Abstract— Blood cancers such as leukemia, Hodgkin

lymphoma, non-Hodgkin lymphoma, myeloma and

myelodysplastic syndromes are cancers that originate in the bone

marrow or lymphatic tissues. They are considered to be related

cancers because they involve the uncontrolled growth of cells

with similar functions and origins. The diseases result from an

acquired genetic injury to the DNA of a single cell, which

becomes abnormal (malignant) and multiplies continuously. The

accumulation of malignant cells interferes with the body's

production of healthy blood cells. Around 1,20,603 people in

India die every and Every 4 minutes one person is diagnosed with

a blood cancer due to leukemia.

I. INTRODUCTION

Blood cancers such as leukemia, Hodgkin lymphoma, non-

Hodgkin lymphoma, myeloma and myelodysplastic

syndromes are cancers that originate in the bone marrow or

lymphatic tissues. They are considered to be related cancers

because they involve the uncontrolled growth of cells with

similar functions and origins. The diseases result from an

acquired genetic injury to the DNA of a single cell, which

becomes abnormal (malignant) and multiplies continuously.

The accumulation of malignant cells interferes with the body's

production of healthy blood cells. Around 1,20,603 people in

India die every and Every 4 minutes one person is diagnosed

with a blood cancer due to leukemia. Leukemia can be

diagnosed:

From a blood test to measure the number of blood cells and

look for any abnormal cells.Slides of blood sample are

prepared and observed under the microscope to detect

abnormal shaped cells such as kidney shape.Or the blood

sample is detected for presence of immature cells in bone

marrow.The Conventional Method Of Diagnosis Suffer From

Following Disadvantages:-

All imaging techniques have to be repeated in cases

of suspected fungal infection.

Higher cost.

Human error of Visual Perception.

Using recent leukemia detection technique may lead to

incorrect conclusions. This may result due to human errors in

observing the peripheral sections under microscope. This may

lead to improper diagnosis of disease. Further numbers of

steps are involved in detection.

The most efficient method to overcome all

this drawbacks of present cancer detection techniq

ues is use of artificial intelligence. Basically here we use

“image processing” followed by “fuzzy logic”. These AI

techniques removes the human errors in detection, number of

steps involved are also less. And more importantly it is cost

effective

1. LEUKEMIA

White blood cells are made in your bone marrow, which is the

soft spongy centre of your bones. White blood cells are

involved in your body's immune system, a defense system that

protects you from infections. Your bone marrow makes the

most basic type of cells (called stem cells), and they can

develop further into three types of cells:

white blood cells - protect your body from infection

red blood cells - carry oxygen around your body

platelets - important for normal blood clotting

White blood cells are involved in your

body's immune system, a defense system that protects you

from infections. There are two main types of white blood

cells: myeloid cells and lymphocytes.

Leukemia affects three times as many adults

as children. It's the most common form of childhood cancer,

but this is because childhood cancers are rare. In the UK,

almost 7,000 people are diagnosed with leukemia every year ,

Figure 1: normal and leukemic cell in bone marrow

In leukemia, some of the white blood cells don't grow

properly. Leukemia is a type of cancer that affects the white

blood cells. In leukemia, white blood cells become abnormal,

and divide and grow in an uncontrolled way. They stay in the

bone marrow and keep reproducing in an uncontrolled way.

These abnormal white blood cells fill up the bone marrow and

Automatic Leukemia Detector Using Digital Image Processing

Ankush Bagwale, Nikhil Rai

Leukemia Detection Using Image Processing

prevent it from making healthy white blood cells. This means

the body is less able to fight off infections.

The abnormal white blood cells also prevent

bone marrow from making enough red blood cells and

platelets. A lack of red blood cells leads to less oxygen being

delivered to the organs and tissues of your body. This is called

anemia, and it can make you feel tired and breathless. A lack

of platelets can lead to problems with the blood-clotting

system, and results in bleeding and bruising much more easily

than usual.

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2. RECENT DETECTION TECHNIQUE

Figure2: (a) & (b) leukemia affected cells

Leukemia can be diagnosed from a blood test to measure

the number of blood cells and look for any abnormal

cells.

