Level based normal abnormal classification of mri brain images

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Level based normal abnormal classification of mri brain images

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME403LEVEL BASED NORMAL- ABNORMAL CLASSIFICATION OF MRIBRAIN IMAGESSumesh M. S.1, GopakumarC.2, RejiRajan Varghese3, Abraham Varghese41(Computer Engineering, College of Engineering, Chengannur, Kerala, India,)2(Dept. of Electronics & Communication Engineering, College of Engineering, Chengannur,India)3(Senior Resident, Radio diagnosis, Cochin Medical College, Cochin, India)4(Dept. of Computer science and Engg, Adi- SankaraInsitute of Engineering and Technology,Kalady, India)ABSTRACTThis work proposes a new concept for the normal- abnormal classification of MRIbrain images, a level based approach, and compare the result with the existing methods. Theexisting works does not consider the anatomical structure of the brain slices for theclassification of MRI brain images. In the aspect of image processing, the anatomicallysimilarity of the brain slices can be treated as the similarity of brain slices in the viewingaspect along with the actual anatomical structure. This work aimed to prove that theconsideration of the anatomical structure for the normal– abnormal classification willimprove the result of the classification.The existing work shows that the feature vector, statistical features along with graylevel co-occurrence matrix (GLCM) features with support vector machine (SVM) classifierproduce better results than other methods. It uses statistical features along with GLCMfeatures as feature vector and SVM classifier. Related works in current literatures for thenormal/abnormal classification of MRI images does not consider the anatomical structure ofthe brain slices. Because of the dissimilarity in the anatomical structure, it may produceundesirable results. In this proposed work, the anatomical structure of the brain slices isconsidered for the classification. To accompany this level based concept is introduced here.In the level based concept, the brain slices are classified into four levels depending on thesimilarity in the anatomical structure to implement the normal/abnormal classification at thatparticular level.INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING& TECHNOLOGY (IJCET)ISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online)Volume 4, Issue 2, March – April (2013), pp. 403-409© IAEME: www.iaeme.com/ijcet.aspJournal Impact Factor (2013): 6.1302 (Calculated by GISI)www.jifactor.comIJCET© I A E M E
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME404Keywords: Brain tumour, Level based classification, Magnetic resonance imaging, Medicalimaging, Support vector machine.1. INTRODUCTIONMedical imaging is widely used for disease diagnosis and treatment evaluation.Medical imaging techniques and analysis tools enable both doctors and radiologists toidentify and diagnose various disorders [1, 2]. The medical image data obtained from bio-medical devices have important roles in disease diagnosis. MRI is a non-hazardous methodwhich detects signals emitted from normal and abnormal tissue, providing clear images ofmost tumours [3, 4]. The radiologist or doctor can identify abnormal tissues by examining theMRI slices based on the visual interpretation. The shortage of radiologists and the largevolume of MRI to be analysed make such readings laborious and cost expensive. Also themanual classification by mere visual interpretation of the radiologists may cause bad resultsdue to vision problems. This leads to automated system to aid the doctors and radiologists inthe identification of abnormal brain slices.To develop an accurate and sensitive automated system for the normal- abnormalclassification of MRI brain slices, it has to identify a good set of feature vectors that can besubstituted instead of the original image without losing its actual meaning and a goodclassifier. The related works suggests several feature vectors and classifiers which are shownin Table 1. This works shows that the combination of statistical features and GLCM features[6, 7] along with SVM classifier [8, 9] provides better results than the other methods.Table 1: Related woks for the classification of MRI brain slicesPre-processing Feature Extraction Feature Reduction ClassificationWAVELETTRANSFORM [10, 1],HISTOGRAMEQUALISATION[15].DWT [1, 8,14,16],GLCM [11,12 ],SLANTLETTRANSFORM[13].PCA [14,15 ],GA [16].SVM [11,12,13,16],ANN [14,15 ],K-NN [11,14,15],MLP [11].The proposed method also use the combination of statistical features along withGLCM features as feature vector and is used as the input to the SVM classifier. The relatedworks for the normal/abnormal classification of MRI images does not consider theanatomical structure of the brain slices. Because of the dissimilarity in the anatomicalstructure, it may produce undesirable results. So in this proposed work, the anatomicalstructure of the brain slices is considered for the classification. In the aspect of imageprocessing, the anatomical similarity of the brain slices can be treated as the similarity ofbrain slices in the viewing aspect along with the actual anatomical structure. To accompanythis level based concept is introduced here. In the level based concept, the brain slices areclassified into four levels depending on the similarity in the anatomical structure of the brainslices [17]. That is, classify the brain slices into one of the four levels and implement thenormal/abnormal classification at that particular level.
