervised and unsupervised
classifications. In the supervised classification,
the set of possible results or classes are known
in advanced. While in the unsupervised
classification, the set of classes are unknown
in advance. Many methods can be used to
form a classification of data that are known as
the classifiers. These classifiers can be used to
classify objects types, such as support vector
machine (SVM), Artificial Neural Network
(ANN), Random forest (RF), KNN (𝐾-Nearest
Neighbor), Naive Bayes (NB), Multilayer
Perceptron (MLP),Hybrid and others, as
summarized in Figure 4 [49]. Once the
features are selected and extracted from the
segmented image, the object's type is
recognized and determined through this stage.
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2.6.1 Support vector machine (SVM)
In machine learning and image processing
techniques, the support vector machine (SVM)
also known as the support vector network
[46,47]. It is a supervised learning model that
analyzes the data to use for both classification
and regression tasks. The main objective of
the SVM algorithm is to find a hyper-plane in N number of features that classify the data
points. The SVM classifier separates the
classes based on the labeled training data. The
main objective of the separate classes is to
find the maximum margin, i.e., find the
maximum distance between the classes. The
maximizing distance provides a capability that
can classify future data points with more
confidence. The SVM classifier is seeking to
find the compromise between the complexities
models according to the training data (limited
sample information). This type of classifier is
suitable for small samples of circumstances.
The SVM classifier algorithm provides four
types of the kernel that include: (1) Sigmoid, (2) Radial Basis Function, (3) Polynomial and
(4) Linear [50]. The main reason for selecting
SVM for detection of leukemia cell is the
efficiently classify between the normal and
abnormal cells [74].
Fig 3: Classification methods for acute
leukemia.
2.6.2 Artificial neural networks Artificial neural network (ANN) is a type of artificial intelligence inspired by imitating the functions from the human brains. In other words, ANN is an interconnected set of virtual neurons that created as the computer systems that work similar to a biological neuron (electronic probes designed to emulate neurons). Generally, it consists of a set of
communication nodes that are known as
neurons, which are connected to form a
network of neurons. Each contact between
these neurons has a set of values called
weights that can contribute to determining the
values that are produced by each node of the
network based on the values of this node [48].
The ANN is a framework that has a different
machine learning algorithm that works
together to process complex data. These types
of algorithms depend on the learning to
perform different tasks by using examples that
being programmed for a similar task, i.e.,
working based on the learning. For example,
in the recognition pattern, the systems learn
by analyzing example images to identify other
images such as to identify the image that
contains flowers by analyzing some of the
flowers images that are manually labeled as
"flower" or "no flower”. The systems
automatically generate some of the
characteristics from the learning material to
identify those objects [49,50].
2.6.3 K-Nearest Neighbor
K-Nearest Neighbor (KNN) is a classifier
technique that widely used, where it uses the
nonparametric to classify data. In this
algorithm, the data are classified by voting
from the nearest neighbors. Based on this
voting, the objects in the image will be
assigned to relevant classes. For acute
leukemia cell classification, the KNN algorithm
is used to get better classification result to
determine if the cells are normal or abnormal
[75].
2.6.4 Random Forest
Random forest (RF) is a classifier that uses an
ensemble learning to classify an object from
the vector. It contains different tree
combinations, which perform voting for the
class, and then selection will be the class who
has a maximum vote [76]. The RF preferred
by the researcher because it corrects the over-
fitting problem. For acute leukemia detection,
the RF classifier has been found to be suitable
for detection of normal and abnormal cells
[77].
