Hippocampal Shape Analysis of Alzheimer Disease Based on Machine Learning Methods

Li, S.; Shi, Feng; Pu, Fang; Li, X.; Jiang, Tianzi; Xie, Shumeng; Wang, Y.

doi:10.3174/ajnr.a0620

Cited by 117 publications

(81 citation statements)

References 52 publications

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“…Hirata et al [41] developed software based on the voxel-based specific region analysis for AD, which can automatically analyse 3D MRI data as a series of segmentation, anatomical standardisation and smoothing using a software and Z -score analysis. Li et al [42] employed a SVM for characterisation of the hippocampal volume changes in AD and differentiation of AD patients from healthy control subjects. Kloppel et al [43] developed a CAD method for diagnosis of AD from MRI scans obtained from two different centres and two different equipment [28] using linear support vector.…”

Section: Dementia Alzheimer's and Parkinson Diseases Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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Section: Dementia Alzheimer's and Parkinson Diseases Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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“…As different parts of the hippocampus play different roles in memory [40] and the head and tail are most severely impaired in AD [19,41] , we further divided each hippocampus and parahippocampus into four subregions: the head, parts 1 and 2 of the body, and the tail (Fig. S2).…”

Section: Exploratory Classifi Cation Analysesmentioning

confidence: 99%

“…Thus 16 features for each participant were used in the discriminative and cross-validation analyses. Next, Fisher's linear discriminative method (see Appendix) and leave-one-out cross-validation analyses were performed to assess the utility of grey-matter volumes to classify patients [41][42][43][44] . We also randomly left 2 to 5 participants out and re-performed the cross-validation analyses to test the robustness of the classification.…”

Section: Exploratory Classifi Cation Analysesmentioning

confidence: 99%

Grey-matter volume as a potential feature for the classification of Alzheimer’s disease and mild cognitive impairment: an exploratory study

et al. 2014

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Specific patterns of brain atrophy may be helpful in the diagnosis of Alzheimer's disease (AD). In the present study, we set out to evaluate the utility of grey-matter volume in the classification of AD and amnestic mild cognitive impairment (aMCI) compared to normal control (NC) individuals. Voxel-based morphometric analyses were performed on structural MRIs from 35 AD patients, 27 aMCI patients, and 27 NC participants. A two-sample two-tailed t-test was computed between the NC and AD groups to create a map of abnormal grey matter in AD. The brain areas with signifi cant differences were extracted as regions of interest (ROIs), and the grey-matter volumes in the ROIs of the aMCI patients were included to evaluate the patterns of change across different disease severities. Next, correlation analyses between the grey-matter volumes in the ROIs and all clinical variables were performed in aMCI and AD patients to determine whether they varied with disease progression. The results revealed significantly decreased grey matter in the bilateral hippocampus/ parahippocampus, the bilateral superior/middle temporal gyri, and the right precuneus in AD patients.The grey-matter volumes were positively correlated with clinical variables. Finally, we performed exploratory linear discriminative analyses to assess the classifying capacity of grey-matter volumes in the bilateral hippocampus and parahippocampus among AD, aMCI, and NC. Leave-one-out crossvalidation analyses demonstrated that grey-matter volumes in hippocampus and parahippocampus accurately distinguished AD from NC. These fi ndings indicate that grey-matter volumes are useful in the classifi cation of AD.

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“…Neuroradiologists attempt to estimate the degree of atrophy by capturing atrophic image features on MR images, but it is very difficult and time consuming in routine clinical practice. Therefore, a number of automated methods have been studied for identification of AD patients among the large number of patients with dementia [27,[80][81][82][83]. In recent years, a whole-brain unbiased objective technique, known as voxel-based morphometry (VBM) [84], has been developed for characterizing differences in the local composition of brain tissue using MR images, and can objectively map gray matter loss on a voxel-by-voxel basis.…”

Section: Alzheimer's Diseasementioning

confidence: 99%

Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

et al. 2009

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This paper reviews the basics and recent researches of computer-aided diagnosis (CAD) systems for assisting neuroradiologists in detection of brain diseases, e.g., asymptomatic unruptured aneurysms, Alzheimer's disease, vascular dementia, and multiple sclerosis (MS), in magnetic resonance (MR) images. The CAD systems consist of image feature extraction based on image processing techniques and machine learning classifiers such as linear discriminant analysis, artificial neural networks, and support vector machines. We introduce useful examples of the CAD systems in the neuroradiology, and conclude with possibilities in the future of the CAD systems for brain diseases in MR images.

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Hippocampal Shape Analysis of Alzheimer Disease Based on Machine Learning Methods

Cited by 117 publications

References 52 publications

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Grey-matter volume as a potential feature for the classification of Alzheimer’s disease and mild cognitive impairment: an exploratory study

Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

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