Pathological Brain Detection Using Weiner Filtering, 2D...

Research ArticlePathological Brain Detection Using Weiner Filtering2D-Discrete Wavelet Transform Probabilistic PCA andRandom Subspace Ensemble Classifier

Debesh Jha1 Ji-In Kim1 Moo-Rak Choi2 and Goo-Rak Kwon1

1Department of Information and Communication Engineering Chosun University 309 Pilmun-Daero Dong-GuGwangju 61452 Republic of Korea2School of Electrical Engineering Korea University 145 Anam-ro Sungbuk-gu Seoul 02841 Republic of Korea

Correspondence should be addressed to Goo-Rak Kwon grkwonchosunackr

Received 10 May 2017 Revised 7 August 2017 Accepted 23 August 2017 Published 3 October 2017

Academic Editor George A Papakostas

Copyright copy 2017 Debesh Jha et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Accurate diagnosis of pathological brain images is important for patient care particularly in the early phase of the diseaseAlthough numerous studies have usedmachine-learning techniques for the computer-aided diagnosis (CAD) of pathological brainpreviousmethods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filteringtechniques neuroimaging biomarkers and limited learning models Magnetic resonance imaging (MRI) is capable of providingenhanced information regarding the soft tissues and therefore MR images are included in the proposed approach In this studywe propose a new model that includes Wiener filtering for noise reduction 2D-discrete wavelet transform (2D-DWT) for featureextraction probabilistic principal component analysis (PPCA) for dimensionality reduction and a random subspace ensemble(RSE) classifier along with the119870-nearest neighbors (KNN) algorithm as a base classifier to classify brain images as pathological ornormal ones The proposed methods provide a significant improvement in classification results when compared to other studiesBased on 5 times 5 cross-validation (CV) the proposed method outperforms 21 state-of-the-art algorithms in terms of classificationaccuracy sensitivity and specificity for all four datasets used in the study

1 Introduction

Magnetic resonance imaging (MRI) of the brain providescomprehensive diagnostic information for diagnosis [1] It isessential because it is noninvasive and safe and yields a higherresolution that cannot be obtained by other techniques MRIismainly utilized to diagnose different types of disorders suchas strokes tumors bleeding injury blood-vessel diseases orinfections and multiple sclerosis (MS) The early diagnosisof pathological brain disease and its prodromal stage arecritical and can decrease or halt the progression of the disease[2]Therefore the classification of normalpathological brainstatus fromMRIs is essential in clinical medicine as it focuseson soft tissue anatomy and generates a large and detaileddataset about the subjectrsquos brain However the use of a largedatabase makes manual interpretation of the brain images

tedious time consuming and costly The major drawback ofthe manual approach is its irreducibility Therefore there isa need for automated image analysis tools such as computer-aided diagnosis (CAD) systems [3]

Considerable research has been carried out to developautomatic tools for the classification of MR images todistinguish between normal and pathological brains El-Dahshan et al [4] utilized a three-level discrete wavelettransform accompanied by principal component analysis(PCA) to decrease features A good success rate was obtainedby using feedforward backpropagation neural networks(BPNNs) and the 119870-nearest neighbor (KNN) Zhang andWu [5] recommended the application of a kernel supportvector machine (KSVM) and presented three new kernelshomogenous polynomial inhomogeneous polynomial andGaussian radial basis for distinguishing between normal

HindawiComputational Intelligence and NeuroscienceVolume 2017 Article ID 4205141 11 pageshttpsdoiorg10115520174205141

2 Computational Intelligence and Neuroscience

and abnormal images Patnaik et al [6] employed DWT toobtain the approximation coefficients Later a support vectormachine (SVM) was utilized to perform the classificationZhang et al [7] recommended a training feedforward neuralnetwork (FNN) with a unique scaled conjugate gradient(SCG) technique Kundu et al [8] proposed combiningthe Ripplet transform (RT) for feature extraction PCA fordimensionality reduction and the least-square SVM (LS-SVM) for classification and the 5 times 5 stratified cross-validation (SCV) offered high classification accuracies El-Dahshan et al [9] utilized the feedback pulse-coupled neuralnetwork for the preprocessing of MR images the DWTfor feature extraction PCA for features reduction and theFBPNN for the classification of pathological and normalbrains Damodharan andRaghavan [10] usedwavelet entropyas the feature space and they then used the traditional naıve-Bayes classifier classification method Wang et al [11] utilizedthe stationary wavelet transform (SWT) to substitute forDWT Likewise they proposed a hybridization of particleswarm optimization (PSO) and the artificial bee colony(HPA) method to obtain the optimal weights and biases ofFNN Nazir et al [12] applied denoising at the beginningand they achieved an overall classification accuracy of 918Harikumar and Vinoth Kumar [13] used wavelet-energy andSVM Padma and Sukanesh [14] used the combined waveletstatistical feature to segment and classify Alzheimerrsquos disease(AD) as well as benign and malignant tumor slices Zhang etal [15] utilizedHumoment invariants (HMI) and generalizedeigenvalue proximal SVM (GEPSVM) for the detection ofpathological brain inMRI scanning and obtained an accuracyof 9889 sensitivity of 9929 and specificity of 9200Later on Zhang et al [16] used multilayer perceptron (MLP)for classification where two pruning techniques like dynamicpruning (DP) and Bayesian detection boundaries (BDB wereused to find the optimal hidden neurons and an adaptivereal coded BBO (ARCBBO) method was implemented todetermine the optimal weights and obtained an accuracy of9812 and 9824 respectively Nayak et al [17] used 2D-DWT PCA and Adaboost algorithm with random forest asits base classifier and obtained an accuracy of 9844 forclassification of pathological brain MR image with Dataset-255 Later on Nayak et al [18] utilized two-dimensionalstationary wavelet transform (SWT) symmetric uncertaintyranking (SUR) filter and Adaboost with SVM classifier forthe detection of pathological brain MR images and obtainedan accuracy of 9843 with Dataset-255 Wang et al [19]employed Pseudo Zernike moment and linear regressionclassifier for classification of Alzheimerrsquos disease and yieldedan accuracy of 9751 sensitivity of 9671 and specificityof 9773 Alam et al [20] utilized dual-tree complex wavelettransform (DTCWT) principal component analysis (PCA)and twin support vector machine (TSVM) for the detectionof Alzheimerrsquos disease classification and obtained an accuracyof 9546 plusmn 126

Scholars have proposed different methods to extract fea-tures for the pathological brain disease [21] After analyzingthe abovemethods we found that all of themethods achievedpromising results which indicated that 2D-DWT is effec-tive in feature extraction for pathological brain detection

However there are two problems (1)Most of themutilize tra-ditional PCA for feature extraction which is computational-intensive for large datasets with a higher dimensions (2)Theclassification performance can be further improved becausethe feature vector contains excessive features which requiredmore memory and increased computational complexityMoreover it required too much time to train the classifiers

