A new method for classification of Alzheimer’s disease combined with structural magnetic resonance imaging texture features_Journal of Biomedical Engineering

Authors：

CHU Tongpeng ,  YANG Chunlan , LU Min , WU Shuicai

College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, P.R.China;

Corresponding author：

YANG Chunlan, Email: clyang@bjut.edu.cn

Keywords：

Alzheimer’s disease; hippocampus; three-dimensional texture; multi-features combination; extreme learning machine

DOI：

10.7507/1001-5515.201803039

Video：

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In this paper, a new method for the classification of Alzheimer’s disease (AD) using multi-feature combination of structural magnetic resonance imaging is proposed. Firstly, hippocampal segmentation and cortical thickness and volume measurement were performed using FreeSurfer software. Then, histogram, gradient, length of gray level co-occurrence matrix and run-length matrix were used to extract the three-dimensional (3D) texture features of the hippocampus, and the parameters with significant differences between AD, MCI and NC groups were selected for correlation study with MMSE score. Finally, AD, MCI and NC are classified and identified by the extreme learning machine. The results show that texture features can provide better classification results than volume features on both left and right sides. The feature parameters with complementary texture, volume and cortical thickness had higher classification recognition rate, and the classification accuracy of the right side (100%) was higher than that of the left side (91.667%). The results showed that 3D texture analysis could reflect the pathological changes of hippocampal structures of AD and MCI patients, and combined with multi-feature analysis, it could better reflect the essential differences between AD and MCI cognitive impairment, which was more conducive to clinical differential diagnosis.

Citation： CHU Tongpeng, YANG Chunlan, LU Min, WU Shuicai. A new method for classification of Alzheimer’s disease combined with structural magnetic resonance imaging texture features. Journal of Biomedical Engineering, 2019, 36(1): 94-100. doi: 10.7507/1001-5515.201803039 Copy

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2.	Jia Jianping, Wang Fen, Wei Cuibai, et al. The prevalence of dementia in urban and rural areas of China. Alzheimers Dement, 2014, 10(1): 1-9.
3.	Jack C R, Albert M S, Knopman D S, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement, 2011, 7(3): 257-262.
4.	Ramani A, Jensen J H, Helpern J A. Quantitative MR imaging in Alzheimer disease. Radiology, 2006, 241(1): 26-44.
5.	Cuingnet R, Gerardin E, Tessieras J, et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage, 2011, 56(2): 766-781.
6.	Falahati F, Westman E, Simmons A. Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. J Alzheimers Dis, 2014, 41(3): 685-708.
7.	Jack C R, Barkhof F, Bernstein M A, et al. Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer’s disease. Alzheimers Dement, 2011, 7(4): 474-485.
8.	Hill D L G, Schwarz A J, Isaac M, et al. Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer’s disease. Alzheimers Dement, 2014, 10(4): 421-429.
9.	Poulin S P, Dautoff R, Morris J C, et al. Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity. Psychiatry Research: Neuroimaging, 2011, 194(1): 7-13.
10.	Tanabe J L, Amend D, Schuff N, et al. Tissue segmentation of the brain in Alzheimer disease. Am J Neuroradiol, 1997, 18(1): 115-123.
11.	Singh V, Chertkow H, Lerch J P, et al. Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer’s disease. Brain, 2006, 129(11): 2885-2893.
12.	Eskildsen S F, Coup P, Garca-Lorenzo D, et al. For the Alzheimer’s Disease Neuroimaging Initiative Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage, 2013, 65: 511-521.
13.	Gerardin E, Chételat G, Chupin M, et al. Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage, 2009, 47(4): 1476-1486.
14.	Achterberg H C, van der Lijn F, den Heijer T A, et al. Hippocampal shape is predictive for the development of dementia in a normal, elderly population. Hum Brain Mapp, 2014, 35(5): 2359-2371.
15.	Chincarini A, Bosco P, Calvini P, et al. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer’s disease. Neuroimage, 2011, 58(2): 469-480.
16.	Sorensen L, Igel C, Hansen N L, et al. Early detection of Alzheimer’s disease using MRI hippocampal texture. Hum Brain Mapp, 2016, 37(3): 1148-1161.
17.	Lillemark L, Sorensen L, Pai A, et al. Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI. BMC Med Imaging, 2014, 14(1): 21.
18.	Klein S, Loog M, van der Lijn F, et al. Early diagnosis of dementia based on intersubject whole-brain dissimilarities//2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Rotterdam, Netherlands: IEEE, 2010: 249-252.
19.	Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol, 1991, 82(4): 239-259.
20.	Colliot O, Chételat G, Chupin M, et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology, 2008, 248(1): 194-201.
21.	胡玲静, 李昕, 夏翃, 等. 基于 MR 图像的阿尔茨海默病和轻度认知障碍患者海马三维纹理分析. 北京工业大学学报, 2012, 38(6): 942-948.
22.	于鲁, 夏翃, 刘卫芳. 基于磁共振图像海马三维纹理特征的阿尔茨海默病及健康对照的分类研究. 生物医学工程学杂志, 2016, 33(6): 1090-1094.
23.	Iglesias J E, Augustinack J C, Nguyen K, et al; Alzheimer’s Disease Neuroimaging Initiative. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. NeuroImage, 2015, 115: 117-137.
24.	Huang Guangbin, Zhou Hongming, Ding Xiaojian, et al. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern, 2012, 42(2): 513-529.
25.	Rajeesh J. Automatic detection of alzheimers disease using hippocampus MRI texture features. Chennai: Anna University, 2011.
26.	Li Xin, Xia Hong, Zhou Zhen, et al. 3D texture analysis of hippocampus based on MR images in patients with Alzheimer disease and mild cognitive impairment. J Beijing Univ Technol, 2012, 38(6): 942-948.
27.	de Oliveira M S, Betting L E, Mory S B, et al. Texture analysis of magnetic resonance images of patients with juvenile myoclonic epilepsy. Epilepsy Behav, 2013, 27(1): 22-28.

