ICA-based feature extraction and automatic classification of AD-related MRI data

2010 Sixth International Conference on Natural Computation (ICNC 2010) ICA-Based Feature Extraction and Automatic Classification of AD-Related MRI DataWenlu Yang,Halei Xia,Bin Xia Information Engineering CollegeShanghai Maritime UniversityShanghai,200135,Chinawenluyang@Lok Ming LuiDepartment of MathematicsHarvard UniversityBoston,MA,USAmalmlui@Xudong HuangDepartment of RadiologyBrigham and Women’s Hospitaland Harvard Medical SchoolBoston,MA,USAxhuang3@Abstract—There is an unmet medical need for identifying neuroimaging biomarkers for Alzheimer’s disease(AD),the most common form of senile dementia.These biomarkers are essential for early and accurate diagnosis of AD,monitoring of AD progression,and assessment of AD-modifying therapies.In volumetric studies of the medial temporal lobe and hippocampus, magnetic resonance imaging(MRI),as a technique that can detect changes in cerebral bloodflow and blood oxygenation,has shown its powerful ability to distinguish AD and mild cognitive impairment(MCI)subjects from normal controls.However, how to identify potential AD neuroimaging biomarkers from magnetic resonance(MR)images is still a very challenging task. We have thus proposed a novel method based on independent component analysis(ICA),an increasingly important biomedical signal processing technique that enables separation of blindly observed signals into original independent signals for identifying potential AD neuroimaging biomarker(s).The ICA-based method has three steps.First,all MRI scans are aligned and normalized by SPM.Then,ICA was applied to the images for extracting a potential neuroimaging biomarker.Finally,the separated in-dependent component coefficients were fed into a classifying machine that is able to discrinate AD and MCI from control subjects.The experimental results on the MRI data from the Open Access Series of Imaging Studies showed that that our ICA-based method can discern AD and MCI cases from age-matched controls.I.I NTRODUCTIONAlzheimer’s Disease(AD)is by far the most common cause of dementia associated with aging and a neurodegenerative disorder.Many studies of AD showed that three typical lesions in AD’s brain have been recognized such as the intraneuronal neurofibrillary tangles,extracellular deposits of senile amyloid plaques,and the loss of neurons[1].To clinically diagnose AD patients at the early stage,many biomedical imaging techniques have been used,such as structural and functional magnetic resonance imaging(sMRI/fMRI),positron emission tomorgraphy(PET),and so on.Structural MRI promises to aid diagnosis and treatment monitoring of mild cognitive impairment(MCI)and AD,of-fering the potential for easily obtainable surrogate biomarkers of diagnostic status and disease progression.The studies of analyzing sMRI brain scans can be generally categorized into two classes:region-of-interest(ROI)analysis[2]and whole brain analysis[1][3].ROI analysis focuses on specific brain regions,especially the hippocampus and the entorhinal cortex [4][5],which show histopathogical changes at early stages of AD[6].ROI analysis of the brain structure is considered as the gold standard,but it has some drawbacks such as operator-dependency,being labor-consuming and time-intensive,and requiring a priori choice of regions to be investigated[7]. To overcome these shortcomings,some automated methods of measuring whole brain atrophy have been developed, such as voxel-based morphometry(VBM)[8],tensor-based morphometry[9],source-based morphometry[10],etc.On the other hand,sMRI researches can be divided by analysis techniques into volume statistics[4][2],cortex shape analysis [5],and blind signal separation/machine learning techniques [3][10][11][12].Independent component analysis(ICA),as one of important techniques of blind signal separation,has been shown to provide a powerful method for neuroimaging data[13][17]. It is one of multivariate data-driven techniques that enable an exploratory analysis of MRI datasets to provide useful information about the relationship between voxels in local substructures of the brain.For diagnosis or classification of patients with AD and MCI,support vector machines(SVM) as one of machine learning techniques has been paid more attentions[3][11].In the current study,we will propose a novel method that applies ICA to extracting features from sMRI images and performs support vector machine for classifying AD and MCI patients from normal control subjects.The rest of the paper is organized as the follows.Section II proposed our novel method based on ICA.In this section, we will explain the framework of the method and the key techniques used in this study.In section III,we will present our experimental results including learned basis functions, representation on the ICA subspace,classification of AD and normal controls.Finally discussions and conclusions will be made in section IV.II.M ETHODA.The framework of the proposed methodThe framework of our ICA-based method is shown in