Slides of blood sample are prepared and observed under

the microscope to detect abnormal shaped cells such as

kidney shape.

Or the blood sample is detected for presence of immature

cells in bone marrow.

People with suspected leukemia are referred

to a specialist doctor, usually a hematologist (an expert in the

treatment of blood disorders). Radiotherapy, chemotherapy

and some other techniques are used for treatment of cancer.

Sometimes doctors carry out further tests, such as:

CT (computerized tomography) scans - these can show

enlarged lymph nodes

MRI (magnetic resonance imaging) scans - to locate the

position of tumors

ultrasound - can be done to see if your spleen has been

affected by anemia

tissue typing - if your doctor has advised you to have a

bone marrow transplant

Diagnosis, investigation, treatment and

follow-up for people with leukemia usually take place at

specialist centers in hospitals.

3. NEED OF IMAGE PROCESSING

Using recent leukemia detection technique

may lead to incorrect conclusions. This may result due to

human errors in observing the peripheral sections under

microscope. This may lead to improper diagnosis of disease.

Further numbers of steps are involved in detection.

The most efficient method to overcome all

this drawbacks of present cancer detection techniques is use of

artificial intelligence. Basically here we use “image

processing” followed by “fuzzy logic”. These AI techniques

removes the human errors in detection, number of steps

involved are also less. And more importantly it is cost

effective.

4. INTRODUCTION TO IMAGE PROCESSING

A technique in which the data from an

image are digitized and various mathematical operations are

applied to the data, generally with a digital computer, in order

to create an enhanced image that is more useful or pleasing to

a human observer, or to perform some of the interpretation and

recognition tasks usually performed by humans.

An image is usually interpreted as a 2-D

array of brightness values. To digitally process an image, it is

first necessary to reduce the image into a number that can be

manipulated by the computer. Each number representing the

brightness value of the image at the particular location is

called a picture element or pixel. It is this pixel that undergoes

various types of operation such as point operation, local

operation, global operation etc. these operations, singly or in

combination , are the means by which the image is enhanced,

restored or compressed.

The figure (3) shows the effect of image processing.

Figure 3: effect of image processing

5. FUNDAMENTAL STEPS IN IMAGE

PROCESSING

5.1 IMAGE ACQUISITION

Image processing for microscopy

application begins with fundamental techniques intended to

most accurately reproduce the information contained in the

microscopic sample. This might include adjusting the

Leukemia Detection Using Image Processing

brightness and contrast of the image, averaging images to

reduce image noise and correcting for illumination non-

uniformities. Such processing involves only basic arithmetic

operations between images (i.e. addition, subtraction,

multiplication and division). The vast majority of processing

done on microscope image is of this nature.

5.2 IMAGE ENHANCEMENT

Enhancement refers to accentuation or

sharpening of image features, such as contrast, boundaries,

edges, etc. The process of image enhancement, however, in no

way increases the information content of the image data. It

increases the dynamic range of chosen features with the final

aim of improving the image quality. This is visible in the

following figure (4).

Figure 4: image enhancement

5.3 IMAGE RESTORATION

Sometimes we receive noisy images which

are degraded by some degrading mechanism. One common

source of degradation is the optical lens system in a digital

camera which acquires visual information.

When the degradation is due to atmosphere

(foggy weather), defocused camera or a relative accelerated

motion between the object and the focal plane of lens, the

conventional techniques of enhancement are not suitable to get

the original objects from the image. This is done by various

mathematical models and is called as reconstruction or

restoration of image.

Figure 5: image restoration

5.4 IMAGE COMPRESSION

Image compression, the art and science of

reducing the amount of data required to represent an image, is

one of the most useful and commercially successful

technologies in field of digital image processing. Compression

refers to the process of reducing the amount of data required to

represent that given quantity of information. Figure (6) shows

image compression

Figure 6 : image compression

5.5 IMAGE SEGMENTATION

In computer vision, segmentation refers to

the process of partitioning a digital image into multiple

segments (sets of pixels) (Also known as super pixels). The

goal of segmentation is to simplify and/or change the

representation of an image into something that is more

meaningful and easier to analyze. Image segmentation is

typically used to locate objects and boundaries (lines, curves,

etc.) in images. More precisely, image segmentation is the

process of assigning a label to every pixel in an image such

that pixels with the same label share certain visual

characteristics.

