Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease
ABSTRACT The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.
SourceAvailable from: Mohamad Amin Pourhoseingholi[Show abstract] [Hide abstract]
ABSTRACT: Background: Tuberculosis (TB) is a major global health problem which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnoses based on cultured specimens is the reference standard, however results take weeks to obtain. Scientists are looking for early detection strategies which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and faster diagnose the disease. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. Objective: In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates support vector machine into AIRS for diagnosing tuberculosis. Materials and Methods: Patient epacris reports obtained from the Pasteur laboratory in Iran were used as the benchmark data set, with the sample size of 175 records (114 positive samples for TB and 60 samples in negative group). Strategy of this study was to ensure the representativeness, it was important to have adequate numbers of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, root mean squared error (RMSE), sensitivity and specificity, Youden’s Index, Area under ROC curve (AUC). Statistical Analysis Statistical analysis was done using Waikato Environment for Knowledge Analysis (WEKA), a suite of machine learning software (1) program for window. Results: With an accuracy of 100%, sensitivity 100%, specificity 100%, Youden’s Index of 1, Area under the Curve (AUC) of 1, and RMSE of 0 the proposed method was able to classify tuberculosis patients successfully. Conclusion: There are many researches aiming at diagnosing tuberculosis faster and more accurate. Our results described a model for diagnosing tuberculosis with 100 sensitivity and 100 specificity. This model can be used as additional tool for experts in medicine to diagnose TBC more accurate and faster.International journal of the Iranian Red Crescent Society 01/2015; DOI:10.5812/ircmj.17(4)2015.24557 · 0.33 Impact Factor
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ABSTRACT: Indexing and classification tools for Content Based Visual Information Retrieval (CBVIR) have been penetrating the universe of medical image analysis. They have been recently investigated for Alzheimer’s disease (AD) diagnosis. This is a normal “knowledge diffusion” process, when methodologies developed for multimedia mining penetrate a new application area. The latter brings its own specificities requiring an adjustment of methodologies on the basis of domain knowledge. In this paper, we develop an automatic classification framework for AD recognition in structural Magnetic Resonance Images (MRI). The main contribution of this work consists in considering visual features from the most involved region in AD (hippocampal area) and in using a late fusion to increase precision results. Our approach has been first evaluated on the baseline MR images of 218 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and then tested on a 3T weighted contrast MRI obtained from a subsample of a large French epidemiological study: “Bordeaux dataset”. The experimental results show that our classification of patients with AD versus NC (Normal Control) subjects achieves the accuracies of 87 % and 85 % for ADNI subset and “Bordeaux dataset” respectively. For the most challenging group of subjects with the Mild Cognitive Impairment (MCI), we reach accuracies of 78.22 % and 72.23 % for MCI versus NC and MCI versus AD respectively on ADNI. The late fusion scheme improves classification results by 9 % in average for these three categories. Results demonstrate very promising classification performance and simplicity compared to the state-of-the-art volumetric AD diagnosis methods.Multimedia Tools and Applications 02/2014; 74(4). DOI:10.1007/s11042-014-2123-y · 1.06 Impact Factor
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ABSTRACT: Multi-atlas based methods have been recently used for classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Compared with traditional single-atlas based methods, multiatlas based methods adopt multiple predefined atlases and thus are less biased by a certain atlas. However, most existing multiatlas based methods simply average or concatenate the features from multiple atlases, which may ignore the potentially important diagnosis information related to the anatomical differences among different atlases. In this paper, we propose a novel view (i.e., atlas) centralized multi-atlas classification method, which can better exploit useful information in multiple feature representations from different atlases. Specifically, all brain images are registered onto multiple atlases individually, to extract feature representations in each atlas space. Then, the proposed view-centralized multi-atlas feature selection method is used to select the most discriminative features from each atlas with extra guidance from other atlases. Next, we design a support vector machine (SVM) classifier using the selected features in each atlas space. Finally, we combine multiple SVM classifiers for multiple atlases through a classifier ensemble strategy for making a final decision. We have evaluated our method on 459 subjects [including 97 AD, 117 progressive MCI (p-MCI), 117 stable MCI (s-MCI), and 128 normal controls (NC)] from the Alzheimer's Disease Neuroimaging Initiative database, and achieved an accuracy of 92.51% for AD versus NC classification and an accuracy of 78.88% for p-MCI versus s-MCI classification. These results demonstrate that the proposed method can significantly outperform the previous multi-atlas based classification methods. Hum Brain Mapp, 2015. © 2014 Wiley Periodicals, Inc.Human Brain Mapping 01/2015; 36(5). DOI:10.1002/hbm.22741 · 6.92 Impact Factor