基于结构磁共振成像海马多特征组合的阿尔茨海默病分类新方法
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摘要
本文提出了一种利用结构磁共振图像多特征组合的阿尔茨海默病(AD)分类新方法。首先,利用FreeSurfer软件进行海马分割及皮层厚度、体积测量。然后,采用直方图、梯度、灰度共生矩阵及游程长度矩阵提取海马三维纹理特征,选取AD、MCI及NC三组间均具有显著差异的参数,与MMSE评分进行相关性研究。最后,利用极限学习机,对AD、MCI及NC进行分类识别。结果显示,无论左侧还是右侧,纹理特征相比于体积特征可以提供更好的分类结果;纹理、体积和皮层厚度互补的特征参量具有更高的分类识别率,且右侧(100%)分类正确率高于左侧(91.667%)。结果表明三维纹理分析可反映AD及MCI患者海马结构的病理变化,并且结合多特征的分析更能反映AD与MCI的认知障碍实质差别,更有利于临床鉴别诊断。
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(3 D) 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 3 D 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.
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(3 D) 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 3 D 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.
引文
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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.
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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,Chetelat 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,Chetelat 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,夏翊,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.IEEETrans 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.
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 AgingAlzheimer'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,Chetelat 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,Chetelat 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,夏翊,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.IEEETrans 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.