2.6.5 Naive Bayes
Naive Bayes (NB) is a classifier that is used to
classify data based on the features values in
the image that are independent of the other
values [51]; also, the Naive Bayes can be
called as simple Bayes. For acute leukemia
detection, the NB has utilized in current
studies, where it used to classify the WBCs and
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determine if the cells are normal or abnormal
[52]. 3. Leukemia literature survey
The literature discusses the methods that are
used to segment and detect acute leukemia
using machine learning and image processing
techniques. In this case, the methods detect
acute leukemia using the microscope blood
sample, whereby the process of detection of
acute leukemia cells can be divided into four
stages that include: Pre-processing blood image
Blast cells Segmentation
Feature Extraction of the blast cell
Detect and Classify the blast Cell
The steps of pre-processing, segmentation,
feature extraction, and classification
techniques are presented in this paper. All of
these steps are important in detecting
leukemia by using machine learning and
image processing techniques. The success of
each step in the detection of acute leukemia
depends on the previous step.
The work by [23] has proposed several phases
to diagnosis and detect acute lymphoblastic
leukemia from the rest of blood images. This
technique has included blood image
acquisition using public dataset (ALL-IDB), the
segmentation of the WBCs is accomplished by
a marker-based segmentation, and the
features are extracted from the segmented
WBCs using gray level co-occurrence matrix
(GLCM) with using Probabilistic Principal
Component Analysis (PPCA) to reduce and
obtain the relevant features. This study
proposed the RF classifier to determine if the
cells are normal or abnormal. While the work
by [53] introduced a new approach for
detection of acute leukemia, which includes:
firstly, preprocessing stage by converting RGB
to a gray-scale image and then performs a
histogram equalization to improve and
enhance the image quality to highlights the
contrast of nucleus; secondly, using
morphological contour segmentation by edges
detection of WBCs, and using HSV conversion
with erosion to segmenting the blast cell;
thirdly, extracting the features of the blast cell
such as geometry, color and texture features;
Finally, classify the acute leukemia cells using
Fuzzy C-means clustering technique.
The work by [54] has proposed the Bare-
bones Particle Swarm Optimization (BBPSO)
technique to diagnosis acute leukemia. This
technique has consists of the following steps:
1) Segmenting the WBCs from the rest of
blood image background using the watershed
technique, 2) Nucleus-cytoplasm separation
phase using a stimulating discriminant
measure (SDM)-based clustering, (3)
Extracting the features such as the statistics,
texture and shape features, 4) Classification
phase using three techniques: 1-Nearest
Neighbour (NN), Radial Basis Function (RBF)
and Support Vector Machine (SVM).
The work by [55] has presented a new
technique used for detection of acute leukemia
using image processing. The proposed
technique is performed by using several
phases. Firstly, the pre-processing by
removing the noise from the images using
median filtering technique followed by unsharp
masking technique and then converting the
blood images from RGB to HSV color space
mode. Secondly, segmenting the RIO using
the thresholding technique (i.e. Otsu‟s
method). After that, the features (shape and
texture features) are extracted from the RIO
that has been segmented to classify the cells
whether is cancerous or healthy by the
Support Vector Machine (SVM).
The work by [18] has presented the
segmentation of WBCs using Fuzzy C-Means
clustering for diagnosis acute leukemia. In this
method, the authors are collected the images
from the net, and then the noises are removed
using median filtering technique. In addition,
enhanced the contrast image using the
histogram equalization techniques. In this
work, the blast cell is segmented using Fuzzy
C-means, and then the features (statistical,
texture, and color) are extracted from the ROI
to classify the cells using the SVM.
The work by [57] has proposed the hybrid
hierarchical techniques to detect acute
leukemia. In this technique, the segmentation
of the blast cell by applying the global
thresholding with a Morphological opening on
the image. In the next step, the features (e.g.,
shape and texture) are extracted from the
segmented blast cell, and then these features
are reduced using Principal Component
Analysis (PCA). Finally, the probabilistic neural
network, SVM, K-NN, adaptive neuron-fuzzy
inference system for the classification of the
WBCs on the features that have been
extracted. Table 1 shows the summary and
comparisons between the four steps
algorithms that are used to detection acute
leukemia.
