Automatic Region Growing Method forSegmentation of Tumor on Mammogram

Image

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Nayab ShaikMSRIT, Bangalore

abdul . nayab c^r gmail.com

Abstract - Breast cancer is a common female

malignancy , does not show any symptoms in its

early stage. Screening tests are therefore important

to reduce the death rates. By far, mammography

test proves to be the test for early detection of breast

cancer . However , this test has limitations such as

darkness and the existence of unwanted noise on the

breast image , which can obscure breast ` tumors.

Many image processing techniques have been

introduced in order to detect the edges or segment

these breast cancer morphologies including the seed

based region growing (SBRG) algorithm . However,

two parameters , namely the seed point and the

threshold value of the conventional SBRG algorithm

need to be determined manually. It is time

consuming process. This paper proposes automatic

region growing algorithm , which find these two

parameters automatically . It also detects and

distinguish breast tumor automatically from the

background.

Keywords-Mammography, Moving K-means

Clustering, GUI, Region growing, seed point and

Threshold value.

1. INTRODUCTION

Cancer is the second leading cause of death in

World. Cancer does not show any symptoms in its early

stage. Especially breast cancer does not show any

Megha.P.ArakeriMSRIT, Bangalore

rnegha_u c(r^yahoo.co.in

trouble in its early stage. Therefore screening tests are

important to reduce the death rates. They are many

screening tests (Imaging Techniques) are available for

different type of cancers. The main imaging techniques

are MRI, CT scan, X-ray (radiography) and

Mammography. In radiography and CT scan, X-ray

photons are transmitting through the body onto an X-

ray sensitive detector forms images. Using this image

doctors find abnormities. In MRI, magnetic and radio

waves are used. This technique used to find brain

abnormities. In same way mammography used for early

detection of breast cancer.

Mammography is a specific type of imaging

technique, which use low-dose x-ray waves to examine

breast. A mammography exam, called a mammogram,

is used to aid in the early detection and diagnosis of

breast diseases in women. Image segmentation

technique is required to extract tumor area on

mammogram image. Image Segmentation subdivides an

image into its constituent parts or objects. The level to

which this subdivision is carried out depends on the

problem being solved. That is, segmentation should

stop when the objects of interest in an application have

been isolated. The Exiting segmentation techniques are

Histogram-based segmentation, Edge detection

segmentation, Region growing and Region splitting

segmentation. In general, autonomous image

segmentation is one of the most difficult'tasks in image

processing. In this paper, the region growing

segmentation technique is used. Because it has

advantages like stability with respecti . e noise and

edges founded are thin and connected . In this method

seed point is input . The regions are iteratively grown by

comparing all unallocated neighboring pixels with

using threshold value . But the drawback of this method

is seed point and threshold values are calculated

manually. These are done on a trial and error basis and

must be repeated until satisfactory results are obtained.

This leads to time-consuming issue. Furthermore, the

results obtained from the processes are highly

subjective to the user . This paper presents a new

automatic region growing method to segment tumor.

This method combines Moving K-means Clustering

algorithm and Proposed Region growing algorithm.

2. LITERATURE SURVEY

In this following section, we present a brief

summary of earlier work carried out in the field

Edward A Sickles [3] discuss about

Mammographic Features of Early Breast Cancer. The

Strength of mammography is its ability to detect breast

cancers before they grow large enough to be palpable.

However, to identify cancers at the earliest possible

stage, when they are small and difficult to recognize,

the radiologist must learn to search not only for the

conventional mammographic features of carcinoma,

albeit on a smaller than usual scale, but also for the

more subtle, indirect signs of malignancy.

P K sinha and Q H hong[4] discuss about an

Improved Median Filter. Median filter is frequently

chosen for image smoothing. However, the level of

noise reduction offered by the median filter may not be

sufficiently high for some applications. Here a modified

median filtering technique • which offers improved

smoothing performance.

i N Otsu [5] discuss about A Threshold

Selection Method from Gray-Level Histograms. A

nonparametric and unsupervised method of automatic

threshold selection for picture segmentation is

presented . An optimal threshold is selected by the

discriminant criterion , namely, so as to maximize the

separability of the resultant classes in gray levels. The

procedure is very simple , utilizing only the zeroth- and

the first-order cumulative moments of the gray-level

histogram. It is straightforward to extend the method to

multi threshold problems. Several experimental results

are also presented to support the validity of the method.

