Journal of Medical Systems

, 43:279 | Cite as

Age Prediction Based on Brain MRI Image: A Survey

  • Hedieh SajediEmail author
  • Nastaran Pardakhti
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing


Human age prediction is an interesting and applicable issue in different fields. It can be based on various criteria such as face image, DNA methylation, chest plate radiographs, knee radiographs, dental images and etc. Most of the age prediction researches have mainly been based on images. Since the image processing and Machine Learning (ML) techniques have grown up, the investigations were led to use them in age prediction problem. The implementations would be used in different fields, especially in medical applications. Brain Age Estimation (BAE) has attracted more attention in recent years and it would be so helpful in early diagnosis of some neurodegenerative diseases such as Alzheimer, Parkinson, Huntington, etc. BAE is performed on Magnetic Resonance Imaging (MRI) images to compute the brain ages. Studies based on brain MRI shows that there is a relation between accelerated aging and accelerated brain atrophy. This refers to the effects of neurodegenerative diseases on brain structure while making the whole of it older. This paper reviews and summarizes the main approaches for age prediction based on brain MRI images including preprocessing methods, useful tools used in different research works and the estimation algorithms. We categorize the BAE methods based on two factors, first the way of processing MRI images, which includes pixel-based, surface-based, or voxel-based methods and second, the generation of ML algorithms that includes traditional or Deep Learning (DL) methods. The modern techniques as DL methods help MRI based age prediction to get results that are more accurate. In recent years, more precise and statistical ML approaches have been utilized with the help of related tools for simplifying computations and getting accurate results. Pros and cons of each research and the challenges in each work are expressed and some guidelines and deliberations for future research are suggested.


Age prediction Brain MRI Brain age BAE Chronological age Deep Learning Image processing Machine Learning 



This research was in part supported by a grant from IPM (No. CS1398-4-69).

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


  1. 1.
    Jana, R., Datta, D., and Saha, R., Age Estimation from Face Image using Wrinkle Features. Procedia Computer Science 46:1754–1761, 2015.CrossRefGoogle Scholar
  2. 2.
    Ng, C., Yap, M., Cheng, Y., and Hsu, G., Hybrid Ageing Patterns for face age estimation. Image Vis. Comput. 69:92–102, 2018.CrossRefGoogle Scholar
  3. 3.
    Antipov, G., Baccouche, M., Berrani, S., and Dugelay, J., Effective training of convolutional neural networks for face-based gender and age prediction. Pattern Recogn. 72:15–26, 2017.CrossRefGoogle Scholar
  4. 4.
    Liu, H., Lu, J., Feng, J., and Zhou, J., Group-aware deep feature learning for facial age estimation. Pattern Recogn. 66:82–94, 2017.CrossRefGoogle Scholar
  5. 5.
    Xing, J., Li, K., Hu, W., Yuan, C., and Ling, H., Diagnosing deep learning models for high accuracy age estimation from a single image. Pattern Recogn. 66:106–116, 2017.CrossRefGoogle Scholar
  6. 6.
    Lee, J. W., Choung, C. M., Jung, J. Y., Lee, H. Y., and Lim, S. K., A validation study of DNA methylation-based age prediction using semen in forensic casework samples. Legal Med. 31:74–77, 2018.PubMedCrossRefPubMedCentralGoogle Scholar
  7. 7.
    Lee, H. Y., Jung, S. E., Oh, Y. N., Choi, A., Yang, W. I., and Shin, K. J., Epigenetic age signatures in the forensically relevant body fluid of semen: a preliminary study. Forensic Science International: Genetics 19:28–34, 2015.CrossRefGoogle Scholar
  8. 8.
    Jang, H., Shin, W., Lee, J., and Do, J., CpG and Non-CpG Methylation in Epigenetic Gene Regulation and Brain Function. Genes 8(6):148, 2017.PubMedCentralCrossRefGoogle Scholar
  9. 9.
    Naue, J., Hoefsloot, H. C. J., Mook, O. R. F., Rijlaarsdam-Hoekstra, L., van der Zwalm, M. C. H., Henneman, P., Kloosterman, A. D., and Verschure, P. J., Chronological age prediction based on DNA methylation: Massive parallel sequencing and random forest regression. Forensic Science International: Genetics 31:19–28, 2017.CrossRefGoogle Scholar
  10. 10.
