8.Nagabhushana - [email protected]

Nagabhushana.R.M.et.al, International Journal of Technology and Engineering Science [IJTES]TM

Volume 2[6], pp: 1947-1951, June 2014

ISSN: 2320 – 8007` 1947

Geometrical Analysis of Leukocyte Nucleus to Detect

Lymphoblast from Microscopic Blood Images 1Nagabhushana R M,

2Rajeshwari P

1M.Tech in Bio-medical Signal Processing and Instrumentation, SJCE, Mysore, Karnataka, India

2Assistant professor department of instrumentation, SJCE, Mysore, Karnataka, India

.

Abstract—In order to improve patient diagnosis

various image processing software are developed to

extract useful information from medical images.

Hematologist makes the microscopic study of human

blood which led to a need of methods, including

microscope color imaging, segmentation, classification

that can allow the identification of patients suffering

from leukemia. Leukemia is related with blast white

blood cell (WBC). The nonspecific nature of the signs

and symptoms of acute lymphoblastic leukemia( ALL)

often leads to wrong diagnosis so hematologist also

find difficulty for classification hence manual

classification of blood cells is time-consuming and

susceptible to error. Therefore fast, accurate and

automatic identification of different blood cells is

required. This paper has proposed automatic Otsu’s

threshold used to segment the nucleus part of a white

blood cell along with image enhancement and

arithmetic operations for WBC nucleus segmentation

then the features of segmented nucleus is extracted to

classify. SVM classifier has been utilized to classify the

cells.

Index Terms—Leukemia, Acute Lymphoblastic

Leukemia (ALL), White Blood Cells (WBC), Support

Vector Machine (SVM), Segmentation, Otsu’s method,

Morphological Features.

I. INTRODUCTION

Medical imaging refers to the techniques and

processes used to create images of the human body for

clinical purposes such as medical procedures for

diagnosing or examining disease and also for medical

science which include the study of normal anatomy and

physiology [8]. The importance of medical analysis and

visualization can be recognized from the recent works

in the medical imaging technologies like segmentation

of kidney from ultrasound images, segmentation of

brain images [6], locating of tumors and other

pathologies [5]. Leukemia is a disease that affects blood

forming cells in the body. Its cancerous condition is

characterized by an abundance of abnormal white blood

cells in the body. Based on the research by M. D.

Anderson Cancer Center, leukemia begins in the bone

marrow and spreads to the other parts of the body [4].

In a healthy body, bone marrow makes white blood

cells to help the body fight an infection. When a person

has leukemia, the bone marrow starts to build a lot of

abnormal white blood cells called leukemia cells. They

grow faster than normal cells and they do not stop

growing when they should. In 2009, it is estimated that approximately 31,490

individuals will be diagnosed with leukemia and 44,510

individuals will die of the disease in the United States.

In Malaysia, a total of 21,773 cancer cases were

diagnosed among Malaysians in Peninsular Malaysia in

2006 and registered in the National Cancer Registry.

Out of this value, 3.6% of male and 1.9% of female

were diagnosed with leukemia. The segmentation and classification of blood cells

from microscopic images allow Evaluation and

diagnosis of many diseases. Leukemia is a blood cancer

that can be detected through the analysis of WBCs or

leukocytes. Leukemia can be of Two types: acute and

chronic. According to the French-American-British

(FAB) Classification model, acute leukemia is

classified into two subtypes: acute lymphoblastic

leukemia (ALL) and acute myeloid leukemia (AML). In

this paper we consider only the ALL, The ALL

primarily affects children and adults over 50 years and

due to its rapid expansion into the bloodstream and vital

organs can be fatal if left untreated [12]. Therefore, it

becomes crucial early diagnosis of the disease for

patients' recover, especially in the case of children. The

use of image processing techniques can help to provide

information on the cells morphology. These techniques

require only one image and are therefore less

expensive, but at the same time more scrupulous in

providing more accurate standards. The main goal of

this work is the processing and analysis of microscopic

images, in order to provide a fully automatic procedure

to support the medical activity and classify.

In pathology manual detection of leukemia is done

which is time consuming as well as costly due to high

cost pathology instruments. Hence automatic technique

is adopted for fast and accurate results. In this technique

image of blood sample is processed and nucleus part is

segmented and finally cells are classified whether they

are blast or normal. The segmentation step is very

crucial because the accuracy of the subsequent feature

extraction and classification depends on the correct


Volume 2[6], pp: 1947-1951, June 2014

ISSN: 2320 – 8007` 1948

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. Threshold techniques cannot always produce

meaningful results since no spatial information is used

during the selection of the segmentation threshold [13].

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) [9]. The K-Mean clustering method is

utilized by Sinha and Ramakrishna. However, the

method of cropping the entire cell in order to get the

real area of the whole cell is not clearly shown [10].

