This repository contains three popular approaches for detecting Retinal Vessels:
- Standard image manipulation and morphology
- Classifiers
- Deep Neural Network
The Deep Neural Network approach used can be found here, it also
has video tutorial here. I won't be placing the copy here because that would be stealing.
For those that do not want to spend time viewing this implementation here is a quick rundown on how each approach is made:
- Image manipulation
- Read image
- Detect background (non retina part of the image), cut it out and save a mask
- Extract green channel (best contrast of vessels to rest of the image), later remove background with mask
- Equalize histogram of colors
- Apply Hessian filter, remove background again
- Apply bilateral filter
- Remove small noise
- Remove white circle around retina created by previous steps
- Classifiers
- Cut out subimages randomly from image of specified size
- Get decision for middle pixel of subimage
- Calculate Hu moments of entire subimage fragment
- Merge Hu moments with pixel intensity
- Feed to KNN classifier
- Repeat on output image during prediction to get output image
- Deep Neural Network
- Construct U-Net network
- Train network
- Use it for prediction
The following measures were calculated for all approaches based on their results for images in images folder:
- Accuracy : (
TP+TN) / (TP+TN+FP+FN) - Sensitivity :
TP/ (TP+FN) - Specificity :
TN/ (TN+FP) - Balanced Accuracy : (Sensitivity + Specificity) / 2
Where:
TP - True Positive
TN - True Negative
FP - False Positive
FN - False Negative
All calculated by comparing every pixel of input and output image.
Below the metrics you can see example image result (left is expected result, right is actual result).
| Image name | Accuracy | Sensitivity | Specificity | Balanced Accuracy |
|---|---|---|---|---|
| 11_dr.jpg | 93% | 53% | 98% | 75% |
| 11_g.jpg | 93% | 53% | 98% | 76% |
| 11_h.jpg | 94% | 55% | 98% | 77% |
| 12_dr.JPG | 93% | 48% | 98% | 73% |
| 12_g.jpg | 93% | 59% | 97% | 78% |
| Image name | Accuracy | Sensitivity | Specificity | Balanced Accuracy |
|---|---|---|---|---|
| 11_dr.jpg | 94% | 68% | 96% | 82% |
| 11_g.jpg | 94% | 71% | 95% | 83% |
| 11_h.jpg | 94% | 75% | 95% | 85% |
| 12_dr.JPG | 94% | 65% | 95% | 80% |
| 12_g.jpg | 93% | 76% | 94% | 85% |
| Image name | Accuracy | Sensitivity | Specificity | Balanced Accuracy |
|---|---|---|---|---|
| 11_dr.jpg | 95% | 86% | 96% | 91% |
| 11_g.jpg | 96% | 85% | 97% | 91% |
| 11_h.jpg | 96% | 86% | 97% | 92% |
| 12_dr.JPG | 96% | 71% | 98% | 85% |
| 12_g.jpg | 96% | 85% | 97% | 91% |
