Deep Learning for Alzheimer’s Disease Classification using Texture Features

Jae-Hong       So; Nuwan       Madusanka; Heung-Kook       Choi; Boo-Kyeong       Choi; Hyeon-Gyun       Park

doi:10.2174/1573405615666190404163233

Abstract

Background: We propose a classification method for Alzheimer’s disease (AD) based on the texture of the hippocampus, which is the organ that is most affected by the onset of AD.

Methods: We obtained magnetic resonance images (MRIs) of Alzheimer’s patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This dataset consists of image data for AD, mild cognitive impairment (MCI), and normal controls (NCs), classified according to the cognitive condition. In this study, the research methods included image processing, texture analyses, and deep learning. Firstly, images were acquired for texture analyses, which were then re-spaced, registered, and cropped with Gabor filters applied to the resulting image data. In the texture analyses, we applied the 3-dimensional (3D) gray-level co-occurrence (GLCM) method to evaluate the textural features of the image, and used Fisher’s coefficient to select the appropriate features for classification. In the last stage, we implemented a deep learning multi-layer perceptron (MLP) model, which we divided into three types, namely, AD-MCI, AD-NC, and MCI-NC.

Results: We used this model to assess the accuracy of the proposed method. The classification accuracy of the proposed deep learning model was confirmed in the cases of AD-MCI (72.5%), ADNC (85%), and MCI-NC (75%). We also evaluated the results obtained using a confusion matrix, support vector machine (SVM), and K-nearest neighbor (KNN) classifier and analyzed the results to objectively verify our model. We obtained the highest accuracy of 85% in the AD-NC.

Conclusion: The proposed model was at least 6–19% more accurate than the SVM and KNN classifiers, respectively. Hence, this study confirms the validity and superiority of the proposed method, which can be used as a diagnostic tool for early Alzheimer’s diagnosis.

Keywords: Alzheimer's disease, deep learning, texture analysis, hippocampus, image processing, classification.