To address the above-mentioned problems we proposeda new pathological brain detection system based on brainMR images which has the potential improvements over theother schemes Weiner filter is used for the preprocessingof the images The proposed method uses 2D DWT forthe extraction of features because of its ability to analyzeimages at different scales PPCA is used in place of PCAfor the reduction of features which has the advantages ofcomputing the efficient dimension reduction in terms of thedistribution of latent variables maximum-likelihood esti-mates probability model dealing with the missing data anda combination of multiple PCA as probabilistic mixture Arelatively new classifier known as random subspace ensemble(RSE) classifier is employed which has the advantage of lowcomputational burden over the traditional classifiers Hencethe novelty of the proposed method lies in the application ofPPCA features and RSE classifier

The article is organized as follows Section 2 presentsdetails about the materials and methods Section 3 describesthe experimental results evaluation procedure and discus-sions Finally Section 4 presents the conclusion and futureresearch

2 Materials and Methods

21 Materials At present there are four benchmark datasets(DS) as DS-66 DS-90 DS-160 and DS-255 of different sizesof 66 90 160 and 255 images respectively All the datasets(DS) contain axial T2-weighted 256 times 256-pixel MR imagesdownloaded from medical school of Harvard University(Boston MA USA) (URL httpwwwmedharvardeduaablibhomehtml) website T2-weighted images are selectedas input image because T2-weighted (spin-spin) relaxationgives better image contrast that is helpful to show differentanatomical structure clearly Also they are better in detectinglesions than T1 weighted images

We selected five slices from each subject The selectioncriterion is that for healthy subjects these slices were selectedat random For pathological subjects the slices should con-tain the lesions by confirmation of these radiologists with tenyears of experiences A sample of diseased slices is shownin Figure 2 In this investigation all diseases are treated aspathological and our task is a binary classification problemthat is to distinguish pathological brain from healthy brainsHere the whole brain is considered as the input image Wedid not select local characteristics like point and edge and weextract global image characteristics that are further learnedby the new cascade model Let us keep in mind that ourprocedure is different from the way neuroradiologists doThey usually take the local features and compare with stan-dard template to check whether focuses exist such as shrinkexpansion bleeding and inflammationWhile our technique

Computational Intelligence and Neuroscience 3

is like AlphaGO the computer researcher gives the machinesufficient data and then the machine can learn how to makeclassification naturally Including patientsrsquo information (agegender handedness memory test education etc) can addadditional information and thus may assist us to improvethe classification performance Nevertheless this new modelproposed in our research is only dependent on the imagingdata Besides the imaging data from the website does notcontain the subjectsrsquo information

The cost of predicting pathological to normal types issevere because the subjects may be told that shehe is normaland thus avoids themild symptoms displayedThe treatmentsof patients may be postponed Nevertheless the cost ofmisclassification of healthy to pathological types is low sincecorrect treatment can be given by other diagnosis meansThe cost-sensitivity (CS) problem was resolved by changingthe class distribution at the beginning state since originaldata was accessible That means we purposely picked upmore pathological brains than healthy ones into the datasetwith the goal of making the classifier biased to pathologicalbrains to solve the CS problem The overfitting problem wassupervised by cross-validation technique

In our experiment DS-66 and DS-160 are extensivelyemployed for brain MR image classifications that consistof normal brain images as well as abnormal brain imagesfrom seven types of diseases namely glioma meningiomaAlzheimerrsquos disease Alzheimerrsquos disease plus visual agnosiaPickrsquos disease sarcoma and Huntingtonrsquos disease DS-90containsMR brain images of a healthy brain AIDS dementiaAlzheimerrsquos disease plus visual agnosia Alzheimerrsquos diseasecerebral calcinosis cerebral toxoplasmosis Creutzfeldt-Jakobdisease glioma herpes encephalitis Huntingtonrsquos diseaseLyme encephalopathy meningioma metastatic adenocarci-noma metastatic bronchogenic carcinoma motor neurondisease MS Pickrsquos disease and sarcoma

The third dataset DS-255 includes images of four newtypes of diseases embedded with the above seven typesof diseased images and normal brain images The fouradditional diseases are chronic subdural hematoma cerebraltoxoplasmosis herpes encephalitis and MS

22 Proposed Methodology The proposed method comprisesfour vital stages namely image preprocessing feature extrac-tion using 2D-DWT feature reduction utilizing PPCA andclassification using the RSE classifier In order to enhance thequality of theMR images Wiener filter is employed followedby the extraction of approximation coefficients from MRimages utilizing a 2D-DWT with three-level decompositionThen we saved these obtained features as our primaryfeatures Thereafter then we employ PPCA for obtaininguncorrelated discriminant set of features Finally we classi-fied the reduced features using the RSE classifier with KNN asa base classifierThe complete block diagram of the proposedsystem is shown in Figure 1 A brief description about all thesefour stages is shown below

221 Preprocessing Using Wiener Filter The gif images weredownloaded individually from the website of the HarvardMedical School Then each of the gif images was converted

into JPG format manually The images were in RGB formatand they were then converted into grayscale intensity imagesNext the intensity image is converted to double precisionAcquired brain MR images require preprocessing to improvethe quality enabling us to obtain better features In our studywe used the popular Wiener filter method

The Wiener filter is used to replace the finite impulseresponse (FIR) filter in order to decrease noise in signals[22] When an image is blurred by a familiar low-pass filter(LPF) we can recover the image by inverse filteringHoweverinverse filtering is extremely sensitive to additive noiseWiener filtering accomplishes an optimal trade-off betweeninverse filtering and noise smoothing in that it eliminatesthe additive noise and inverts the blurring simultaneouslyIn addition it reduces the overall mean-square error duringthe course of inverse filtering plus noise smoothing TheWiener filtering method generates a linear approximation ofthe original image and is based on the stochastic frameworkThe orthogonality principle indicates that theWiener filter inthe Fourier domain can be articulated as follows

119882(1198911 1198912) = 119867lowast (1198911 1198912) 119878119909119909 (1198911 1198912)1003817100381710038171003817119867 (1198911 1198912)10038171003817100381710038172 119878119909119909 + 119878119899119899 (1198911 1198912) (1)

Here 119878119909119909(1198911 1198912) is the power spectrum of the originalimage 119878119899119899(1198911 1198912) is the adaptive noise and 119867(1198911 1198912) is theblurring filter

23 2D-DWT

231 Advantage of Wavelet Transform The FT is the mostcommonly used tool for the analysis of signals and it breaksdown a time-domain signal into constituent sinusoids ofvarious frequencies thus changing the signal from the timedomain to the frequency domain Nevertheless the FT has aserious disadvantage as it removes the time information fromthe signal For instance an investigator cannot determinewhen a specific event took place based on a Fourier spectrumTherefore the classification accuracy decreases as the timeinformation is lost