Rolf Adams and Leanne Bischof [6] Discuss

about seeded region growing the segmentation of

intensity images in which the individual objects or

regions in the image are characterized by connected

pixels of similar value. Thus, the method presented may

not be applicable to highly textured images or to range

images . It may be applied to images affected by lighting

variation but only after suitable preprocessing. The

new algorithm for segmentation of intensity images

which is robust, rapid , and free of tuning parameters.

The method, however, requires the input of a number of

seeds , either individual pixels or regions , which will

control the formation of regions into which the image

tivili be segmented.

%' N. Ikonomakis, K.N. Plataniotis, M. Zervakis,

A.N. Venetsanopoulos [7] Discuss about Region

growing and region merging image, segmentation.

Image segmentation refers to partitioning an image into

different regions that are homogeneous or similar in

some image characteristics. It is usually the first task of

any image analysis process module and thus,

subsequent tasks rely strongly on the quality of

segmentation. Here a seeded region growing and

merging algorithm was created to segment grey scale

i

•

and colour images. The approach starts with a set of

seed pixels and from these grows regions by appending

to each seed pixel those neighboring pixels that satisfy

a certain predicate. Small regions of far away values

were merged to neighboring regions while regions of

similar value were also merged. Homogeneity functions

are introduced for both grey scale and color images.

Ahmad Fadzil Mohd Han, Umi Kalthum

Ngah, Venkatachalam'Lim Eng Eng [8] Discuss about

Processing of Abdominal Ultrasound Images Using

Seed based Region Growing Method.• They are many

diseases relating to abdomen. Patients suffering by

abdominal diseases will be experiencing chronic or

acute abdominal pain or suspects of an abdominal mass.

Abdomen has two major ports: liver and gall bladder.

Gallbladder and liver diseases are very common in all

over the globe. An abdominal ultrasound image is a

useful way of examining internal organs, including the

liver, gallbladder, spleen and kidneys. In general raw

ultrasound images contain lot of imbedded noises. So

digital processing can improve the quality of raw

ultrasound images. In this work a software tool called

Ultrasound Processing Tool (UPT) has been developed

by employing the histogram equalization and region

growing approach to give a clearer view of the affected

regions in the abdomen.

Tien Dzung Nguyen, Viet Dzung Nguyen,

Thuan Duong Ba Hong, Nam Chul Kim [9] Discuss

about Fast segmentation based on a hybrid of

clustering and morphological approaches. The

clustering is to partition an input image into a number

of clusters such that the gray levels within each cluster

are similar. The clustered image is further processed by

using morphological segmentation approach, in which a

seeded region growing however plays a role of the

decision tool instead of a watershed algorithm for a..r

remarkable improvement of processing time. The

performance of the proposed method is evaluated by

comparing its region-based coding results with those of

the morphological watershed-based segmentation

method and the split-and-merge algorithm. The

experiments results showed that region-based coding

using the proposed algorithm yields PSNR

improvement of about 1.5 dB over the morphological

watershed-based method. Especially, the total time

elapsed to segment an image using the proposed

method is reduced about 116 and 113 compared with

those of the watershed-based segmentation and the

split-and-merge methods, respectively.

3. ANALYSIS AND DESIGN

3.1 ANALYSIS

The purpose of analysis phase in the project

development is to find out what services the system

should provide, the required performance of the system,

application domain, and so on. Analysis is an important

process. The acceptance of the system depends on how

well it provides expected functionality and meets the

requirements that were defined in the analysis phase.

The analysis phase mainly focuses on

determining which technique or which algorithms are

suitable for solving given problem efficiently. The

existing systems successfully detect the edges or

segment the tumour. But, the two parameters, namely

seed point and threshold value are determined manually

by the user. These are done on a trial and error basis

and must be repeated until satisfactory results are

obtained from the process. But it is highly subjective to

the user. This project mainly focuses on calculating

these two parameters and segments the tumour

automatically.

For determining the threshold value, the data

mining technique like clustering is used . M any existing

s"stems use clustering algorithm, mainly k-means

clustering algorithm for classifying set of clusters. But

this k-means clustering algorithm did not always

produce good performance due to centre redundancy

and trapped centre at local minima problem. To

overcome this problem , in this project Moving K-means

algorithm is used to find the threshold value.

