Structural MRI texture analysis for detecting Alzheimer's disease

Purpose: Alzheimer’s disease (AD) has the highest worldwide prevalence of all neurodegenerative disorders, no cure, and low ratios of diagnosis accuracy at its early stage where treatments have some effect and can give some years of life quality to patients. This work aims to develop an automatic method to detect AD in 3 different stages, namely, control (CN), mild-cognitive impairment (MCI), and AD itself, using structural magnetic resonance imaging (sMRI). Methods: A set of co-occurrence matrix and texture statistical measures (contrast, correlation, energy, homogeneity, entropy, variance, and standard deviation) were extracted from a two-level discrete wavelet transform decomposition of sMRI images. The discriminant capacity of the measures was analyzed and the most discriminant ones were selected to be used as features for feeding classical machine learning (cML) algorithms and a convolution neural network (CNN). Results: The cML algorithms achieved the following classification accuracies: 93.3% for AD vs CN, 87.7% for AD vs MCI, 88.2% for CN vs MCI, and 75.3% for All vs All. The CNN achieved the following classification accuracies: 82.2% for AD vs CN, 75.4% for AD vs MCI, 83.8% for CN vs MCI, and 64% for All vs All. Conclusion: In the evaluated cases, cML provided higher discrimination results than CNN. For the All vs All comparison, the proposed method surpasses by 4% the discrimination accuracy of the state-of-the-art methods that use structural MRI.
Purpose:: Alzheimer’s disease (AD) has the highest worldwide prevalence of all neurodegenerative disorders, no cure, and low ratios of diagnosis accuracy at its early stage where treatments have some effect and can give some years of life quality to patients. This work aims to develop an automatic method to detect AD in 3 different stages, namely, control (CN), mild-cognitive impairment (MCI), and AD itself, using structural magnetic resonance imaging (sMRI). Methods:: A set of co-occurrence matrix and texture statistical measures (contrast, correlation, energy, homogeneity, entropy, variance, and standard deviation) were extracted from a two-level discrete wavelet transform decomposition of sMRI images. The discriminant capacity of the measures was analyzed and the most discriminant ones were selected to be used as features for feeding classical machine learning (cML) algorithms and a convolution neural network (CNN). Results:: The cML algorithms achieved the following classification accuracies: 93.3% for AD vs CN, 87.7% for AD vs MCI, 88.2% for CN vs MCI, and 75.3% for All vs All. The CNN achieved the following classification accuracies: 82.2% for AD vs CN, 75.4% for AD vs MCI, 83.8% for CN vs MCI, and 64% for All vs All. Conclusion:: In the evaluated cases, cML provided higher discrimination results than CNN. For the All vs All comparison, the proposedmethod surpasses by 4% the discrimination accuracy of the state-of-the-art methods that use structural MRI.