<strong>Introduction:</strong> The purpose of this study is to evaluate discriminating power of two texture analysis, linear discriminant analysis and nonlinear discriminant analysis, in classifying atroph...<strong>Introduction:</strong> 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. <strong>Methods:</strong> 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. <strong>Result:</strong> Nonlinear discriminant analysis indicates the best performance for classification of atrophy of Alzheimer’s disease and atrophy of aging. <strong>Conclusion:</strong> Our result showed computer aided diagnosis has high potential discriminating power in classifying Alzheimer’s disease in early stage.展开更多
文摘<strong>Introduction:</strong> 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. <strong>Methods:</strong> 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. <strong>Result:</strong> Nonlinear discriminant analysis indicates the best performance for classification of atrophy of Alzheimer’s disease and atrophy of aging. <strong>Conclusion:</strong> Our result showed computer aided diagnosis has high potential discriminating power in classifying Alzheimer’s disease in early stage.
