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Application of 3D Whole-Brain Texture Analysis and the Feature Selection Method Based on within-Class Scatter in the Classification and Diagnosis of Alzheimer’s Disease

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Abstract

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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Funding

This study was funded by National Natural Science Foundation of China (No. 61802074), Guangdong Medical Scientific Research Foundation, China (No. A2021402), Guangdong Basic and Applied Basic Research Foundation, China (No. 2020A1515010760), Science and Technology Project of Zhanjiang, China (No. 2019A01016), and the Research Fund Project of Guangdong Medical University, China (No. GDMUM2019002).

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Authors and Affiliations

  1. School of Biomedical Engineering, Guangdong Medical University, Zhanjiang, 524023, China

    Ke Zhou, Wenguang He & Jie Cai

  2. Department of Medical Imaging Technology, Capital Medical University Yanjing Medical College, Beijing, 101300, China

    Lingjing Hu

  3. Department of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524023, China

    Zhou Liu

Authors
  1. Ke Zhou
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  2. Zhou Liu
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  3. Wenguang He
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  4. Jie Cai
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  5. Lingjing Hu
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Corresponding authors

Correspondence to Jie Cai or Lingjing Hu.

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Zhou, K., Liu, Z., He, W. et al. Application of 3D Whole-Brain Texture Analysis and the Feature Selection Method Based on within-Class Scatter in the Classification and Diagnosis of Alzheimer’s Disease. Ther Innov Regul Sci 56, 561–571 (2022). https://doi.org/10.1007/s43441-021-00373-x

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  • DOI: https://doi.org/10.1007/s43441-021-00373-x

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