The automatic image segmentation is one of the

most difficult tasks in image processing . Many edge

detection techniques are used to detect the tumor edges.

The main techniques are threshold technique , boundary

based methods and region based methods. Threshold

technique uses only gray level information. They

neglect all the spatial information of the image and do

not manage well with noise or blurring at boundaries

which generally encountered in ultrasound images.

Boundary based methods use the pixel values that

varies rapidly at the boundary between adjacent

regions . in this method the edge pixels are identified

and then these pixels are modified to produce closed

curves . But to convert the edge pixels into close

boundary is difficult for the ultrasound image

segmentation Region based segmentation is based on

the principle that neighboring pixels within the one

region have similar value . Split and merge algorithm is

best known region based category for the segmentation.

In this project , the region growing segmentation

technique is used . Because it has advantages like

stability with respective noise and edges founded are

thin and connected . Modifications are made to the

region growing algorithm to find the seed point

automatically . This project mainly involves two stages.

In the first stage, clustering algorithm is applied to the

image to find the threshold value . This threshold value

will be used in the modified region growing algorithm

to segment the tumor. The graphical user interface is

needed to help the user to interact with the application

3.2 DESIGN

In the design phase the architecture is

established. This phase starts with the requirement

document and maps the requirements into architecture.

The architecture defines the components, their

interfaces and behaviors. The deliverable design

document is the architecture. The design document

describes a complete plan • to implement the

requirements. The figure 3.1 shows class diagram for

automatic region growing method for segmentation of

tumor on mammogram image.

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4. PROPOSED APPROACH

The proposed region growing algorithm involves

two stages. in the first stage, clustering algorithm is

applied to the image to find the threshold value. The

number of clusters depends on the number of region to

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Step 7: Compare each neighbor pixel with the initial

seed pixel. Add a pixel to a region if it

qualified for the region through either one of

the two conditions listed below

(a) If the gradient of the neighbor pixel is less

than 95% of the equalized histogram and

its grey level value is more than or equal

to the preselected threshold, b.

(b) If the gradient of the neighbor pixel is

more than or equal to 95% of the

equalized histogram and the grey level of

the pixel is not more than or equal to one

standard deviation away from the region

mean.

Step 8: Set the neighbor pixel, which is added to the

region, as the new seed location.

Step 9: Repeat Steps (6) to (8) until the region cannot

be grown or all the pixels have been

considered.

Step 10: Change the grey level of the pixel that cannot

be grown with the value of O or 255.

Step I l: Set next pixel with grey level more than a as

the new initial seed location, po(x, y) if the

pixel is not been grown yet. Repeat Steps (6)

to (10) until all the pixels of the image have

been considered.

4.3 GUI

For display the results user interface was developed

here. For developing User Interface GUI Components

like buttons and image display components was used.

The outputs of each algorithm showed on user

interface.

5. EXPERIMENTAL RESULTS

The software used for the implementation of the

above method is Mat lab. It can be run on any Windows

Operating system, with a minimum 512MB of RAM

and 680MB of free disk space.

Here also implemented a GUI (Graphical User

Interface) for display output images . This GUI work

with mat lab GUIDE and call functions and other GUI

components . Mainly 3 buttons and 3 image display

components were used. The buttons are Get Image, K

Means Clustering and Segmentation. When click I

button , it display all original mammogram image

names. Here I selected patientl image and it displayed

on User Interface. When click button 2, it shows

clustered image of original mammogram image and

when click 3rd button, it shows segmented tumor image

for original mammogram image.

Auto iiatic Peon Cr:win j Method for S .c,n e^tafion of T umour

on Mommog^am Image

Fig I : GUI for This paper

The figures 2, 3 show automatic region growing

method and its propose algorithm outputs. Here Fig2

(a) and Fig3 (a) shows Original Mammogram Images.

Fig2(b) and Fig3(b) are outputs for K Means Clustering

algorithm for given original mammogram images and

Fig2(c)and Fig3(c) shows segmented tumor images by

proposed region growing algorithm.

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be segmented on the image. In the second stage, this

threshold value will be used in the Region Growing

algorithm. Modifications are made to the conventional

Region Growing algorithm where the seed point and

threshold values are automatically determined. The

block diagram for automatic region growing method for

segmentation of tumor on mammogram image is shown

in figure 4.1.