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME4052. METHODOLOGYThe proposed methodology for the normal- abnormal classification of MRI brainslices has 2 steps, feature extraction and classification. Significant difference in tissue types,observed in variety of texture measures of MRI, is used for this classification. The classifierhas 2 phases, training and testing phases. In training phase the statistical and GLCM texturefeatures of MRI brain slices along with its label and normality or abnormality details, aregiven as input to the classifier. In testing phase, if the feature vector of a new slice is given asinput to the classifier, a well-trained classifier can accurately classify it according to theparameters formed in the training phase. In the level based approach, the brain slices aregrouped into four classes according to the similarity of anatomical structure in visual aspectof the image and the above texture extraction, training and testing processes are done at eachlevel independently.2.1 FEATURE EXTRACTIONThe purpose of feature extraction is to reduce the original data set by measuringcertain properties, or features, that distinguish one input pattern from another [18]. Theextracted features provide the characteristics of the input type to the classifier by consideringthe description of the relevant properties of the image into a feature space. Most of thetumour is heterogeneous tissues and the mean values of relaxation times are not at allsufficient to characterize the heterogeneity of the different tumour types [3]. An alternativeapproach, which is being investigated within the framework of this study, is to apply textureanalysis to the T2 FLAIR images to describe quantitatively the brightness and texture of theimages. Texture analysis covers a wide range of techniques based on first- and second orderimage texture parameters. In the present study the statistical features based on image intensitylike mean & variance and features from gray level co-occurrence matrices (GLCMs) such asentropy, contrast, energy, inverse difference moment and correlation [ 6,7 ,11] are used toinvestigate the adequacy for the discrimination of normal and abnormal patient.The gray level co- occurrence matrix (GLCM) calculates how often a pixel with graylevel value occurs either horizontally, vertically, or diagonally to adjacent pixels with thevalue j, where i and j are the gray level values in the image. Haralick features [6, 7] based onGLCM is a proven technique to analyse the object with irregular outlines [6, 7]. Haralickintroduced fourteen textural features from the GLCM and out of these fourteen features fiveof the textural features are considered to be the most relevant. Those textural features areEnergy, Entropy, Contrast, Correlation and Inverse Difference Moment. Energy is also calledAngular Second Moment (ASM) where it measures textural uniformity [19]. If an image iscompletely homogeneous, its energy will be maximum. Entropy is a measure, which isinversely correlated to energy. It measures the disorder or randomness of an image [19].Next, contrast is a measure of local gray level variation of an image. This parameter takeslow value for a smooth image and high value for a coarse image. On the other hand, inversedifference moment is a measure that takes a high value for a low contrast image. Thus, theparameter is more sensitive to the presence of the GLCM elements, which are nearer to thesymmetry line x (i, i) [19]. The last feature, correlation, measures the linear dependencyamong neighbouring pixels. It gives a measure of abrupt pixel transitions in the image [20].
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME4062.2.1 FEATURES USEDStatistical FeaturesMeanܺത ൌ1X ‫כ‬ Y෍୶୧ୀଵ෍ xሺi. jሻ୷୨ୀଵVarianceV ൌଵଡ଼‫כ‬ଢ଼∑୶୧ୀଵ ∑ ሺxሺi. jሻ୷୨ୀଵ െ xതሻGLCM FeaturesEntropyൌ െ ∑୒୧ୀଵ ∑ ቀሺ୔ሺ୧.୨ሻୖቁ୒୨ୀଵ ݈‫݃݋‬ሺሺ୔ሺ୧.୨ሻୖሻ Energyൌ െ ∑୒୧ୀଵ ∑ ቀሺ୔ሺ୧.୨ሻୖቁଶ୒୨ୀଵContrastൌ െ ∑୒୧ୀଵ ∑ ሺi െ jሻ ቀሺ୔ሺ୧.୨ሻୖቁ୒୨ୀଵCorrelationൌെ ∑୒୧ୀଵ ∑ቀ౟ౠሺౌሺ౟.ౠሻ౎ቁିµ౮µ౯σ౮σ౯୒୨ୀଵInverse Difference Momentൌ ∑୒୧ୀଵ ∑ቀሺౌሺ౟.ౠሻ౎ቁଵାሺ୧ି୨ሻమ, i ് j୒୨ୀଵWhere P(i, j) is the GLCM Matrix, R is the total number of pixel pairs used for thecalculation of GLCM and ߤ௫, ߤ௬, ߪ௫ and ߪ௬ are the mean and standard deviation values ofGLCM values accumulated in the x and y directions respectively.2.2 CLASSIFICATIONThe aim of classification is to group items that have similar feature values intogroups. Classifier achieves this by making a classification decision based on the value of thelinear combination of the features. SVM is a binary classification method that takes as inputlabelled data from two classes and outputs a model file for classifying new unlabelled orlabelled data into one of two classes [1, 9,11].2.3 SUPPORT VECTOR MACHINESupport Vector Machine (SVM) is a binary classifier based supervised learningtheory, a recent advances in statistical learning theory. SVMs deliver state-of-the-artperformance in real-world applications such