Graphical Abstract

[1] 
Alzheimer's Association. Alzheimer’s disease facts and figures. Alzheimers Dement  2018; 14(3): 367-62.
[http://dx.doi.org/10.1016/j.jalz.2018.02.001] 
[2] 
Brookmeyer R, Abdalla N, Kawas CH, Corrada MM. Forecasting
the prevalence of preclinical and clinical Alzheimer’s disease in the
United States. Alzheimers Dement  2018; 14(2): 121-29.
[http://dx.doi.org/10.1016/j.jalz.2017.10.009] [PMID:  29233480] 
[3] 
Huang J, Auchus AP. Diffusion tensor imaging of normal appearing
white matter and its correlation with cognitive functioning in
mild cognitive impairment and Alzheimer’s disease. Ann N Y
Acad Sci  2007; 1097(1): 259-64.
[http://dx.doi.org/10.1196/annals.1379.021] [PMID:  17413027] 
[4] 
Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol  1999; 56(3): 303-08.
[http://dx.doi.org/10.1001/archneur.56.3.303] [PMID:  10190820] 
[5] 
Mangialasche F, Solomon A, Winblad B, Mecocci P, Kivipelto M. Alzheimer’s disease: clinical trials and drug development Lancet
Neurol  2010; 9(7): 702-16.
[http://dx.doi.org/10.1016/S1474-4422(10)70119-8] [PMID:  20610346] 
[6] 
Mueller SG, Weiner MW, Thal LJ, et al. Ways toward an early
diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging
Initiative (ADNI). Alzheimers Dement  2005; 1(1): 55-66.
[http://dx.doi.org/10.1016/j.jalz.2005.06.003] [PMID:  17476317] 
[7] 
Querbes O, Aubry F, Pariente J, et al. Early diagnosis of Alzheimer’s
disease using cortical thickness: impact of cognitive reserve. Brain  2009; 132(Pt8): 2036-47.
[http://dx.doi.org/10.1093/brain/awp105] [PMID:  19439419] 
[8] 
Ovais M, Zia N, Ahmad I, et al. Phyto-therapeutic and nanomedicinal
approaches to cure Alzheimer’s disease: Present status and future
opportunities. Front Aging Neurosci  2018; 10: 284.
[http://dx.doi.org/10.3389/fnagi.2018.00284] [PMID:  30405389] 
[9] 
Chételat G. Multimodal neuroimaging in Alzheimer’s disease:
Early diagnosis, physiopathological mechanisms, and impact of
lifestyle. J Alzheimers Dis  2018; 4(s1): S199-211.
[http://dx.doi.org/10.3233/JAD-179920] [PMID:  29504542] 
[10] 
Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ. Prediction of MCI to AD conversion, via MRI, CSF biomarkers,
and pattern classification. Neurobiol Aging  2011; 32(12): 2322.e19.
[http://dx.doi.org/10.1016/j.neurobiolaging.2010.05.023] [PMID:  20594615] 
[11] 
Samper-González J, Burgos N, Bottani S, et al. Reproducible evaluation
of classification methods in Alzheimer’s disease: Framework
and application to MRI and PET data. Neuroimage  2018; 183: 504-21.
[http://dx.doi.org/10.1016/j.neuroimage.2018.08.042] [PMID:  30130647] 
[12] 
Callen DJA, Black SE, Gao F, Caldwell CB, Szalai JP. Beyond the
hippocampus: MRI volumetry confirms widespread limbic atrophy
in AD. Neurology  2001; 57(9): 1669-74.
[http://dx.doi.org/10.1212/WNL.57.9.1669] [PMID:  11706109] 
[13] 
Lancaster JL, Tordesillas-Gutiérrez D, Martinez M, et al. Bias
between MNI and Talairach coordinates analyzed using the ICBM-
152 brain template. Hum Brain Mapp  2007; 28(11): 1194-205.
[http://dx.doi.org/10.1002/hbm.20345] [PMID:  17266101] 
[14] 
Mori S, Oishi K, Jiang H, et al. Stereotaxic white matter atlas based
on diffusion tensor imaging in an ICBM template. Neuroimage  2008; 40(2): 570-82.
[http://dx.doi.org/10.1016/j.neuroimage.2007.12.035] [PMID:  18255316] 
[15] 
Wesarg S, Seitel M, Firle EA, Dold C. AHA conform visualization
of conventionally acquired cardiac CT data using the toolkits itk
and vtk. CARS 2004.
[http://dx.doi.org/10.1016/j.ics.2004.03.322] 
[16] 
Madusanka N, Choi H-K, So J-H, Choi B-K. Alzheimer’s disease classification based on multi-feature fusion. Curr Med Imaging Rev  2018; 15(2): 161-8.
[http://dx.doi.org/110.2174/1573405614666181012102626] 
[17] 
Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an
image computing platform for the quantitative imaging network. Magn Reson Imaging  2012; 30(9): 1323-41.
[http://dx.doi.org/10.1016/j.mri.2012.05.001] [PMID:  22770690] 
[18] 
Fedorov A, Tuncali K, Fennessy FM, et al. Image registration for
targeted MRI-guided transperineal prostate biopsy. J Magn Reson
Imaging  2012; 36(4): 987-2.
[http://dx.doi.org/10.1002/jmri.23688] [PMID:  22645031] 
[19] 
Gabor D. Theory of communication. Part 1 the analysis of information. Journal of the Institution of Electrical Engineers-Part III Radio and Communication Engineering  1946; 93(26): 429-12.
[http://dx.doi.org/10.1049/ji-3-2.1946.0074] 
[20] 
Zhang J, Tan T, Ma L. Invariant texture segmentation via circular Gabor filters. In:Proceedings of the 16th IAPR International Conference on Pattern Recognition (ICPR)  2002; Quebec, Canada. 901-4.
[21] 
Kong WK, Zhang D, Li W. Palmprint feature extraction using 2-D Gabor filters. Pattern Recognit  2003; 36(10): 2339-8.
[http://dx.doi.org/10.1016/S0031-3203(03)00121-3] 
[22] 
Jain AK, Ratha NK, Lakshmanan S. Object detection using Gabor filters. Pattern Recognit  1997; 30(2): 295-15.
[http://dx.doi.org/10.1016/S0031-3203(96)00068-4] 
[23] 
Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell  1996; 18(8): 837-5.
[http://dx.doi.org/10.1109/34.531803] 
[24] 
Dunn D, Higgins WE, Wakeley J. Texture segmentation using 2-d gabor elementary functions. IEEE Trans Pattern Anal Mach Intell  1994; 16(2): 130-9.
[http://dx.doi.org/10.1109/34.273736] 
[25] 
Robert M. Haralick, K Shanmugam, Its’Hak Dinstein. Textural features for image classification. IEEE Trans Cybern  1973; 3(6): 610-1.
[26] 
Gadelmawla ES. A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT Int  2004; 37(7): 577-11.
[http://dx.doi.org/10.1016/j.ndteint.2004.03.004] 
[27] 
Partio M, Cramariuc B, Gabbouj M, Visa A. Rock texture retrieval
using gray level co-occurrence matrix. In: Proceedings of the 5th
Nordic Signal Processing Symposium, NORSIG  2002; Norway;. 1-5.
[28] 
Tuceryan M, Jain AK. The handbook of pattern recognition and computer vision.  2nd ed. New Jersey: World Scientific, Inc. 1998.
[29] 
Soh L, Tsatsoulis C. Texture analysis of sar sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens  1999; 37(2): 780-15.
[http://dx.doi.org/10.1109/36.752194] 
[30] 
Pydipati R, Burks TF, Lee WS. Identification of citrus disease using color texture features and discriminant analysis. Comput Electron Agric  2006; 52(1-2): 49-10.
[http://dx.doi.org/10.1016/j.compag.2006.01.004] 
[31] 
Wang H, Guo X-H, Jia Z-W, et al. Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image. Eur J Radiol  2010; 74(1): 124-9.
[http://dx.doi.org/10.1016/j.ejrad.2009.01.024] [PMID:  19261415] 
[32] 
Kayitakire F, Hamel C, Defourny P. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens Environ  2006; 102(3-4): 390-11.
[http://dx.doi.org/10.1016/j.rse.2006.02.022] 
[33] 
Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen  1936; 7(2): 179-9.
[http://dx.doi.org/10.1111/j.1469-1809.1936.tb02137.x] 
[34] 
Plata S. A note on Fisher’s correlation coefficient. Appl Math Lett  2006; 19(6): 499-3.
[http://dx.doi.org/10.1016/j.aml.2005.02.036] 
[35] 
Saqlain SM, Sher M, Shah FA, et al. Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowl Inf Syst  2019; 58(1): 139-67.
[36] 
Narang A, Batra B, Ahuja A, Yadav J, Pachauri N. Classification of EEG signals for epileptic seizures using Levenberg-Marquardt algorithm based multilayer perceptron neural network. J Intell Fuzzy Syst  2018; 34(3): 1669-8.
[http://dx.doi.org/10.3233/JIFS-169460] 
[37] 
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature  2015; 521(7553): 436-4.
[http://dx.doi.org/10.1038/nature14539] [PMID:  26017442] 
[38] 
Bayat FM, Prezioso M, Chakrabarti B, Nili H, Kataeva I, Strukov D. Implementation of multilayer perceptron network with highly
uniform passive memristive crossbar circuits. Nat Commun  2018; 9(1): 2331.
[http://dx.doi.org/10.1038/s41467-018-04482-4] [PMID:  29899421] 
[39] 
Chaudhuri BB, Bhattacharya U. Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomputing  2000; 34(1-4): 11-27.
[http://dx.doi.org/10.1016/S0925-2312(00)00305-2] 
[40] 
Pham BT, Bui DT, Pourghasemi HR, Indra P, Dholakia MB. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol  2017; 128(1-2): 255-18.
[http://dx.doi.org/10.1007/s00704-015-1702-9] 
[41] 
Zeiler MD, Ranzato M, Monga R, et al. On rectified linear units for speech processing. ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing  2013; Vancouver, BC, Canada. 3517-4.
[42] 
Maas Andrew L, Awni Y. Hannun, Andrew Y Ng. Rectifier nonlinearities improve neural network acoustic models. International Conference on Machine Learning (ICML)  2013; Atlanta; USA. 3-5.
[43] 
Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. AISTATS  2010; 9: 249-7.
[44] 
Townsend JT. Theoretical analysis of an alphabetic confusion matrix. Percept Psychophys  1971; 9(1): 40-10.
                    10.3758/BF03213026
                
