IMAGE SEGMENTATION BY USING K-MEANS CLUSTERINGALGORITHM

Ramaraj.M*1, Dr.Antony Selvadoss Thanamani*2

1Research Scholar Department of Computer Science NGM College

Pollachi India

ramaraj302@gmail.com.2Associate Professor & Head Department of Computer Science

NGM College Pollachi

India

selvdoss@gmail.com

Abstract In this paper, we discuss above

the medical image segmentation

and also comparative study has

been made between the

conventional k-MEANS and fuzzy c

means algorithm. Image

segmentation is the first step

in image analysis. Segmentation

is the process which partitions

an image into its constituent

regions or objects. The main aim

of the image segmentation is the

process of analyze and easier to

identify the pixel clustering of

the image as helpful for the MRI

brain images. Segmentation is

the process of images which

divided into multiple parts and

typically used for relevant

objects or other relevant object

in the digital image for the MRI

scan images. Image segmentation

is the various or other

different application are widely

used for the medical images in

order to the diagnose disease.

An efficient clustering

algorithm is used for the

medical image for the k-means

algorithm in image segmentation.

Medical image segmentation had

been a vital organ of research as

it inherited complex problems

for the proper diagnosis of

incomplete developed mind. In

this research, it provides a

foundation of segmentation and

find out the tumor and

clustering the tumor in the

brain tissue.

Key words: Clustering, K-Means

clustering, Fuzzy c-Means

Clustering, brain tumor, Image

Segmentation.

I. INTRODUCTION Clustering is a method of

grouping data objects into

different groups, such that

similar data objects belong to

the same group and dissimilar

data objects to different

clusters. Current research

increasing interest in digital

image searching, classification,

identification, management and

storage. Some common but

important applications of are

person identification in movie

clips and festive home videos,

recognition in biometric system,

natural scene classification for

robot vision, commercials

filtering, segmentation of

important topics in lectures and

meetings. The image clustering,

an important technology for

image processing, has been

actively researched for a long

period of time and explosive

growth of the Web, image

clustering has even been a

critical technology to help

users digest the large amount of

online visual information.

Clustering approach is widely

used in biomedical image

segmentation and it is

application are used for brain

tumor detection as the normal

and abnormal to find out the

tumor on the brain [4].

The many different segmentation

techniques are used in the image

mining and image segmentation

approaches can be divided into

many parts as: clustering, edge

detection, thresholding and

region extraction. In this

paper, we discuss above the

clustering concept in the image

mining on the image segmentation

process of the clustering and

each object can be have its

place of more than one clusters

to be provisional upon the

degree of relationship

association on it.

Many of things and real

situation, like image,

restricting an object into only

one cluster will be take on the

different tasks.

A technique which is handles the

mining of information,

association of the image data or

additional patterns not

unambiguously stored in the

image data base of the image

segmentation process [11]. The

methods are handling with the

image processing, image stored

and retrieval, data mining,

machine learning, database on

the image to be stored and

artificial intelligence. A huge

amount of image databases have

being implements with as rule

mining concept. It handles with

several features of very huge

image databases which comprises

of indexing methods, image

storages, and image retrieval,

all regarding in an image mining

system.

Image mining is to considerable

patterns without any information

of the image content, and

patterns types are different: it

is classification patterns,

correlation patterns, temporal

patterns and spatial patterns

for the main intention of the

image mining techniques. In this

techniques are implemented with

the good quality of image mining

system provides the end users

with a useful to access the

image on the databases to be

storage area at the time it

recognizing the patterns to be

generates with under the

knowledge of this image

representation. Image

segmentation is the primary

phase in image mining. In other

words, image mining is simply an

expansion of data mining in the

field of image processing. Image

mining handles with the hidden

knowledge extraction, image data

association and additional

patterns which are not clearly

accumulated in the images.

