1. health and fitness
2. disease
3. cancer

ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
Detection of Leukemia with Blood
Microscopic Images
A. Arputha Regina
PG Scholar, Department of Computer Science and Engineering, Regional Centre of Anna University
Tirunelveli, Tamil Nadu, India
ABSTRACT: The work done here is based upon processing of blood microscopic images to identify the Acute
Myelogenous Leukemia. According to different classification of leukemia, this work focuses on the Acute
Myelogenous Leukemia (AML) a type of acute leukemia that affects mostly the adult and children. This makes a need
to detect and classify the AML automatically. Premature work is done by color conversion of the image from RGB to
CIELAB color space to make the segmentation method perform well. In segmentation method, the widely used
technique is K-means algorithm. K-means is an unsupervised learning algorithm based on clustering of similar
behavior of the objects. Feature extraction technique includes the Hausdorff dimension (HD) and Local Binary Pattern.
Support Vector Machine is used for classification. The evaluation of various result analysis parameters is analyzed to
achieve accuracy.
KEYWORDS: Segmentation, K-means algorithm, Local Binary Pattern, Hausdorff Dimension, Support Vector
Machine.
I.
INTRODUCTION
Medical imaging is a technique for creating the visual representation of the interior body for diagnosis of diseases. It
reveals the internal structure of the body to detect the diseases. A record is created to store the captured image so that it
will be easy to identify the diseases [2]. Blood is fundamental component to human life. A human body has
approximately 70 liters of water of which five liters are blood. Blood is essential for maintaining homeostasis. That
refers to hydration, temperature regulation and ion concentration [1]. A White Blood Cell (WBC) is larger than a Red
Blood Cell (RBC). White Blood Cell (WBC) composition in the blood gives valuable information in the diagnosis of
different diseases. The mesoderm gives raise to the blood cells. Through hematopoietic process the blood cells are
differentiated as Red Blood Cells (RBC) or White blood Cells (WBC). The immature growth in White Blood Cells
(WBC) causes Leukemia. The immature growth is considered about 30% of blast cells. These cells provide the greatest
defense beside infections, and their individual concentration can help specialists to distinguish between the presences of
pathologies [1]. Cancer is a data-intensive region of study, with growing speed of development in data collection
technology. Analysis and classification of blast cell is a valuable requirement for the diagnosis of leukemia and has a
positive impact on treatment. Leukemia cause is unknown where the bone marrow produces large numbers of abnormal
cells (White Blood Cells) that stop developing before maturity. Acute leukemia patients are referred to specialist units
for evaluation. Treatment is based on chemotherapy through the veins, lasting four to six months, which kills also the
usual body cell. Acute leukemia is the cancer of the White blood Cells (WBC). Two types of acute leukemia are Acute
Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). The analysis of leukemia cells were based on
its morphology. Acute leukemia is a disease in which the malignant transformation causes accumulation of early bone
marrow. The formation of cellular blood components is hematopoietic. The methodical presentation of leukemia is
usually bone marrow failure caused by accumulation of blast cells. Chemotherapy, a better supportive care and Central
Nervous System (CNS), concerning one-third of these patients can be expecting disease-free survival for more than five
years. Furthermore, advance in action have increased the cure rate for AML .This increase is the result of accurate
diagnosis.Acute leukemia is diagnosed with more than 20% of blast cells in the bone marrow.Acute Myelogenous
Leukemia (AML) is an illness caused by the abnormal growth and development of immature White Blood Cells
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27
ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
(WBC). It starts in the bone marrow, blast cells which develop to shape granule. This work is aimed on the analysis of
AML images. The AML blasts do not mature and become too numerous in the bone marrow. As the cells build up, they
affect the body's ability to fight infection and stop bleeding. So it is necessary to treat this disease within a short time
after analysis. The gratitude of the blast cell in the bone marrow of patients suffering from AML is a very important
step in identifying the stage of the illness and choosing an appropriate treatment. Clinicians need to identify these
abnormal cells under a microscope to conclude that a patient suffers from leukemia. The patient's bone marrow is
examined to count the blast cells and confirm the dieses. For classification of AML, it is necessary to identify the types
of blast present in the blood smear. AML is a general form of acute leukemia that is increasingly common but may
occur in all age groups. Fig.1.1 represents the architecture diagram.
Fig.1.1Architecture Diagram
II.
