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3D texture analysis on MRI images of Alzheimer’s disease

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Abstract

This study investigated three-dimensional (3D) texture as a possible diagnostic marker of Alzheimer’s disease (AD). T1-weighted magnetic resonance (MR) images were obtained from 17 AD patients and 17 age and gender-matched healthy controls. 3D texture features were extracted from the circular 3D ROIs placed using a semi-automated technique in the hippocampus and entorhinal cortex. We found that classification accuracies based on texture analysis of the ROIs varied from 64.3% to 96.4% due to different ROI selection, feature extraction and selection options, and that most 3D texture features selected were correlated with the mini-mental state examination (MMSE) scores. The results indicated that 3D texture could detect the subtle texture differences between tissues in AD patients and normal controls, and texture features of MR images in the hippocampus and entorhinal cortex might be related to the severity of AD cognitive impairment. These results suggest that 3D texture might be a useful aid in AD diagnosis.

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References

  • Alzheimer’s Association (2010) Alzheimer’s disease facts and figures. Includes a Special Report on Race, Ethnicity and Alzheimer’s Disease. 2010.

  • Boniha, L., Kobayashi, E., Castellano, G., Coelho, G., Tinois, E., Cendes, F., et al. (2003). Texture analysis of hippocampal sclerosis. Epilepsia, 44, 1546–1550.

    Article  Google Scholar 

  • China Alzheimer’s Project, http://www.memory360.org/en/. Accessed in Sept., 2011.

  • Colliot, O., Chételat, G., Chupin, M., Desgranges, B., Magnin, B., Benali, H., et al. (2008). Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology, 248, 194–201.

    Article  PubMed  Google Scholar 

  • Cummings, J. L., Vinters, H. V., Cole, G. M., & Khachaturian, Z. S. (1998). Alzheimer’s disease: etiologies, pathophysiology, cognitive reserve, and treatment opportunities. Neurology, 51(supl 1), 2–17.

    Google Scholar 

  • Dash, M., & Liu, H. (1997). Feature selection for classification. Elsevier Science Inc., http://www-east.elsevier.com/ida/browse/0103/ida00013/article.htm/

  • Delacourte, A., David, J. P., Sergeant, N., Buee, L., Wattez, A., Vermersch, P., et al. (1999). The biochemical pathway of neurofibrillary degeneration in aging and Alzheimer’s disease. Neurology, 52, 1158–1165.

    PubMed  CAS  Google Scholar 

  • Desikan, R. S., Cabral, H. J., Hess, C. P., Dillon, W. P., Glastonbury, C. M., Weiner, M. W., et al. (2009). Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer’s disease. Brain, 132(8), 2048–2057.

    Article  PubMed  Google Scholar 

  • Drzezga, A., Lautenschlage, Y. U., Siebner, H., Riemenschneider, M., Willoch, F., Minoshima, S., et al. (2003). Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer’s disease: a PET follow-up study. European Journal of Nuclear Medicine and Molecular Imaging, 30(8), 1104–1113.

    Article  PubMed  Google Scholar 

  • Du, A. T., Schuff, N., Zhu, X. P., Jagust, W. J., Miller, B. L., Reed, B. R., et al. (2003). Atrophy rates of entorhinal cortex in AD and normal aging. Neurology, 60, 481–486.

    PubMed  CAS  Google Scholar 

  • El-Baz, A., Casanova, M. F., Gimel’farb, G., Mott, M., & Switala, A. E. (2007). Autism diagnostics by 3D texture analysis of cerebral white matter gyrifications. Medical Image Computing and Computer-Assisted Intervention, 10(Pt 2), 882–890.

    PubMed  Google Scholar 

  • Folstein, M., Folstein, S., & McHugh, P. (1975). “Mini-Mental State”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.

    Article  PubMed  CAS  Google Scholar 

  • Fox, N. C., & Freeborough, P. A. (1997). Brain atrophy progression measured from registered serial MRI: validation and application to Alzheimer’s disease. Journal of Magnetic Resonance Imaging, 7, 1069–1075.

    Article  PubMed  CAS  Google Scholar 

  • Fox, N. C., Scahill, R. I., Crum, W. R., & Rossor, M. N. (1999). Correlation between rates of brain atrophy and cognitive decline in AD. Neurology, 52, 1687–1689.

    PubMed  CAS  Google Scholar 

  • Fox, N. C., Cousens, S., Scahill, R., Harvey, R. J., & Rossor, M. N. (2000). Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer’s disease. Archives of Neurology, 57, 339–443.