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Table 1. Shows the Summarizes of current methods
Author,
Year
Methods
Performance
STRENGTHS
And WEAKNESS
Accuracy
J. Su ,
et al
2017
[7]
Preprocessi
ng
-
STRENGTHS:
this method has
improved the
segmentation
results in both
of the whole and
cropped images
WEAKNESS:
K values are
difficult to
estimate and not
suitable for
non-convex
clusters.
This technique achieved a
segmentation accuracy 96%
(average) and it is compared
with a manual segmentation that
per implemented by an expert
and other existing methods
Segmentatio
n
This technique used k-
means cluster, and
builds cell images to
represent-ing model by
HMRF (Hidden-Markov
Random Field)
Feature
Extraction -
Classificat
ion
-
S. Negm,
et al
2017
[58]
Preprocessi
ng
Histogram green
color of RGB
component that has
most contrast
information
STRENGTHS:
This technique
got good
accuracy by
detect ALL cells
and separate
overlapping
cells.
WEAKNESS:
K values are
difficult to
estimate and it
not suitable for
non-convex
clusters.
The technique achieved the
following accuracies:
The accuracy technique is
99.517%.
The sensitivity accuracy is
99.348%.
The specificity accuracy is
99.529%.
Segmentatio
n
Using K-means
clustering technique
Feature
Extraction
Geometry features,
statistics features,
textures features
and size ratio from
regions selection
(Nucleus, cytoplasm,
and whole cell).
classificat
ion
Artificial Neural
Network (ANN)
J. Rawat.
, et al,
2017
[8]
Preprocessi
ng
Convert RGB image into
gray scale image. Then
enhance the blood image
quality using histogram
equalization
STRENGTHS:
This method used
GA-Gaussian with
radial basis
kernel (RBF) for
classify acute
leukemia
WEAKNESS:
- need high contrast between
the objects and
background
- It is very sensitive to
gray-scale
unevenness and
noise in the
image
This method got accuracy for
the classification of leukemia
cells 99.5% .
The accuracy for the FAB
subtypes of ALL is 97.1%.
The accuracy for the FAB
subtypes of AML is 98.5%
Segmentatio
n
Divide the image to sub-
images and used
Thresholding using
Otsu’s technique
Feature
Extraction
This method used the
statistical, texture ,
and geometrical features
classificat
ion
genetic algorithm GA-
Gaussian radial basis
kernel
W.
Srisukkha
m, et al
2017
[54]
Preprocessi
ng
converted the image to
Sub-images
STRENGTHS:
this method has
good performance
by using marker-
This technique achieved
the performances of 94.94%
and 96.25% as accuracy Segmentatio
n
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Using marker-controlled
watershed to getting the
blast cells
controlled
watershed with
Particle Swarm
Optimization
WEAKNESS:
- over
segmentation
- sensitivity to
noise
Feature
Extraction
Particle Swarm
Optimization (PSO),
Dragonfly Algorithm
(DA)and Cuckoo Search
(CS)
classificat
ion
SVM
S.
Mishra,
et al
2017
[23]
Preprocessi
ng
using histogram
equalization and weiner
filtering to enhance
blood images
STRENGTHS:
marker-based
watershed
segmentation
algorithm is
used to
segmentation and
separation
overlapping
cells and it is
achieved good
accuracy
WEAKNESS:
- over
segmentation
- sensitivity to
noise
The presented method achieved
results as following :
The segmentation accuracy ,
96.29%
The classification accuracy is
99.004%
Segmentatio
n
By using marker-based
watershed segmentation
scheme to separate the
overlapping cells
Feature
Extraction
gray level co-occurrence
matrix (GLCM)
and the probabilistic
principal component
analysis (PPCA) is used
to extract the relevant
Feature reduction
classificat
ion
Random Forest (RF)
technique
T.