Original Image

a pixel to be clustered and C is the j-th cluster (centre)i

(x = 1, 2, ....NI , y = 1, 2, ..., N and j =1, 2, _.., n). The

moving K-means clustering alggorithm finds the

threshold value . This algorithm is implemented as:

Step 1: Start

Step 2: Initialize the cluster centers and a , and set a =(1 u

a= a (where a is a small constant value, 0h U 0

< a <1 /3nd should be chosen to be inversely0

proportional to the number of centers).

IMoving K-means

Clustering

Al gorithmv

Modified Region

Growing Algorithm

Threshold

Value

Segmented Tumor

Image

1

Figure 4.1 Block Diagram for automatic region

growing method for segmentation of tumor on

mammogram image

4.1 Moving K means Cluster Algorithm

The clustering algorithm is implemented on the

mammogram image to automatically find the threshold .

value for classifying two regions of clusters, i.e. the

object of interest (tumor) and the background.

Consider an image with MxN pixels (where hI and

N are number of row and column of the image

respectively) to be clustered into n clusters . Let p(x,y) is

Step 3: Assign all pixels to the nearest cluster and

calculate the centre positions using

1C = - ^yeC >x E c p(x, y) ............ (I)

1 ° j J J

Where j = 1, 2... n

x=1,2...1\1

y=1, 2...N

Step 4: Check the fitness of each cluster using equation:

f(C)= ^y^X(^ p(x,y) - C I)2 ......(2)

Step 5: Find C and C, the cluster that has the smallest.s i

and the largest value off (•).

Step 6: Iff(C) < a f(C^,s^ a

(i) Assign the members (pixels) of C to C if

(

1

P(x,y) < C , Where x,y E C ,and leavei r

the rest of the members (pixels) to C.i

i) Recalculate the positions of C and C

according to:

C = Il (n )^yEc LIxEC p(x, y) .......... (3).^^ s S S

D

I"

D

Fig 2(a): Original Image

Fig 2(b): Clustered Image

Fig 3(a): Original Image

Fig 3(c): Segmented image

6. CONCLUSION

The aim of this work is to develop an

automatic algorithm for segmentation of tumor on

mammogram image which is unique challenge in

mammogram image segmentation . The above result

shows that our algorithm is one of the best automatic

methods of segmenting tumor on mammogram images.

The proposed method automatically determines the

seed point and threshold value. The results obtained are

favorable as the proposed Region Growing algorithm

provides more meaningful images. The region of tumor

is successfully detected . The size and shape of tumor

region has also been preserved.

7. REFERENCES

[1] Reifael C. Gonzalez and Richard E. Woods,"Digital

Image Processing' Pearson Edition,

2002.

[2] B Chanda, D Dutta Majumder, "Digital Image

Processing and Analysis", Prentice Hall, 1st

Edition 2004.

[3] Edward A Sickles, Mammographic Features of

Early Breast Cancer , the annual meeting of the

American Roentgen Ray Society, Las Vegas, April

1984.

[4] P K sinha and Q H hong , An Improved Median

Filter., IEEE Transactions on Medical Imaging,

Vol. 9, Sep 1990, pp 51-54.

[5] N Otsu , A Threshold Selection Method from Gray-

Level Histograms, IEEE Transactions on systems,

Vol.l, Jan 1979, pp 149-151.

[6] Rolf Adams and Bischof L, Seeded region

growing , IEEE Transactions on Pattern Analysis

and Machine Intelligence, Vol 16(6):641-647,Oct

1994. pp 34-38.

[7] N. Ikonomakis, K.N. Plataniotis , M. Zervakis, A.N.

Venetsanopoulos , Region growing and region

merging image segmentation , IEEE Transactions

on Medical Imaging, Vol 13, April 1997,pp 82-87.

[8J

[9J

Ahmad Fadzil Mohd Han , Umi Kaithum Ngah,

Venkatachalam ' Lim Eng Eng , Processing of

Abdominal Ultrasound Images Using Seed based

Region Growing Method , IEEE Transactions, Vol

33, May 2004,pp 52-60.

Tien Dzung Nguyen, Viet Dzung Nguyen, Thuan

Duong Ba Hong, Nam Chul Kim , Fast

segmentation based on a hybrid of clustering and

morphological approaches , IEEE Transactions, Vol

23 , June 2008, pp 315-319.

[I0] w'vw.wikipedia.org