Background:
In this study, we investigated the fusion of texture and morphometric features
as a possible diagnostic biomarker for Alzheimer’s Disease (AD).
Methods:
In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment
(MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric
categorization provides the ground truth for AD and MCI diagnosis. This can then be
supported by biological data such as the results of imaging studies. Cerebral atrophy has been
shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of
the brain are important resources for AD diagnosis. In the proposed method, we used three different
types of features identified from structural MR images: Gabor, hippocampus morphometric,
and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix
(GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine
(SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we
achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval
(CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity
and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects,
thus outperforming recent works found in the literature. For the classification of MCI against AD,
the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34,
76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity.
Results and Conclusion:
The results of the third experiment, with MCI against NC, also showed
that the multiclass SVM provided highly accurate classification results. These findings suggest that
this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC
classification performance.
Wear Debris Classification of Steel Production Equipment using Feature Fusion and Case-based Reasoning
