Page 1 of 6

European Journal of Business &

Social Sciences

Available at https://ejbss.org/

ISSN: 2235-767X

Volume 07 Issue 03

March 2019

Available online: https://ejbss.org/ P a g e | 441

Segmentation and Classification of Brain Tumor from MRI Brain

Images for Medical Diagnosis

Dr. B.Subrahmanyeswara Rao1

, K. Gayathri2

, S.Harshitha3

, L.V.S.Ganesh4

, P.Ramesh5

boyinasrao@gmail.com,gayathrisadhana68@gmail.com,harshithasayala@gmail.com

venkatasivaganesh111@gmail.com, rameshpechetti1998@gmail.com

Department of ECE, Swarnandhra College of Engineering and Technology, Narsapur,W.G.Dst,A.P

Abstract

This work aimed was to initiate an

automated tumor diagnostic system based on T1

and T2 weighted magnetic resonance images

(MRI) as the detection of brain tumor is

complicated procedure in medical field. This

system incorporates steps for pre-processing, image

segmentation with morphological operations in

multiple steps to segment benign and malignant

tumor or tissue, feature extraction and image

classification. This can be done by SVM (support

vector machine) and k-means algorithm. The

textural and shape based features were extracted by

discrete wavelet transform moments from the

segmented tumor regions. Finally, the

implementation of the suggested system was

evaluated with extreme learning machine (ELM)

algorithm to discriminate two types of tumor on

MR images. The Diagnostic efficiency of the

proposed system was evaluated with sensitivity,

specificity and accuracy.

Keywords

Pre-processing, SVM, Image

segmentation, morphological operations, benign

and malignant tumor.

Introduction

In human nervous system, brain is in the

center. Automated identification of brain tumor

based on magnetic resonance (MR) images is of

excessively beneficial in neurosurgery and

treatment designing. The brain tumors are varied in

size and characteristic properties, which are

important elements in the segmentation of for

anatomical structures. Hence automatic detection of

size and location of tumor on MR image is very

difficult task, due to the intensity variations and

magnetic field in homogeneities. By using semi- automated [5] and automated techniques, number

of computational brain tumor segmentation has

been increased for tumor structures over the past

two decades. To control the outcome of the

magnetic field in homogeneities and cavities, most

of the existing approaches utilized bias-field

correction [4] and wavelet transformation [2] as

preprocessing techniques for the segmentation

process.

There are various learning algorithms such as

neural networks were applied for MRI object

identification. The methods were introduced for

brain tumor and tissue surface localization.

Recently, K-means clustering [3] have shown its

potential in tumor segmentation, however it

increase the complexity in extract of features in

deep hierarchy network.

Hence, we suggest a successful and efficient

technique for segmentation, classification of benign

and malignant brain tumor identification form MR

based images. Textural and shape based features

were obtained by using wavelet transform and

Zernike moments. Furthermore, extreme machine

learning algorithm was applied for discriminating

benign and malignant tumor.

Existing method:

The proposed method consists of number

of steps such as enhancement to sharpen the edges,

segmentation of region of interest (ROI), extracting

texture and shape based features and finally,

classifying by machine learning technique to

identify brain tumor or tissue is described in fig.1.

Fig. 1. Illustration of brain tumor segmentation and

classification.

Page 2 of 6

European Journal of Business &

Social Sciences

Available at https://ejbss.org/

ISSN: 2235-767X

Volume 07 Issue 03

March 2019

Available online: https://ejbss.org/ P a g e | 442

MATERIALS AND METHODS

Dataset description

We conducted experiment on real patient data

obtained from the Tamil Nadu Government Multi

super specialty hospital from January to August

2016. It consists of 17 benign and 11 malignant

brain tumor or tissue MR images with an image

matrix of 500 X 540 pixels. Structural data

included both of high resolution T1 and T2

weighted MRI scans received on a GE Sigma HDxt

1.5T with 5mm slice thickness and display field of

view in the range from 24.0 – 28.0cm.

Enhancement

The gray scale image conversion whose

entries are between 0 and 255, with 0 to black and

255 to white. After the image conversion, the

enhancement technique was essential to remove

low frequency noise and sharpening the edges of

the of the brain image. Median filter was utilized to

remove noise by estimating the new neighboring

pixels. To emphasize the fine details in the image

we applied high pass filtering technique by

removing the low

frequency noise. Then the resultant high pass

filtering image is converted into the binary image

by using the mean pixel values of all the pixel

image.