    Maggio, A., The skeletal age estimation potential of the knee: Current scholarship and future directions for research. Journal of Forensic Radiology and Imaging 9:13–15, 2017.CrossRefGoogle Scholar
  11. 11.
    Monum, T., Mekjaidee, K., Pattamapaspong, N., and Prasitwattanaseree, S., Age estimation by chest plate radiographs in a Thai male population. Sci. Justice 57:169–173, 2017.PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    Darmawan, M. F., Yusuf, S. M., Kadir, M. R. A., and Haron, H., Age estimation based on bone length using 12 regression models of left hand X-ray images for Asian children below 19 years old. Int. J. Legal Med. 17:71–78, 2015.CrossRefGoogle Scholar
  13. 13.
    Schmidt, S., Nitz, I., Ribbecke, S., Schulz, R., Pfeiffer, H., and Schmeling, A., Skeletal age determination of the hand: a comparison of methods. Int. J. Legal Med. 127:691–698, 2013.PubMedCrossRefPubMedCentralGoogle Scholar
  14. 14.
    Schmeling, A., Schulz, R., Reisinger, W., Muhler, M., Wernecke, K. D., and Geserick, G., Studies on the time frame for ossification of the medial clavicular epiphyseal cartilage in conventional radiography. Int. J. Legal Med. 118:5–8, 2004.PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Wittschieber, D., Schulz, R., Vieth, V., Kuppers, M., Bajanowski, T., Ramsthaler, F. et al., The value of sub-stages and thin slices for the assessment of the medial clavicular epiphysis: a prospective multi-center CT study. Forensic Science, Medicine, and Pathology. 10:163–169, 2014.PubMedCrossRefPubMedCentralGoogle Scholar
  16. 16.
    Wittschieber, D., Ottow, C., Vieth, V., Kuppers, M., Schulz, R., Hassu, J. et al., Projection radiography of the clavicle: still recommendable for forensic age diagnostics in living individuals? Int. J. Legal Med. 129:187–193, 2015.PubMedCrossRefPubMedCentralGoogle Scholar
  17. 17.
    Zhang, K., Chen, X. G., Zhao, H., Dong, X. A., and Deng, Z. H., Forensic age estimation using thin-slice multidetector CT of the clavicular epiphyses among adolescent Western Chinese. J. Forensic Sci. 60:675–678, 2015.PubMedCrossRefPubMedCentralGoogle Scholar
  18. 18.
    Cameriere, R., Giuliodori, A., Zampi, M., Galic, I., Cingolani, M., Pagliara, F. et al., Age estimation in children and young adolescents for forensic purposes using fourth cervical vertebra (C4). Int. J. Legal Med. 129:347–355, 2015.PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    Nagaoka, T., and Kawakubo, Y., Using the petrous part of the temporal bone to estimate fetal age at death. Forensic Sci. Int. 248:188 e1–188 e7, 2015.CrossRefGoogle Scholar
  20. 20.
    de Oliveira, F. T., Soares, M. Q., Sarmento, V. A., Rubira, C. M., Lauris, J. R., Rubira-Bullen, I. R. et al., Int. J. Legal Med. 129:195–201, 2015.PubMedCrossRefPubMedCentralGoogle Scholar
  21. 21.
    Ge, Z. P., Ma, R. H., Li, G., Zhang, J. Z., and Ma, X. C., Age estimation based on pulp chamber volume of first molars from cone-beam computed tomography images. Forensic Sci. Int. 253:133.e1–133.e7, 2015.CrossRefGoogle Scholar
  22. 22.
    Lewis, A. J., Boaz, K., Nagesh, K. R., Srikant, N., Gupta, N., Nandita, K. P. et al., Demirjian's method in the estimation of age: a study on human third molars. J. Forensic Dent. Sci. 7:153–157, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Surfer, 2018., Accessed date: 6/8/2018.
  24. 24.
    Talabani, R. M., Baban, M. T., and Mahmood, M. A., Age estimation using lower permanent first molars on a panoramic radiograph: a digital image analysis. J. Forensic Dent. Sci. 7:158–162, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Scoles, P. V., Salvagno, R., Villalba, K., and Riew, D., Relationship of iliac crest maturation to skeletal and chronologic age. J. Pediatr. Orthop. 8:639–644, 1998.CrossRefGoogle Scholar
  26. 26.
