optimization technique, to detect brain tumor in mri€¦ · optimization technique, to detect...

DOI: 10.23883/IJRTER.2017.3300.IMERL 234

Optimization Technique, To Detect Brain Tumor in MRI

Depika Patel1, Prof. Amit kumar Nandanwar

2

1Student, M.Tech, CSE, VNSIT Bhopal

2Computer Science andEngineering, VNSIT Bhopal

Abstract- Image Segmentation is an important and challenging factor in the field of medical

sciences. It is widely used for the detection of tumours. This paper deals with detection of brain

tumour from MR images of the brain. There are a variety of function of Image Segmentation among

them Medical Image Analysis is a popular application. Analysis of Medical Image provides in

sequence to the doctor for the treatment from MRI or CT images. Volumes of tissues, Brain Tumor

detection are some of the applications of image segmentation in medical image analysis. Many

researches have been occurred in order to detect tumor present in the brain, the area of tumor, the

type of tumor present. The following paper proposes a new algorithm for the detection of brain

tumor using MRI. The proposed method is implemented using Optimization Technique, to detect

tumor. The proposed method proves to be efficient, 16.39% accurate and 9.53% précised then the

existing work.

Keywords- MRI image, Brain Tumor, Genetic Algorithm, Curve Fitting and Support Vector

Machine.

I. INTRODUCTION

Human body is made up of several type of cells. Brain is a highly specialized and sensitive organ of

human body. Brain tumor is a very harmful disease for human being. The brain tumor is intracranial

mass made up by abnormal growth of tissue in the brain or around the brain. Brain tumour can be

detected by benign or malignant type. The benign being non-cancerous and malignant is cancerous.

Malignant tumour is classified into two types; primary and secondary tumour benign tumour is less

harmful than malignant. The malignant tumour it spread rapidly entering other tissues of the brain

therefore, worsening condition patients are loosed. Brain tumour detection is very challenging

problem due to complex structure of brain [1]Image Segmentation is a procedure that creates the

group of those pixels which has similar attributes. The results of image segmentation are used in

medical field for planning of treatment, recognition of tumor and analyzing the enlargement of tumor

[1]. Brain tumor is irregular and unrestrained growth of brain cells. Magnetic Resonance Imaging

provides minute information about brain tumor, its cellular structure, and it is an important tool for

diagnosis. According to Brain and Central Nervous System 372 people were diagnosed in TATA

Memorial Hospital in Mumbai, India. Out of 372, 250 were males (67%) and 122 were females

(33%). National Cancer Institute has approximate 22,070 novel cases of tumor and 12,920 deaths

due to brain tumor in the US for 2009. The Central Brain Tumor Registry of US (CBTRUS) has

made the survey which shows that 64,530 new cases of primary brain and central nervous system

tumors were diagnosed plough end of 2011. In United States in the year 2013 estimated new cases

are 23,130 and deaths are 14,080 from brain and other nervous system cancers [4].

In there time, there are numerous tools and software are available to detect and analyze tumor in the

brain. Since the last century has yielded much progression from which the chance of survival has

increased by diagnosing the tumor by means of the help of tools. But still there is a lot more left to

cure the brain tumor more efficiently, several researchers and authors have come forward in this field

International Journal of Recent Trends in Engineering & Research (IJRTER)

Volume 03, Issue 06; June - 2017 [ISSN: 2455-1457]

@IJRTER-2017, All Rights Reserved 235

and has designed several methods through which helps doctors in diagnosing and planning the

treatment [5].

There are several optimization technique in attendance which is being used in detecting tumor

efficiently and truthfully. because very last few years genetic algorithm has been in demand because

it has the advantage that it can extract features present in the image which is further used in

determining and identifying information present in the tumor. It is a searching algorithm based on the

process of natural evolution. Genetic Algorithm is used to generate the best solutions for

optimization and search problems. inherent algorithms are a subclass of evolutionary algorithms

which gives the best solutions to optimization problems using the techniques of natural evolution [2].