Fig.1. First of all,all MRI data are normalized into a template[14], and then reconstruct the brain images in which all nonbrain voxels are masked out.Next,the normalized brain images are decomposed into MRI basis functions and the correspondingFig.1.The framework of the method for analysis of structural MRIdata.Fig.2.An example of the atlas-registered gainfield-corrected and brain-masked images.coefficients using the FastICA algorithm[15].Finally,the separated coefficients are fed into a SVM-based classifier for diagnosis of individuals with or without AD.B.Overview of the Data SetThe Open Access Series of Imaging Studies(OASIS)is a series of magnetic resonance imaging data set that is publicly available for study and analysis.The data set consists of a cross-sectional collection of416subjects aged18to96years, including218subjects aged18to59years and198subjects aged60to96years.Of the older subjects,98had CDR score of0,indicating no dementia,and100had a CDR score greater than zero(70CDR=0.5,28CDR=1,2CDR=2),indicating a diagnosis of very mild to moderate AD.The detailed statistics of the data set was described in the literature[14].To alleviate our computing burden,we used the processed image data in the OASIS database,that is,the atlas-registered gainfield-corrected and brain-masked image in the processed directory.An example is shown in Fig.2.Fig.3.A spatial ICA model for decomposing MRIimages.Fig.4.An example of reconstruction of MRI images.C.Spatial ICA model for MRI dataThe basic goal of independent component analysis is to solve the blind signal separation problem by expressing a set of random variables(observations)as linear combinations of statistically independent component variables(source signals). According assumptions of sources independent over space or time,ICA can be further described as spatial ICA(sICA)and time ICA(tICA).SICA seeks a set of mutually independent component source images and a corresponding set of uncon-strained time courses.By contrast,tICA seeks a set of IC source time courses and a corresponding set of unconstrained images.In concrete sMRI data,sICA embodies the assumption that each image in X is composed of a linear combination of spatially and statistically independent images.The spatial ICA model for MRI images is shown in Fig.3.In Fig.3, the data X denotes voxels of all MRI images,The voxels in each MRI image are arranged into one row in X.S and A are unsupervisedly and simultaneously learned from X.Each row in A denotes a base,also called a basis function,or a feature.In this study,these bases also are considered as potential neuroimaging biomarkers.After ICA computation,any MRI image can be recon-structed by linearly combining the set of basis functions and corresponding coefficients,for example,shown in Fig.4. D.Classification Using Support Vector MachineSupport vector machine(SVM)[16]is one of very popular classifiers and recently has been used to help distinguish AD subjects from elderly control subjects using anatomical MR imags[3].SVM conceptually implements the idea that vectors are nonlinearly mapped to a very high dimension feature space. In this feature space,a linear separation surface is created to separate the training data by minimizing the margin between the vectors of the two classes.In this study we will use the LIBSVM toolbox (.tw/cjlin/libsvm)as the classifier to diagnose AD subjects from normal controls.Fig.5.Examples of learned MRI image bases from MRI data with AD and NC using a spatial ICA algorithm built-in the FastICA toolbox.III.EXPERIMENTAL RESULTSA.Feature extraction using ICAUsing the FastICA algorithm to decompose the brain im-ages,we can obtain MRI image basis functions shown in Fig.5.From these bases,we can easily notice that each base has only coded a local part of a brain MRI image.Differently bases locally code different parts of the brain image.If a corresponding coefficient is bigger,we can say the base is more important in the individual MRI scans.B.Experimental DatasetThe whole data set is divided four groups:Group1(100 subjects with the CDR score greater than0),and Group2, 3,and4respectively included116,100,and100subjects divided from the316control subjects without dementia in the descending order.Ages in Group2are in the range from18 to19,averaged18.44±0.56;Group3form25to59,averaged42.16±17.16;Group4from59to94,averaged75.58±18.42.C.Experimental Results1)Representation on the ICA feature subspace:After using the FastICA tool to separate all MRI images with AD and normal subjects,the dimensionality of all independent com-ponents is reduced using principal component analysis(PCA). Next,we project all MRI scan samples onto the ICA feature subspace.The Fig.6shows distribution of MRI data on the3D subspace spanned by Age,principle component(PC1),and PC2.Here,PC1and PC2are reconstructed from decomposed components using principle component analysis(PCA).And the Fig.7denotes distribution in3D subspace of PC1,PC2, and PC3,same as the meaning in the Left.These twofigures indicate that brains are different,gradually changing with ages. The results are consistent with that presented by Marcus et al.