    Wittschieber, D., Vieth, V., Domnick, C., Pfeiffer, H., and Schmeling, A., The iliac crest in forensic age diagnostics: evaluation of the apophyseal ossification in conventional radiography. Int. J. Legal Med. 127:473–479, 2013a.PubMedCrossRefPubMedCentralGoogle Scholar
  27. 27.
    Wittschieber, D., Vieth, V., Wierer, T., Pfeiffer, H., and Schmeling, A., Cameriere's approach modified for pelvic radiographs: a novel method to assess apophyseal iliac crest ossification for the purpose of forensic age diagnostics. Int. J. Legal Med. 127:825–829, 2013b.PubMedCrossRefPubMedCentralGoogle Scholar
  28. 28.
    Buckberry, J. L., and Chamberlain, A. T., Age estimation from the auricular surface of the ilium: a revised method. Am. J. Phys. Anthropol. 119:231–239, 2002.PubMedCrossRefPubMedCentralGoogle Scholar
  29. 29.
    Eich, G. F., Babyn, P., and Giedion, A., Pediatric pelvis: radiographic appearance in various congenital disorders. RadioGraphics 12:467–484, 1992.PubMedCrossRefPubMedCentralGoogle Scholar
  30. 30.
    Hao, D., Xiren, X., and Rubiao, P., The evaluation of the apophyseal ossification in conventional radiography in Hai Nan Han Group. Forensic Science and Technology 6:24–26, 1996.Google Scholar
  31. 31.
    Bunge, S. A., and Whitaker, K. J., Brain Imaging: Your Brain Scan Doesn't Lie About Your Age. Curr. Biol. 22(18):R800–R801, 2012.PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    Aycheh, H. M. et al., Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study. Front. Aging Neurosci. 10:252, 2018.PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Clarkson, M. J. et al., A comparison of voxel and surface based cortical thickness estimation methods. Neuroimage 57(3):856–865, 2011.PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    Peters, R., Ageing and the brain. Postgrad. Med. J. 82(964):84–88, 2006.PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Huizinga, W., Poot, D. H. J., Vernooij, M. W., and Roshchupkin, G. V., A spatio-temporal reference model of the aging brain. NeuroImage 169:11–22, 2018.PubMedCrossRefPubMedCentralGoogle Scholar
  36. 36.
    Taki, Y., Kinomura, S., Sato, K., and Goto, R., A longitudinal study of gray matter volume decline with age and modifying factors. Neurobiol. Aging 32:907–915, 2011.PubMedCrossRefPubMedCentralGoogle Scholar
  37. 37.
    Tisserand, D. J., van Boxtel, M. P. J., Pruessner, J. C., Hofman, P., Evans, A. C., and Jolles, J., A Voxel-based Morphometric Study to Determine Individual Differences in Gray Matter Density Associated with Age and Cognitive Change Over Time. Cereb. Cortex 14:966–973, 2004.PubMedCrossRefPubMedCentralGoogle Scholar
  38. 38.
    Wang, B., and Pham, T. D., MRI-based age prediction using hidden Markov models. J. Neurosci. Methods 199:140–145, 2011.PubMedCrossRefGoogle Scholar
  39. 39.
    Luders, E., Cherbuin, N., and Gaser, C., Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitioners. NeuroImage 134:508–513, 2016.PubMedCrossRefGoogle Scholar
  40. 40.
    Steffener, J., Habeck, C., O’Shea, D., Razlighi, Q., Bherer, L., and Stern, Y., Differences between chronological and brain age are related to education and self-reported physical activity. Neurobiol. Aging 40:138–144, 2016.PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Huang, T., Chen, H., Fujimoto, R., Ito, K., Wu, K., Sato, K., Taki, Y., Fukuda, H., and Aoki, T., Age estimation from brain MRI images using deep learning. Melbourne: IEEE 14th International Symposium on Biomedical Imaging, 2017Google Scholar
  42. 42.
    Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N. et al., Prediction of individual brain maturity using fMRI. Science 329:1358–1361, 2010.PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Brown, T. T., Kuperman, J. M., Chung, Y., Erhart, M., McCabe, C., Hagler, D. J., Venkatraman, V. K., Akshoomoff, N., Amaral, D. G., Bloss, C. S. et al., Neuroanatomical assessment of biological maturity. Curr. Biol. 22:1693–1698, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., and Montana, G., Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage 163:115–124, 2017.PubMedCrossRefPubMedCentralGoogle Scholar
  45. 45.
