1. International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME
148
DETECTION OF ACUTE LEUKEMIA USING WHITE BLOOD CELLS
SEGMENTATION BASED ON BLOOD SAMPLES
Ms. Minal D. Joshi1
, Prof. A.H.Karode2
1
Department of E&TC, SSBT College of Engineering, NMU University, Jalgaon
2
Department of E&TC, SSBT College of Engineering, NMU University, Jalgaon
ABSTRACT
In order to improve patient diagnosis various image processing software are
developed to extract useful information from medical images. An essential part of the
diagnosis and treatment of leukemia is the visual examination of the patient’s peripheral
blood smear under the microscope. Morphological changes in the white blood cells are
commonly used to determine the nature of the malignant cells, namely blasts. Morphological
analysis of blood slides are influenced by factors such as hematologists experience and
tiredness, resulting in non standardized reports. So there is always a need for a cost effective
and robust automated system for leukemia screening which can greatly improve the output
without being influenced by operator fatigue. This paper presents an application of image
segmentation, feature extraction, selection and cell classification to the recognition and
differentiation of normal cell from the blast cell. The system is applied for 108 images
available in public image dataset for the study of leukemia. The methodology demonstrates
that the application of pattern recognition is a powerful tool for the differentiation of normal
cell and blast cell leading to the improvement in the early effective treatment for leukemia.
Keywords: Acute Lymphoblastic Leukemia (ALL), WBC segmentation, Public image
dataset, Feature extraction, kNN classifier.
1. INTRODUCTION
Leukemia is a group of hematological neoplasia which usually affects blood, bone
marrow, and lymph nodes. It is characterized by proliferation of abnormal white blood cells
(leukocytes) in the bone marrow without responding to cell growth inhibitors [1]. Acute
leukemia is classified according to the French-American- British (FAB) classification into
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2. International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME
149
two types: Acute Lymphoblastic Leukemia (ALL) and Acute Myelogenous Leukemia
(AML). In acute leukemia disease, cells that are not fully differentiated get affected. ALL is
most common in children while AML mainly affects adults but can occur in children and
adolescents. In this paper the main focus is on ALL. The early and fast identification of the
leukemia type, greatly aids in providing the appropriate treatment for the particular type. Its
detection starts with a complete blood count (CBC) [2]. If the count is abnormal, the patient
is suggested to perform bone marrow biopsy. Therefore, to confirm the presence of leukemic
cells, a study of morphological bone marrow and peripheral blood slide analysis is done.
Manual examination of the slides are subjected to bias i.e. operator experience, tiredness etc.
resulting with inconsistent and subjective reports. This paper represents blood slide image
segmentation and classification for automatic detection of leukemia. For study purpose a
public supervised image datasets (ALL-IDB) [3] is provided by Fabio Scotti to test and fairly
compare algorithms for cell segmentation and classification of the ALL disease.
Fig.1 The proposed system
The proposed system for ALL detection is shown in Fig 1. It consists of various
functional modules [4]. The input image of blood slide is fed to the system. As the blood
contains various elements only WBCs are separated using segmentation. White blood cells
fall into five categories: Neutrophil, Eosinophil, Basophil, Monocyte and Lymphocyte.
Lymphocytes are responsible for ALL. Hence lymphocytes are detected and their features
such as area, perimeter, circularity etc. are calculated using feature extraction module. Using
these extracted features, classifier classifies the normal cell and blast cell. The most common
method adopted for leukemia classification is the FAB method [3].
The segmentation step is very crucial because the accuracy of the subsequent feature
extraction and classification depends on the correct segmentation of white blood cells. It is
also a difficult and challenging problem due to the complex nature of the cells and
uncertainty in the microscopic image. Many researchers have given different methods for
image segmentation. Cseke used automatic thresholding method (1979). Threshold
techniques cannot always produce meaningful results since no spatial information is used