[http://dx.doi.org/10.3758/BF03213026] 
[45] 
Patil TR, Sherekar SS. Performance analysis of Naive Bayes and J48 classification algorithm for data classification. Int J Comput Appl  2013; 6(2): 256-5.
[46] 
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification
of skin cancer with deep neural networks. Nature  2017; 542(7639): 115-8.
[http://dx.doi.org/10.1038/nature21056] [PMID:  28117445] 
[47] 
Veropoulos K, Campbell C, Cristianini N. Controlling the sensitivity
of support vector machines. In: Proceedings of the International
Joint Conference on AI  1999; Sweden. 60-5.
[48] 
Ruuska S, Hämäläinen W, Kajava S, Mughal M, Matilainen P, Mononen J. Evaluation of the confusion matrix method in the validation
of an automated system for measuring feeding behaviour of
cattle. Behav Processes  2018; 148: 56-62.
[http://dx.doi.org/10.1016/j.beproc.2018.01.004] [PMID:  29330090] 
[49] 
Ohsaki M, Wang P, Matsuda K, Katagiri S, Watanabe H, Ralescu A. Confusion-matrix-based kernel logistic regression for imbalanced data classification. IEEE Trans Knowl Data Eng  2017; 29(9): 1806-13.
[http://dx.doi.org/10.1109/TKDE.2017.2682249] 
[50] 
Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild
cognitive impairment due to Alzheimer’s disease: recommendations
from the National Institute on Aging-Alzheimer’s Association
workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers
Dement  2011; 7(3): 270-79.
[http://dx.doi.org/10.1016/j.jalz.2011.03.008] [PMID:  21514249] 
[51] 
Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D. Early diagnosis of
Alzheimer’s disease with deep learning. In: IEEE 11th International
Symposium on Biomedical Imaging (ISBI);  2014; Beijing, China;. 1015-8.
[http://dx.doi.org/10.1109/ISBI.2014.6868045] 
[52] 
Gray KR, Wolz R, Keihaninejad S, et al. Regional analysis of
FDG-PET for use in the classification of Alzheimer’s Disease. In:
IEEE International Symposium on Biomedical Imaging: From
Nano to Macro  2011; Chicago, IL, USA. 1082-5.
[http://dx.doi.org/10.1109/ISBI.2011.5872589] 

Rights & Permissions Print Cite

Article Metrics

47

6

Journal Information

For Authors

For Editors

For Reviewers

Explore Articles

For Visitors

DOI https://dx.doi.org/10.2174/1573405615666190404163233	Print ISSN 1573-4056
Publisher Name Bentham Science Publisher	Online ISSN 1875-6603

Current Medical Imaging

Deep Learning for Alzheimer’s Disease Classification using Texture Features

Abstract Play Pause

Graphical Abstract

Related Books

Related Articles

Abstract