    Liem, F., Varoquaux, G., Kynast, J., Beyer, F. et al., Predicting brain-age from multimodal imaging data captures cognitive impairment. NeuroImage 148:179–188, 2017.PubMedCrossRefPubMedCentralGoogle Scholar
  46. 46.
    Meng, X., Jiang, R., Lin, D., Bustillo, J., Jones, T., Chen, J., Yu, Q., Du, Y., Zhang, Y., Jiang, T., Sui, J., and Calhoun, V. D., Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data. NeuroImage 145:218–229, 2017.PubMedCrossRefPubMedCentralGoogle Scholar
  47. 47.
    Bowman, F. D. B., Brain imaging analysis. Annual Review of Statistics and its Application 1:61–85, 2014.PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Beheshti, I., and Demirel, H., Feature-ranking-based Alzheimer’s disease classification from structural MRI. Magn. Reson. Imaging 34:252–263, 2016.PubMedCrossRefPubMedCentralGoogle Scholar
  49. 49.
    Kumar, S., Dabas, C., and Godara, S., Classification of Brain MRI Tumor Images: A Hybrid Approach. Procedia Computer Science 122:510–517, 2017.CrossRefGoogle Scholar
  50. 50.
    Lu, W., Li, Z., and Chu, J., A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning. Comput. Biol. Med. 83:157–165, 2017.PubMedCrossRefPubMedCentralGoogle Scholar
  51. 51.
    Linna, K. A., Gaonkarc, B., Satterthwaiteb, T. D., Doshic, J., Davatzikosc, C., and Shinoharaa, R. T., Control-Group Feature Normalization for Multivariate Pattern Analysis of Structural MRI Data using the Support Vector Machine. NeuroImage 132:157–166, 2016.CrossRefGoogle Scholar
  52. 52.
    Mohsen, H., El-Dahshan, E. A., El-Horbaty, E. M., and Salem, A. M., Classification using Deep Learning Neural Networks for Brain Tumors. Future Computing and Informatics Journal 3(1):68–71, 2018.CrossRefGoogle Scholar
  53. 53.
    Pashaei, A., Sajedi, H., and Jazayeri, N., Brain tumor classification via convolutional neural network and extreme learning machines, 2018 8th International Conference on Computer and Knowledge Engineering, ICCKE, 8566571, pp. 314-319, 2018.Google Scholar
  54. 54.
    Berger, A., Magnetic resonance imaging. Br. Med. J. 324:35, 2002.CrossRefGoogle Scholar
  55. 55.
    Case Western Reserve University, 2018. Accessed date: 9/11/2018.
  56. 56.
    Pooley, R. A., AAPM/RSNA Physics Tutorial for Residents. RadioGraphics 25(4):1087–1099, 2005.PubMedCrossRefPubMedCentralGoogle Scholar
  57. 57.
    Chau, W., and McIntosh, A. R., The Talairach coordinate of a point in the MNI space: how to interpret it. NeuroImage 25(2):408–416, 2005.PubMedCrossRefPubMedCentralGoogle Scholar
  58. 58.
    Bakir, B., Sanli, S., Bakir, V. L., Avas, S. et al., Role of diffusion weighted MRI in the differential diagnosis of endometrial cancer, polyp, hyperplasia, and physiological thickening. Clin. Imaging 41:86–94, 2017.PubMedCrossRefPubMedCentralGoogle Scholar
  59. 59.
    Brennan, M. E., McKessar, M., Snook, K. et al., Impact of selective use of breast MRI on surgical decision-making in women with newly diagnosed operable breast cancer. Breast 32:135–143, 2017.PubMedCrossRefPubMedCentralGoogle Scholar
  60. 60.
    Eiber, M., Weirich, G., Holzapfel, K., Souvatzoglou, M. et al., Simultaneous 68Ga-PSMA HBED-CC PET/MRI Improves the Localization of Primary Prostate Cancer. Eur. Urol. 70:829–836, 2016.PubMedCrossRefPubMedCentralGoogle Scholar
  61. 61.
    Cole, J., Cole, H., Leech, R., and Sharp, D. J., Prediction of Brain Age Suggests Accelerated Atrophy after Traumatic Brain Injury. Ann. Neurol. 77:571–581, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Lancaster, J., Lorenz, R., Leech, R., and Cole, J. H., Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Prediction. Front. Aging Neurosci. 10:28, 2018. eCollection 2018.CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Cole, J. H., Ritchie, S. J., Bastin, M. E., and Hernández, M. C. V., Brain age predicts mortality. Mol. Psychiatry 23:1385–1392, 2018.PubMedCrossRefPubMedCentralGoogle Scholar
  64. 64.