3. International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME
150
during the selection of the segmentation threshold [5]. Edge detection method can also be
meaningful for segmentation but it is applicable only when there is good contrast between
foreground and background. (Piuri and Scotti) [6]. The K-Mean clustering method is utilized
by Sinha and Ramakrishnan. However, the method of cropping the entire cell in order to get
the real area of the whole cell is not clearly shown [7]. Theera-Umpon used a fuzzy C-Mean
clustering to segment single cell images of white blood cells in the bone marrow into two
regions, i.e., nucleus and non-nucleus. The computational time increases if the clusters
number is greater than 2 [8].
2. IMAGE SEGMENTATION
This section shows steps of the image segmentation algorithm used in this system [9].
1) Input the colour blood slide image to the system.
2) Convert the colour image into grayscale image.
3) Enhance contrast of the grayscale image by histogram equalization method (A).
4) To adjust image intensity level apply linear contrast stretching to gray scale image
(B).
5) Obtain the image I1=B+A to brighten all other image components except cell nucleus.
6) Obtain the image I2=I1-A to highlight the entire image objects along with cell
nucleus.
7) Obtain the image I3=I1+I2 to remove all other components of blood with minimum
effect of distortion over nucleus.
8) To reduce noise, preserve edges and increase the darkness of the nuclei implement 3-
by-3 minimums filter on the image I3.
9) Apply a global threshold Otsu’s method on image I3 to get image I4.
10) Using the threshold value in above step convert I3 to binary image.
11) To remove small pixel groups use morphological opening.
12) To form objects connect the neighboring pixels.
13) By applying the size test removal of all objects that are less than 50% of average RBC
area is done.
It is observed that this method of segmentation yields better results than that of previous
methods.
3. FEATURE EXTRACTION
Feature extraction means to transfer the input data into different set of features. In this
paper three features of lymphocyte cells have been observed viz, area, perimeter and
circularity because shape of the nucleus is important feature for differentiation of blasts.
1) Area: The area was determined by counting the total number of none zero pixels
within the image region.
2) Perimeter: It was measured by calculating distance between successive boundary
pixels.
3) Circularity: This is a dimensionless parameter which changes with surface
irregularities and is defined in equation (1):
Circularity = 4* Pi* Area/ Perimerer2
………………….. (1)

4. International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME
151
4. CLASSIFICATION
Based on the features extracted in above steps classifier classifies the lymphocyte
cells as blast or normal cells. The K-nearest neighbor (kNN) decision rule has been a
ubiquitous classification tool with good scalability. In this system kNN classifier of k=1 is
utilized.
5. EXPERIMENTAL RESULTS
The proposed technique has been applied on 108 peripheral blood smear images
obtained from the public dataset as mentioned earlier. A microscopic blood image of size
2592 × 1944 is considered for evaluation [3]. The results of segmentation steps have shown
below.
(a) (b) (c)
(d)
Fig.2: a) Original image b) Gray scale image c) Histogram equalized image A d) Linear
contrast stretched image B
In this paper Otsu’s method of segmentation is utilized along with image arithmetic.
Fig. 2 shows the image histogram equalization and linear contrast stretching method results.
After this step simple arithmetic operations are used along with Global thresholding method
to detect white blood cell nucleus as shown in fig. 3.

5. International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME
152
Image I1 Image I2 Image I3
Image I4
Fig. 3: Images after application of arithmetic operations and thresholding method
according to segmentation algorithm
After image arithmetic operation, minimum filter is applied to remove noise. Finally
WBCs are shown with white spots all around its nucleus. This execution requires time period
of millisecond. And in the last stage, using kNN classifier, normal lymphocytes and blast
lymphocytes are classified. Fig. 4 shows the resultant images.
(a) (b) (c)
Fig.4: a) Minimum filter effect b) WBCs with white spots over nuclei c) Final
lymphocyte blast segmented image
6. CONCLUSION
A WBC nucleus segmentation of stained blood smear images followed by relevant
feature extraction for leukemia detection is the main theme of the paper. The paper mostly
concentrates on measuring area, circularity, perimeter etc. features for better detection
accuracy. Leukemia detection with the proposed features were classified with kNN classifier.
The system is applied on 108 images from public dataset giving accuracy of 93%.
Furthermore the system should be robust to excessive staining and touching cells.

6. International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME
153
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