Assessment of Linear Discrimination and Nonlinear Discrimination Analysis in Diagnosis Alzheimer’s Disease in Early Stages

The purpose of this study is to evaluate discriminating power
of two texture analysis, linear discriminant analysis and nonlinear discriminant
analysis, in classifying atrophy of Alzheimer’s disease and atrophy of
aging. Methods: The database included 24 regions of interest of Alzheimer
patients and 24 regions of interest of aging people in hippocampus region.
Linear discriminant analysis and nonlinear discriminant analysis were used
for texture analysis. The first nearest neighbor classifier was applied to features
resulting from linear discriminant analysis. Nonlinear discriminant
analysis features were classified by using an artificial neural network. The
confusion matrix and Receiver Operating Characteristic (ROC) curve analysis
were used to examine the performance of texture analysis method. Result:
Nonlinear discriminant analysis indicates the best performance for classification
of atrophy of Alzheimer’s disease and atrophy of aging. Conclusion: Our
result showed computer aided diagnosis has high potential discriminating
power in classifying Alzheimer’s disease in early stage.