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A Survey Paper on Magnetic

Resonance Image (MRI) Classifier

W. Jenifa

Department of ECE

Mepco Schlenk Engineering College, Sivakasi.

 Abstract - Magnetic resonance imaging (MRI) is a

technique boon to the medical field, which is used in radiology

to diagnosis the anatomy and physiological processes of the

body.  It can be used in detecting brain tumour, Liver Cancer,

spinal column and also in blood vessels malfunctioning. Mostly

MRI image is used compared to compute topography (CT) scan

image because MRI give clear differences between normal and

abnormal tissue and it also has better contrast of image. It

provides High Resolution Image.

 

I.  INTRODUCTION

 It is very important to provide a clear Image of

diseased organ so only physician can correctly diagnose and

provide an appropriate treatment to the patient. The recent

technique used for MR image classification is explained.

Many methods use a supervised classification framework.

Supervised classification includes a) training images with

known labels are aligned to a common space, b) distinguished

feature extraction, c) extracted features calculated from

regions of interest (ROIs). These ROIs are either predefined

or learned from the data.

II.  CLASSIFIER

The classifier-training algorithm uses pre-classified examples

to determine the set of parameters required for proper

discrimination.The algorithm then encodes these parameters

into a model called a classifier.

Image Classifier can be divided into three types are as

follows:

A. Supervised Classification Framework

 This Framework can be established by using concepts of

machine learning,

1) Ensemble Learning

 It involves training multiple classifiers on different

feature sets. It gives better performance than individual

classifier.

2) Feature Ranking

 It mainly focuses on ordering features based on their

relevance to classification.In supervised classifier has higher

accuracy than unsupervised classifier.

B. Semi- Supervised Classification Framework

It is the combination of both supervised and unsupervised

framework.

C. Unsupervised Classification Framework

Unsupervised techniques are self organizing method.

III.  TYPES OF CLASSIFIER

There are different types of classifier are discussed in this

section as follows.

A. Neural Network Classifier

 Neural network is a set of connected input/output units

and connection has its unique weight. This networks have the

remarkable ability to derive meaning from complicated or

blurred image and can be used to extract original information

and to detect image that are too complex to be noticed by

either physician or other computer techniques. These are well

suited for continuous valued inputs and outputs the learning

phase, network learns by adjusting weights so as to be able to

predict the correct information. The neural network is trained

for either continuous or discrete valued features to analyze the

efficiency of the network for varying inputs to identify the

tumor detection In Medical Resonance images noise gets

added due to operator performance. This may further leads to

inaccuracies categorization which is very severe. Artificial

intelligent methods with neural networks have revealed

excellent potential for experimentation .Neural Networks

generalize better on unseen data and is easier to train and

doesn’t have any local optima.

1) Advantage

Neural network training algorithm is judged by a set of

conflicting requirements such as simplicity, flexibility, and

efficiency.

B.  Support Vector Machine (SVM) Classifier

Support vector machine (SVM) is widely used classifier

in Medical Image and it gives more accuracy comparable to

neural-network. It also used in object detection & recognition,

content-based image retrieval, text recognition, biometrics,

speech recognition.

1) Support Vector Machine (SVM) Algorithm

Step 1: Choose a kernel function

  Step2.: Choose a value for C

  Step3: Solve the quadratic programming problem (many   

          software packages available)

  Step4:Construct the discriminate function from the

support vectors .

2) Approaches of SVM Classifier

    a)  Nonlinear Separable Case

 SVM model using a sigmoid kernel function is

equivalent to a two-layer, feed-forward neural network.

b)   Linear Separable Case

In most of the real-world applications of SVM we

combine the kernel trick and the soft margin and use them

together.

c) Kernal Trick

A kernal function is defined as a function that

corresponds to a dot product of two feature vectors in some

expanded feature space

SVMs classifier techniques suitable for binary

classification takes as input labeled data from two classes and

outputs a model file for classifying new unlabeled/labeled

data into one of two classes. It is proved that this kennel

technique helps to get more accurate result.

do not have to be defined. Do not use abbreviations in the title

unless they are unavoidable.

3)  Advantages

SVM have high approximation capability and much

faster convergence.

4)  Disadvantages

 SVM does not provide accurate results for a large

data due to the training complexity of SVM.  

 It is highly dependent on the size of data.

C. Hybrid Classifier

The term hybrid refers to combination of any two classifier

which gives better efficiency than the individual classifier

Adaptive neuro-fuzzy inference systems (ANFIS technology)

it includes the advantages of both the ANN and the fuzzy

logic systems the classification of different brain images using

hybrid classifier .The hybrid systems have wider scope, dual

advantages  this technique increases accuracy.

1)  Advantages

 It does not require prior human expertise.

 It uses membership functions and desired dataset to

approximate.  

 It provides greater choice of membership functions.

D.  Naïve Bayes Classifier

All naive Bayes  classifiers has the value of a particular

feature is independent of the value of any other feature, given

the class variable.For example, in a brain  if tumor is detected

a normal it may be round, and about15 cm in diameter

depends upon its above features. A naive Bayes classifier

considers each of these features to contribute independently to

the probability that this tumour is an, regardless of any

possible correlation between the color, roundness, and

diameter features.

1)  Advantages

Naive Bayes requires a small number of training data to

estimate the parameters which is necessary for classification

E.   k-means Clustering

k–Means clustering classifier was proposed to segregate

different intensity pixels in group of pixels having similar

pattern or characteristics.

The basic K-Means  clustering algorithm is given below:

  Step 1: K cluster centers are selected based on some       

              heuristic or random.

  Step 2: Assigning pixels to those cluster center having  

           minimum distance from that center based on  

           certain characteristics.

  Step 3: Recomposing the cluster centers by averaging

all  

           the pixels in the clusters.