It is a technique used for feature selection and extraction. For the classification of features there are

several algorithms available. According to the training methods classifiers are divided to supervised

and unsubstantiated methods. The most popularly used classification method is Support Vector

Machine (SVM). It is a supervised learning method which classifies the several points into two

disjoint short spaces separated by a linear classifier [6]. This paper is prepared hooked on six

sections. Section II illustrates the work which has been done. Section III deals with a brief of the

methods used. Section IV explains the method designed to achieve the desired results. Section V

show the results obtained during the research. Finally, Section VI gives the termination of the

following paper.

The brain tumor detection can be done through MRI images. In image processing and image

enhancement tools are used for medical image processing to improve the quality of images. The

contrast adjustment and threshold techniques are used for highlighting the features of MRI images.

The Edge detection, Histogram, Segmentation and Morphological operations play a vital role for

classification and detecting the tumor of brain. The main objective of this paper is too studied and

reviewed the different research papers to find the various filters and segmentation techniques,

algorithms to brain tumor detection.

II. LITERATURE REVIEW Minakshi Sharma and Dr. Saurabh Mukherjee [1] in 2016 had compared the algorithms for

determining the tumor present in the brain. The comparison has been done among FCM, FCM+K-

Means, GA and ANFIS which gives improved accuracy, sensitivity and specificity. Amanpreet Kaur

and Gangandeep Jindal [2] in 2015 has given an approach through which tumor can be detected

effectively using Genetic Algorithm. It has been applied to reduce the population and then detecting

the tumor present in the brain. K. Selvanayaki and Dr. P.Kalugasalam [3] in 2013 have suggested an

algorithm for detecting tumor. The work has been done to compare the results of PSO, ACO and GA.

Meghana Nagori, Shivaji Mutkule and Praful Sonarkar [4] in 2013 has proposed a new approach of

detecting tumor in the brain. The comparison has been shown among various algorithms to achieve

accuracy in the detection of brain tumor. Dina Abdul Pahab, Samy S.A. Ghoniemy and Gamal

M.Selim [5] in 2012 have given an approach to detect tumor present in the brain by using

Probabilistic Neural Network Techniques. The PNN method is based on LVQ. Mehdi Jafari and

Reza Shafaghi [6] in 2012 have presented a method to detect brain tumor using GA and SVM. The

set of features has been extracted by GA and then these features have been classified using SVM.

Rabeb Mezgar, Ali Mahjoub. Mohamed, Salem. Randa asnd Mtibaa. Abdellatif [7] in 2012 has

proposed a method for detection of brain tumor by applying an algorithm of Expectation-

Maximization. A. Padma and R. Sukanesh [8] in 2011 has given an approach in which tumor has

been detected. The work shows that features have been extracted using Wavelet based and SGLDM

method. M. Guslen, A.E. Smith and D.M. Tate [9] has used genetic algorithm with curve fitting. The

research paper has used a sequential evolution mechanism which has been used to overcome

premature convergence to local minima when some terms are dominating others in magnitude. The

literature review shows that various methods have been employed to detect tumor in the brain. The




existing work has detected the tumor by segmenting the image by applying segmentation techniques.

But the rate of detection of tumor has been evaluated on the basis of sensitivity, specificity which is

not efficient for the detection and for the treatment of tumor. Hence, a new algorithm has been

proposed which will prove to be more efficient in detection of tumor and the performance will be

evaluated on the basis of sensitivity and specificity.

III. THEORY A. Genetic Algorithm:

A genetic algorithm is a search heuristic and iterative procedure. It involves a set of individuals

which is represented by a finite length of symbols, and encoded them into a possible solution in a

given problem. The procedure of genetic algorithm is as follows: the initial population is selected

randomly. At each evolutionary step which is called as generation, the individual in the selected

population is decoded according to some predefined parameters which is known as fitness function.