Gabor modified the FT to examine only a small part ofthe signal at a timeThis approach is known as windowing orthe short-time FT (STFT) [23] It accumulates a window ofappropriate shape to the signal STFT can be considered asa compromise between the time information and frequencyinformation Nevertheless the precision of the information islimited by the window size

The wavelet transform (WT) constitutes the next logicalstep It uses a windowing method with variable size and theprogress of the signal analysis is shown in Figure 3 Anotherbenefit of the WT is that it selects a ldquoscalerdquo in place of thetraditional ldquofrequencyrdquo that is it does not generate a time-frequency view of a specific signal but a time-scale view Thetime-scale view is another way of visualizing data and is morecommonly used and effective

232 DWT This is an effective implementation of the WTand it utilizes the dyadic scales and positions [24] The


2D DWTProduce

feature matrix

Calculate three-level approximation coefficients

Reduction of feature using PPCA

Create reduced feature matrix

Subspace KNNNormal brain

Pathological brain

Wiener filter

Figure 1 Block diagram of the proposed system

(a) (b) (c) (d) (e) (f)

(g) (h) (i) (j) (k) (l)

(m) (n) (o) (p) (q) (r)

Figure 2 Brain MR images (a) healthy brain (b) AIDS dementia (c) Alzheimerrsquos disease plus visual agnosia (d) Alzheimerrsquos disease (e)cerebral calcinosis (f) cerebral toxoplasmosis (g) Creutzfeldt-Jakob disease (h) glioma (i) herpes encephalitis (j) Huntingtonrsquos disease (k)Lyme encephalopathy (l) meningioma (m) metastatic adenocarcinoma (n) metastatic bronchogenic carcinoma (o) motor neuron disease(p) multiple sclerosis (q) Pickrsquos disease and (r) sarcoma

fundamentals of theDWT are as follows Let 119909(119905) be a square-integral function The continuous WT of the signal 119909(119905)relative to a real-valued wavelet 120595(t) is defined as

119882(119886 120591) = intinfinminusinfin

119909 (119905) 1radic119886120595 lowast (119905 minus 120591

119886 ) d119905 (2)

where 119882(119886 120591) is the WT 120591 indicates the function across119909(119905) and the variable 119886 is the dilation factor (both realand positive numbers) Here the asterisk (lowast) indicates thecomplex conjugate

Equation (1) can be discretized by restraining 119886 and 120591 toa discrete lattice (119886 = 2119895 and 120591 = 2119895119896) to provide the DWTwhich is given as follows

119888119860119895119896 (119899) = DS[sum119899

119909 (119899) 119897lowast119895 (119899 minus 2119895119896)]

119888119863119895119896 (119899) = DS[sum119899

119909 (119899) ℎlowast119895 (119899 minus 2119895119896)] (3)

Here 119888119860119895119896 and 119888119863119895119896 refer to the coefficients ofthe approximation components and detailed components


Time

Scal

e

Scal

e

Scal

e

TimeFrequency

Short-time Fouriertransform

Fouriertransform

Wavelettransform

Figure 3 Progress of signal analysis

s

Ca1

Ca2

Ca3

Cd1

Cd2

Cd3

Figure 4 Three-level wavelet decomposition tree

respectively 119897(119899) and ℎ(119899) represent the LPF and high-passfilter (HPF) respectively 119895 and 119896 represent the waveletscale and translation factors respectively The DS operatorrepresents downsampling The approximation componenthas low-frequency components of the image whereas thedetailed components contain high-frequency componentsFigure 4 shows a three-level decomposition tree

233 2D-DWT In a case involving 2D images the DWTis employed in each dimension separately A sample of apathological brain MR image with its three-level waveletdecomposition is shown in Figure 5 Consequently thereare four subband images (LL LH HH and HL) at eachscale The subband LL is utilized for the other 2D-DWTand can be considered as the approximation component ofthe image whereas the LH HL and HH subbands can beconsidered as the detailed components of the image As thelevel of the decomposition is increased a more compact butcoarser approximation component is accessedThus waveletsgive a simple hierarchical foundation for clarifying the imageinformation

There are various types of wavelets for example Daub-echies symlets 1 coiflets 1 and biorthogonal wavelets and

reverse biorthogonal 11 We tested our result with eachtype of the wavelet family as shown in Table 2 In ourresearch the approximation coefficient of three-level waveletdecomposition along with a Haar wavelet yields promisingresults when compared to others in thewavelet family HenceHaar wavelet was selected in the experiment It is also thesimplest and most significant wavelet of the wavelet familyMoreover it is very fast and can be used to extract basicstructural information from an image All the features arepresent for all the images and a feature matrix is generated

24 Probabilistic Principal Component Analysis The PPCAalgorithm proposed by Tipping et al [36ndash38] is based onthe estimation of the principal axes when any input vectorhas one or more missing values The PPCA reduces thehigh-dimensional data to a lower-dimensional representationby relating a 119901-dimensional observation vector 119910 to a k-dimensional latent (or unobserved) variable119909 that is regardedas normal with zero mean and covariance 119868(119896) MoreoverPPCA depends on an isotropic error model The relationshipcan be established as

119910119879 = 119882 lowast 119909119879 + 120583 + 120576 (4)

where 119910 denotes the row vector of the observed variable 120576denotes the isotropic error term and 119909 is the row vector oflatent variablesThe error term 120576 is Gaussian with zeromeanand covariance V lowast 119868(119896) where V is the residual varianceTo make the residual variance greater than 0 the value of119896 should be smaller than the rank A standard principalcomponent where V equals 0 is the limiting condition ofPPCA The observed variables y are conditionally indepen-dent for the given values of the latent variables 119909 Thereforethe correlation between the observation variables is explainedby the latent variables and the error justifies the variabilityunique to 119910119894 The dimension of the matrix 119882 is 119901 times 119896 andit relates both latent and observation variables The vector 120583allows themodel to acquire a nonzeromean PPCA considers


1 （1

（1 （1（（1 （（1

2 （2 （2

（2（2

（（2（（2

（2

（1 （1

（（2

3 （3

（3 （（3

Figure 5 Pathological brain image and its wavelet coefficient at three-level decomposition

the values to be missing and arbitrary over the dataset Fromthis model

119910 sim 119873(120583119882 lowast 119882119879 + V lowast 119868 (119896)) (5)

Given that the solution of119882 and V cannot be determinedanalytically we used the expectation-maximization (EM)algorithm for the iterativemaximization of the correspondinglog-likelihood functionTheEMalgorithm considersmissingvalues as additional latent variables At convergence thecolumns of 119882 span the solution subspace PPCA then yieldsthe orthonormal coefficients

With respect to our research the size of the image is 256times 256 After three-level decomposition the vector featurebecomes 32 times 32 = 1024 Here all the features are not relevantfor the classification Because of the high computationalcost we utilized PPCA for the dimensionality reductionTheadvantage of PPCA over PCA is its computational efficiency

