Abstract
Introduction: 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.
Highlights
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
Criteria were used to select region of interest (ROI) include two Region of Interest (ROIs) were selected for each subject, ROI placed in right and left of hippocampus region and all ROIs has the same size and chooses in the same place of hippocampus
Over 270 texture features extracted based on Histogram, Absolute gradient (spatial variation of grey level values, Run-length matrix, Co-occurrence matrix, Auto regressive model and Wavelet decomposition image frequency and different scales)
Summary
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. 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. The group intended to establish and to describe clinical criteria for the diagnosis of Alzheimer’s disease of particular importance for research protocols and to describe approaches that would be useful for assessing the natural history of the disease [1].
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call