II. RELATED WORK

Clustering is a process of

partitioning or grouping of the

dataset and to a given sector

unlabeled pattern into a number

of clusters in the K-means

clustering and application in

color image segmentation, such

that similar patterns is

assigning to be a group, which

is consideration for a

cluster[9]. It is a brain tumor

segmentation using Fuzzy c-means

clustering to tumor region for

the manual segmentation of brain

tumor from MRI images [2].

Imaging techniques to establish

stage of diagnosis and follow

patients with brain tumors are

central to the clinical

management. MRI is the most

commonly used technique for

injury finding in the definition

of extent detection of spread

and in evaluation of either

residual or recurrent

disease[6]. The existing tumor

detection methods broadly

classified into

Three categories:

Atlas Based Segmentation

Segmentation in MRI images

Segmentation of Brain Tumor

In another definition of the

brain tumor in a growth of

abnormal cells in the tissues of

the brain. Brain tumor can be

begin (not cancer) or malignant

(cancer).

Brain Cancer Statistics:

Every year about 1400 malignant

brain tumors are diagnosed in

Australia and about 100 of these

are in children data about

benign brain tumors are not

collected but an estimated 2000

people and including children on

affected into the every year.

Fig 1: black diagram for

identification of the brain

tumor using image segmentation

method

Fig 1: it’s used for the black

diagram is a brain tumor

identification and the

algorithms of segmenting the

image and using with the k-

medoids (as for the k-means)

algorithms are implemented.

In this study, a new

approach has been discussing of

brain tumor the image, by

applying K-medoids algorithm and

Fuzzy c-means algorithm for

comparison of this algorithms

and its more effective

classification accuracy to find

out the any one of the

clustering algorithm for using

the image [3]. The tumor is

calculated to statistical

parameter are evaluated and

clustering the tumor.

III. METHODOLOGY

K-Means Clustering:

It’s the on the techniques for

the clustering concept in the

data mining process and is very

famous algorithm for the K-means

clustering, because it is

similar or simpler and easier in

computation of an efficient K-

means clustering algorithm it is

the simplest unsupervised

learning algorithms that solve

the well known clustering

problems[12]. It’s the K-means

algorithm is an unsupervised

clustering algorithm that

classified in the input data

points into multiple classes

based on their intrinsic

distance from other dataset

points of his cluster [7].

Image Databases

Pre-processing

Brain Tumor

NomalTissue

Training And Testing Datasets And Both Trained

Segmentation Of The Tumor Using K-medoids

Feature Extraction

Its assume that the

data features from a vector

space and tries to find natural

clustering.

Hierarchical Clustering:

Hierarchical clustering is

any valid measure of distance

can be used for a traditional

clustering method that can be

generates a tree structure or a

dendrograms and it’s a matrix of

distance.

The two types of approaches are:

Bottom- up approaches

To- down approaches

The hierarchical clustering

algorithms are classified into

two stages are given as

Agglomerative hierarchical

clustering

K-means algorithms

Where x, y is a set of two

elements in the cluster and

d(x,y) denote the min-distance

between the two elements of x

and x.

Complete linkage clustering

Its calculate the maximum

distance between the large

set of clusters. Formula as

d(x,y)= maxx∈X,y∈Y

d(x,y) …..

(1)

where x, y is a set of two

elements in the cluster and d(x,

y) denote the max-distance

between the two elements of x

and x.

Its calculate the minimum

distance between the large set

of clusters. Formula as

d(x,y)= minx∈X,y∈Y

d(x,y) …..

(2)

where x, y is a set of two

elements in the cluster and d(x,

y) denote the min-distance

between the two elements of x

and x.

dis (x,y )=∑i=1

d

‖xi−ci‖ ……….. (3)

Presently, the feature vectors

are grouped into k means

clusters using a selected

distance measure such as

Euclidean distance may be

calculated.

Image Segmentation:

Segmenting a MRI images into

homogeneous texture regions

representing disparate tissue

types is often a useful

preprocessing step in the

computer-assisted detection of

brain tumor[11]. That is why we

proposed new algorithm to detect

cancer in mammogram breast

cancer images. The best approach

to image segmentation may vary

between different

applications[1].