Karthikey
an, et al
2017
[18]
Preprocessi
ng
-Image acquisition
-Noise removal using
Adaptive median filter
-Contrast enhancement
using histogram
equalization
STRENGTHS:
this technique
got high
accuracy by used
Fuzzy c-mean
with SVM
WEAKNESS:
- determination of fuzzy
membership is
complex.
- Sensitive to the noises in
images.
-
Computationally
expensive due to
long time.
The Fuzzy c-means method gives
90% as accuracy
Segmentatio
n
Fuzzy c- means
clustering
Feature
Extraction
Gabor Texture Extraction
method is used to
extract color
Features
classificat
ion
Support Vector Machine
(SVM)
A. Gajul,
et al
2016
[56]
Preprocessi
ng
Convert RGB image to
CIELAB L*a*b* color
space.
STRENGTHS:
This good
technique to
detect AML and
separate the
overlapping cell
by using K-mean
clustering with
SVM
WEAKNESSES:
- K values are
difficult to
estimate - time
consuming
This technique achieved result
as following :
-Sensitivity 71.43 %
-Specificity 100 %
-Precision 100 %
-overall classification
performance of 83.33 %
Segmentatio
n
k-means clustering
Feature
Extraction
-Hausdorff Dimension (HD
- Local Binary Pattern
(LBP)
-Shape Features:
-GLCM Features:
-Color Feature: Cell
Energy
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classificat
ion
support vector machines
(SVM)
- it is not
suitable for
non-convex
sclusters.
M.
Sukanya,
et al
2016
[60]
Preprocessi
ng
Convert RGB image to
CIEL*a*b color space
STRENGTHS This
technique used
K-Means
Clustering with
DRLBP and DRLTP
to gives a
perfect decision
about the
disease.
WEAKNESS:
- K values are
difficult to
estimate - time
consuming
- it is not
suitable for
non-convex
sclusters.
This technique presented system
performs well
Segmentatio
n
K-Means Clustering.
Feature
Extraction
This technique used the
edge-texture feature,
Discriminative Robust
Local Binary Pattern
(DRLBP) and
Discriminative Robust
Local ternary Pattern
(DRLTP)
classificat
ion
support vector machines
(SVM)
K.
ElDahshan
, et al
2015
[59]
Preprocessi
ng
Convert RGB to HSV color
space
STRENGTHS:
This technique
used the FPGA
with Xilinx for
segmentation
WEAKNESS:
- over
segmentation
- sensitivity to
noise
-
Segmentatio
n
-FPGA pre-processing
-Xilinx models for FPGA
segmentation
-FPGA post-processing
Feature
Extraction
-
classificat
ion
-
G. Patil,
et al
2015
[55]
Preprocessi
ng
convert the image from
RGB color space to HSV
domain and perform the
median filtering
STRENGTHS:
Its fast ,simple
and got good
result
WEAKNESS:
- need high
contrast between
the objects and
background
-it is very
sensitive to
gray-scale
unevenness and
noise in the
image
Segmentatio
n
This method have used
the Thresholding is
done by Otsu’s method
with bounding box
technique to getting
sub-image
Feature
Extraction
Shape features and
Densitom-etric features:
Energy, GLCM,
Correlation
classificat
ion
Support Vector
Machine(SVM)
.T.
Madhloom,
et al
2012
[61]
Preprocessi
ng
This technique used
CIELAB Color Space,
Transform to HSV Color
Space and extract (H)
Channel
STRENGTHS
this method
give good
accuracy for
segmentation by
using Marker-
Controlled
Watershed and
This technique presented
segmentation results of 96%
accuracy
Segmentatio
n
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Marker- Controlled
Watershed
and SRG algorithm
SRG algorithm
WEAKNESS:
- over segmentation
- sensitivity to noise
Feature
Extraction -
classificat
ion
-
L. Putzu,
et al
2014
[21]
Preprocessi
ng
Convert the images from
RGB to CMYK colour
model, then perform the
Histogram equalization
to improve the image
quality
STRENGTHS: this
technique
achieved good
accuracy by
using the Zack
algorithm with
SVM .
