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Application of 3D Whole-Brain Texture Analysis and the Feature Selection Method Based on within-Class Scatter in the
Ke Zhou1, Zhou Liu2, Wenguang He1
1School of Biomedical Engineering, Guangdong Medical University, Zhanjiang, 524023, China.
Background:
Patients with mild cognitive impairment (MCI) are a high-risk group for Alzheimer's disease (AD). Thus, a reliable prediction of the conversion from MCI to AD based on three-dimensional (3D) texture features of MRI images could help doctors in developing effective treatment protocols.
Methods:
The 3D texture features of the whole-brain were deduced based on the gray-level co-occurrence matrix. Then, the embedded feature selection method based on least squares loss and within-class scatter (LSWCS) was employed to select the optimal subsets of features that were used for binary classification (AD, MCI_C, MCI_S, normal control in pairs) based on SVM. A tenfold cross validation was repeated ten times for each classification. LASSO, fused_LASSO, and group LASSO are used in feature selection step for comparison.
Results:
The accuracy and the selected features are the focus of clinical diagnosis reports, indicating that the feature selection algorithm is effective.
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