1. Wilson R S, Segawa E, Boyle P A, et al. The natural history of cognitive decline in Alzheimer’s disease. Psychol Aging, 2012, 27(4): 1008-1017.
2. Jia Jianping, Wang Fen, Wei Cuibai, et al. The prevalence of dementia in urban and rural areas of China. Alzheimers Dement, 2014, 10(1): 1-9.
3. Jack C R, Albert M S, Knopman D S, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement, 2011, 7(3): 257-262.
4. Ramani A, Jensen J H, Helpern J A. Quantitative MR imaging in Alzheimer disease. Radiology, 2006, 241(1): 26-44.
5. Cuingnet R, Gerardin E, Tessieras J, et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage, 2011, 56(2): 766-781.
6. Falahati F, Westman E, Simmons A. Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. J Alzheimers Dis, 2014, 41(3): 685-708.
7. Jack C R, Barkhof F, Bernstein M A, et al. Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer’s disease. Alzheimers Dement, 2011, 7(4): 474-485.
8. Hill D L G, Schwarz A J, Isaac M, et al. Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer’s disease. Alzheimers Dement, 2014, 10(4): 421-429.
9. Poulin S P, Dautoff R, Morris J C, et al. Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity. Psychiatry Research: Neuroimaging, 2011, 194(1): 7-13.
10. Tanabe J L, Amend D, Schuff N, et al. Tissue segmentation of the brain in Alzheimer disease. Am J Neuroradiol, 1997, 18(1): 115-123.
11. Singh V, Chertkow H, Lerch J P, et al. Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer’s disease. Brain, 2006, 129(11): 2885-2893.
12. Eskildsen S F, Coup P, Garca-Lorenzo D, et al. For the Alzheimer’s Disease Neuroimaging Initiative Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage, 2013, 65: 511-521.
13. Gerardin E, Chételat G, Chupin M, et al. Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage, 2009, 47(4): 1476-1486.
14. Achterberg H C, van der Lijn F, den Heijer T A, et al. Hippocampal shape is predictive for the development of dementia in a normal, elderly population. Hum Brain Mapp, 2014, 35(5): 2359-2371.
15. Chincarini A, Bosco P, Calvini P, et al. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer’s disease. Neuroimage, 2011, 58(2): 469-480.
16. Sorensen L, Igel C, Hansen N L, et al. Early detection of Alzheimer’s disease using MRI hippocampal texture. Hum Brain Mapp, 2016, 37(3): 1148-1161.
17. Lillemark L, Sorensen L, Pai A, et al. Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI. BMC Med Imaging, 2014, 14(1): 21.
18. Klein S, Loog M, van der Lijn F, et al. Early diagnosis of dementia based on intersubject whole-brain dissimilarities//2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Rotterdam, Netherlands: IEEE, 2010: 249-252.
19. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol, 1991, 82(4): 239-259.
20. Colliot O, Chételat G, Chupin M, et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology, 2008, 248(1): 194-201.
21. 胡玲静, 李昕, 夏翃, 等. 基于 MR 图像的阿尔茨海默病和轻度认知障碍患者海马三维纹理分析. 北京工业大学学报, 2012, 38(6): 942-948.
22. 于鲁, 夏翃, 刘卫芳. 基于磁共振图像海马三维纹理特征的阿尔茨海默病及健康对照的分类研究. 生物医学工程学杂志, 2016, 33(6): 1090-1094.
23. Iglesias J E, Augustinack J C, Nguyen K, et al; Alzheimer’s Disease Neuroimaging Initiative. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. NeuroImage, 2015, 115: 117-137.
24. Huang Guangbin, Zhou Hongming, Ding Xiaojian, et al. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern, 2012, 42(2): 513-529.
25. Rajeesh J. Automatic detection of alzheimers disease using hippocampus MRI texture features. Chennai: Anna University, 2011.
26. Li Xin, Xia Hong, Zhou Zhen, et al. 3D texture analysis of hippocampus based on MR images in patients with Alzheimer disease and mild cognitive impairment. J Beijing Univ Technol, 2012, 38(6): 942-948.
27. de Oliveira M S, Betting L E, Mory S B, et al. Texture analysis of magnetic resonance images of patients with juvenile myoclonic epilepsy. Epilepsy Behav, 2013, 27(1): 22-28.

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