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 [7]. In this work we

used thresholding along with morphological operators

to segment and SVM classifier is used for better

performance of classification.

1 METHODS

A segmentation method: figure (1) shows the block

diagram of a proposed system it consists of various

functional modules. The main two steps in the proposed

system are image segmentation and classification. The

input image of blood slide is fed to the system. Blood

has four main elements to ensure it fulfills its functions

they are red blood cells, white blood cells, platelets,

plasma [1]. White blood cells fall into five categories:

Neutrophil, Eosinophil, Basophil, Monocyte and

Lymphocyte [2].White blood cell composition and

concentration in the blood gives valuable information

and plays a crucial role in the diagnosis of different

diseases. Hence the very first module in the proposed

system is image acquisition and followed by leukocyte

nucleus segmentation, then features of lymphocyte are

extracted from the segmented binary image the features

extracted are used for classification as normal or blast.

Figure(2) shows the block diagram of the

nucleus segmentation of leukocytes or WBCs. The

WBCs segmentation was made by converting the color

microscopic image to grayscale image, to enhance the

contrast of a grayscale image we used histogram

equalization which is given by the equation (1)

Figure 1. Block diagram of Proposed Work

1

Where sk is the resultant histogram equalized

output and nj is the pixel value of jth pixel and n is the

total number of pixel. and the next step is contrast

stretching is a simplest piecewise linear function

contrast stretching is used to enhance the dynamic

range of a image because the images are having poor

illumination so to remove the noise of illumination

contrast stretching is used to the original microscopic

sub images .after contrast stretching to enhance the

contrast of a leukocyte we used some arithmetic

operations like addition and subtraction of both

histogram equalized image as well as contrast stretched

image To reduce noise, preserve edges and increase the

darkness of the nuclei implement 3-by-3 minimum

filter on the resultant image. The segmentation is

realized using a threshold where white regions are

clearly shows the leukocyte nucleus and black as the

background.

There are many threshold techniques available

in literature [11] here we used Otsu’s thresholding [14]

A measure of region homogeneity is variance (i.e.,

regions with high homogeneity will have low variance).

Otsu’s method selects the threshold by minimizing the

within-class variance of the two groups of pixels

separated by the thresholding operator. It does not

depend on modeling the probability density functions,

however, it assumes a bimodal distribution of gray-

level values (i.e., if the image approximately fits this

constraint, it will do a good job).Otsu threshold is

calculated by minimizes the weighted within-class

variance. This is given by equation (2)

Microscopic blood sub image

Leukocyte nucleus segmentation

Feature extraction

Classification

)/(0

nnsk

j

jk


Volume 2[6], pp: 1947-1951, June 2014

ISSN: 2320 – 8007` 1949

Figure 2.Block Diagram Leukocyte Segmentation

2

3

4

5

6

7

8

where q1(t) and q2(t) in equation (3) and (4) are the

class probabilities of fore ground and background

respectively.µ1(t) , µ2(t) in equation (5) and (6) are the

mean of fore ground and background �21(t), �22(t) in

equation (7) and (8 )are the variance of the fore ground

and back ground of a grayscale image. Once the

threshold value is calculated then depending upon the

value grayscale image is converted in to binary image.

The binary image have some unwanted regions we have

to remove these regions we used some morphological

operations .Edge detection is used to determine the

edges of a segmented image canny edge detection is

used because of its accuracy in detection of edges, once

the edges are determined then next step is to do image

opening which is given by the equation (9)

9

Where A is the image to be opened by the structuring

element B, opening is nothing but erosion followed by

dilution. Finally by using filling of connected

components by holes done to fill the connected

component to get a complete segmented image.

B Feature Extraction

Feature extraction means to transfer the input data into

different set of features. In image processing this is a

technique of redefining a large set of redundant data

into a set of features (or feature vector) of reduced

dimension .In this paper four features of lymphocyte

cells have been observed Shape Features According to

hematologist the shape of the nucleus is an essential

feature for discrimination of blasts. Region and

boundary based shape features are extracted for shape

analysis of the nucleus. All the features are extracted

from the binary equivalent image of the nucleus with

none zero pixels representing the nucleus region. The

quantitative evaluation of each nucleus is done using

the extracted features under two classes i.e. region

based and boundary based. The features are as follows:

AREA: The area was determined by counting the total

number of none zero pixels within the image region.

PERIMETER: It was measured by calculating distance

between successive boundary pixels.