METHODOLOGY
a)
Pre-processing
The images generated by digital microscopes are usually in RGB color space.Usually the blood cells and image
background vary greatly with respect to color and intensity. This is caused by multiple reasons such as camera settings,
varying enlightenment, and aging blemish. Cell segmentation isdifferent with respect to these variations, so a process is
used to convert RGB input image into the CIELAB color space [1].The a and b components is used to make accurate
color balance corrections. The L*a*b*color space with dimension L represents the lightness of the color, element
a*that represents its position between red/magenta and green, and element b*that represents its position between yellow
and blue.
b)
Segmentation
Segmentation is performed for extracting the nuclei of the White Blood Cells using color-based clustering. Cluster
analysis is the official learning of methods and algorithms for grouping objects according to characteristics or
similarity. Cluster analysis does not use class labels. K-means is most popular unsupervised knowledge algorithms.
Here cluster correspond to nucleus, background, and other cells. Each and every pixel is assigned to one of these
assigned classes using the properties of the cluster center [8]. The k-means algorithm has three user-specified
parameters: total number of cluster k, initialization of cluster, and distance metric. A k-means cluster algorithm is used
to assign every pixel to one of the clusters. Every pixel is assign to one of these classes using the properties of the
cluster center that are fixed arbitrarily. On the corresponding ∗a and ∗b values in the L∗a∗b color space each pixel is
classified into k cluster. Every pixel in the L∗a∗b color space is classified into any of the k clusters and is calculated
using Euclidean distance between the pixel center and each color pointer. These three clusters are assigned as nucleus,
background and other cell. The cluster that contains the blue nucleus are measured, which is necessary for the feature
extraction.
Transformation of the input data into the set of features is called feature extraction. Feature selection influences the
classifier performance so that a correct choice of features must be identified. I have considered the following feature for
the efficient work.
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28
ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
c )Feature Extraction
i)
Hausdorff dimension
HD is used to detect the edge of the nucleus and it is considered an essential feature. Box counting technique is
implemented to detect the edge of nucleus. The edges are detected to gain the perimeter roughness of the nucleus.
When the grid becomes finer and finer the perimeter roughness of the nucleus increases.
ii)
Local Binary Pattern
The Local Binary Pattern (LBP) is used for texture classification [6]. LBP quality features have the subsequent
characteristics: LBP is robust against illumination variations; Fast to compute; Do not require many parameters; A local
feature;LBP is invariant to monotonic gray scale transformations and scaling It has performed very well in much
computer vision application. The LBP technique has proved to outperform in, Linear Discriminated Analysis (LDA)
and the Principal Component Analysis (PCA). To covenant with textures at different scales, the LBP operator is
stretched to use regions of different dimensions.
iii)
Shape Features
Area: The area is defined by counting the total number of none zero pixels within the image region.
Perimeter: Computing distance between consecutive boundary pixels.
Compactness: Measure of a nucleus is called compactness.
…(1)
Solidity: The ratio of actual area and convex hull area is known as solidity.
…(2)
Eccentricity: How much the shape of a nucleus deviates from being spherical?
…(3)
Elongation: Abnormal bulging of the nucleus is defined by elongation.
…(4)
Form factor: The measure of surface irregularities.
…(5)
iv)
GLCM Features
Homogeneity: Measurement of degree of variance.
…(6)
Energy: Measurement used to measure uniformity.
…(7)
Correlation: To measure correlation between pixel values and its neighborhood.
…(8)
Entropy: Measurement of randomness.
d)
Classification
Support Vector Machine (SVM) is used for constructing a decision surface in the feature space that bisects the two
categories. Here the classification is based upon two classes i.e., cancerous and noncancerous. A two class classifier is
used to categorize the classes. The feature values are plotted on the decision surface and maximum hyper-plane are
drawn separating the two classes. The support vector is drawn by making a wide margin. To evade misclassification the
boundary are wider from the hyper plane. The technique is cheap and does not need kernel trick. It is said to be
performed good.
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29
ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
III.
RESULT AND CONCLUSION
The work is developed for automatically detecting and classifying the AML in Blood infinitesimal images. Ten blood
infinitesimal images are classified and the evaluation is also measured according to the performance evaluation
technique. The preprocessing method seems to be effective for robust segmentation. The segmentation technique suits
for color images so that easy to identify the blue nucleus. The feature extracted is useful for classification of the data.
The performance assessment shows the classifier performance.
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