Segmentation and Detection of Acute Leukemia Using 1,2 ... · Therefore, several of image...

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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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




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

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Table 1: Examples State of Tablet's Pill

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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.

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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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Recent and Innovation Trends in

Computing and Communication, vol. 5,

no. 2, pp. 94–98, 2017. [76]. R. J. A. Cabrera, C. A. P. Legaspi,

E. J. G. Papa, R. D. Samonte, and D. D.

Acula, “HeMatic: An automated leukemia






985–987.






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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