The new population is created by selecting population according to the fitness function obtained in

the previous iteration. The individual having high fitness function have the chance of reproducing

new population and those who have low fitness function are discarded. After then crossover is

applied to generate new offspring from the two selected individual called as parent who have best

fitness function by exchanging their properties. Then, the mutation is applied to prevent premature

convergence by selecting new points randomly. The bits are flipped with small probability PM. The

termination condition is specified by specifying fixed number of generation or achieving an

acceptable fitness level [3]

B. Sustains Vector Machine: Support Vector Machine is technique which is used for classification and has been proved that it has

better accuracy and computational advantages then conventional classification methods. The

technique performs training at the initial step which is based on predefining some features [6]. The

non-linear classification is performed with Kernel Trick. It is independent of the dimensions of the

feature space. It is a supervised learning technique and can be used for classification of n classes. It

can perform as a powerful tool by combining linear or non-linear functions. It has got its application

in medical imaging and data mining [8].

C. Curve Fitting: Curve Fitting is the process which approximates a function of closed form from a given data set. The

mathematical equations are used for analyzing and interpretation of data set to express it in closed

form. It is a preliminary step to several techniques which is used to solve and model problems. To

approach curve fitting there are several ways and methods available and few techniques are available

for complex functions as well [9].

IV.PROPOSED METHOD

The proposed work has been implemented using Genetic Algorithm, Curve Fitting and Support

Vector Machine. In this paper, Genetic Algorithm has been old to produce segment of the image.

The segments obtain with applying GA might be losing some information in neighboring segments.

To section image properly, with no the loss of information Curve Fitting has been used. Curve

Fitting has been used to get better the practice of segmenting. After segmenting the image, the

features have been extracted from the segments. These features are then classified using Support

Vector Machine. The classified data helps in determining the tumor of the brain using features which

have been extracted.

The proposed method has been explained with the assist of flow chart in Fig.1:-




Start

Input Image

Apply Genetic Algorithm

Apply Curve Fitting

Feature Extraction Repeat Algo for Better Result

Classification

Result

Stop

Fig.1 Flow Chart of Proposed Method

In this paper, initially training has been performed using Support Vector Machine on few sets of

tumor and non-tumor images as shown in Fig 2. The narrative like mean, median, range filter and

entropy has been calculated for tumor and non-tumor images which helps in classify among healthy

and non-healthy image. The resources then proceeds to section the input MRI brain image. The GA

has been applied to division the images by passing on the entropy as optimization parameters. Going

by the number of generations the Curve Fitting has been applied to get better the threshold which has

been derived using GA. The threshold determined for segmenting the MRI image strength behind

their information. because a result, Curve Fitting has been employed. On formative the threshold

obtained from Curve Fitting the MRI image segments. The features are extracted of the segments

approximating mean, median, standard deviation, texture, energy, edge, dissimilarity and entropy.

a. b. c. d.

Fig. 2 a) non- tumor image b) non- tumor image c) tumor image d) tumor image

The description extracted are classify according to the training performed. The categorization has

been performed by using sustain Vector Machine. The segments are classified on the basis of

features extracted. It results in detecting the tumor close by in the segment.

The proposed means is organism compared through the existing method in which Mahalanobis

Distance is used. It determines the detachment of two voxels [7].




V. EXPERIMENTAL RESULTS The proposed method begins by performing training on few sets of tumorous and non-tumorous

image as shown in Fig 2.

Fig. 3 Input MRI Image

Fig. 4 Best and Mean Fitness Value

The Fig.4 gives the Best and Mean Fitness Value as - 16.167 and 12.3222 acquire on pertain Genetic

Algorithm.

Fig.5 Curves obtained on Segmentation




The Fig.5 displays the curves obtained on segmenting the images into 5 segments

Fig. 6 Five Segments

The Fig.6 shows the five segments acquire following applying curve fitting.

Fig.7 Tumor Detected

The Fig.7 shows that finally tumor has been detected which has been shown by black curve.

