Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning
Abstract
:1. Introduction
- To use texture analysis to examine the minor changes in the hippocampus caused by AD.
- To extract features by implementing texture analysis technique on MRI.
- To classify subjects into AD and cognitively normal (CN) groups by providing the information extracted as input for training machine learning models.
2. Materials and Methods
2.1. Alzheimer’s Disease Neuroimaging Initiative
2.2. MRI Data and Subjects
2.3. ROI Selection
2.4. Hippocampus Segmentation
2.5. Texture Analysis
2.6. Classification Models
2.6.1. Support Vector Machine (SVM)
2.6.2. Decision Trees
2.6.3. Random Forest
3. Results
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Group | Description | Subject Age |
---|---|---|
AD | Number of Cases- 119 | - |
Minimum | 55 | |
Maximum | 91 | |
Mean (Standard Deviation) | 75.10 (±8.60) | |
CN | Number of Cases- 115 | - |
Minimum | 59 | |
Maximum | 90 | |
Mean (Standard Deviation) | 74.80 (±7.95) |
Sr. No. | Directions |
---|---|
1 | (0, 0, d) |
2 | (0, d, −d) |
3 | (0, d, 0) |
4 | (d, 0, −d) |
5 | (0, d, d) |
6 | (d, d, −d) |
7 | (−d, d, −d) |
8 | (d, 0, 0) |
9 | (d, 0, d) |
10 | (d, d, 0) |
11 | (−d, d, 0) |
12 | (d, d, d) |
13 | (−d, d, d) |
Sr. No. | Feature | Formula | Notations |
---|---|---|---|
1 | Autocorrelation | –number of grey levels. i–row number j–column number | |
2 | Contrast | –Element i,j of the normalized symmetrical GLCM –number of paired data | |
3 | Correlation | –the GLCM mean (being an estimate of the intensity of all pixels in the relationships that contributed to the GLCM) –the variance of the intensities of all reference pixels in the relationships that contributed to the GLCM (symmetric) | |
4 | Cluster Prominence | ||
5 | Cluster Shade | ||
6 | Dissimilarity | ||
7 | Energy | ||
8 | Entropy | ||
9 | Homogeneity | ||
10 | Maximum Probability | ||
11 | Variance | ||
12 | Sum average | ||
13 | Sum variance | ||
14 | Sum entropy | ||
15 | Difference variance | ||
16 | Difference entropy | ||
17 | Information measure of correlation1 | and are entropies of and | |
18 | Information measure of correlation2 | ||
19 | Inverse difference | ||
20 | Inverse difference moment normalized |
Sr. No | Model | Classification Accuracy (%) |
---|---|---|
1 | Decision Trees | 86.8 |
2 | SVM | 87.2 |
3 | Ensemble | 86.3 |
Distance and Directions | Classification Model Accuracy | ||||||
---|---|---|---|---|---|---|---|
With Textural Data Only | With Textural and Structural Data | ||||||
Decision Trees | SVM | Ensemble | Decision Trees | SVM | Ensemble | ||
d = 1 | 011 | 81.6 | 82.5 | 82.1 | 86.8 | 85.9 | 84.6 |
100 | 81.2 | 84.6 | 82.9 | 88 | 86.8 | 88.5 | |
101 | 82.9 | 82.9 | 83.8 | 86.3 | 85.5 | 86.8 | |
110 | 82.5 | 84.2 | 85 | 85.9 | 87.2 | 87.6 | |
111 | 79.5 | 83.3 | 82.1 | 86.3 | 87.2 | 86.8 | |
11–1 | 79.9 | 82.1 | 80.3 | 88 | 85.5 | 90.2 | |
001 | 82.5 | 83.3 | 82 | 88.5 | 86.6 | 86.3 | |
01–1 | 82.9 | 83.3 | 82.9 | 88 | 86.8 | 86.3 | |
010 | 80.8 | 84.2 | 83.8 | 85.9 | 85.9 | 88 | |
10–1 | 81.2 | 83.8 | 81.6 | 88.5 | 85.9 | 88.9 | |
−11–1 | 82.1 | 84.2 | 82.9 | 86.3 | 86.3 | 86.8 | |
−110 | 82.9 | 84.2 | 82.5 | 88 | 86.3 | 87.6 | |
−111 | 81.6 | 84.6 | 80.8 | 88.9 | 85.9 | 86.8 | |
d = 2 | 022 | 81.6 | 81.2 | 83.8 | 85.9 | 85.9 | 84.6 |
200 | 80.3 | 85.9 | 81.6 | 85 | 85.5 | 86.3 | |
202 | 80.3 | 83.3 | 84.2 | 87.2 | 86.8 | 86.8 | |
220 | 81.6 | 84.2 | 81.2 | 88 | 86.8 | 88 | |
222 | 79.1 | 82.9 | 80.3 | 87.2 | 85.9 | 88.5 | |
22–2 | 79.9 | 82.1 | 82.9 | 88 | 86.8 | 86.3 | |
002 | 80.8 | 83.3 | 81.6 | 88.5 | 85 | 87.2 | |
02–2 | 82.5 | 82.5 | 79.9 | 85.9 | 87.2 | 86.3 | |
020 | 82.5 | 83.8 | 83.8 | 88.9 | 86.3 | 87.6 | |
20–2 | 80.8 | 84.2 | 80.8 | 84.2 | 85.9 | 87.6 | |
−22–2 | 83.3 | 84.2 | 84.2 | 85 | 85.5 | 88 | |
−220 | 80.3 | 84.2 | 82.9 | 86.3 | 86.3 | 88.5 | |
−222 | 82.1 | 80.8 | 82.1 | 87.2 | 85.5 | 85.5 |
Author | Structure | Texture Analysis Method (Features) | Machine Learning Technique | Accuracy (%) |
---|---|---|---|---|
Xiao, Z. et al. [6] | Brain | GLCM, Gabor filter | SVM | For AD-CN data: 85.71 |
Oishi, K. et al. [33] | Gray Matter, White Matter, Cerebral Spinal Fluid and Background | Coefficient of Probability Changes | SVM | Maximum: 95 Average: 70 |
Kumar, K. et al. [34] | Brain | GLCM | k-NN | 74.73 |
Luk, C.C. et al. [35] | Brain | 3D GLCM | - | Maximum: 90.5 Average: 76 |
Chaddad, A. et al. [36] | Brain | 3D GLCM | Ensemble | 74.19 |
Madusanka, N. et al. [37] | Hippocampus | 3D GLCM | SVM | 86.61 |
Ranjbar, S. et al. [38] | Hippocampus | Hippocampal Volume | Diagonal Quadratic Discriminant Analysis (Naïve Bayes) | 89 |
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Salunkhe, S.; Bachute, M.; Gite, S.; Vyas, N.; Khanna, S.; Modi, K.; Katpatal, C.; Kotecha, K. Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning. Appl. Syst. Innov. 2021, 4, 49. https://doi.org/10.3390/asi4030049
Salunkhe S, Bachute M, Gite S, Vyas N, Khanna S, Modi K, Katpatal C, Kotecha K. Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning. Applied System Innovation. 2021; 4(3):49. https://doi.org/10.3390/asi4030049
Chicago/Turabian StyleSalunkhe, Sumit, Mrinal Bachute, Shilpa Gite, Nishad Vyas, Saanil Khanna, Keta Modi, Chinmay Katpatal, and Ketan Kotecha. 2021. "Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning" Applied System Innovation 4, no. 3: 49. https://doi.org/10.3390/asi4030049