ISSN Vol.08,Issue.23, December-2016, Pages:

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1 ISSN Vol.08,Issue.23, December-2016, Pages: A Review Paper on Different techniques of Feature Extraction and Classification of MRI Images SHEETAL S. SHIRKE 1, JYOTI A. KENDULE 2 1 Dept of E & TC, Sveri's College of Engineering, Pandharpur, India, shiatalshirke32@gmail.com. 2 Dept of E & TC, Sveri's College of Engineering, Pandharpur, India, jyotikendule@rediffmail.com. Abstract: In this paper, a survey has been made on various existing MRI image processing techniques used in the brain tumor detection. MRI plays a very important role for radiologists to diagnose and treat brain tumor patients. Study of the medical image by the radiologist is a time consuming process and also the accuracy depends upon their experience. Thus, the computer aided systems becomes very necessary as they overcome these limitations. There are various feature extraction and classification methods which are used for detection of brain tumor from MRI images. Keywords: Gray Level Co-Occurrence Matrix, Principle Component Analysis, Neural Network, MRI Image, Tumour Detection, Feature Extraction, Discrete Wavelet Transform, Support Vector Machine. I. INTRODUCTION This paper presents review indented to give an overview of the state of the art in magnetic resonance imaging (MRI) - based medical image analysis for brain tumor detection. Magnetic resonance imaging (MRI) of nervous system uses magnetic fields and radio waves to produce high quality twoor three-dimensional images of nervous system structures without use of ionizing radiation (X-rays) or radioactive tracers. Human brain is a complicated organ. It commands our body, receives information, examines information, and reserves information (our memories). Brain tumors are abnormal and uncontrolled proliferations of cells. Some originate in the brain itself, in which case they are termed primary. Others spread to this location from somewhere else in the body through metastasis, and are termed secondary. Primary brain tumors do not spread to other body sites, and can be benign. Secondary brain tumors are always malignant. Benign tumors are noncancerous cells and malignant tumors are cancerous cells.. The first types do not invade brain or other tissues. But they need to be treated because they might harm the neighboring tissues or other vital organs. A malignant brain tumor invades normal tissue or contains cancerous cells either from the brain or other parts of the body. Both types are potentially disabling and life threatening. Because the space inside the skull is limited, their growth increases intracranial pressure, and may cause edema, reduced blood flow, and displacement, with consequent degeneration, of healthy tissue that controls vital functions. Brain tumors are, in fact, the second leading cause of cancer related deaths in children and young adults. So patients with either benign or malignant tumors, needs immediate recovery treatment after the diagnosis. Classification of tumor is to identify what type of tumor it is. The conventional methods, which are present in diagnosis, are Biopsy, Human inspection, Expert opinion and etc. In early stage brain tumor diagnose mainly includes Computed Tomography(CT) scan, Magnetic Resonance Imaging (MRI) scan, Nerve test, Biopsy etc. At present, doctor usually refer MRI image and make the report about the MRI analysis of the patient. Texture analysis is an important task in many computer applications of Computer image analysis for classification, detection or segmentation of image II. FEATURE EXTRACTION Large amount of information is needed to represent an image and this information occupies large amount of memory. The features are extracted from the image. The extracted features contain the relevant information about the image. The extracted features are used as input to the classifier for classification. Features are said to be properties that describes the whole image. It can also refer as an important piece of information which is relevant for solving the computational task related to specific application. The purpose of feature extraction is to reduce the original dataset by measuring certain features. The extracted features acts as input to classifier by considering the description of relevant properties of image into feature space. There are various techniques of feature extraction from an image. Some important techniques are DWT (Discrete Wavelet Transform), GLCM (Gray level Co occurrence Matrix), PCA (Principal Component Analysis), etc. These are most powerful techniques used for purpose of feature extraction from MRI images. After extracting statistical features the role of feature reduction technique comes in, it tries to reduce less centric features form all the features, and ultimately reduce the dimension of the data. A. Feature Extraction Using Discrete Wavelet Transform (Dwt) The discrete wavelet transform (DWT) is a linear transformation that operates on a data vector whose length is an integer power of two, transforming it into a numerically 2016 IJATIR. All rights reserved.