WEAKNESS:
- over segmentation
- sensitivity to noise
- need high contrast image
Using this technique achieved
the results as following :
as accuracy from 33images is
92%
identification of ALL, with an
accuracy 93%
and sensitivity 98%
Segmentatio
n
threshold with Zack
algorithm,
and then separation of
overlapping cells by
watershed segmentation
technique with Solidity
to remove the nois
Feature
Extraction
-shape features
-color features
-texture features
classificat
ion
support vector machine
(SVM)
M.
MoradiAmi
n, et al
2015
[12]
Preprocessi
ng
Convert RGB to HSV
Color space, and then
Histogram equalization
on V band
STRENGTHS:
This technique
achieved a good
accuracy by used
fuzzy c-mean
with SVM .Also
its success to
find the sub-
type of ALL.
WEAKNESS:
- determination of fuzzy
membership is
complex.
- Sensitive to the noises in
images.
-
Computationally
expensive due to
long time.
The presented method achieved
results as following :
The sensitivity , 98%
The specificity
95 %
The classification accuracy is
97%
Segmentatio
n
By using fuzzy c-means
clustering and then
using watershed
algorithm to separate
overlapping objects
Feature
Extraction
- geometric feature for size and shape of a
nucleus
- statistical features for gray scale image
histogram
classificat
ion
support vector machine
(SVM)
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4. Analysis and Discussion
A survey of image processing techniques that
are used for segmentation and classification of
acute leukemia cells have presented in this
paper. Moreover, we have discussed the
techniques and methods that have used in
previous works. To check how the previous
works were implemented and compared these
methods to the manual methods for leukemia
cells detection. These studies focused on the
results of each following steps: preprocessing,
segmenting, features extracting, and then the
detection of acute leukemia. The
computerized methods are more reliable,
accurate, and efficient compared to the
manual techniques that were time-consuming
and less efficient.
In the segmentation stage, many techniques
have been proposed by previous studies to
obtain the ideal results for segmenting and
detecting of acute leukemia cells. The higher
results of this phase were achieved using a
marker-based watershed technique to
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segment and separate the overlapping cells
with 96.29% of accuracy. In addition, the
researchers also use some of the
segmentation techniques such as Fuzzy C-
Means and K-Means, thresholding, watershed,
HSV color based and others to segment the
blast cell accurately. Most of these methods
have difficulties in segmenting complex blood
images due to some major shortcomings, such
as lack of contrast between objects and
background and sensitivity to noise in images.
Therefore, it is indispensable to develop an
effective technique for segmenting blast cells
from peripheral blood smears. In addition, the
scientists are still seeking for a more accurate
technique to segment the blast cells.
In the phase of feature extraction, various
features used include the color, geometrical
and texture features. The numbers of the
features are numerous; therefore, it is better
to make the feature selection by using T-SNE,
LDA or PCA. Fisher's Discrimination Ratio
(FDR) can also be used followed by Exhaustive
Search (ES) to obtain the best extraction
features.
The work by W. Srisukkham, et al [54] have
divided the image to Sub-images for
preprocessing of blood smear images and then
marker-controlled watershed was used to
detect and segment the blast cells and after
extracting features using Particle Swarm
Optimization (PSO), Dragonfly Algorithm (DA)
and Cuckoo Search (CS); the SVM was used
for detection and classification of blasted cells.
This method able to achieve 94.94% as
accuracy result. While the work by S. Negm,
et al [58] have improved the accuracy to
99.517% using Histogram green color of RGB
component for preprocessing of blood smear
images and then Using K-means clustering
technique was used to segment the blast cells
and after extracting features using Geometry
features, statistics features, and textures
features; the Artificial Neural Network (ANN)
was used for detection and classification of
blasted cells.
In the classification phase, different
techniques were carried out by researchers to
obtain the accurate results, where the ANN
and SVM techniques were the most accurate.