25 RSE Classifier Ensemble classification includes combin-ing multiple classifiers to obtain more accurate predictionsthan those obtained utilizing individual models In additionensemble learning techniques are considered very useful forupgrading prediction accuracy Nevertheless base classifiersmust be as precise and diverse as possible to increase thegeneralization capability of an ensemble model

For the classification of normal and pathological brainMRI images we used a random subspace classifier thatuses KNN as a base classifier The main idea behind thesuccess of ensemble classification is the diversification in theclassification that makes the ensemble classifier With theensemble classification approach each classifier provides adifferent error for different instantTherefore we can developa strong classifier that can decrease the error The ran-dom subspace classifier is a machine-learning classifier thatdivides the entire feature space into subspaces Each subspacerandomly selects features from the original feature space Itmust be guaranteed that the boundaries of the particular baseclassifier are significantly different To realize this an unstableor weaker classifier is utilized as base classifier because theycreate sufficiently varied decision boundaries even for smalldisturbances in the training data parameters

We used the majority voting method to obtain the finaldecision of the class membership In the proposed algorithmwe used KNN as the base classifier owing to its simplicityAfter selecting a random subspace a new set of KNNsis estimated The majority voting method was utilized tocombine the output of each base classifier for the decisionpreparing test class

Table 1 Confusion matrix for a binary classifier to discriminatebetween two classes (1198601 and 1198602)True class Predicted class

1198601 (patients) 1198602 (controls)1198601 (patients) TP FN1198602 (controls) FP TNHere TP (true positive) correctly categorized as positive cases TN (true neg-ative) correctly categorized as negative cases FP (false positive) incorrectlycategorized as negative cases FN (false negative) incorrectly categorized aspositive cases

Table 2 Comparison of different wavelet families

Wavelet family AccuracyHaar 9920Daubechies 2 9860Coiflets 1 9698Symlets 1 9901Biorthogonal 11 9864

26 Pseudocode of Proposed System Our proposed systemcan be outlined in four major stages The steps involved aredepicted in Pseudocode 1

27 Performance Measures Various techniques are used toevaluate the classifierrsquos efficiency The performance is deter-mined based on the final confusion matrix The confusionmatrix holds correct and incorrect classification resultsTable 1 illustrates a confusion matrix for binary classificationwhere TP TN FP and FN depict true positive true negativefalse positive and false negative respectively

Here pathological brains are assumed to hold the valueldquotruerdquo and normal control (NC) ones are assumed to hold thevalue ldquofalserdquo following normal convention Now we calculatethe performance of the proposed approach on the basis ofsensitivity specificity accuracy and precision as follows

(i) Sensitivity (true positive rate) this is the tendency orability to determine that the diagnostic test is positive whenthe person has the disease

Sensitivity = TPTP + FN

(6)

(ii) Specificity (true negative rate) this is the tendency orability to determine that the diagnostic test is negative whenthe person does not have the disease

Specificity = TNTN + FP

(7)


Input T2-weighted MR brain imagesParameter119873 total number of imagesStep 1 (weiner filter)for 119894 = 1 119873Read the images and apply wiener filterendStep 2 (2D-DWT)For 119894 = 1 119873Read in the image fileApply the DWT using for the 3rd level using ldquoHaarrdquo wavelet to extract the wavelet coefficientsA matrix119883 [119872 times 119873] is employed to store all the coefficientsEndStep 3 Reduce the features from the coefficients using PPCAfor 119895 = 1 119873Apply PPCA transformation on the obtained wavelet coefficientsPut the new dataset in a matrix 119884EndStep 4 (RSE classification using 5 times 5 cross-validation)Divide the input data 119868 and target data 119879 into 5 different groups randomlyFor 119896 = 1 5Use the 119896th group for test and other 4 groups to train the RSE algorithmClassify test imageEndCalculate average specificity sensitivity and accuracy

Pseudocode 1 Pseudocode of the proposed system

Investigation 1

Investigation 2

Investigation 3

Investigation 4

Investigation 5

TrainingValidation

Figure 6 Illustration of 119896-fold cross-validation

(iii) Accuracy this is a measure of how many diagnostictests are correctly performed

Accuracy = TP + TNTP + TN + FP + FN

(8)

(iv) The precision and the recall are formulated by

Precision = TPTP + FP

(9)

28 Cross-Validation Cross-validation (CV) is a model-assessment method that is used to evaluate the performanceof a machine-learning algorithm prediction on a new DS onwhich it has not been trained It helps to solve the overfitting

problems Each cross-validation round involves randomlyportioning the original DS into a training set and a validationsetThe illustration of the 119896-fold CV is shown in Figure 6Thetraining set is used to train a supervised learning algorithmwhile a test set is used to evaluate its performance

Tomake the RSE classifier more reliable and generalize toindependent datasets a 5 times 6-fold stratified cross-validation(SCV) and 5 times 5-fold SCV are employed A 5 times 6-fold SCVis employed for DS-66 and 5 times 5-fold SCV is used for DS-90 DS-160 and DS-255 For DS-66 55 MR images are usedfor training whereas 75 128 and 204 images are used for DS-90 DS-160 and DS-255 respectively The validation imagesfor DS-66 DS-90 DS-160 and DS-255 are 11 15 32 and 51respectively


Table 3 Comparison result of the proposed method

Proposed method Feature DS-66 DS-90 DS-160 DS- 255Logistic regression 13 10000 10000 10000 9250QDA 13 10000 9890 9890 9650KNN 13 10000 10000 10000 9730RSE classifier 13 10000 10000 10000 9920

Table 4 Classification comparison with DS-90

Existing methods Success cases Sensitivity () Specificity () Precision () Accuracy ()DWT + PCA + BPNN [25] 388 8800 5600 9714 8622DWT + PCA + RBF-NN [25] 411 9247 7200 9825 9133DWT + PCA + PSO-KSVM [25] 440 9812 9200 9952 9778WE + BPNN [26] 390 8847 5600 9716 8667WE + KSVM [27] 413 9318 6800 9802 9178DWT + PCA + GA-KSVM [28] 439 9788 9200 9952 9756WE + PSO-KSVM [29] 437 9765 8800 9928 9711WE + BBO-KSVM [29] 440 9812 9200 9952 9778WE + QPSO-KSVM [30] 442 9859 9200 9952 9822WFRFT + PCA + GEPSVM [31] 446 9953 9200 9953 9911HMI + SEPSVM [15] 445 9906 9600 9889HMI + TSVM [15] 445 9929 9200 9889Proposed2D- DWT + PPCA + RSE (proposed) 450 10000 10000 10000 10000

3 Results and Discussion

In this study we implemented a new machine-learningframework using MATLAB 2016a on an Intel computer witha Core-i5 processor and 16GB RAM running under theWindows 7 operating system This program can be tested orrun on any computer platform where MATLAB is available