2 different vector of the same length. It is a tool that separates data into different frequency components, and then studies each component with resolution matched to its scale. The wavelet is a powerful mathematical tool for feature extraction, and has been used to extract the wavelet coefficient from MR images. Wavelets are localized basis functions, which are scaled and shifted versions of some fixed mother wavelets. The main advantage of wavelets is that they provide localized frequency information about a function of a signal, which is particularly beneficial for classification. In Numerical analysis and functional analysis, a discrete wavelet transform (DWT) is any Wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transform is temporal resolution: it captures both frequency and location information (location in time). The discrete wavelet transform (DWT) yields a fast computation of wavelet transform through a series of filters. The DWT is realized through successive low pass and high pass filtering of the discrete time signal, three level wavelet decomposition. B. Feature Extraction Using Pca PCA is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (i.e., uncorrelated with) the preceding components. Principal components are guaranteed to be independent only if the data set is jointly normally distributed. PCA is sensitive to the relative scaling of the original variables. Depending on the field of application, it is also named the discrete Karhunen Loève transform (KLT). The major drawback of using this technique is that Principle component analysis (PCA) reduces the dimensions and overcomes the computational complexity. PCA has also been used for best feature extraction. PCA is a transformation that converts the set of correlated variables into set of uncorrelated variables. PCA has reduced to 1024 feature vectors. DWT along with PCA & KSVM with GRB kernel achieved the best accurate classification result 99.38% than other kernels [12] C. Feature Extraction Using Glcm Gray-level co-occurrence matrix (GLCM) is the statistical method of investigating the textures that considers the spatial relationship of the pixels. The GLCM functions characterize the texture of an image by calculating how frequently pairs of pixel with specific values and in a specified spatial relationship that present in an image, forms GLCM. This forms the extraction of statistical measures from this matrix. [10]. Here we are using Statistical approach to texture analysis among the four approaches (Structural, SHEETAL S. SHIRKE, JYOTI A. KENDULE Statistical, model based and Transform). It is the most broadly used and more generally applied method because of its high accuracy and less computation time. A gray level cooccurrence matrix (GLCM) contains information about the positions of pixels having similar gray level values. It was initially proposed by R.M. Haralick, the co occurrence matrix illustration of texture features explores the grey level spatial dependence of texture. A co-occurrence matrix is characterized for an image by the method of partitioning of co-occurring ideals at a given offset. Whether considering the gray scale values of the image or various measures of colour, the Gray level co-occurrence matrix is mainly used for the measurement of texture of the image. Because Gray level co-occurrence matrices are consistently large and occasional, Features generated using this method is generally defined as Haralick features. GLCM calculates the cooccurrence matrix of an image by computing how often a pixel with a certain intensity i occurs in relation with other pixel j at a certain distance d and orientation. Features Extracted by using GLCM and classified with RB-Kernel gives 100% classification accuracy better than PCA. [8]. Texture based feature selection using GLCM and SVM classifier combination has proved to get accurate results but only for smaller dataset [13]. III. ClASSIFICATION Approaches used for classification falls into two categories. The first category is supervised learning technique such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) which are used for classification purposes. The neural network classifier gives less accuracy because it requires large amount of training data set for high accuracy which is practically not feasible. The neural network classifier system is work with learning capabilities. Feed Forward Back Propagation method is used in the neural classifier. This is a supervised learning based method in which desired output is available. In this proposed method comparing the result by adjusting the weight and get the good result. Neuro-Fuzzy classifier is more complex and time consuming. Neuralfuzzy hybrid model is performing successfully where other individual methods do not. The classification performance parameters such as accuracy, specificity, sensitivity are comparatively higher in hybrid neuro-fuzzy model than the individual fuzzy and neural classifiers. But the convergence time period of neuro-fuzzy logic classifier is more. To improve the convergence time, for Optimization Genetic Algorithm technique is used for optimization process. This reduces the processing time. A. Classification using PNN-RBF Training Probabilistic Neural Network is a classification of Radial Basis Function (RBF) network. The fundamental architecture of this Neural Network is having various layers, Input Weights Layer, a Rule Layer, and an exhibiting layer called Output Layer. The Rule layer or the Pattern Layer constitutes a neural implementation of a classifier and their performances. The class dependent Probability Density Functions (PDF) is approximated using an estimator called