    Article  PubMed  CAS  Google Scholar 

  • Freeborough, P. A., & Fox, N. C. (1998). MR image texture analysis applied to the diagnosis and tracking of Alzheimer’s disease. IEEE Transactions on Medical Imaging, 17(3), 475–479.

    Article  PubMed  CAS  Google Scholar 

  • Galloway, M. (1975). Texture analysis using gray level run lengths. Computer Graphics and Image Processing, 4, 171–179.

    Article  Google Scholar 

  • Gerardin, E., Chetelat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H. S., et al. (2009). Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. NeuroImage, 47(4), 1476–1486.

    Article  PubMed  Google Scholar 

  • Haralick, R. M., Shanmugam, K., & Dinstein, J. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621.

    Article  Google Scholar 

  • Harrison, L., Dastidar, P., Eskola, H., Järvenpää, R., Pertovaara, H., Luukkaala, T., et al. (2008). Texture analysis on MRI images of non-Hodgkin lymphoma. Computers in Biology and Medicine, 38, 519–524.

    Article  PubMed  CAS  Google Scholar 

  • Hughes, C. P., Berg, L., Danziger, W. L., Coben, L. A., & Martin, R. L. (1982). A new clinical scale for the staging of dementia. British Journal of Psychiatry, 240, 566–572.

    Article  Google Scholar 

  • Jack, C. R., Petersen, R. C., Xu, Y., O’Brien, P. C., Smith, G. E., Ivnik, R. J., et al. (1998). Rate of medial temporal lobe atrophy in typical aging and Alzheimer’s disease. Neurology, 51(4), 993–999.

    PubMed  Google Scholar 

  • Jack, C. R., Shiung, M. M., Gunter, J. L., O’Brien, P. C., Weigand, S. D., Knopman, D. S., et al. (2004). Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology, 62(4), 591–600.

    PubMed  Google Scholar 

  • Jack, C. R., Weigand, S. D., Shiung, M. M., Przybelski, S. A., O’Brien, P. C., Gunter, J. L., et al. (2008). Atrophy rates accelerate in amnestic mild cognitive impairment. Neurology, 70(19 Pt 2), 1740–1752.

    PubMed  Google Scholar 

  • Kaeriyama, T., Kodama, N., Shimada, T., & Fukumoto, I. (2002). Application of run length matrix to magnetic resonance imaging diagnosis of Alzheimer-type dementia. Nippon Hoshasen Gijutsu Gakkai Zasshi, 58(11), 1502–1508.

    Google Scholar 

  • Kalaria, R. N., Maestre, G. E., Arizaga, R., Friedland, R. P., Galasko, D., Hall, K., et al. (2008). Alzheimer’s disease and vascular dementia in developing countries: prevalence, management, and risk factors. Lancet Neurology, 7(9), 812–826.

    Article  PubMed  Google Scholar 

  • Karas, G. B., Burton, E. J., Rombouts, S. A., Van Schijndel, R. A., O’Brien, J. T., Scheltens, P., et al. (2003). A comprehensive study of gray matter loss in patients with Alzheimer’s disease using optimized voxel-based morphometry. NeuroImage, 18, 895–907.

    Article  PubMed  CAS  Google Scholar 

  • Kesslak, J. P., Nalcioglu, O., & Cotman, C. W. (1991). Quantification of magnetic resonance scans for hippocampal and parahippocampal atrophy in Alzheimer’s disease. Neurology, 41, 51–54.

    PubMed  CAS  Google Scholar 

  • Klöppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., et al. (2008). Automatic classification of MR scans in Alzheimer’s disease. Brain, 131(Pt 3), 681–689.

    Article  PubMed  Google Scholar 

  • Kovalev, V. A., Kruggel, F., Gertz, H. J., & Yves von Cramon, D. (2001). Three-dimensional texture analysis of MRI brain datasets. IEEE Transactions on Medical Imaging, 20(5), 424–433.

    Article  PubMed  CAS  Google Scholar 

  • Kumar, S. V. B., Mullick, R., & Patil, U. (2005). Textural content in 3T MR: An image-based marker for Alzheimer’s disease. Proc. of SPIE Medical Imaging.

  • Liu, Y., Teverovskiy, L., Carmichael, O., Kikinis, R., Shenton, M., Carter, C. S., et al. (2004). Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer’s disease classification (pp. 393–400). Heidelberg: Springer. MICCAI 2004 (LNCS 3216).

    Google Scholar 

  • Mathias, J. M., Tofts, P. S., & Losseff, N. A. (1999). Texture analysis of spinal cord pathology in multiple sclerosis. Magnetic Resonance in Medicine, 42, 929–935.

    Article  PubMed  CAS  Google Scholar 

  • McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 34(7), 939–944.