The result of image segmentation (figure

(7)) is a set of segments that collectively cover the entire

image, or a set of contours extracted from the image (see edge

detection). Each of the pixels in a region is similar with

respect to some characteristic or computed property, such as

color, intensity, or texture. Adjacent regions are significantly

different with respect to the same characteristics.

Leukemia Detection Using Image Processing

Figure 7 : image segmentation

5.6 COLOR IMAGE PROCESSING

The color of images can be altered in a

variety of ways. Colors can be faded in and out, and tones can

be changed using curves or other tools. The color balance can

be improved. In addition, more complicated procedures such

as the mixing of color channels are possible using more

advanced graphics. Image editors have provisions to

simultaneously change the contrast of images and brighten or

darken the image. Underexposed images can often be

improved by using this feature. Recent advances have allowed

more intelligent exposure correction whereby only pixels

below a particular luminosity threshold are brightened,

thereby brightening underexposed shadows without affecting

the rest of the image..

6. APPLICATION IN LEUKEMIA DETECTION

The acute leukemia is a disease of the

leukocytes and their precursors. It is characterized by the

appearance of immature, abnormal cells in the bone marrow

and peripheral blood. The aspirated marrow is found to be

infiltrated by abnormal cells. The recognition of the blast cells

in the bone marrow of the patients suffering from

myelogenous leukemia is a very important step in the

recognition of the development stage of the illness and proper

treatment of the patients.

Image segmentation is a division of the

image into different regions, each having certain properties. In

a segmented image, the picture elements are no longer the

pixels, but connected set of pixels, all belonging to the same

region. The segmentation techniques are used to separate the

individual cells from the set of cells creating the image. The

recognition and separation of individual cells from the image

of the blood cells is a very difficult task, since different

regions are of little grey level variations and the borders of

individual cells are hardly visible. Fig.8 presents the

exemplary image of the blast cells of the bone marrow

containing different types of cells.

Figure 8: the exemplary image of the bone marrow smear of

the acute leukemia patient containing different blast cells.

The individual cells are close to each other

and the borders among them are not well defined. The task of

segmentation of the image is focused on the automatic

recognition and separation of each cell for further processing.

6.1 EDGE DETECTION

Edge detection highlights image contrast.

Detecting contrast, which is difference in intensity, can

emphasize the boundaries of features within an image, since

this is where image contrast occurs. This is how human vision

can perceive the perimeter of an object, since the object is of

different intensity to its surrounding. Essentially, the boundary

of an object is a step change in the intensity levels. The edge is

at the position of step change. An ideal edge has the properties

of the model shown in figure 9(a) an ideal edge according to

this model is a set of connected pixel, each of which is located

at step transition in gray level.

In practice, optics, sampling, and other

image acquisition imperfections yield edges that are blurred,

with degree of blurring being determined by factors such as

the quality of image acquisition system, the sampling rate and

illumination condition under which the image is acquired. As

a result, edges are more closely modeled as having a “ramp

like” profile, such as the one shown in figure 9(b)

Figure 9 : (a) ideal edge (b) Ramp edge

The slope of the ramp is inversely

proportional to the degree of blurring ill the edge. In this

model, we no longer have a thin (one pixel thick) path.

Instead, an edge point now is any point contained in the ramp,

and edge would then be a set of such points that are connected.

Leukemia Detection Using Image Processing

The "thickness" of the edge is determined by the length of the

ramp, as it transitions from an initial to a final gray level. This

length is determined by the slope, which, in turn, is

determined by the degree of blurring. This makes sense:

Blurred edges tend to be thick and sharp edges tend to be thin.

.