as text categorisation, hand-written characterrecognition, image classification, bio sequences analysis, etc. The basis of this approach isthe projection of the low-dimensional training data in a higher dimensional feature space,because in this higher dimensional feature space it is easier to separate the input data. Thisprojection is achieved by using kernel functions. So kernel functions provides the bridgebetween non-linear to linear. Thus kernel function is used to map the low dimensional datainto the high dimensional feature space where data points are linearly separable. There aremany types of kernels are available for SVM and this work uses the following kernels:Linear, Polynomial and radial basis function (RBF) [1, 9, 11].3. RESULTS AND DISCUSSIONSIn the proposed work, T2 FLAIR weighted axial MRI Brain images as input data set.Here two types of databases are used1. Simulated Brain Database.2. Brain Database of a Hospital
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME407The input data involved 100 patients (50 abnormal and 50 normal). At first thenormal- abnormal classification is done without considering the anatomical structure. In thisstage of the work a set of 320 brain slices, 160 normal and 160 abnormal, are used. Out ofthese two hundred images, 160 slices, 80 normal and 80 abnormal, are used for training andremaining hundred are used for testing.For level based normal- abnormal classification, the whole normal and abnormalimages are divided into 4 levels according to the similarity of the brain slices based on aviewing aspect of the images. Thus each level contains a total of 160 images with 80 normaland 80 abnormal. Out of these 160 images 80, 40 normal and 40 abnormal are used fortraining phase and remaining 80 are used for testing phase. Results are summarised in Tables3, 4 and 5.Table 3: Classification using Polynomial KernelLevel 1 Level 2 Level 3 Level 4 All LevelsTP 40 39 39 40 78FN 0 1 1 0 2TN 40 40 39 40 78FP 0 0 1 0 2Sensitivity (TPR) 1 0.975 0.975 1 0.975(FPR) 0 0.025 0.025 0 0.025Specificity (TNR) 1 1 0.975 1 0.975(FNR) 0 0 0.025 0 0.025Accuracy 1 0.9875 0.975 1 0.975Table 4: Classification using RBF KernelLevel 1 Level 2 Level 3 Level 4 All LevelsTP 40 39 39 40 73FN 0 1 1 0 7TN 40 39 38 40 72FP 0 1 2 0 8Sensitivity (TPR) 1 0.975 0.975 1 0.9125(FPR) 0 0.025 0.025 0 0.0875Specificity (TNR) 1 0.975 0.95 1 0.9(FNR) 0 0.025 0.05 0 0.1Accuracy 1 0.975 0.9625 1 0.90625
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME408Table 5: Classification using Linear KernelLevel 1 Level 2 Level 3 Level 4 All LevelsTP 40 40 39 39 72FN 0 0 1 1 8TN 40 38 39 40 73FP 0 2 1 0 7Sensitivity (TPR) 1 1 0.975 0.975 0.9(FPR) 0 0 0.025 0.025 0.1Specificity (TNR) 1 0.95 0.975 1 0.9125(FNR) 0 0.05 0.025 0 0.0875Accuracy 1 0.975 0.975 0.9875 0.90625The results shows that level based normal-abnormal classification got better result thannon-level based classification. Also it shows that SVM with Polynomial kernel got better resultthan those with RBF and Linear kernels.4. CONCLUSIONThis work is intended to prove that, the consideration of anatomical structure of the MRIBrain slices, for the normal/abnormal classification, will help to get more accurate result. Levelbased normal abnormal classification got better results than non- level based classification. Heresupport vector machine with polynomial kernel of degree 3 shows better results than those withlinear or RBF kernel.This work will surely help the radiologists and doctors in the identification of abnormalbrain slices. Magnetic Resonance Images are examined by radiologists based on visualinterpretation of the films to identify the presence of tumour abnormal tissue. The shortage ofradiologists and the large volume of MRI to be analysed make such readings labour intensive,cost expensive and often inaccurate. The sensitivity of the human eye in interpreting largenumbers of images decreases with increasing number of cases, particularly when only a smallnumber of slices are affected. Hence this automated systems for analysis and classification ofsuch medical image will surely become an aid for both radiologists and doctors in tumouranalysis and detection. Also it will be the key step for the automated tumour detection systemdevelopment.REFERENCES[1] Abdullah, N, Ngah, U.K.; Aziz, S.A., “Image classification of brain MRI using supportvector machine” Imaging Systems and Techniques (IST), 2011 IEEE International Conference on17-18 May 2011.[2] T Kesavamurthy, S SubhaRani, ``Pattern Classification using imaging techniques forInfarct and Hemorrhage Identification in the Human Brain"Calicut Medical Journal 2006.[3] http://www.braintumor.org/TumorTypes[4] http://www.bio-medicine.org/Biology[5] D. Selvathi, R.S. Ram Prakash, Dr.S.ThamaraiSelvi, “Performance Evaluation of KernelBased Techniques for Brain MRI Data Classification"International Conference on ComputationalIntelligence and Multimedia Applications 2007.