31 Feature Extraction and Optimum Wavelet In the pro-posed system the three-level 2D-DWT of the Haar waveletbreaks down the input image into 10 subbands as illustratedin Figure 5The top left corner of thewavelet coefficient image(Figure 5) represents the approximation coefficients of thethree-level decomposition of the image whose size is only 32times 32 = 1024 These obtained features are the initial featuresThe size of these features is still large and the matrix sizeneeds to be reduced Now these reduced features are sent asthe input to the PPCA

32 Feature Reduction The use of PPCA as a dimension-reduction tool reduces the feature size to its desired sizeHere we can take the feature as desired It is better thatthe desired number of features should at least preserve morethan 90 of the variance However in this study we did nottake 95 of the variance because it may lead to a highercomputational cost Researchers have considered differentnumbers of features In our case we first used a smallnumber of features but the accuracy was poor However theresult with 13 principal components was excellent Hence

the proposed method uses 13 principal components to earnhigher classification accuracy

33 Classification Results The reduced features were sentto the classifier and the results obtained with the differentclassifier are promising From the experiment it is seenthat the proposed method works well for all four DSs using13 principal components The performances obtained withlogistic regression quadratic discriminant analysis KNNand RSE classifier with KNN as a base classifier are shownin Table 3 From the table we see that the proposed methodoutperforms other methods We utilized a 5-fold CV for DS-90 DS-160 and DS-255 whereas we utilized a 6-fold CV forDS-66 The RSE classifier obtained an accuracy of 1000010000 10000 and 9920 with DS-66 DS-90 DS-160and DS-255 respectively The result obtained with the cubicSVM is the same as the RSE classifier for the dataset besideDS-66 where it could only achieve 9850

34 Comparison with Existing Schemes To further demon-strate the effectiveness of the proposed approach we com-pared 21 existing algorithms The algorithms and their cor-responding results are listed in Tables 4 and 5 Table 4shows the comparison result with DS-90 It is evident fromTable 4 that our proposedmethod correctly matched all caseswith 100 sensitivity 100 specificity 100 precision and100 accuracy A comparison of the obtained results showsthat our algorithm is superior to the others This shows theeffectiveness of the preprocessing technique combined with


Table 5 Classification comparison (DS-66 DS-160 and DS-255)

Approaches Feature Run Accuracy ()DWT + SVM + POLY [24] 4761 DS-66 DS-160 DS-255DWT + SVM + RBF [24] 4761 5 9800 9715 9637DWT + PCA + 119896-NN [4] 7 5 9800 9733 9618DWT + PCA + FNN + ACPSO [32] 19 5 9800 9754 9679DWT + PCA + FNN + SCABC [33] 19 5 10000 9875 9738DWT + PCA + BPNN + SCG [7] 19 5 10000 9893 9781DWT + PCA + KSVM [5] 19 5 10000 9829 9714RT + PCA + LS-SVM [34] 9 5 10000 9938 9882SWT + PCA + IABAP-FNN [11] 7 10 10000 9888 9843WT + PCA + ABC-SPSO-FNN [11] 7 10 10000 9944 9918WE + NBC [35] 7 10 9258 9962 9902DWT + PCA + ADBRF [17] 13 5 10000 9930 9844DWT + SUR + ADBSVM [18] 7 5 10000 9922 9843FRFE + DP-MLP + ARCBBO [16] 12 10 10000 9919 9824FRFE + BDP-MLP + ARCBBO [16] 12 10 10000 9931 9812DWT + PCA + RSE 13 5 10000 9957 9890DWT + PPCA + RSE (proposed) 13 5 10000 10000 9920

features extracted using theWT and PPCA Table 4 shows theresult of 5 runs of the proposed system Table 5 demonstratesthe comparison results over the three DSs in terms of thenumber of features number of runs and average accuracyHere some of the recent schemes were run 10 times whileothers were run five times From Tables 4 and 5 we see thatmost of the techniques achieved excellent classification whensubjected to DS-66 as it is smaller in size However noneof the algorithms achieved 10000 with DS-90 and DS-160because DS-255 is larger in size and includes more types ofdiseased brains therefore no current CAD system can earna perfect classification

Finally this proposed ldquoDWT + PPCA + RSErdquo achievedan accuracy of 100 for DS-66 DS-90 and DS-160 and anaccuracy of 9920 for DS-255 which is comparable withother recent studies and greater than the entire algorithmpresented in Table 5 The improvement realized by therecommended scheme appears to bemarginal comparedwithother schemes but we obtained this result based on a carefulstatistical analysis (five repetitions of 119896-fold CV) Thus thisimprovement is reliable and robust

4 Conclusion

This paper proposed a new cascade model of ldquo2D-DWT +PPCA + RSErdquo for the detection of pathological brains Theexperiments validated its effectiveness as it achieved an accu-racy of 9920 Our contributions lie in three points Firstwe introduced the Wiener filter and showed its effective-ness Besides this we introduced the PPCA and RSE classifierand proved it gives the better performance when comparedwith other state-of-the-art algorithms In this work we trans-formed the PBD problem to a binary classification task Wepresented a novel method that replaced PCA and introducedRSE classifier The experiment showed the superiority of ourmethods to existing approaches

The proposed algorithm can also be employed in otherfields for example face recognition breast cancer detectionand fault detectionMoreover thismethod has been validatedon the publically available datasets which are limited in sizeAlso in the selected dataset the images are collected duringthe late andmiddle stage of diseases however the imageswithdisease at early stages need to be considered

In future research we may consider images from othermodalities like MRSI PET and CT to increase robustnessto our scheme The proposed method can be validated ona larger clinical dataset utilizing modern machine-learningtechniques like deep learning extreme learning and so onafter collecting the enough brain images from the medi-cal institutes Internet of things can be another promisingresearch field to embed this PBDS

Nomenclature

MR(I) Magnetic resonance (imaging)DWT Discrete wavelet transformPPCA Probabilistic principal component analysisKNN 119896-nearest neighborCV Cross-validationBPNN Backpropagation neural networkKSVM Kernel support vector machineSCG Scale conjugate gradientLS-SVM Least-square support vector machineFBPNN Feedforward backpropagation neural

networkSWT Stationary wavelet transformPSO Particle swarm optimizationCAD Computer-aided diagnosisSTFT Short-time Fourier transformQDA Quadratic discriminant analysisSUR Symmetric uncertainty ranking


PZM Pseudo Zernike momentSWT Stationary wavelet transformDTCWT Dual-tree complex wavelet transformRBFNN Radial basis function neural networkCT Computed tomographyTSVM Twin support vector machineHMI Hu moment invariantsMLP Multilayer perceptronARCBBO Adaptive real coded biogeography-based

optimizationDP Dynamic pruning

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This research was supported by the Brain Research Programthrough the National Research Foundation of Korea fundedby the Ministry of Science ICT amp Future Planning (NRF-2014M3C7A1046050) And this work was supported by theNational Research Foundation of Korea Grant funded by theKorean Government (NRF-2017R1A2B4006533)