Figure 10 :(a) vertical edge (b) Gray level first and second

derivative Profile

Detection of edge involves the use of first

and second derivatives. Figure 10(a) shows a horizontal gray-

level profile of the edge between the two regions. This figure

also shows the first and second derivatives of the .gray-level

profile. The first derivative is positive at the points of

transition into and out, of the ramp as we move from left to

right along the profile; it is constant for points in the ramp;

and-is zero in areas of constant gray level.

The second derivative is positive at the

transition associated with the dark side of the edge, negative at

the transition-associated with the light side of the edge, and

zero along the ramp and in areas of constant gray level. The

signs of the derivatives in Fig. 10(b) would be reversed for an

edge that transitions from light to dark.

We conclude from these observations that

the magnitude of the, first derivative can be used to detect the

presence of an edge at a point in an image (i.e., to determine if

a point is on a ramp). Similarly, the sign of the second

derivative can be used to determine whether an edge pixel lies

on the dark or light side of an edge.

We note two additional properties of the

second derivative around an edge: (1) It produces two values

for every edge in an image (an undesirable feature); and (2) an

imaginary straight line joining the extreme positive and

negative values of the second derivative would cross zero near

the midpoint of the edge. This zero-crossing property of the

second derivative is quite useful for locating the centers of

thick edges. 6.2 GRADIENT OPERATORS

First-order derivatives of a digital image are

based on various approximations of the 2-D gradient. The

gradient of an image f (x. y) at location (x, y) is defined as the

vector

It is well known from vector analysis that

the gradient vector points in the direction of maximum rate of

change of f at coordinates (x, y).An important quantity in edge

detection is the magnitude of this vector, denoted f,where'

This quantity gives the maximum rate of

increase of f(x, y) per unit distance in the direction of f. It

is a common (although not strictly correct) practice to refer to

f also as the gradient. We will adhere to convention and

also use this term interchangeably, differentiating between the

vector and its magnitude only in cases in which confusion is

likely. The direction of the gradient vector also is an important

quantity. Let α (x, y) represent the direction angle of the

vector f at (x, y). Then, from vector analysis,

where the angle is measured with respect to the x-axis. The

direction of an edge at (x, y) is perpendicular to the direction

of the gradient vector at that point.

7.3 LAPLACIAN OPERATOR

Second derivative can be computed using

laplacian operator.

Digital approximation

Leukemia Detection Using Image Processing

1) 7.4 THRESHOLDING AND LINKING

Once we have computed a measure of edge

strength (typically the gradient magnitude), the next stage is to

apply a threshold, to decide whether edges are present or not

at an image point. The lower the threshold, the more edges

will be detected, and the result will be increasingly susceptible

to noise, and also to picking out irrelevant features from the

image. Conversely a high threshold may miss subtle edges, or

result in fragmented edges.

If the edge thresholding is applied to just the

gradient magnitude image, the resulting edges will in general

be thick and some type of edge thinning post-processing is

necessary. A commonly used approach to handle the problem

of appropriate thresholds for thresholding is by using

thresholding with hysteresis. This method uses multiple

thresholds to find edges. We begin by using the upper

threshold to find the start of an edge. Once we have a start

point, we then trace the path of the edge through the image

pixel by pixel, marking an edge whenever we are above the

lower threshold. We stop marking our edge only when the

value falls below our lower threshold. This approach makes

the assumption that edges are likely to be in continuous

curves, and allows us to follow a faint section of an edge we

have previously seen, without meaning that every noisy pixel

in the image is marked down as an edge.

8. PROCEDURE

Based on above discussed concepts of

segmentation, the segmentation of cancerous blood cell (i.e.

edge detection) can be implemented with the help of following

steps in matlab programming.

Step 1: Read Image

Read in 'cell.tif', which is an image of a cancer cell.

I = imread('cell.tif'),figure, imshow(I), title('original image');

Original image

Figure:11

Thus figure (11) shows the original image to be processed.

Step 2: Detect Entire Cell

Two cells are present in this image, but only

one cell can be seen in its entirety. We will detect this cell.