COMPACTNESS: Compactness or roundedness is the

measure of a nucleus as given by

Compactness =Perimeter2/area

SOLIDITY: The ratio of actual area and convex hull

area is known as solidity and is also an essential feature

for blast cell classification given by

Solidity =area/ convex area

ECCENTRICITY: This parameter is used to measure

how much a shape of a nucleus deviates from being

circular. It’s an important feature since lymphocytes are

more circular than the blast. Eccentricity given by

Eccentricity= /a

)()()()()( 2

22

2

11

2 ttqttqtwt

i

iptq1

1 )()(

I

ti

iptq1

2 )()(

t

i tq

iipt

1 1

1)(

)()(

I

ti tq

iipt

1 2

2)(

)()(

)(

)()]([)(

11

2

1

2

1tq

iptit

t

i

)(

)()]([)(

21

2

2

2

2tq

iptit

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ti


Volume 2[6], pp: 1947-1951, June 2014

ISSN: 2320 – 8007` 1950

Where a is the major axis and b is the minor axis of the

equivalent ellipse representing the nucleus region.

ELONGATION: Abnormal bulging of the nucleus is

also a symbol which signifies leukemia. Hence the

nucleus bulging is measured in terms of a ratio called

elongation. This is defined as the ratio between

maximum distance Rmax and minimum distance Rmin

from the center of gravity to the nucleus boundary and

is given by

Elongation= Rmax/ Rmin

Where Rmaxand Rmin are maximum and minima radii

respectively.

FORM FACTOR: It is a dimensionless parameter which

changes with surface irregularities and it is given by

form factor=4 × pi × area/ perimeter2

C. Classification

Based on the features extracted in above step,

classifier classifies the lymphocyte cells as blast or

normal cells. Classification is the task of assigning to

the unknown test vector, a label from one of the known

classes. Since the patterns are very close in the feature

space, support vector machine (SVM) support vector

machine is employed for classification. SVM is a

powerful tool for data classification based on hyper

plane classifier. This classification is achieved by a

separating surface (linear or non linear) in the input

space of the data set. They are basically two class

classifiers that optimize the margin between the classes

.The classifier training algorithm is a procedure to find

the support vectors. SVM has been utilized to classify

blast cells from normal white blood cells.

2 .DATASET

Automated systems based on artificial vision methods

can speed up this operation and they can increase the

accuracy of the response also in telemedicine

applications. Unfortunately, there are not available

public image datasets to test and compare such

algorithms. In this project public dataset of blood

samples is used, specifically designed for the evaluation

and the comparison of algorithms for segmentation and

classification [10]. For each image in the dataset, the

classification of cell is given, and it is provided a

specific set of figures of merits to be processed in order

to fairly compare different algorithms when working

with the proposed dataset. The annotation of ALL-

IDB1 is as follows[3]. The ALLIDB1 image files are

named with the notation ImXXX Y.jpg where XXX is a

3-digit integer counter and Y is a Boolean digit equal to

0 is no blast cells are present, and equal to 1 if at least

one blast cell is present in the image. Please note that

all images labeled with Y=0 are from for healthy

individuals, and all images labeled with Y=1 are from

ALL patients we used sub images of main image where

the sub image consisting of one leukocyte per slide.

3.EXPIREMENT RESULTS

The proposed technique has been applied on

peripheral blood smear sub images obtained from the

public dataset as mentioned earlier. A microscopic

blood sub image is considered for evaluation [10]. As

mentioned in section 3, algorithm applied to input

image. The resulting images of segmentation are shown

in figure (3).

In figure three (a1)and (a2) are input colored image

,(b1) and (b2) are the histogram equalized images,

similarly(c1)and (c2) are thesholded output images

,(d1) and (d2) are the final segmented images.

a(1) b(1) c(1) d(1)

a(2) b(2) c(2) d(2)

Figure (3): Experiment results: (a) original image; (b) preprocessed

image; (c) Threshold image; (c) final segmented image.


Volume 2[6], pp: 1947-1951, June 2014

ISSN: 2320 – 8007` 1951

Table I

The performance of the proposed CAD method

Features Image A Image B

Area in pixels 9352 7259

Perimeter in pixels 462.17 318.2

Eccentricity 0.840 0.535

Circularity 0.550 0.900

Orientation -70.94 13.96

compactness 22.840 13.952

solidity 0.8596 0.9876

The above table shows the features extracted from both

Images a and b where A is the lymphoblast image and

image B is the normal lymphocyte

4. CONCLUSIONS AND FUTURE WORK

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,

orientation, eccentricity, circularity, perimeter etc.

features for better detection accuracy. Leukemia

detection with the proposed features were classified

with SVM classifier. The system is applied on 144 sub

images from public dataset giving accuracy of 88%

Furthermore the system should be robust to excessive

staining cells. Results obtained encourage future works

like stain independent blood smear image segmentation

and leukemia type classification.

ACKNOWLEDGMENT

We would like to express our thanks to Fabio Scotti for

providing dataset.

REFERENCES

[1] Waidah Ismail, Department of Information

Systems, Computing and Mathematics, Brunel

University, “Automatic Detection And

Classification Of Leukemia Cells”, June

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Biological and Therapeutic Aspects of Infant

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[2] Mostafa Mohamed, Behrouz Far, Department of

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8.Nagabhushana - [email protected]