References

[1] D Jha and G-R Kwon ldquoAlzheimers disease detection in MRIusing curvelet transform with K-NNrdquo Journal of KIIT vol 14no 8 2016

[2] S Alam M Kang J-Y Pyun and G-R Kwon ldquoPerformanceof classification based on PCA linear SVM and Multi-kernelSVMrdquo in Proceedings of the 8th International Conference onUbiquitous and Future Networks ICUFN 2016 pp 987ndash989Vienna Austria July 2016

[3] FThorsen B Fite L M Mahakian et al ldquoMultimodal imagingenables early detection and characterization of changes intumor permeability of brain metastasesrdquo Journal of ControlledRelease vol 172 no 3 pp 812ndash822 2013

[4] E-S A El-Dahshan T Hosny and A-B M Salem ldquoHybridintelligent techniques for MRI brain images classificationrdquoDigital Signal Processing vol 20 no 2 pp 433ndash441 2010

[5] Y Zhang and L Wu ldquoAn MR brain images classifier via prin-cipal component analysis and kernel support vector machinerdquoProgress in Electromagnetics Research vol 130 pp 369ndash3882012

[6] LM Patnaik S Chaplot andN R Jagannathan ldquoClassificationof magnetic resonance brain images using wavelets as input tosupport vector machine and neural networkrdquo Biomedical SignalProcessing and Control vol 1 no 1 pp 86ndash92 2006

[7] Y Zhang Z Dong L Wu and S Wang ldquoA hybrid methodfor mri brain image classificationrdquo Expert Systems with Appli-cations vol 38 no 8 pp 10049ndash10053 2001

[8] M K Kundu M Chowdhury and S Das ldquoBrain MR imageclassification using multi-scale geometric analysis of rippletrdquoProgress in Electromagnetics Research vol 137 pp 1ndash17 2013

[9] E A-S El-Dahshan H M Mohsen K Revett and A-BM Salem ldquoComputer-aided diagnosis of human brain tumorthrough MRI A survey and a new algorithmrdquo Expert Systemswith Applications vol 41 no 11 pp 5526ndash5545 2014

[10] S Damodharan and D Raghavan ldquoCombining tissue sege-mentation and neural network for brain tumor detectionrdquo TheInternational Arab Journal of InformationTechnology vol 12 no1 2015

[11] S Wang Y Zhang Z Dong et al ldquoFeed-forward neuralnetwork optimized by hybridization of PSO and ABC forabnormal brain detectionrdquo International Journal of ImagingSystems and Technology vol 25 no 2 pp 153ndash164 2015

[12] M Nazir F Wahid and S A Khan ldquoA simple and intelligentapproach for brain MRI classificationrdquo Journal of Intelligent ampFuzzy Systems Applications in Engineering and Technology vol28 no 3 pp 1127ndash1135 2015

[13] R Harikumar and B Vinoth Kumar ldquoPerformance analysisof neural networks for classification of medical images withwavelets as a feature extractorrdquo International Journal of ImagingSystems and Technology vol 25 no 1 pp 33ndash40 2015

[14] A Padma and R Sukanesh ldquoSegementation and classificationof brain CT images using combined wavelet statistical texturefeaturesrdquo Arabian Journal for Science Engineering vol 39 no 22014

[15] Y Zhang J Yang S Wang Z Dong and P Phillips ldquoPathologi-cal brain detection in MRI scanning via Hu moment invariantsand machine learningrdquo Journal of Experimental andTheoreticalArtificial Intelligence vol 29 no 2 pp 299ndash312 2017

[16] Y Zhang Y Sun P Phillips G Liu X Zhou and S Wang ldquoAmultilayer perceptron based smart pathological brain detectionsystem by fractional fourier entropyrdquo Journal of Medical Sys-tems vol 40 no 7 article 173 2016

[17] D R Nayak R Dash and B Majhi ldquoBrain MR image classi-fication using two-dimensional discrete wavelet transform andAdaBoost with random forestsrdquo Neurocomputing vol 177 pp188ndash197 2016

[18] D R Nayak R Dash and B Majhi ldquoStationary wavelettransform and AdaBoost with SVM based pathological braindetection in MRI scanningrdquo CNS and Neurological Disorders -Drug Targets vol 16 no 2 pp 137ndash149 2017

[19] S-H Wang S Du Y Zhang et al ldquoAlzheiemers diseasedetection by pseudo zernike moment and linear regressionclassifierrdquo CNS amp Neurologicla Disorders vol 16 no 1 pp 11ndash15 2017

[20] S Alam M Kang and G Kwon ldquoAlzheimer disease classifica-tion based on TSVM and Kernel SVMrdquo in Proceedings of the2017 Ninth International Conference on Ubiquitous and FutureNetworks (ICUFN) pp 565ndash567 July 2017

[21] D Jha J Kim and G Kwon ldquoDiagnosis of Alzheimerrsquos diseaseusing dual-tree complex wavelet transform PCA and feed-forward neural networkrdquo Journal of Healthcare Engineering vol2017 Article ID 9060124 13 pages 2017

[22] H Naimi A B H Adamou-Mitiche and L Mitiche ldquoMedicalimage denoising using dual tree complex thresholding wavelettransform and Wiener filterrdquo Journal of King Saud University- Computer and Information Sciences vol 27 no 1 pp 40ndash452015

[23] L Durak ldquoShift-invariance of short-time FOUrier transform infractional FOUrier domainsrdquo Journal of the Franklin InstituteEngineering and Applied Mathematics vol 346 no 2 pp 136ndash146 2009

[24] S Chaplot LM Patnaik andN R Jagannathan ldquoClassificationof magnetic resonance brain images using wavelets as input tosupport vector machine and neural networkrdquo Biomedical SignalProcessing and Control vol 1 no 1 pp 86ndash92 2006


[25] Y Zhang S Wang G Ji and Z Dong ldquoAn MR brain imagesclassifier system via particle swarm optimization and kernelsupport vector machinerdquoThe ScientificWorld Journal vol 2013Article ID 130134 9 pages 2013

[26] R Choudhary S Mahesh J Paliwal andD S Jayas ldquoIdentifica-tion of wheat classes using wavelet features from near infraredhyperspectral images of bulk samplesrdquo Biosystems Engineeringvol 102 no 2 pp 115ndash127 2009

[27] M R KMookiahU RajendraAcharya CM LimA Petznickand J S Suri ldquoData mining technique for automated diagnosisof glaucoma using higher order spectra and wavelet energyfeaturesrdquo Knowledge-Based Systems vol 33 pp 73ndash82 2012