    PubMed  CAS  Google Scholar 

  • Minoshima, S., Giordani, B., Berent, S., Frey, K. A., Foster, N. L., & Kuhl, D. E. (1997). Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Annals of Neurology, 42, 85–94.

    Article  PubMed  CAS  Google Scholar 

  • Morris, J. C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43, 2412–2414.

    PubMed  CAS  Google Scholar 

  • Mucciardi, A. N., & Gose, E. E. (1971). A comparison of seven techniques for choosing subsets of patter recognition properties. IEEE Transactions on Computers, c-20(9), 1023–1031.

    Article  Google Scholar 

  • Namer, I. J., Yu, O., Mauss, Y., & Chambron, J. (2001). Existence of contralateral abnormalities revealed by texture analysis in unilateral intractable hippocampal epilepsy. Magnetic Resonance Imaging, 19, 1305–1310.

    Article  PubMed  Google Scholar 

  • National Institute of Ageing (NIA), NIH (2009). http://www.nia.nih.gov/Alzheimers/Publications/Unraveling/Part2/hallmarks.htm.

  • Petrella, J. R., Coleman, R. E., & Doraiswamy, P. M. (2003). Neuroimaging and early diagnosis of Alzheimer disease: a look to the future. Radiology, 226(2), 315–336.

    Article  PubMed  Google Scholar 

  • Rangayyan, R. M., Banik, S., & Desautels, J. E. (2010). Computer-aided detection of architectural distortion in prior mammograms of interval cancer. Journal of Digital Imaging. 2010 Feb 2.

  • Sayeed, A., Petrou, M., Spyrou, N., Kadyrov, A., & Spinks, T. (2002). Diagnostic features of Alzheimer’s disease extracted from PET sinograms. Physics in Medicine and Biology, 47(1), 137–148.

    Article  PubMed  CAS  Google Scholar 

  • Saeed, N., & Piri, B. K. (2002). Cerebellum segmentation employing texture properties and knowledge based image processing: applied to normal adult controls and patients. Magnetic Resonance Imaging, 20, 425–429.

    Article  PubMed  CAS  Google Scholar 

  • Schürman, J. (1996). Pattern classification. John Wiley & Sons.

  • Silverman, D. H., Small, G. W., Chang, C. Y., Lu, C. S., Kung De Aburto, M. A., Chen, W., et al. (2001). Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome. JAMA, 286, 2120–2127.

    Article  PubMed  CAS  Google Scholar 

  • Small, G. W., Ercoli, L. M., Silverman, D. H., Huang, S. C., Komo, S., Bookheimer, S. Y., et al. (2000). Cerebral metabolic and cognitive decline in persons at genetic risk for Alzheimer’s disease. Proceedings of the National Academy of Sciences United States of America, 87, 6037–6042.

    Article  Google Scholar 

  • Strzelecki, M., Materka, A., & Szczypinski, P. M. (2006). MaZda. In Texture analysis for magnetic resonance imaging (pp. 105–111). Prague, Czech: Med4publishing, 2006.

  • Szczypinski, P. M., Strzelecki, M., Materka, A., & Klepaczko, A. (2009). MaZda—A software package for image texture analysis. Computer Methods and Programs in Biomedicine, 94, 66–76.

    Article  PubMed  Google Scholar 

  • Zhang, J., Tong, L. Z., Wang, L., & Li, N. (2008). Texture analysis of multiple sclerosis: a comparative study. Magnetic Resonance Imaging, 26(8), 1160–1166.

    Article  PubMed  Google Scholar 

  • Zhang, Y., Zhu, H., Ferrari, R., Wei, X., Eliasziw, M., Metz, L. M., et al. (2003). Texture analysis of MR images of minocycline treated MS patients, MICCAI (LNCS 2878) (pp. 786–793). Heidelberg: Springer.

    Google Scholar 

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Acknowledgements

The project was partially supported by the Program for New Century Excellent Talents in University (NCET-07-0568), the Natural Science Foundation of China (Grant No. 30670575) and Beijing Natural Science Foundation (Grant No. 3073015).

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Authors and Affiliations

  1. School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People’s Republic of China

    Jing Zhang, Guilian Jiang, Weifang Liu & Longzheng Tong

  2. Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, People’s Republic of China

    Chunshui Yu

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  1. Jing Zhang
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  2. Chunshui Yu
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  3. Guilian Jiang
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  4. Weifang Liu
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Correspondence to Longzheng Tong.

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Zhang, J., Yu, C., Jiang, G. et al. 3D texture analysis on MRI images of Alzheimer’s disease. Brain Imaging and Behavior 6, 61–69 (2012). https://doi.org/10.1007/s11682-011-9142-3

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