Fig.2. Benign image (A) original (B) Original (C)

highpass filter (D) highpass filter (E) binary and

malignant (F) binary

Labeling procedure

High density pixels were estimated from

the proportion of the pixels in the region by

applying the labeling of objects to the binarized

image and extract the objects composed greater

than 50 pixels. The object location of the maximum

value pixels of the extracted highly dense object

pixels was considered for further processing of the

segmentation of the brain tumor process. The

morphological operation of the dilation using a

square structuring element of the extracted object

removed small holes and determined exact

boundary of the tumor or tissue in the image as

shown in fig. 3

Fig.3. Tumor boundary of (A) benign and

(B) malignant

Segmentation

It separate image objects into number of

discrete regions such that pixels sharing high

similarity belong to the same region. We

incorporated traditional k-means clustering

algorithm [3] because of its simple and fast

computation than the other hierarchical clustering.

It targets to group number of evaluations into k

clusters in which each evaluation associates to the

cluster with the nearest mean. First k cluster centers

were chosen to accept with k arbitrarily chosen

model inside the hyper volume containing the

model C. Then allocate each model to the nearest

cluster center. Again the cluster centers are

recomputed based on current cluster memberships U.

If the convergence of criterion is not satisfied then

the process is repeated with new cluster centers

until to get minimal squared error. The resultant

image of the benign and the malignant and the

tumor shown in the following below figures.

Fig. 4. Segmentation using k-mean algorithm (A)

benign and (B) malignant

Page 3 of 6

European Journal of Business &

Social Sciences

Available at https://ejbss.org/

ISSN: 2235-767X

Volume 07 Issue 03

March 2019

Available online: https://ejbss.org/ P a g e | 443

Feature extraction

In feature extraction, multi-resolution

transform called wavelet transform followed by

Zernike moments were applied by which textural

and shape feature vectors were extracted from the

identified tumor or tissue region from the MR

images. The wavelet extraction of an image is

acquired to examine the different frequencies of an

image using different scales. Zernike moments

were used to explain the properties of an image

with no redundancy or overlap information

between the moments. Thus, four frequency band

utilizing the hard features from wavelet and two

rotational invariant features from Zernike were

included.

Classification

Extreme learning machines (ELM) are

introduced for classification based on single layer

of hidden nodes, with good generalization

performance. The weights between hidden nodes

and outputs are learned using a linear model. Since

it is gradient based algorithm, it analytically solves

the problem by calculating the optimal weights of

the single-hidden Layer feed-forward Neural

Networks (SLFN). Hence β of the linear system

Hβ=T:

||H(w1,,,,wn,q1,,,,qn)β-T||=

minβ||H(w1,,,,wn,q1,,,,qn)β-T

...................(1)

In many occurrences the number of hidden nodes

are quite lesser than the number of training, thus H

is not a square matrix, and there may not remain wi

,

q i

,

β i

, such that Hβ=T. The smallest norm least

square solution of the linear system is(2)

B = H*T.............................(2)

Where H*

is the Moore-Penrose

generalized inverse of matrix.

The classification of the extracted features

from the tumor or tissue was evaluated using

sigmoidal kernel function. The performance of the

suggested system can be validated by sensitivity,

specificity and accuracy.

Sensitivity = TP/(TP+FN)........(3)

Specificity = TN/(FP+TN).......(4)

Accuracy=(TP+TN)/(TP+TN+FN+FP)

.............(5)

Accuracy = (TP+TN)/(TP+TN+FN+FP)

.............(6)

These are the previous algorithms which are

different from us.

Proposed system

In this proposed system, there obtained the

number of steps such as enhancement of the

sharpened edges, segmentation by using k-means

algorithm and segmentation with multistage of

morphological operations, feature extraction and

then classification. There by here also consists of

segmentation. We are aiming to present the

different MRI images segmentation methods by

using k-means algorithm and morphological

operations in multistages, discrete wavelet and

SVM (support vector machine) based on the

features of MRI (magnetic resonance image)

Fig-1. Stages in brain tumor segmentation.

The below figure shows the stages in brain

tumor. In this paper we are presenting to take

review on different methods of brain tumor image

segmentation. We are aiming to present the

different MRI images segmentation methods by

using k-means algorithm and morphological

operations in multistages.

As compared with the above exist method;

the illustration of brain tumor segmentation and