[28] S Wang G Ji P Phillips and Z Dong ldquoApplication of geneticalgorithm and kernel support vector machine to pathologicalbrain detection in MRI Scanningrdquo in Proceedings of the 2ndNational Conference Information Technology Comp Science pp450ndash456 Shanghai China 2015

[29] G Yang Y Zhang J Yang et al ldquoAutomated classificationof brain images using wavelet-energy and biogeography-basedoptimizationrdquo Multimedia Tools and Applications vol 75 no23 pp 1ndash17 2015

[30] Y Zhang G Ji J Yang et al ldquoPreliminary research on abnormalbrain detection by wavelet-energy and quantum-behaved PSOrdquoTechnology and Health Care vol 24 pp S641ndashS649 2016

[31] Y-D Zhang S Chen S-H Wang J-F Yang and P PhillipsldquoMagnetic resonance brain image classification based onweighted-type fractional Fourier transform and nonparallelsupport vector machinerdquo International Journal of Imaging Sys-tems and Technology vol 25 no 4 pp 317ndash327 2015

[32] Y Zhang S Wang and L Wu ldquoA novel method for magneticresonance brain image classification based on adaptive chaoticPSOrdquo Progress in Electromagnetics Research vol 109 pp 325ndash343 2010

[33] Y Zhang LWu and SWang ldquoMagnetic resonance brain imageclassification by an improved artificial bee colony algorithmrdquoProgress In Electromagnetics Research vol 130 pp 369ndash3882012

[34] S Das M Chowdhury and M K Kundu ldquoBrain MR imageclassification using multi-scale geometric analysis of rippletrdquoProgress in Electromagnetics Research vol 137 pp 1ndash17 2013

[35] X Zhou S Wang W Xu et al ldquoDetection of pathologicalbrain in MRI scanning based wavelet-entropy and naıve Bayesclassifierrdquo in Proceedings of the International Conference onBioinformatics and Biomedical Engineering pp 201ndash209 2015

[36] M E Tipping and C M Bishop ldquoProbabilistic principalcomponent analysisrdquo Journal of the Royal Statistical SocietySeries B Statistical Methodology vol 61 no 3 pp 611ndash622 1999

[37] S Roweis ldquoEM algorithms for PCA and SPCArdquo in Advancein Neural Information Proccessing System vol 10 pp 626ndash632MIT Press Cambridge MA USA 1998

[38] A Ilin and T Raiko ldquoPractical approaches to principal com-ponent analysis in the presence of missing valuesrdquo Journal ofMachine Learning Research vol 11 pp 1957ndash2000 2010

Submit your manuscripts athttpswwwhindawicom

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Human-ComputerInteraction

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and abnormal images Patnaik et al [6] employed DWT toobtain the approximation coefficients Later a support vectormachine (SVM) was utilized to perform the classificationZhang et al [7] recommended a training feedforward neuralnetwork (FNN) with a unique scaled conjugate gradient(SCG) technique Kundu et al [8] proposed combiningthe Ripplet transform (RT) for feature extraction PCA fordimensionality reduction and the least-square SVM (LS-SVM) for classification and the 5 times 5 stratified cross-validation (SCV) offered high classification accuracies El-Dahshan et al [9] utilized the feedback pulse-coupled neuralnetwork for the preprocessing of MR images the DWTfor feature extraction PCA for features reduction and theFBPNN for the classification of pathological and normalbrains Damodharan andRaghavan [10] usedwavelet entropyas the feature space and they then used the traditional naıve-Bayes classifier classification method Wang et al [11] utilizedthe stationary wavelet transform (SWT) to substitute forDWT Likewise they proposed a hybridization of particleswarm optimization (PSO) and the artificial bee colony(HPA) method to obtain the optimal weights and biases ofFNN Nazir et al [12] applied denoising at the beginningand they achieved an overall classification accuracy of 918Harikumar and Vinoth Kumar [13] used wavelet-energy andSVM Padma and Sukanesh [14] used the combined waveletstatistical feature to segment and classify Alzheimerrsquos disease(AD) as well as benign and malignant tumor slices Zhang etal [15] utilizedHumoment invariants (HMI) and generalizedeigenvalue proximal SVM (GEPSVM) for the detection ofpathological brain inMRI scanning and obtained an accuracyof 9889 sensitivity of 9929 and specificity of 9200Later on Zhang et al [16] used multilayer perceptron (MLP)for classification where two pruning techniques like dynamicpruning (DP) and Bayesian detection boundaries (BDB wereused to find the optimal hidden neurons and an adaptivereal coded BBO (ARCBBO) method was implemented todetermine the optimal weights and obtained an accuracy of9812 and 9824 respectively Nayak et al [17] used 2D-DWT PCA and Adaboost algorithm with random forest asits base classifier and obtained an accuracy of 9844 forclassification of pathological brain MR image with Dataset-255 Later on Nayak et al [18] utilized two-dimensionalstationary wavelet transform (SWT) symmetric uncertaintyranking (SUR) filter and Adaboost with SVM classifier forthe detection of pathological brain MR images and obtainedan accuracy of 9843 with Dataset-255 Wang et al [19]employed Pseudo Zernike moment and linear regressionclassifier for classification of Alzheimerrsquos disease and yieldedan accuracy of 9751 sensitivity of 9671 and specificityof 9773 Alam et al [20] utilized dual-tree complex wavelettransform (DTCWT) principal component analysis (PCA)and twin support vector machine (TSVM) for the detectionof Alzheimerrsquos disease classification and obtained an accuracyof 9546 plusmn 126

Scholars have proposed different methods to extract fea-tures for the pathological brain disease [21] After analyzingthe abovemethods we found that all of themethods achievedpromising results which indicated that 2D-DWT is effec-tive in feature extraction for pathological brain detection

However there are two problems (1)Most of themutilize tra-ditional PCA for feature extraction which is computational-intensive for large datasets with a higher dimensions (2)Theclassification performance can be further improved becausethe feature vector contains excessive features which requiredmore memory and increased computational complexityMoreover it required too much time to train the classifiers

To address the above-mentioned problems we proposeda new pathological brain detection system based on brainMR images which has the potential improvements over theother schemes Weiner filter is used for the preprocessingof the images The proposed method uses 2D DWT forthe extraction of features because of its ability to analyzeimages at different scales PPCA is used in place of PCAfor the reduction of features which has the advantages ofcomputing the efficient dimension reduction in terms of thedistribution of latent variables maximum-likelihood esti-mates probability model dealing with the missing data anda combination of multiple PCA as probabilistic mixture Arelatively new classifier known as random subspace ensemble(RSE) classifier is employed which has the advantage of lowcomputational burden over the traditional classifiers Hencethe novelty of the proposed method lies in the application ofPPCA features and RSE classifier