Moreover, the detection phase was dependent
on the previous stages (segmentation and
feature extraction). The studies that used
Support Vector Machine (SVM) have
presented higher accuracy compared to other
classifiers. Moreover, other studies also found
the Fuzzy logic-based classifier was greater
than SVM in terms of accuracy. Therefore, it is
indispensable to develop an effective classifier
to detect blast cells from peripheral blood
smears images.
After we have analyzed the previous
techniques, we have come to the main point:
the scientists still need to seek for a more
accurate technique to detect acute leukemia
cells. Acute leukemia detection is a very
sensitive issue and it is related to humans
health. Therefore, the Accuracy of diagnosis
process should be flawless in order to replace
human operators with computerized
diagnoses. But the replacement is a very
challenging task because the complexities of
blood cells are high. 5. Limitations
Although the database of sources that we have
used are reliable; but the identification was
difficult. Moreover, the timeliness of this study
was one of limitations because of the
increasing progress in this area. In addition,
the response of the researchers to this field is
the objective of this review. 6. Conclusion
Many studies have been presented in this
paper. These studies were conducted for the
segmentation and detection of acute leukemia
by using image processing and machine
learning techniques. Image processing
techniques can be easily and rapidly diagnose
leukemia, and this increases the chance of
patients to be saved and allowing them to be
given the appropriate treatment. Image
processing and machine learning techniques
may replace blood analysis experts in the
detection and classification of leukemia in
terms of accuracy and time.
In future work, researchers could focus on
other methods of segmenting acute leukemia
cells such as using a combination of two or
three segmentation techniques to make full
use of the different algorithms advantages to
achieve better segmentation for the blast
cells. In addition, we can suggest using a
machine learning algorithm (e.g., support
vector machine, neural network, and others)
to improve the segmentation of the blast cell.
Moreover, there will be more medical image
segmentation techniques widely used; as well
as more than one chance to find other suitable
features that can detect the type of Acute
Leukemia. The features selection techniques
and dimensional reduction can be performed
to achieve greater efficiency.
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6. Acknowledgment
We would like to thank the Higher Education
Minatory at Malaysia and University
Technology Malaysia for their educational and
financial support. This work is conducted
under Cyber Physical Systems Research Group
(CPSRG) at Razak Faculty of Technology and
Informatics that funded by University
Technology Malaysia (UTM). References
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Table 1: Examples State of Tablet's Pill
681
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Ima
ge
.No
Mean
Pill 1
Mean
Pill 2
Mean
Pill 3
Mean
Pill 4
Mean
pill5
Mean
Pill 6
Mean
Pill 7
Mean
Pill 8
Mean
Pill 9
Mean
Pill
10
Max
mean
pill
*75
%
no
.
pi
State
1
98.76
2
97.11
4
99.70
1
92.23
8
98.87
5
92.83
5
96.01
9
100.0
0
99.32
99.62
75.00
1
0
Safety
2 99.90
0
100.0
0
98.50
7
96.11
9
99.40
2
94.13
3
98.76
2
99.77
5
100.0
0
97.75
0
75.00
10
Safety
3
98.00
9
99.40
2
99.10
4
88.25
8
99.10
4
89.75
12
99.50
7
97.31
34
99.88
75
99.70
1
74.91
10
Safety
4
99.71
2
99.60
1
98.31
2
99.30
3
98.80
5
98.70
64
100.0
0
99.40
29
98.60
69
75.00
9 Dama
ge
5
99.88
7
99.32
2
98.20
0
98.50
7
99.00
4
99.20
39
98.60
6
94.31
1
98.87
51
74.91
5
9 Dama
ge
6
100.0
0
98.98
7
99.77
5
99.77
5
100.0
0
98.40
79
98.60
9
100.0
0
99.80
0
75.00
9 Dama
ge
7 97.07
5
98.61
2
97.63
7
99.11
7
48.35
0
99.43
75
98.31
2
97.91
04
98.27
8
99.11
7
74.57
8
1 0
Dama
ge
8 88.44
4
95.72
5
92.46
3
42.29
4
90.43
8
92.93
53
94.93
8
86.95
16
90.88
8
96.06
2
72.04
7
1 0
Dama
ge
5. Experimental Results
In this section, the performance of the
implemented system is tested for safety and
damage tablet images. The first experiment is
tested on fitted tablet images. The second
experiment is tested on damage tablet images
include broken one of the pills. The third
experiment is tested on damage tablet images
include missing one of the pills from the tablet.