[14].2)Classification of AD and normal controls:After feature extraction and representation on independent subspace,all MRI data will be diagnosed and clustered into two groups-AD and normal controls-using a classifier based on support vector machine.We have made two experiments on the data.Fig.6.Distribution of MRI data on the3D subspace spanned by Age,PC1, and PC2.Fig.7.Distribution in3D subspace of PC1,PC2,and PC3.TABLE IA CCURACY BY RANDOM METHOD(%)Mean Sensitivity SpecificityGroup1-298.497.499.4Group1-393.389.996.1Group1-462.856.968.7One is random training and testing method,and other is leave-one-out method.Thefirst method randomly selects half of the data as the training set,the rest as testing data.And more experiments with90%training dataset have improved the classification accuracy.This is because the classifier is based on statical learning theory.The more samples there are, the more reliable the classification result is.The leave-one-out method means leaving one data as the testing data,the rest as the training data.The classification accuracy is shown as tables I,II,and III.In the table I,the mean accuracy was averagely obtainedTABLE IIA CCURACY BY RANDOM METHOD(%)Mean Sensitivity SpecificityGroup1-299.198.399.8Group1-394.893.596.0Group1-465.460.670.4TABLE IIIA CCURACY BY LEAVE-ONE-OUT METHOD(%)Mean Sensitivity SpecificityGroup1-299.098.0100Group1-395.094.096.0Group1-467.562.073.0after we repeated over100times the experiments using usual training and testing method.In the same way,the mean accuracy in the table III was averagely obtained after each sample was tested.We also quantified the specificity related to AD patients and sensitivity related to control subjects. Given TP the number of true positives:number of AD pa-tients correctly classified,TN the number of true negatives, FP the number of false positives,and FN the number of false negatives,the classification accuracy can be definedas accuracy=T P+T NT P+F P+T N+F N ,the specificity can bedefined as specificity=T PT P+F P ,and the sensitivity assensivity=T NT N+F N .From these tables,it is obvious that the differences between group1and other groups are more remarkable with increasing age gap.In group1,there are100subjects of age over60, and in groups2,3,4,the average ages are18.44,42.16,and 75.58,respectively.IV.DISCUSSIONS AND CONCLUSIONSA.Why ICA to structural imaging analysis?ICA is one of the data-driven,multivariate,and unsuper-vised methods with an advantage of no any a priori in-formation.It has become an increasing popular biomedical data-mining technique,also for processing functional and structural MRI data[17].To our knowledge,there are few reports about application of ICA to structural MRI data of AD patients.However,ICA might also be a useful tool for early AD diagnosis of structural MRI data analysis because it has shown its powerful ability of processing sMRI data with schizophrenia[17].Therefore,in this study,we tried to apply ICA to analysis of AD-related MRI data.The results we have presented show that the proposed method based on ICA is very useful for classification of AD and normal controls. B.Dimensionality reduction of the MRI dataDue to the high dimensionality of the MRI data in OASIS database,say XYZ178×208×178,there are a problem with computing resources.To solve it,dimensionality reduction step is needed.Wefirst cut MRI images to160×208×160because the deleted voxels are outermost in the MRI images with gray values about zero.Then resize them to40×52×40using a bilinear method.Even so,the total voxels in one image are still83200.Finally we calculate the standard variances of the voxels in each image and select the voxels with variances over a threshold.In other words,these voxels are strongly related to different kinds of subjects and they are useful for classification of AD and normal controls as the proof of this study.As abovementioned,we used spatial ICA model to separate the MRI data into image bases and independent components.Be-fore ICA computation,PCA was used to reduce dimensionality and noisy information including in the MRI data.Finally,we separated out200independent components as the input to SVM-based classifier distinguishing AD subjects from normal control subjects.C.Related worksMarcus et al.[14]used the FAST program in the FSL soft-ware suite(/fsl)to compute normalized whole-brain volume(nWBV)and plotted nWBV distribution line across the adult life span.Our result shown in Fig.6is similar to that presented in the paper.This indicates that the feature extraction method based on ICA preserves the usefully changing information with the development of Alzheimer’s disease.Therefore,there are same manifolds on both originally statistical space(nWBV-ages)and ICA subspace.Magnin et al.[3]presented a method based on SVM to classify16AD and22elderly control subjects and showed their promising results.Theyfirst parcellated MR images into regions of interest(ROIs)and then apply an SVM algorithm to the characteristics of gray matter extracted from each ROI for distinguishing patients with AD from elderly controls. Savio et al.