    Su, L., Wang, L., Shen, H., and Hu, D., Age-related Classification and Prediction Based on MRI: A Sparse Representation Method. Procedia Environ. Sci. 8:645–652, 2011.CrossRefGoogle Scholar
  65. 65.
    Franke, K., Ziegler, G., Klöppel, S., and Gaser, C., Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters. NeuroImage 50:883–892, 2010.PubMedCrossRefPubMedCentralGoogle Scholar
  66. 66.
    Gaser, C., Volz, H. P., Kiebel, S., Riehemann, S., and Sauer, H., Detecting structural changes in whole brain based on nonlinear deformations-application to schizophrenia research. NeuroImage 10:107–113, 1999.PubMedCrossRefPubMedCentralGoogle Scholar
  67. 67.
    Loeffler, M., Engel, C., and Ahnert, P., The LIFE-Adult-Study: objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany. BMC Public Health 15:691, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  68. 68.
    Nooner, K., Colcombe, S. J., Tobe, R. H. et al., The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatrym. Front. Neurosci. 6:152, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Lin, L., Jin, C., Fu, Z., Zhang, B., Bin, G., and Wu, S., Predicting healthy older adult’s brain age based on structural connectivity networks using artificial neural networks. Comput. Methods Prog. Biomed. 125:8–17, 2016.CrossRefGoogle Scholar
  70. 70.
    Larobina, M., and Murino, L., Medical image file formats. J. Digit. Imaging 27(2):200–206, 2014.PubMedCrossRefPubMedCentralGoogle Scholar
  71. 71.
    Sonka M., Hlavac, V., and Boyle, R., Image pre-processing. In: Image Processing, Analysis and Machine Vision. Boston: Springer, 1993.CrossRefGoogle Scholar
  72. 72.
    Krig, S., Image Pre-Processing. In: Computer Vision Metrics. Berkeley: Apress, 2014.CrossRefGoogle Scholar
  73. 73.
    Jude Hemanth, D., and Anitha, J., Image Pre-processing and Feature Extraction Techniques for Magnetic Resonance Brain Image Analysis. In: Kim, T., Ko, D., Vasilakos, T., Stoica, A., Abawajy, J. (eds) Computer Applications for Communication, Networking, and Digital Contents. FGCN 2012. Communications in Computer and Information Science, vol 350. Berlin: Springer, 2012.Google Scholar
  74. 74.
    Bo, Z., Jalal, M. F., and Jean-Luc, S., Wavelets, ridgelets and curvelets for Poisson noise removal. IEEE Trans. Image Process. 17(7):1093–1108, 2008.CrossRefGoogle Scholar
  75. 75.
    Marianne, M., Russell, G., Jorg, S., Albert, M., and Mark, S., Learning a classification based glioma growth model using MRI data. J. Comput. 1(7):21–31, 2006.Google Scholar
  76. 76.
    Nicu, S., and Michael, S.L., Wavelet based texture classification. In: 15th International Conference on Pattern Recognition. 3: 3959–3962, 2000.Google Scholar
  77. 77.
    Manjón, J. V., MRI Preprocessing. In: Martí-Bonmatí L., Alberich-Bayarri A. (eds) Imaging Biomarkers. Cham: Springer, 2017.Google Scholar
  78. 78.
    Guo, C., Machine Learning Methods for Magnetic Resonance Imaging Analysis, 2012. PhD thesis, University of Michigan.Google Scholar
  79. 79.
    SPM webpage,, Accessed date: 8/4/2018.
  80. 80.
    Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., and Gee, J. C., N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6):1310–1320, 2010. Scholar
  81. 81.
    Ashburner, J., A fast diffeomorphic image registration algorithm. NeuroImage 38(1):95–113, 2007.PubMedCrossRefPubMedCentralGoogle Scholar
  82. 82.
    Ashburner, J., and Friston, K., Voxel-based morphometry—the methods. NeuroImage 11:805–821, 2000.PubMedCrossRefPubMedCentralGoogle Scholar
  83. 83.
    Liu, Y., Kot, A., Drakopoulos, F., Yao, C., Fedorov, A., Enquobahrie, A., and Chrisochoides, N. P., An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery. Frontiers in Neuroinformatics 8:33, 2014.PubMedPubMedCentralGoogle Scholar
  84. 84.