3 A Review Paper on Different techniques of Feature Extraction and Classification of MRI Images the Parzen estimator. It determines the Probability Density Functions by reducing the awaited danger in classifying the training set incorrectly. Using the Parzen estimator, the classification gets closer to the true underlying class density functions as the number of training samples increases, the pattern layer consists of a processing element corresponding to each input vector in the training set. Fig.1. Architecture of Probabilistic Neural Network All the output parameters in the pattern layer is tested and trained based on the Neural Network once. An element is trained to return a high output value when an input vector matches the training vector. To obtain more generalization a factor is included to smooth the signals while training the network. The pattern layer classifies the input vectors based on ranking level, where only the highest smoothing vector to an input vector wins and generates an output. Hence only one classification category is generated for any given input vector. If there is no relation between input patterns and the patterns programmed into the pattern layer, then no output is generated. The Probabilistic networks classify on the basis of Bayesian theory, it is essential to classify the input vectors into one of the two classes in a Bayesian optimal manner. This theory provides a cost function to comprise the fact that it may be worse to misclassify a vector that is actually a member of class A than it is to misclassify a vector that belongs to class B. The Bayes rule classifies an input vector belonging to class A as: (1) Where, PA - Prior probability of occurrence of patterns in class A CA - Cost associated with classifying vectors JA(x) - Probability density function of class A There are many advantages of using PNN, Some of them are Training speed is Fast, Training is Easy, Robust in noise conditions, Can be used in Real-time, PNN operations are organized into multi-level feed. PNN classification of the image encryption avoides explovation of the image. Modified Probabilistic neural network model provides 100% accuracy whereas Back propagation network based classification produces 77.56% of accuracy. CBIR based on texture retrieval along with SVM classification suitable for detecting multiple sclerosis and tumors. Probabilistic Neural Network (PNN) with mathematical technique called Principal Component Analysis (PCA) is used to give more accurate and fast solution than the Conventional methods of brain tumor classification [15]. PNN Model based on Learning Vector Quantization (LVQ) performance is measured with 100% accuracy [4]. B. Mri Brain Classification Using Neural Network In this paper Ibrahim et al. [24] proposed a Neural Network technique for the purpose of better classification of brain magnetic resonance images. The technique consists of three stages preprocessing, dimensionality reduction and classification. They apply the contrast adjustment in preprocessing stage. The result of the preprocessing has much sharper and contrast enhanced image. The dimensionality reduction is performed using PCA (Principal Component Analysis). PCA is one of most fundamental linear dimensionality reduction technique. In classification back propagation Neural Network has been implemented. The whole research is performed over MRI brain images. The experimental result of proposed algorithm has shown a efficiency with an accuracy of 96.33% in the classification. The accuracy is 70% using KNN classifier and 72.5% by using BPNN classifier [9]. C. Classification Using Artificial Neural Network The main objective of this study is to examine the pros and cons of two algorithms in a variable environment. Here Deepa and Devi [17] develop the system to exploit the capability of Back Propagation Neural Network and Radial Basis Function Neural Network for task of classification of brain magnetic resonance image to either cancerous or non cancerous. The major problem of the classification is optimal selection of features to describe the images. Classification is done on basis of symmetry of the brain image. The results demonstrate that performance of Radial Based Function Network (RBFN) is better when compared with the Back Propagation Network (BPN) with the classification accuracy. D. Classification using Extension Neural Network Since MRI has capability to generate multiple slices of same section of tissues. As a consequence the analysis of the slice becomes more complicated. In this paper Wang et al. [18] present a solution to this drawback by implementing Extension Neural Network theory. The intent of this term paper is to combine the Artificial Neural Network and the Extension Theory. The concept of adding a double layer Neural Network with extended distance will enhance the training interval for Neural Network and also make the recognition rate high E. Classification of Brain Cancer using Artificial Neural Network Joshi et al. [20] here presented a computer based system to classify various types of brain tumor using magnetic resonance image. The features are extracted using Gray Level Co- occurrence Matrix. A Neuro Fuzzy Classifier is developed for the classification. The whole technique is sub