All the experiments are performed Matlab in
32-bit system with 2.40 GHz core i3 processor
and 3 GB of RAM, run with the MS Win.7
operating system.
The pill image inputted into inspection green
circle pill system and pass through all stages
as described in the following stages: Stage 1:
Input pill image mentioned above as
shown in Figure 4, to pass through
preprocessing steps.
Figure 4: Original green tablet images
Stage2:
The results of preprocessing step shown in
Figure 5.
682
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683
Figure 5: preprocessing green tablet images
Stage3: The results of Pill Radii for circle
detection step shown in Table 2.
Table 2: PILL RADII
No.
Pill
1 2 3 4 5 6 7 8 9 10
radii 18 18 18 16 18 18 18 17 17 19
Stage4: analysis circle detection of green pill,
table 3 shows the mean of pill detection.
Table 3: Mean of Pill Detection
Stage5:
Print number of detected circles & print
number of broken circles shown in figure 6
Figure 6: Eventual Image of the proposed
system 6. Conclusion
The experimental results indicated that CHT
can be effectively applied to detect the centers
and radii of pill circle and color transforms that
led to higher enhance the contrast and
emphasis of green color for image these
descriptors lead to correct circle detection. The
obtained results indicated that this method
leading to high speed, high precision, noise
resistance and false fault detection. This
system is time efficient as it requires just
0.975 seconds on an average for executing the
program and predicting the result.
The performance of an inspection green pill
system is 100% for each database that
contain a) 50 image of the tablet fill pill and
not broken. b) 50 image of the tablet empty
one of the pills. c) 50 image of the tablet
broken one of the pills. Noticeable the
performance of an inspection green bell
system Excellent and nearer from Human
vision (The person checking the manual).
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Saif S Al-jaboriy was born in Babil city, Iraq in 1980. He received
the B.Sc. Degree from the College of Computer
Science, University of Baghdad, Baghdad, Iraq,
and the Master of IT from Universiti Tenaga
Nasional Malaysia 2011,. He is currently
pursuing the Ph.D. degree with the Razak
Faculty of Technology and Informatics,
Universiti Teknologi Malaysia, Malaysia. His
research interests include segmentation and
detection diseases using image processing and machine learning technique. He has published
2 papers in international journals.
Nilam Nur Binti Amir Sjarif a Senior Lecturer at Razak Faculty of
Technology and Informatics (RFTI), Universiti
Teknologi Malaysia. Currently, she is the
Quality Coordinator at RFTI,UTM. She received
her PhD from the Universiti Teknologi Malaysia,
Skudai in 2015, B.Sc. Degree from the College
of Computer Science, Universiti Teknologi Malaysia,2010, She is active in the field of
Machine Learning, Deep Learning, Pattern
Recognition, Image Processing and Big Data
Analytics, she has published 33 papers in international
journals.
Nilam Nur Binti Amir Sjarif a Senior Lecturer at Razak Faculty of
Technology and Informatics (RFTI), Universiti
Teknologi Malaysia. Currently. She received
her PhD from the Universiti Teknologi
Malaysia, , B.Sc., She is active in the field of Cloud Computing, Network Security
Information, Security QoS Requirements,
Engineering Embedded, System Design, Real-
Time Systems, Scheduling Algorithms and Big
Data Analytics, she has published 13 papers
in international journas
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