  Step 4: Repeat the last two steps till no more new

cluster  

           center is formed. The segregation of pixels is  

           done on the basis of intensity, size colour,  

           texture, location etc.   

1)  Advantages

 K-MEANS clustering algorithm is simple

 It has relatively low computational complexity

2) Disadvantage

 Sometimes it fails to give accurate results

F. K- Nearest Neignbour(KNN)

 KNN is a non parametric classifier. This classifier

provides output in the form of class membership and the

object is related to the class with maximum resemblance. In

the training stage of the KNN classifier the input dataset is

divided into k classes, with each class containing only the

inputs belonging to its class. This is done by finding the

spearman distance between all inputs and distributing them

into k classes.Different distance measures can be adopted as

the reference measure for classification.  

1)  KNN Algorithm

Step 1: Training set includes classes.

Step 2: Examine K items near item to be classified.

Step 3: New item placed in class with the most number of  

         close items.

denotes +1

denotes -1

Step 4: O (q) for each tuple to be classified (Here q is the  

         size of the training set.).

   The K-nearest neighbour classification is performed by

using a training data set which contains both the input and

the target variables and then by comparing the test data which

contain only the input variables to that reference set the

distance of the unknown to K nearest neighbours determines

its class assignment by either averaging the class numbers of

the K nearest neighbour points

2) Advantage

 It is simplest technique conceptually

 It provide better accuracy

 Computationally easier

G. Radial Basis Function (RBF)

RBF network is trained by supervised manner. The

generalization property of RBF is to train a network on a set

of input vectors and get the result without even training the

network on all the possible input and output pairs. The Radial

Basis Function (RBF) network and Back Propagation network

performs similar function mapping. BP network is the global

network where RBF network is the local network.

1)  Advantage

 The Radial Basis Function (RBF) is used for adjusting

the weights and minimizes the errors and gives the

approximate results.

H. Artifical Neural Network (ANN)

Artificial neural networks have Proven themselves as

proficient classifiers and are particularly well suited for tumor

classification.ANN models widely used in early detection of

cancer. An ANN is a computational model consisting of a

number of highly interconnected processing elements called

neurons.  

These neurons are organized in layers. The neurons are

connected with each other through links (connections). Each

link is assigned a weight. A neuron also has an associated

bias. The output of a neuron is the output of the activation

function; argument of this function is the sum of incoming

signals multiplied with respective weights, plus the bias.

Activation function is usually sigmoid. The geometry and

functionality of neural network resemble the human brain.

The basic form of ANN is the Multilayer Perceptron (MLP)

which is a neural network that updates the weights through

back propagation. The neural network has been trained to

adjust the connection weights and biases in order to produce

the desired mapping.

1)  Advantage

 It accurately segments tumorous and normal brain

tissues.

 It  is reliable, fast, automatic and robust diagnosis

system

a. Neuro Fuzzy Classifier

Neuro-fuzzy classifier is used to find whether the input

image is tumor image or not. The Neuro fuzzy and the Feed

Forward Neural Network (FFNN) and the obtained results are

analyzed in terms of     sensitivety and accuracy.Due to the

fuzzy and deterministic characteristics of MRI brain image,

various tissues are complex and heterogeneous.

 Fuzzy C-means algorithm (FCMA) is a nonlinear

iteration optimization method based on objective functions

I. Decision Tree

  This classifier  divides datasets into smaller and more

uniform datasets. The subsets are broken down into further

subsets via different variables and attributes. The data is

partitioned based on maximum reduction in deviance over all

splits of all nodes to choose the next split

1)  Advantage

  It requires very little computational load.

 does not require extensive network training.

 It is best suited for non-parametric data

I. CONCLUSION

 This survey gives an information regarding MRI Image

Classifier which is used in medical field. Various Classifiers

are discussed in the above survey sections. Each classifier has

its own merits as well as demerits .Depending upon the

application, suitable classifier is used to get the better results

in diagnosis purpose.

REFERENCES

[1] Amer AI-Badarneh, Hassan Najadat, and Ali M AIraziqi. \"A                               

classifier to detect tumor disease in MRI brain images\". In:  Advances in

Social Networks Analysis and Mining (ASONAM), 2012 IEEEIACM

1nternational Conference on. IEEE. 2012, pp. 784787.  

[2] Ketan Machhale et al. \"MRI brain cancer classification using hybrid

classifier (SVM-KNN)\". In: Industrial Instrumentation and Control (1CIC),

2015 1nternational Conference on. IEEE. 2015, pp. 6065.  

[3] Yudong Zhang et al. \"A hybrid method for MRI brain image classification\".

In: Expert Systems with Applications 38.8 (2011), pp. 10049-10053

[4] .Jayachandran .A & R.Dhanasekaran ,”Brain tumor Detection and

Classification of MRI Using Texture Feature and Fuzzy SVM Classifiers”,

Research Journal of Applied Sciences, Engg and Tech 6(12):2264-2269,

2013  

[5] C. Ramalakshmi, A.Jaya Chandran,”Automatic Brain Tumor Detection in

MR Images Using Neural Network Based Classification”, int Journal of

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M, Parkin DM, Forman D, Bray, F. GLOBOCAN v1.1, 2012

[7] Matei Mancas, Bernard Gosselin, and Benoît Macq, “Tumor Detection using

Airways Asymmetry”, Engineering in Medicine and Biology Society, 2005.

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[8] V. Ulagamuthalvi, D. Sridharan; “Automatic Identification of Ultrasound

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[9] Vanitha. L. and Venmathi. A.R, “Classification of Medical Images Using

Support Vector Machine”, International Conference on Information and

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[10] S. N. Sulaiman and N. A. Mat Isa, \"Adaptive fuzzy-K-means clustering

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