Brain Tumor Identification Using K-Means Clustering

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International Journal of Engineering Trends and Technology- Volume4Issue3- 2013

ISSN: 2231-5381 http://www.internationaljournalssrg.org Page 354

Brain Tumor Identification Using K-Means

ClusteringManali Patil Samata Prabhu Sonal Patil Sunilka Patil

Mrs.Prachi Kshirsagar

I. T. Department,Padmabhushan Vasantdada Patil Pratishthan’s College Of Engineering.

Sion (East), Mumbai-400 022, India.

Abstract

The project is entitled as “BRAIN TUMOR

IDENTIFICATION”. The idea behind choosing this topic

was to simplify the process of tumor identification. The

system will be computerized and hence time consumed

will be less. The system will also keep records of patientswho are affected by tumor and who are not. So the

doctors can schedule the further treatments for the

patients.

KeywordsTumor, Brain, Clustering, MRI image, identifying tumor.

I. INTRODUCTION

A brain Image consists of four regions i.e. gray matter (GM),

white matter (WM), cerebrospinal fluid (CSF) and background. These regions can be considered as four different classes. Therefore, an input image needs to be

divided into these four classes. In order to avoid the chances

of misclassification, the outer elliptical shaped object should

be removed. By removing this object we will get rid of non brain tissues and will be left with only soft tissues. Brain

tumor identification is used to identify tumor from particular

image. Brain tumor identification image application is

typically based on clustering concept of image pixels matrix. Brain tumor identification is used to identify tumor affected

image based on clustering and centroid concept.

The basic concept is that local textures in the images can

reveal the typical regularities of the biological structures.

Thus, the textural features have been extracted using a co-

occurrence matrix approach. The level of recognition, amongthree possible types of image areas: tumor, non-tumor and

back ground.

The main objective of this project is to study the design of acomputer system able to detect the presence of a tumor in the

digital images of the brain, and to accurately define its

borderlines

A. Images clustering

Convert 2-D report into 3-D images because brain is mass

body to calculate centroid so it has 3 dimensions and we need to calculate centroid, as also we can get more accurate results

with 3 dimensions.

B. Algorithm k-means

1. Place K points into the space represented by the objects

that are being clustered.2. These points represent initial group of centroids.

3. Assign each object to the group that has the closest

centroid.

When all objects have been assigned, recalculate the

positions of the K centroids.Repeat Steps 2 and 3 until the centroids no longer move. This

produces a separation of the objects into groups from which

the metric to be minimized can be calculated.





II.PROPOSED SYSTEM

In our project, first the MRI report of the patient is scanned

and made into computerized form. As it becomes in computerized form, detection of the tumor becomes simpler

as clustering is done on that MRI image and manual checking

by doctors is avoided. So the results generated are morespecific.

The major role of this application is to identify the tumor inthe brain image and reconstruct the area which the tumor is

affected and based on the threshold value the system will

identify whether the image is affected by the tumor or not.Following are the functionality which is involved in the

tumor identification module.

Identification Reconstruction

Testimony

A. Identification

K-Means algorithm is used to implement the Identification

of the MRI brain image. Clustering can be considered the

most important unsupervised learning problem; so, as everyother problem of this kind, it deals with finding a structure in

a collection of Unlabeled data. A loose definition of

clustering could be “the process of organizing objects intogroups whose members are similar in some way”. A cluster is therefore a collection of objects which are “similar”

between them and are “dissimilar” to the objects belonging to

other clusters.

We are implementing the k-means algorithm with brain

image. Algorithm will cluster the brain image and

differentiate the cells into the affected cluster region and unaffected cluster region.

B.Reconstruction

The affected area will be selected and as a cluster and constructed as an image and it is displayed in the label. Based

on the constructed area threshold values will calculated and

the tumor identification process will performed based on the

threshold values. Our system will show the option panemessage dialog contain the image affected or not.

_ _ +

Fig: K-means clustering

C.Testimony

The report is generated based on the affected and theunaffected image. The users have to select the option the

affected or un affected patients. The reports contain the

patient id and the name of the candidate.

III.STEPS OF IMPLEMENTATION

1. Register patient details with image of his/her brain.

2. Select operation.

3. Select patient ID for identification of image.4. Select clustering for image clustering.

5. Reconstruction of image with proper result.

A. Block Diagram

DatabaseUser Login

Clustering of

3D MRI image

using threshold

value

Report

generation

No of

clusters

centroid

Distance objects to centroid

endNo obj

move

Start

Grouping based on min

distance







V.CONCLUSION

The “BRAIN TUMOUR IDENTIFICATION” has beendeveloped to satisfy all proposed requirements.

The system is highly scalable and user friendly. Almost allthe system objectives have been met. The system has beentested under all criteria. The system minimizes the problem

arising in the existing manual system and it eliminates the

human errors to zero level.

The design of the database is flexible ensuring that the

system can be implemented. It is implemented and gone

through all validation.

VI.REFERENCES

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[2]. Kjaer, L., Ring, P., Thomson, C., Henriksen, O.: Texture

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[3]. War_eld, S., Dengler, J., Zaers, J., Guttman, C., Wells,W., Ettinger, G., Hiller, J., Kikinis, R.: Automatic

identi_cation of gray matter structures from MRI to improve the Identification of white

matter lesions. Journal of Image Guided Surgery 1 (1995)326{338

[4]. Vinitski, S., Gonzales, C., Mohamed, F., Iwanaga, T.,Knobler, R., Khalili, K., Mack, J.: Improved intracranial

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Med (1997) 457{469

[5]. Zhu, Y., Yan, H.: Computerized tumor boundary

detection using a hop_eld neural network. IEEE-TMI (1997)55{67

[6]. Dickson, S., Thomas, B.: Using neural networks to

automatically detect brain tumours in MR images. Int J Neural Syst (1997) 91{99

[7]. Just, M., Thelen, M.: Tissue characterization with T1,

T2, and proton density values: Results in 160 patients with brain tumors.

[8]. O’reilly, Java Swings, Tata McGraw Hill, Fifth Edition,2002

[9]. Java Handbook, Patrick Naughton & Michael Morrison,

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[10]. The Java AWT Reference, O'Reilly & Associates, Inc. ,John Zukowski,1-565-92240-9,1997

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Brain Tumor Identification Using K-Means Clustering