Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Xiao, Zhe; Ding, Yi; Lan, Tian; Zhang, Cong; Luo, Chuanji; Qin, Zhiguang

doi:10.1155/2017/1952373

Cited by 70 publications

(39 citation statements)

References 41 publications

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“…Various types of cortical morphological features, such as cortical thickness, GM volume, metric distortion, and GM densities, have shown promising capacities for the classification between aMCI and controls [43][44][45]. Our study revealed the relatively high classification accuracy by using each of parameters extracted from the whole brain (GM volume and density), which was in line with previous studies.…”

Section: The Reliability Of Combined Visual Rating Scalessupporting

confidence: 90%

“…Li et al extracted six cortical features for each aMCI subject and demonstrated the highest discriminative power (84%) by combining the metric distortion and cortical thickness features in the left hemisphere [46]. Xiao et al also reported a relatively high classification accuracy of 86.11% for aMCI based on the combination of texture features and morphometric features [45], indicating that multi-feature combination was better than the single-feature method.…”

Section: The Reliability Of Combined Visual Rating Scalesmentioning

confidence: 99%

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Combining visual rating scales of medial temporal lobe atrophy and posterior atrophy to identify amnestic mild cognitive impairment from cognitively normal older adults: evidence based on two datasets

Sheng

Sun

Wang

et al. 2019

Preprint

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Background: Structural magnetic resonance imaging (MRI)-based visual rating scales are considered as the primary method for rapid and cost-effective evaluation of regional brain atrophy in patients with Alzheimer’s disease (AD) in routine clinical practice. Both medial temporal lobe atrophy (MTA) and posterior atrophy (PA) visual rating scales have been reported to be useful in AD diagnosis. However, very few existing studies have investigated the efficacy of combined MTA and PA for identification of amnestic mild cognitive impairment (aMCI) from cognitively normal elderly.Methods: This study included T1-weighted MRI images acquired inXuanwu Hospital of Capital Medical University, Beijing, China from two different cohorts. In the first cohort, we recruited 73 patients with aMCI and 48 group-matched cognitively normal controls for the training and validation. Visual assessments of MTA and PA, including left MTA, right MTA, mean MTA, left PA, right PA and mean PA, were carried out from each participant. Global gray matter (GM) volume and density was estimated using voxel-based morphometry analysis as the objective reference. Based on the receiver operating characteristic (ROC) analysis, we investigated the discriminative power of single visual rating scale and the combination of MTA and PA for successful classification between the two diagnostic groups, respectively, and then compared them to GM measures. The second cohort, consisted of 33 aMCI patients and 45 NCs, was used to verify the reliability of the discriminative power of visual assessments.Results: In the first cohort, visual rating scales of MTA and PA showed different potential to distinguish aMCI from controls. Moreover, the combination of MTA and PA exhibited the best discriminative power, with the AUC of 0.818 ± 0.041, which was similar to the diagnostic accuracy of GM volumetric measures (0.857 ± 0.034). The discriminative power was verified in the second cohort when combining MTA and PA visual rating scales, with the AUC of 0.824 ± 0.058.Conclusion: The combined visual rating scales of MTA and PA demonstrated practical diagnostic values for distinguishing aMCI patients from controls, suggesting its potential to serve as a convenient and reproducible method to assess the degree of atrophy in the clinical setting.

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Section: The Reliability Of Combined Visual Rating Scalessupporting

confidence: 90%

Section: The Reliability Of Combined Visual Rating Scalesmentioning

confidence: 99%

Combining visual rating scales of medial temporal lobe atrophy and posterior atrophy to identify amnestic mild cognitive impairment from cognitively normal older adults: evidence based on two datasets

Sheng

Sun

Wang

et al. 2019

Preprint

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“…Additionally, sMRI measurements of brain atrophy are promising biomarkers for tracking disease progression in AD patients. Recently, VBM has been applied in a number of studies [54–56] for the detection of AD. It can be used to study the volumetric atrophy of the gray matter (GM) that exists in the neocortex of the brain, which can be used to distinguish AD patients from HC individuals.…”

Section: Methodsmentioning

confidence: 99%

Early diagnosis of Alzheimer’s disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images

et al. 2019

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In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer’s disease (AD) or its prodromal phase {i.e., mild cognitive impairment (MCI)} and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects. In this paper, we propose a novel classification technique that precisely distinguishes individuals with AD, aAD (stable MCI, who had not converted to AD within a 36-month time period), and mAD (MCI caused by AD, who had converted to AD within a 36-month time period) from HC individuals. The proposed method combines three different features extracted from structural MR (sMR) images using voxel-based morphometry (VBM), hippocampal volume (HV), and cortical and subcortical segmented region techniques. Three classification experiments were performed (AD vs. HC, aAD vs. mAD, and HC vs. mAD) with 326 subjects (171 elderly controls and 81 AD, 35 aAD, and 39 mAD patients). For the development and validation of the proposed classification method, we acquired the sMR images from the dataset of the National Research Center for Dementia (NRCD). A five-fold cross-validation technique was applied to find the optimal hyperparameters for the classifier, and the classification performance was compared by using three well-known classifiers: K-nearest neighbor, support vector machine, and random forest. Overall, the proposed model with the SVM classifier achieved the best performance on the NRCD dataset. For the individual feature, the VBM technique provided the best results followed by the HV technique. However, the use of combined features improved the classification accuracy and predictive power for the early classification of AD compared to the use of individual features. The most stable and reliable classification results were achieved when combining all extracted features. Additionally, to analyze the efficiency of the proposed model, we used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to compare the classification performance of the proposed model with those of several state-of-the-art methods.

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“…Basically, the first two diagnosis processes are non-invasive and uses medical imaging while the third process is surgical. Magnetic Resonance Imaging (MRI) is one of the dominant medium of diagnosing the abnormalities present in human body [4]. Brain is considered as one of the essential as well as complex human organ where diagnosis of the abnormalities are bit challenging.…”

Section: Introductionmentioning

confidence: 99%

An optimized approach for extensive segmentation and classification of brain MRI

Harish¹,

Ahammed²

2020

IJECE

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With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.

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Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Cited by 70 publications

References 41 publications

Combining visual rating scales of medial temporal lobe atrophy and posterior atrophy to identify amnestic mild cognitive impairment from cognitively normal older adults: evidence based on two datasets

Combining visual rating scales of medial temporal lobe atrophy and posterior atrophy to identify amnestic mild cognitive impairment from cognitively normal older adults: evidence based on two datasets

Early diagnosis of Alzheimer’s disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images

An optimized approach for extensive segmentation and classification of brain MRI

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