The choice between manual,

semiautomatic or fully automatic

methods depends on the image

quality and the number of

objects needs to be matmetric in

the amount of accessible user

time, and the required

classification accuracy of the

images. The segmentation process

is usually based on gray

intensity, color, shape or

texture.

Texture can be

characterized by local

variations of pixel values that

repeat in a regular or random

pattern on the entity or

image[8]. It can also be defined

as a repetitive arrangement of

patterns over a region. A wide

variety of texture segmentation

techniques have been reported in

the literature [13]. We decided

to choose a set of existing

texture features which can

provide us good discriminating

power and are easy to compute as

compare to Segmentation using

gray level co-occurrence matrix

required huge time for tumor

demarcation with less accuracy

[5].

Segmentation by Gray level co-

occurrence matrix basic

watershed algorithm and

LindeBuzo Gray algorithm (LBG)

which are used for comparative

performance of tumor detection.

There are several types of the

segmentation algorithms as

follow

Gray level co-occurrence

matrix.

Watershed algorithm.

K-Means Algorithms:

The principle of the k-

medoids algorithm (its same for

the k-means algorithm) for

cluster the data and k-medoids

algorithm is the one of the best

unsupervised learning clustering

algorithm [10].

Clustering the image is

according to the group of pixels

and the k-medoids algorithm is

initially we have to a number of

cluster k, then k-cluster center

are chosen randomly. The

distance between the each pixel

to each cluster centers are

calculated. The distance may be

of simple Euclidean function.

Single pixel is compared to all

cluster centers using the

distance formula.

∑c (j)=k∨|Xj−Xi|∨22 ……………..

(4)

To calculate the number

clustering in the equation of

the k is medoids point of

cluster.

∑k=1

k

∑C(i)=k

||Xi−Ck||22 …………… (5)

Minimum cover of C

For each i=1….n, to find the

cluster center of ck closest to

xi,

and let c(i)=k

k-medoids algorithm is generate

and returns the higher value is

calculated as

Algorithm:

1. Give the no of cluster value

as k.

2. Randomly choose the k cluster

centers

3. Calculate mean or center of

the cluster

4. Calculate the distance

between each pixel to each

cluster center

5. If the distance is near to

the center then move to that

cluster.

6. Otherwise move to next

cluster.

7. Re-estimate the center.

Fuzzy C Means

Clustering, a major area of

study in the scope of

unsupervised learning, deals

with recognizing meaningful

groups of similar items. Among

all clustering algorithms, the

Fuzzy C-Means (FCM) clustering

algorithm

FCM calculates membership

values for each data point to

all clusters it usually

generates a more consistent

and trust-worthy results. We

applied the FCM into our image

segmentation project to

develop a framework for the

multi-level FCM clustering

Jm=∑i=1

N

∑j=1

Cuij⃦ ⃦m ‖xi−cj❑‖❑2.1≤m<∞....

................................

..............................

6 uy=

1

∑k−1

c¿¿¿

……………... 7

where m, the fuzzification factor, is any

real number greater than 1, uij is

the degree of membership of xi in

the cluster j, xi is the ith of d-

dimensional measured data, cj is

the d-dimension center of the

cluster, and ||*|| is any norm

expressing the similarity

between any measured data and

the center. Fuzzy partitioning

is carried out through an

iterative optimization of the

objective function shown above,

with the update of membership uij

and the cluster centers cj.

IV. EXPREMENTAL RESULTS

MATLAB stands for matrix

laboratory. It’s also a

software program that allows us

to do data manipulation and

visualization, image analysis,

calculations, math and

programming. It can be used to

do very simple as well as very

sophisticated tasks. MATLAB is a

high-performance language for

technical computing. MATLAB is

an interactive system whose

basic data element is an array

that does not require

dimensioning. MATLAB can

import/export several image

formats such as BMP (Microsoft

Windows Bitmap), GIF (Graphics

Interchange Files), HDF

(Hierarchical Data Format), JPEG

(Joint Photographic Experts

Group), PCX (Paintbrush), PNG

(Portable Network Graphics),

TIFF (Tagged Image File Format),

XWD (X Window Dump). MATLAB can

also load raw-data or other

types of image data.