    Johnson, H. J., McCormick, M. M., and Ibanez, L., The ITK software guide book 1: Introduction and development guidelines fourth edition updated for ITK version 4.7. Clifton Park: Kitware, Inc., 2015.Google Scholar
  85. 85.
    Mengler, L., Khmelinskii, A., Diedenhofen, M., Po, C., Staring, M., Lelieveldt, B. P., and Hoehn, M., Brain maturation of the adolescent rat cortex and striatum: changes in volume and myelination. Neuroimage 84:35–44, 2014.PubMedCrossRefPubMedCentralGoogle Scholar
  86. 86.
    Keihaninejad, S., Zhang, H., Ryan, N. S., Malone, I. B., Modat, M., Cardoso, M. J., and Ourselin, S., An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease. Neuroimage 72:153–163, 2013.PubMedCrossRefPubMedCentralGoogle Scholar
  87. 87.
    Sorzano, C. O. S., Vargas, J., Pascual-Montano, A. D., A survey of dimensionality reduction techniques, ArXiv, 2014.Google Scholar
  88. 88.
    Jolliffe, I. T., Principal Component Analysis. Springer Series in Statistics. New York: Springer, 2002.Google Scholar
  89. 89.
    Perlaki, G., Horvath, R., Nagy, S. A., Bogner, P., Doczi, T., Janszky, J., and Orsi, G., Comparison of accuracy between FSL’s FIRST and Freesurfer for caudate nucleus and putamen segmentation. Sci. Rep. 7(1):2418, 2017.PubMedPubMedCentralCrossRefGoogle Scholar
  90. 90.
    Chen, H., Dou, Q., Yu, L., Qin, J., and Heng, P. A., VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. Neuro Image. 170:446–455, 2018.PubMedPubMedCentralGoogle Scholar
  91. 91.
    van Opbroek, A., van der Lijn, F., and de Bruijne, M., Automated brain-tissue segmentation by multi-feature SVM classification. In: Proceedings of the MICCAI Workshops—The MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS’13), 2013.Google Scholar
  92. 92.
    Litjens, G., et al. A survey on deep learning in medical image analysis. Med. Image Anal. 42:60–88, 2017.PubMedCrossRefPubMedCentralGoogle Scholar
  93. 93.
    Beliveau, V., Ganz, M., Feng, L., Ozenne, B., Højgaard, L., Fisher, P., Svarer, C., Greve, D., and Knudsen, G., A High-Resolution In Vivo Atlas of the Human Brain's Serotonin System. J. Neurosci. 37(1):120–128, 2017.PubMedPubMedCentralCrossRefGoogle Scholar
  94. 94.
    Wang, H., Suh, J. W., Das, S. R., Pluta, J. B., Craige, C., and Yushkevich, P. A., Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35(3):611–623, 2013.PubMedCrossRefPubMedCentralGoogle Scholar
  95. 95.
    Spulber, G., Niskanen, E., MacDonald, S., Smilovici, O., Chen, K., Reimanet, E. M. et al., Whole brain atrophy rate predicts progression from MCI to Alzheimer’s disease. Neurobiology of Ageing 31:1601–1605, 2010.CrossRefGoogle Scholar
  96. 96.
    Ge, Y., Grossman, R. I., Babb, J. S., Rabin, M. L., Mannon, L. J., and Kolson, D. L., Age-Related Total Gray Matter and White Matter Changes in Normal Adult Brain. Part I: Volumetric MR Imaging Analysis. Am. J. Neuroradiol. 23(8):1327–1333, 2002.PubMedPubMedCentralGoogle Scholar
  97. 97.
    Cole, J. H., and Franke, K., Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 40(12):681–690, 2017.PubMedCrossRefPubMedCentralGoogle Scholar
  98. 98.
    Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. R., ORB: An efficient alternative to SIFT or SURF. ICCV 11(1):2, 2011.Google Scholar
  99. 99.
    Lowe, D. G., Object recognition from local scale-invariant features. ICCV 99(2):1150–1157, 1999.Google Scholar
  100. 100.
    Calonder, M., Lepetit, V., Strecha, C., and Fua, P., Brief: Binary robust independent elementary features. In: European conference on computer vision, pp. 778-792, Berlin: Springer, 2010.Google Scholar
  101. 101.
    Simonyan, K., and Zisserman, A., Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, 2014.Google Scholar
  102. 102.