4 divided in two phases, first phase of learning and training, based on the features evaluated using GLCM and the second phase of recognition and testing of the affected MRI images. The system designed is tested efficiently for the classification of the brain MRI having any abnormalities. The scope of the system can be advanced to improve using some other medical images. ANN provides high classification accuracy [25]. Grey Level Co-occurrence Matrix (GLCM), Artificial Neural Network (ANN) and Back Propagation Network [14]. It achieves a balance between the net s memorization and generalization. It detects Astrocytoma type of tumors efficiently. F. Mri Brain Classification Using Support Vector Machine In this paper Nandpuru et al. [22] proposed a classification technique using SVM (Support Vector Machine) with linear and different kernels, to identify the normal and abnormal of brain MRI images. The technique follows the three method of image classification. The stage of feature extraction is performed using GLCM and all the texture features are calculated. After sorting of all features the authors has applied the feature reduction through application of PCA (Principal Component Analysis). And finally the classification is done by applying SVM. The result is tested with SVM having different essence, linear, quadratic and polynomial. The final result shown is that quadratic kernel has maximum accuracy rate of classification SVM works well with this combination proves to be robust and produces high quality results. Texture feature coding Method and SVM method provides 88% accuracy. Multi classification SVM helps to extracts the boundaries of 7 kinds of encephalic tissues successful and proved satisfactory generalization accuracy. G. Classification Using Hybrid Least Square Support Vector Machine and Chaotic PSO In this paper Sivapriya et al[23] explore the implementation of (Least Square Support Vector Machine) training LS-SVM with Chaotic PSO (Particle Swarm Optimization) for the purpose of distinguish of brain MRI images. Implementing an efficient method is more objective than a manual process of medical analysis of the brain MRI. The whole projected algorithm includes three major steps feature extraction, feature selection and classification. The final result of classification is compared with two different classifiers SVM-PSO and LSSVM-PSO. The proposed technique i.e. LS-SVM have better performance as compared with above when it trained with Chaotic PSO, in term of parameter sensitivity specificity and accuracy. Least Squares Support Vector Machines (LS-SVM) compared with k-nearest Neighbor, Multi layer Perception and Radial Basis Function Networks [39]. Analysis of the statistical features like sensitivity, specificity, and classification accuracy proved that LS-SVM yields better. IV. CONCLUSION AND FUTURE SCOPE The classification of the brain MRI provides strength to the radiologist and physician and leads them to confident SHEETAL S. SHIRKE, JYOTI A. KENDULE decision about the patient diagnosis. The radiologist can predict the patient s situation better. Although the classification of the brain MRI has performed optimal in detecting the abnormality however the algorithm of the classification of the brain MRI should be optimized for the all the brain disease. This optimization can be achieved through the perfect selection of which classification technique should be use. The result of the classification predicts the number of the slices having abnormalities. In this paper categorization of feature extraction and classification of brain MRI images is performed. This paper focus on the various combined techniques and it also focus on sequence of MRI image processing i.e. feature extracting and then classifying about what type of tumour it is. This result can further be utilized for detection of the brain tumor in human being whether it is critical and calculate the stage of the tumor i.e. the size and the critical level of the patients V. REFRENCES [1]Natarajan P, Krishnan.N, Natasha Sandeep Kenkre, Shraiya Nancy, Bhuvanesh Pratap Singh, "Tumor Detection using threshold operation in MRI Brain Images", IEEE International Conference on Computational Intelligence and Computing Research, [2]Dipali M. Joshi, N. K. Rana, V. M. Misra, " Classification of Brain Cancer Using Artificial Neural Network", IEEE International Conference on Electronic Computer Technology,ICECT,2010. 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