Another word for object detection is segmentation. The object

to be segmented differs greatly in contrast from the

background image. Changes in contrast can be detected by

operators that calculate the gradient of an image. One way to

calculate the gradient of an image is the Sobel operator, which

creates a binary mask using a user-specified threshold value.

A threshold value is determined using the gray thresh

function. To create the binary gradient mask, we use the edge

function.

BWs = edge(I, 'sobel', (graythresh(I) * .1));

Figure, imshow (BWs), title ('binary gradient mask');

Binary gradient mask

Figure: 12

The binary gradient mask shows lines of

high contrast in the image as can be seen in the figure (12).

These lines do not quite delineate the outline of the object of

interest. Compared to the original image, you can see gaps in

the lines surrounding the object in the gradient mask. These

linear gaps will disappear if the Sobel image is dilated using

linear structuring elements, which we can create with the strel

function.

se90 = strel ('line', 3, 90);

se0 = strel ('line', 3, 0);

Step 3: Dilate the Image

The binary gradient mask is dilated using

the vertical structuring element followed by the horizontal

Leukemia Detection Using Image Processing

structuring element. The imdilate function dilates the

image.the image now appears as shown in figure (13).

BWsdil = imdilate (BWs, [se90 se0]);

figure, imshow(BWsdil), title('dilated gradient mask');

Dilated gradient mask

Figure:13

Step 4: Fill Interior Gaps

The dilated gradient mask shows the outline

of the cell quite nicely, but there are still holes in the interior

of the cell. To fill these holes we use the imfill function and

get the image as shown in figure (14).

BWdfill = imfill(Bwsdil, „holes‟);

figure, imshow(Bwdfill);

title(„binary image with filled holes‟);

Binary image with filled holes

Figure:14

Step 5: Remove Connected Objects on Border

The cell of interest has been successfully

segmented, but it is not the only object that has been found.

Any objects that are connected to the border of the image can

be removed using the imclearborder function. The

connectivity in the imclearborder function was set to 4 to

remove diagonal connections.

BWnobord = imclearborder (BWdfill, 4);

figure, imshow(BWnobord), title('cleared border image');

Cleared border image

Figure:15

Step 6: Smoothen The Object

Finally in order to make the segmented object look natural,

smoothening of the image is done by eroding the image twice

with a diamond structuring element. The diamond structuring

element can be created using the strel function

seD = strel('diamond',1);

BWfinal = imerode (BWnobord,seD);

BWfinal= imerode(BWfinal,seD);figure, imshow(BWfinal),

title ('segmented image');

segmented image

Figure:16

Step 7:Final Segmented Image

An alternate method for displaying the

segmented object would be to place an outline around the

segmented cell. The outline is created by bwperim function

BWoutline=bwperim(BWfinal);

segout=I;segout(BWoutline)=225;

figure, imshow(segout),title(„outlined original image‟);

Thus we obtain the final image as shown below in figure(17).

Outlined original image

Figure:17

Leukemia Detection Using Image Processing

9. CONCLUSION Thus using image processing segmentation,

edge of the cancerous blood cell can be obtained. Matlab

programming is used efficiently involving the concepts of

image segmentation to give the required edge to be checked

for any abnormal shape which assures cancer in a patient. This

image in which edge of cancerous cell is obtained can be

processed further for exact detection of cancer by any AI

technique.

Acknowledgment We would like to thank Dr. BHARAT PATIL of All India

Institute Of Medical Sciences (AIIMS ) New Delhi,Had it not

been the inspiration and motivation provided by Dr. Bharat

patil this research would definitely not progressed up the way

it is now. We plan to finish the work with Dr.Bharat Patil so

that we could provode this technology to the citizens of india.

at the least cost we can provide.we would also like to thank

Dr.Roopesh Ranjan for the financial support they provided by

funding this project.

References [1]„Digital image processing‟ by Woods and Gonzalez.

[2]http://www.cancerbackup.org.uk/

[3]http://www.leukaemiacare.org.uk/

[4]Feature extraction and image processing‟ by Mark S. Nixon

and Albrto S. Aguado

[5]Wikipedia- The Free Encyclopedia