    Sajedi, H., Mohammadi Panah, F., and Kazemi Shariat Panah, S. H., An image analysis-aided method for redundancy reduction in differentiation of identical Actinobacterial strains. Future Microbiol 13(3):313–329, 2018.PubMedCrossRefPubMedCentralGoogle Scholar
  103. 103.
    Afshar, L. K., and Sajedi, H., Age Prediction based on Brain MRI Images using Extreme Learning Machine, 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS, 2019.Google Scholar
  104. 104.
    Pardakhti, N., and Sajedi, H., Age Prediction based on brain MRI images using Feature Learning, Subotica: SISY 2017, IEEE 15th International Symposium on Intelligent Systems and Informatics, 2017.Google Scholar
  105. 105.
    Wang, D., and Tan, X., Unsupervised feature learning with C-SVDDNet. Pattern Recogn. 60:473–485, 2016.CrossRefGoogle Scholar
  106. 106.
    Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., and Buckner, R. L., Open Access Series of Imaging Studies (OASIS): Cross-Sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. J. Cogn. Neurosci. 19:1498–1507, 2007.PubMedCrossRefPubMedCentralGoogle Scholar
  107. 107.
    Tax, D. M., and Duin, R. P., Support Vector Data Description. Mach. Learn. 54:45–66, 2004.CrossRefGoogle Scholar
  108. 108.
    Wu, Z., Lu, X., and Deng, Y., Image edge detection based on local dimension: A complex networks approach. Physica A: Statistical Mechanics and its Applications 440:9–18, 2015.CrossRefGoogle Scholar
  109. 109.
    Auria, L., and Moro, R. A., Support Vector Machines (SVM) as a Technique for Solvency Analysis. Mach. Learn. 54:45–66, 2004.CrossRefGoogle Scholar
  110. 110.
    Raji, C. A., Lopez, O. L., Kuller, L. H., Carmichael, O. T., and Becker, J. T., Age, Alzheimer disease, and brain structure. Neurology 73(22):1899–1905, 2009.PubMedPubMedCentralCrossRefGoogle Scholar
  111. 111.
    Mastery Farahani, R., Aliaghaei, A., Abdolmaleki, A., Abbaszadeh, H. A., Shaerzadeh, F., Norozian, M., and Moayeri, A., Sexual Dimorphism and Age-Related Variations of Corpus Callosum Using Magnetic Resonance Imaging. Anatomical Sciences 13(3):159–166, 2016.Google Scholar
  112. 112.
    Resnick, S. M., Goldszal, A. F., Davatzikos, C., Golski, S., Kraut, M. A., Metter, E. J., Bryan, R. N., and Zonderman, A. B., One-year age changes in MRI brain volumes in older adults. Cereb. Cortex 10(5):464–472, 2000.PubMedCrossRefPubMedCentralGoogle Scholar
  113. 113.
    Asim, Y., Raza, B., Malik, A. K., Rathore, S., Hussain, L., and Iftikhar, M. A., A multi-modal, multi-atlas-based approach for Alzheimer detection via machine learning. Int. J. Imaging Syst. Technol. 28(2):113–123, 2018.CrossRefGoogle Scholar
  114. 114.
    Liu, M., Zhang, D., Shen, D., and Alzheimer’s Disease Neuroimaging Initiative, View-centralized multi-atlas classification for Alzheimer's disease diagnosis. Hum. Brain Mapp. 36(5):1847–1865, 2015.PubMedCrossRefPubMedCentralGoogle Scholar
  115. 115.
    Min, R., Wu, G., Chen, J., Wang, Q., Shen, D., and Alzheimer's Disease Neuroimaging Initiative, Multi-atlas based representations for Alzheimer's disease diagnosis. Hum. Brain Mapp. 35(10):5052–5070, 2014.PubMedPubMedCentralCrossRefGoogle Scholar
  116. 116.
    Pang, S., Yu, Z., and Orgun, M. A., A novel end-to-end classifier using domain transferred deep convolution neural networks for biomedical images. Comput. Methods Prog. Biomed. 140:283–293, 2017.CrossRefGoogle Scholar
  117. 117.
    Lu, S., Lu, Z., and Zhang, Y., Pathological brain detection based on AlexNet and transfer learning. J. Comput. Sci. 30:41–47, 2019.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematics, Statistics and Computer Science, College of ScienceUniversity of TehranTehranIran
  2. 2.School of Computer ScienceInstitute for Research in Fundamental Science (IPM)TehranIran

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