The article is organized as follows Section 2 presentsdetails about the materials and methods Section 3 describesthe experimental results evaluation procedure and discus-sions Finally Section 4 presents the conclusion and futureresearch

2 Materials and Methods

21 Materials At present there are four benchmark datasets(DS) as DS-66 DS-90 DS-160 and DS-255 of different sizesof 66 90 160 and 255 images respectively All the datasets(DS) contain axial T2-weighted 256 times 256-pixel MR imagesdownloaded from medical school of Harvard University(Boston MA USA) (URL httpwwwmedharvardeduaablibhomehtml) website T2-weighted images are selectedas input image because T2-weighted (spin-spin) relaxationgives better image contrast that is helpful to show differentanatomical structure clearly Also they are better in detectinglesions than T1 weighted images

We selected five slices from each subject The selectioncriterion is that for healthy subjects these slices were selectedat random For pathological subjects the slices should con-tain the lesions by confirmation of these radiologists with tenyears of experiences A sample of diseased slices is shownin Figure 2 In this investigation all diseases are treated aspathological and our task is a binary classification problemthat is to distinguish pathological brain from healthy brainsHere the whole brain is considered as the input image Wedid not select local characteristics like point and edge and weextract global image characteristics that are further learnedby the new cascade model Let us keep in mind that ourprocedure is different from the way neuroradiologists doThey usually take the local features and compare with stan-dard template to check whether focuses exist such as shrinkexpansion bleeding and inflammationWhile our technique










119882(1198911 1198912) = 119867lowast (1198911 1198912) 119878119909119909 (1198911 1198912)1003817100381710038171003817119867 (1198911 1198912)10038171003817100381710038172 119878119909119909 + 119878119899119899 (1198911 1198912) (1)


23 2D-DWT






2D DWTProduce

feature matrix





Pathological brain

Wiener filter


(a) (b) (c) (d) (e) (f)

(g) (h) (i) (j) (k) (l)

(m) (n) (o) (p) (q) (r)





119886 ) d119905 (2)



119888119860119895119896 (119899) = DS[sum119899

119909 (119899) 119897lowast119895 (119899 minus 2119895119896)]

119888119863119895119896 (119899) = DS[sum119899

119909 (119899) ℎlowast119895 (119899 minus 2119895119896)] (3)



Time

Scal

e

Scal

e

Scal

e

TimeFrequency


Fouriertransform

Wavelettransform


s

Ca1

Ca2

Ca3

Cd1

Cd2

Cd3







119910119879 = 119882 lowast 119909119879 + 120583 + 120576 (4)



1 （1

（1 （1（（1 （（1

2 （2 （2

（2（2

（（2（（2

（2

（1 （1

（（2

3 （3

（3 （（3


















(6)



(7)




Investigation 1

Investigation 2

Investigation 3

Investigation 4

Investigation 5

TrainingValidation




(8)



(9)





















4 Conclusion




Nomenclature








Acknowledgments


References















































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in












119882(1198911 1198912) = 119867lowast (1198911 1198912) 119878119909119909 (1198911 1198912)1003817100381710038171003817119867 (1198911 1198912)10038171003817100381710038172 119878119909119909 + 119878119899119899 (1198911 1198912) (1)


23 2D-DWT






2D DWTProduce

feature matrix





Pathological brain

Wiener filter


(a) (b) (c) (d) (e) (f)

(g) (h) (i) (j) (k) (l)

(m) (n) (o) (p) (q) (r)





119886 ) d119905 (2)



119888119860119895119896 (119899) = DS[sum119899

119909 (119899) 119897lowast119895 (119899 minus 2119895119896)]

119888119863119895119896 (119899) = DS[sum119899

119909 (119899) ℎlowast119895 (119899 minus 2119895119896)] (3)



Time

Scal

e

Scal

e

Scal

e

TimeFrequency


Fouriertransform

Wavelettransform


s

Ca1

Ca2

Ca3

Cd1

Cd2

Cd3







119910119879 = 119882 lowast 119909119879 + 120583 + 120576 (4)



1 （1

（1 （1（（1 （（1

2 （2 （2

（2（2

（（2（（2

（2

（1 （1

（（2

3 （3

（3 （（3


















(6)



(7)




Investigation 1

Investigation 2

Investigation 3

Investigation 4

Investigation 5

TrainingValidation




(8)



(9)





















4 Conclusion




Nomenclature








Acknowledgments


References















































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in




2D DWTProduce

feature matrix





Pathological brain

Wiener filter


(a) (b) (c) (d) (e) (f)

(g) (h) (i) (j) (k) (l)

(m) (n) (o) (p) (q) (r)





119886 ) d119905 (2)



119888119860119895119896 (119899) = DS[sum119899

119909 (119899) 119897lowast119895 (119899 minus 2119895119896)]

119888119863119895119896 (119899) = DS[sum119899

119909 (119899) ℎlowast119895 (119899 minus 2119895119896)] (3)



Time

Scal

e

Scal

e

Scal

e

TimeFrequency


Fouriertransform

Wavelettransform


s

Ca1

Ca2

Ca3

Cd1

Cd2

Cd3







119910119879 = 119882 lowast 119909119879 + 120583 + 120576 (4)



1 （1

（1 （1（（1 （（1

2 （2 （2

（2（2

（（2（（2

（2

（1 （1

（（2

3 （3

（3 （（3


















(6)



(7)




Investigation 1

Investigation 2

Investigation 3

Investigation 4

Investigation 5

TrainingValidation




(8)



(9)





















4 Conclusion




Nomenclature








Acknowledgments


References















































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in




Time

Scal

e

Scal

e

Scal

e

TimeFrequency


Fouriertransform

Wavelettransform


s

Ca1

Ca2

Ca3

Cd1

Cd2

Cd3







119910119879 = 119882 lowast 119909119879 + 120583 + 120576 (4)



1 （1

（1 （1（（1 （（1

2 （2 （2

（2（2

（（2（（2

（2

（1 （1

（（2

3 （3

（3 （（3


















(6)



(7)




Investigation 1

Investigation 2

Investigation 3

Investigation 4

Investigation 5

TrainingValidation




(8)



(9)





















4 Conclusion




Nomenclature








Acknowledgments


References















































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in




1 （1

（1 （1（（1 （（1

2 （2 （2

（2（2

（（2（（2

（2

（1 （1

（（2

3 （3

（3 （（3


















(6)



(7)




Investigation 1

Investigation 2

Investigation 3

Investigation 4

Investigation 5

TrainingValidation




(8)



(9)





















4 Conclusion




Nomenclature








Acknowledgments


References















































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in






Investigation 1

Investigation 2

Investigation 3

Investigation 4

Investigation 5

TrainingValidation




(8)



(9)





















4 Conclusion




Nomenclature








Acknowledgments


References















































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in




















4 Conclusion




Nomenclature








Acknowledgments


References















































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in








Acknowledgments


References















































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in

























Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in



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