[18]applied four different models of ANNs to classification of98female patients of AD vs.control subjects. They reported the result of83%accuracy based on VBM analysis.Garcia-Sebastian et al.[19]studied feature extraction processes based on VBM analysis to classify MRI volumes of AD patients and normal subjects.They applied SVM to perform classification on MRI volumes of98females and obtained better results with80.6%accuracy.Unlike their less samples,we processed total416MR images and presented the more reliable and statistical results. Our more results showed that if we classified100AD and316 control subjects we would obtain the average92.4%accuracy.D.Future workAlthough we have presented the acceptable classification results of AD and normal control subjects,there are still have a lot of work to ing spatial ICA model,the locality of MRI data in the original image space has been extracted.However, some questions should befigured out.For example,which ICA base is more important for diagnosis of AD?What is the relationship between ICA bases and substructures in the brain? Which ICA base is a potential neuroimaging biomarker for identifying AD subjects?We will further study these questions in order to discover AD-related neuroimaging biomarkers.A CKNOWLEDGMENTThe authors would like to express their gratitude for the sup-ports from Shanghai Maritime University Foundation(Grant No.20080471,20090175),the Science and Technology Com-mission of Shanghai Municipality(Grant No.0951*******), the national natural science foundation of China(Grant No. 60905056)the NIA/NIH(5R21AG028850),Alzheimer’s As-sociation(IIRG-07-60397),and the research funds from BWH Radiology Department.R EFERENCES[1]Katz,B.and S.Rimmer,Ophthalmologic manifestations of alzheimers-disease.Survey of Ophthalmology,1989,34(1):31-43.[2]Jack CR,Petersen RC,Obrien PC,Tangalos EG.MR-based hip-pocampal volumetry in the diagnosis of Alzheimers-disease.Neurology, 1992;42(1):183-8.[3]Magnin B,Mesrob L,Kinkingnehun S,Pelegrini-Issac M,Colliot O,Sarazin M,Dubois B,Lehericy S,Benali H.Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI.Neuroradiology2009;51(2):73-83.[4]Chupin M,Gerardin E,Cuingnet R,Boutet C,Lemieux L,Lehericy S,Be-nali H,Garnero L,Colliot O.Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI.Hippocampus2009;19(6):579-87.[5]Zhou L,Lieby P,Barnes N,Reglade-Meslin C,Walker J,Cherbuin N,Hartley R.Hippocampal shape analysis for Alzheimer’s disease using an efficient hypothesis test and regularized discriminative deformation.Hippocampus2009;19(6):533-40.[6]Braak H,Braak E.Staging of Alzheimer-related cortical destruction.IntPsychogeriatr1997;9Suppl1:257-61;discussion69-72.[7]Giesel FL,Thomann PA,Hahn HK,et al.,Comparison of manual directand automated indirect measurement of hippocampus using magnetic resonance imaging.European Journal of Radiology,2008,(66):268-273.[8]Ashburner J,Friston KJ.V oxel-based morphometry-The methods.Neuroimage2000;11(6):805-21.[9]Hua X,Leow AD,Parikshak N,Lee S,Chiang MC,Toga AW,JackCR,Jr.,Weiner MW,Thompson PM.Tensor-based morphometry as a neuroimaging biomarker for Alzheimer’s disease:an MRI study of676 AD,MCI,and normal subjects.Neuroimage2008;43(3):458-69. [10]Xu L,Groth KM,Pearlson G,Schretlen DJ,Calhoun VD.Source-basedmorphometry:the use of independent component analysis to identify gray matter differences with application to schizophrenia.Hum Brain Mapp 2009;30(3):711-24.[11]Kloppel S,Stonnington CM,Chu C,Draganski B,Scahill RI,RohrerJD,Fox NC,Jack CR,Jr.,Ashburner J,Frackowiak RS.Automatic classification of MR scans in Alzheimer’s disease.Brain2008;131(Pt3):681-9.[12]Walhovd KB,Fjell AM,Brewer J,McEvoy LK,Fennema-NotestineC,Hagler DJ,Jr.,Jennings RG,Karow D,Dale bining MR Imaging,Positron-Emission Tomography,and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease.AJNR Am J Neuroradiol 2010.[13]McKeown MJ,Sejnowski TJ.Independent component analysis of fMRIdata:Examining the assumptions.Human Brain Mapping1998;6(5-6):368-72.[14]Marcus D.S.,et al.,Open access series of imaging studies(OASIS):Cross-sectional MRI data in young,middle aged,nondemented,and demented older adults.Journal of Cognitive Neuroscience,2007,19(9): 1498-1507.[15]Hyvarinen A.,Fast and robustfixed-point algorithms for independentcomponent analysis.Ieee Transactions on Neural Networks,1999,10(3): 626-634.[16]Cortes C.and Vapnik V.SUPPORT-VECTOR NETWORKS.MachineLearning,1995,20(3):273-297.[17]Xu L.,Pearlson G.,and Calhoun V.D.Joint source based morphometryidentifies linked gray and white matter group differences.Neuroimage, 2009,44(3):777-789.[18]Savio A.,Garcia-Sebastian M.,et al.Classification results of artificialneural networks for Alzheimer’s Disease Detection.Lecture Notes in Computer Science,2009,5788:641-648.[19]Garcia-Sebastian M.,Savio A.,Grana M.,Villanua J.On the Use ofMorphometry Based Features for Alzheimers Disease Detection on MRI.Lecture Notes in Computer Science,2009,5517:957-964.。