TABLE 1.Parameters designed for obtaining segmentation

Parameters

Set

Values

Obtained

Values

Total Segments 5 5

Solution 0 0

Tolerance

Population Size 16 16

Maximum Generation 1000 1

Maximum Time(sec) 60 0.2990




It has explained in Table 1. that five segments are used with population size of 16 maximum

generation used is 1 out of 1000 in maximum time of 0.2990 sec with solution tolerance 0.

TABLE 2.Parameter values obtained when MRI applied 50 times

Parameters Mahalanobis Distance Proposed Method

Training Time(sec) 0.038982 0.056412

Matching Time 0.0128832 0.024135

True Positive 0.7846 0.9866

True Negative 0.9035 0.9798

False Positive 0.0965 0.0202

False Negative 0.2154 0.0134

Accuracy 0.8259 0.9898

Precision 0.8914 0.9867

Recall 0.7846 0.9866

F-measure 0.8156 0.9866

The Table 2. shows the parameters used to determine the efficiency of the proposed method with the

Mahalanobis Distance. It has been cleared from the table that the proposed method gives 16.39%

accuracy and 9.53% precision then the Mahalanobis Distance.

VI. CONCLUSION

In this paper, an algorithm has been proposed for detect the tumor. Genetic Algorithm, Curve Fitting

and Support Vector Machine has been employed to detect the image which shows the location where

tumor is present.

The proposed method has been compared with the previous method using Mahalanobis Distance on

the basis of few parameters on the Tumor images 50 times which has optimized the results. 16.39%

accuracy and 9.53% precision has been obtained on tumor images when applied 50 times. This result

shows that the proposed method proves to be highly beneficial for detection of tumor.

REFERENCES [1]. Minakshi Sharma, Dr. Saurabh Mukherjee, “Fuzzy C- Means, ANFIS and Genetic Algorithm for Segmenting

Astrocytoma –A Type of Brain Tumor” International Journal of Advanced Research in Computer Science and Software

Engineering, Vol. 3, Issue 6, June-2016, pp. 852-858.

[2]. Amarpreet Kaur, Gagandeep Jindal, “Overview of Tumor Detection using Genetic Algorithm”, IJIET, Vol. 2, Issue

2, April 2015.

[3]. K.Selvanayaki, Dr.P.Kalugasalam, “Intelligent Brain Tumor TissueSegmentation from Magnetic

Resonance Image (MRI) using MetaHeuristic Algorithms”, JGRCS, Vol. 4, February 2015.

[4]. Meghana Nagori, Shivaji Mutkule, Praful Sonarkar, “Detection of Brain Tumor by Mining MRI Images”,IJARCCE ,

Vol.2, Issue 4, January 2013.

[5]. Dina Aboul Dahab, Samy S. A. Ghoniemy, Gamal M. Selim,“Automated Brain Tumor Detection and

Identification Using Image Processing and Probabilistic Neural Network Techniques”, IJIPVC, Vol. 1, Issue 2, October

2012.

[6]. Mehdi Jafari, Reza Shafaghi, “A Hybird Approach for Automatic Tumor Detection of Brain MRI Using Support

Vector Machine and Genetic Algorithm”, GJSET, Issue 3, 2012, pp.1-8.

[7]. Mezgar. Rabeb, Ali Mahjoub. Mohamed, Salem. Randa and Mtibaa. Abdellatif, “Brain MRI Image

Segmentation in View Tumor Detection: Application to Multiple Sclerosis”, Springer, 2012, pp. 380-390.

[8]. A.Padma, R. Sukanesh, “Automatic Classification and Segmentation of Brain Tumor in CT Images using

Optimal Dominant Gray level Run length Texture Features”, IJACEA, Vol.2, No. 10, 2011.

[9]. Gulsen. M, Smith. A. E and Tate. D. M, “A genetic algorithm approach to curve fitting”, International Journal of

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optimization technique, to detect brain tumor in mri€¦ · optimization technique, to detect...

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