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME409[6] R. M Haarlick, “Statistical and structural approaches to texture", Proc. IEEE, vol. 67, pp.786-804, 1979.[7] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for ImageClassification", IEEE transaction on systems, man, and cybernetics, Vol.3, No 6, pp. 610-621,1973.[8] S. Chaplot, L.M. Patnaik, N.R. Jaganathan, “Classification of magnetic resonance brainimages using wavelet as input to support vector machine and neural network" Biomed. SignalProcess. Control (2006).[9] V.N. Vapnik, “Statistical Learning Theory", Wiley, New York, 1998.[10] VerlagDr. Mueller, “Image De-noising Using Wavelet Transforms" April 2008.[11] H. Selvaraj, S. ThamaraiSelvi, D. Selvathi, L. Gewali, “Brain MRI Slices ClassificationUsing Least Squares Support Vector Machine”, IC-MED, Vol. 1, No. 1, Issue 1, Page 21 of 33[12] Ching-Tsorng Tsai, Hsian Min Chen, Jyh-Wen Chai, Chen, Chein-I Chang,“Classification of Magnetic Resonance Brain Images by Using Weighted Radial Basis Functionkernels",IEEE International Conference of Electrical and Control Engineering (ICECE), 2011,Page(s): 5784 - 5787.[13] MadhubantiMaitra, AmitavaChatterjee and FumitoshiMatsuno, “A Novel Scheme forFeature Extraction and Classification of Magnetic Resonance Brain Images Based on SlantletTransform and Support Vector Machine",IEEE SICE Annual Conference 2008, Page(s): 1130 -1134.[14] N. HemaRajini, R.Bhavani, “ Classification of MRI Brain Images using K-NearestNeighbour and Artificial Neural Network",IEEE-International Conference on Recent Trends inInformation Technology, ICRTIT 2011, Page(s): 563-568.[15] ShahlaNajafi, Mehdi ChehelAmirani and Zahra Sedgh, “A New Approach to MRI BrainImages Classification",IEEE 19th Iranian Conference on Electrical Engineering (ICEE), 2011,Page(s): 1.[16] Ahmed Kharrat, Karim Gasmi n, Mohamed Ben Messaoud, NacéraBenamrane andMohamed Abid, “Automated Classification of Magnetic Resonance Brain Images Using WaveletGenetic Algorithm and Support Vector Machine",IEEE International Conference on CognitiveInformatics (ICCI 10), 2010, Page(s): 369-374 .[17] Abraham Varghese, RejiRajan Varghese, KannanBalakrishnan, J. S. Paul, “Axial T2Weighted MR Brain Image Retrieval Using Moment Features",Springer- Advances in IntelligentSystems and Computing Volume 177, 2013, pp 355-363.[18] Alberto Martin and SobriTosunoglu, “Image Processing Techniques for MachineVision", Florida University, 2000[19] A. Baraldi, F. Parmiggiani, “An Investigation Of The Textural Characteristics AssociatedWith GLCM Matrix Statistical Parameters”,IEEE Trans. on Geos. and Rem. Sens., vol. 33(2), pp.293-304, 1995.[20] A. Ukovich, G. Impoco, G. Ramponi, “A tool based on the GLCM to measure theperformance of dynamic range reduction algorithms”,IEEE Int. Workshop on Imaging Sys. &Techniques, pp. 36-41, 2005.[21] Selvaraj.D and Dhanasekaran.R, “MRI Brain Tumour Detection by Histogram andSegmentation by Modified Gvf Model”, International Journal of Electronics and CommunicationEngineering & Technology (IJECET), Volume 4, Issue 1, 2013, pp. 55 - 68,ISSN Print: 0976- 6464, ISSN Online: 0976 –6472.[22] B.Venkateswara Reddy, Dr.P.Satish Kumar, Dr.P.Bhaskar Reddy and B.Naresh KumarReddy, “Identifying Brain Tumour from MRI Image using Modified FCM and Support VectorMachine”, International journal of Computer Engineering & Technology (IJCET), Volume 4,Issue 1, 2013, pp. 244 - 262, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.

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