Figure 2: Flowchart represented

by the cluster algorithms to be

implemented with images to

produce the result.

Original image

Fig1: The original image on the

brain

Tumor image on brain

Fig 2&3: Tumor image on brain and

highlight the tumor on affected in

the area of brain.

Fig 3: to cluster the k means

cluster for tumor image

Fig 4: it represent the original

image on the human brain

V. CONCLUSION:

In this paper

described a large scale soft

clustering algorithm that relies

on topic segmentation and

clustering the brain tissue. We

have developed an automatic

image based method to detect

tumor in 2D MRI head scans. The

classification accuracy on image

in 93.74%. In the proposed

method an efficient detection of

brain tumor region from cerebral

image is done using k-medoids

clustering and Fuzzy c means

clustering to be compared with

this algorithm is more effective

and efficient in the extract on

tumor from brain and helpful for

the MRI brain images. In the

future work we can increase

accuracy on the images.

REFERENCES:

[1]

A.R.Kavitha,Dr.C.Chellamuthu,

Ms.Kavin Rupa, “An Efficient

Approach for Brain Tumour

Detection Based on Modified

Region Growing and Network in

MRIImages,”IEEE, 2012.

[2] Wen-Liange, De-Hua Chen,

Mii-shen Yang, “Suppressed

fuzzy-soft learning vector

quantization for MRI

segmentation,”Elsevier ltd,

2011.

[3] Vida Harati, Rasoul

Khayati, Abdolreza Farzan,

“Fully automated tumor

segmentation based on improved

fuzzy connectedness algorithm in

brain MR images,”Elsevier ltd,

2011.

[4] R.B.Dubey, M.Hanmandlu,

Sr.Member, Shantaram Vasikarla,

“Evaluation of ThreeMethods for

MRI Brain Tumor segmentation,”

IEEE, 2011.

[5] Shaheen Ahmed, Khan

M.Iftekharuddin, “Efficacy of

Texture,Shape,and Intensity

Feature Fusion for Posterior-

Foss Tumor Segmentation in

MRI,”IEEE,2011.

[6] T.Logeswari, M.Karnan,

“Hybrid Self Organizing Map for

improved Implementation of Brain

MRI Segmentation,”IEEE, 2010.

[7] P. Dhanalakshmi & T.

Kanimozhi “

“Automated Segmentation of Brain Tumor using K-MeansClustering and its Area Calculation”IJAREEE” 2013

[8] Jayashri Joshi1,

Mrs.A.C.Phadke, “A New Approach

to Image Segmentation for Brain

Tumor detection using Pillar K-

means Algorithm” IJARCCE,vol

3,issue 3, March 2013.

[9] Oyelade, O. J, Oladipupo, O.

O, Obagbuwa, I. C “Application

of k-Means Clustering algorithm

for prediction of Students’

Academic Performance” International

Journal of Computer Science and

Information Security, vol. 7, pp.292-295,

2010.

[10] Meghana Nagori, Shivaji

Mutkule, Praful Sonarkar

“Detection of Brain Tumor by

Mining fMRI Images”IJARCCE,vol

2,issue 4,January 2013.

[11] Hakeem Aejaz Aslam,

Tirumala Ramashri, Mohammed

Imtiaz Ali Ahsan “A New Approach

to Image Segmentation for Brain

Tumor detection using Pillar K-

means Algorithm” IJARCCE,vol

2,issue 3, March 2013.

[12]

Google:http://sites.google.com/s

ite/

dataclusteringalgorithms/k-mean-

clustering-algorithm.

[13] D. Klein, S.D. Kamvar, and

C. Manning, “Feature Extraction

and Texture Classification in

MRI”, International Conference

[ICCT-2010], 3rd-5th December

2010