%0 Journal Article %J J Alzheimers Dis %D 2016 %T Detection of Alzheimer's Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging. %A Wang, Shuihua %A Zhang, Yudong %A Liu, Ge %A Phillips, Preetha %A Yuan, Ti-Fei %K Aged %K Aged, 80 and over %K Alzheimer Disease %K Brain %K Brain Mapping %K Datasets as Topic %K Female %K Humans %K Imaging, Three-Dimensional %K Machine Learning %K Magnetic Resonance Imaging %K Male %X

BACKGROUND: Within the past decade, computer scientists have developed many methods using computer vision and machine learning techniques to detect Alzheimer's disease (AD) in its early stages.

OBJECTIVE: However, some of these methods are unable to achieve excellent detection accuracy, and several other methods are unable to locate AD-related regions. Hence, our goal was to develop a novel AD brain detection method.

METHODS: In this study, our method was based on the three-dimensional (3D) displacement-field (DF) estimation between subjects in the healthy elder control group and AD group. The 3D-DF was treated with AD-related features. The three feature selection measures were used in the Bhattacharyya distance, Student's t-test, and Welch's t-test (WTT). Two non-parallel support vector machines, i.e., generalized eigenvalue proximal support vector machine and twin support vector machine (TSVM), were then used for classification. A 50 × 10-fold cross validation was implemented for statistical analysis.

RESULTS: The results showed that "3D-DF+WTT+TSVM" achieved the best performance, with an accuracy of 93.05 ± 2.18, a sensitivity of 92.57 ± 3.80, a specificity of 93.18 ± 3.35, and a precision of 79.51 ± 2.86. This method also exceled in 13 state-of-the-art approaches. Additionally, we were able to detect 17 regions related to AD by using the pure computer-vision technique. These regions include sub-gyral, inferior parietal lobule, precuneus, angular gyrus, lingual gyrus, supramarginal gyrus, postcentral gyrus, third ventricle, superior parietal lobule, thalamus, middle temporal gyrus, precentral gyrus, superior temporal gyrus, superior occipital gyrus, cingulate gyrus, culmen, and insula. These regions were reported in recent publications.

CONCLUSIONS: The 3D-DF is effective in AD subject and related region detection.

%B J Alzheimers Dis %V 50 %P 233-48 %8 2016 %G eng %N 1 %1 http://www.ncbi.nlm.nih.gov/pubmed/26682696?dopt=Abstract %R 10.3233/JAD-150848 %0 Journal Article %J J Alzheimers Dis %D 2016 %T Three-Dimensional Eigenbrain for the Detection of Subjects and Brain Regions Related with Alzheimer's Disease. %A Zhang, Yudong %A Wang, Shuihua %A Phillips, Preetha %A Yang, Jiquan %A Yuan, Ti-Fei %K Adolescent %K Adult %K Aged %K Aged, 80 and over %K Alzheimer Disease %K Brain %K Datasets as Topic %K Early Diagnosis %K Female %K Humans %K Image Interpretation, Computer-Assisted %K Imaging, Three-Dimensional %K Magnetic Resonance Imaging %K Male %K Middle Aged %K Sensitivity and Specificity %K Young Adult %X

BACKGROUND: Considering that Alzheimer's disease (AD) is untreatable, early diagnosis of AD from the healthy elderly controls (HC) is pivotal. However, computer-aided diagnosis (CAD) systems were not widely used due to its poor performance.

OBJECTIVE: Inspired from the eigenface approach for face recognition problems, we proposed an eigenbrain to detect AD brains. Eigenface is only for 2D image processing and is not suitable for volumetric image processing since faces are usually obtained as 2D images.

METHODS: We extended the eigenbrain to 3D. This 3D eigenbrain (3D-EB) inherits the fundamental strategies in either eigenface or 2D eigenbrain (2D-EB). All the 3D brains were transferred to a feature space, which encoded the variation among known 3D brain images. The feature space was named as the 3D-EB, and defined as eigenvectors on the set of 3D brains. We compared four different classifiers: feed-forward neural network, support vector machine (SVM) with linear kernel, polynomial (Pol) kernel, and radial basis function kernel.

RESULTS: The 50x10-fold stratified cross validation experiments showed that the proposed 3D-EB is better than the 2D-EB. SVM with Pol kernel performed the best among all classifiers. Our "3D-EB + Pol-SVM" achieved an accuracy of 92.81% ± 1.99% , a sensitivity of 92.07% ± 2.48% , a specificity of 93.02% ± 2.22% , and a precision of 79.03% ± 2.37% . Based on the most important 3D-EB U1, we detected 34 brain regions related with AD. The results corresponded to recent literature.

CONCLUSIONS: We validated the effectiveness of the proposed 3D-EB by detecting subjects and brain regions related to AD.

%B J Alzheimers Dis %V 50 %P 1163-79 %8 2016 %G eng %N 4 %1 http://www.ncbi.nlm.nih.gov/pubmed/26836190?dopt=Abstract %R 10.3233/JAD-150988