1. Similarity Index = 2*TP/(2*TP+FP+FN)
2. Correct detection Ratio = TP/TP+FN
3. Segmentation errors (OSE, USE, TSE)
4. Hausdorff Distance
5. Average Surface distance
6. Accuracy = (TP+TN)/(FN+FP+TP+TN);
7. Recall = TP/(TP+FN);
8. Precision = TP/(TP+FP);
9. F-measure = 2*TP/(2*TP+FP+FN);
10. MCC = (TP*TN-FP*FN)/sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN));
11. Dice = 2*TP/(2*TP+FP+FN);
12. Jaccard = Dice/(2-Dice);
13. Specitivity = TN/(TN+FP);
14. Sensitivity = TP/(TP+FN);
Dear Muhammad Imad ,
Suggest best performance evaluation metrics for image segmentation?
Similarity Index = 2*TP/(2*TP+FP+FN).
Correct detection Ratio = TP/TP+FN.
Segmentation errors (OSE, USE, TSE).
Hausdorff Distance.
Average Surface distance.
Accuracy = (TP+TN)/(FN+FP+TP+TN).
Recall = TP/(TP+FN).
Precision = TP/(TP+FP).
https://www.researchgate.net/post/Suggest_best_performance_evaluation_metrics_for_image_segmentation
Regards,
Shafagat
Precision, Recall, Accuracy, F1-score are some evaluation metrics.
Here are some papers related to evaluation of image segmentation.
Rhesus Teeth Segmentation by Use of Multi-Atlas Library – Invicro https://invicro.com/case-studies/rhesus-teeth-segmentation-by-use-of-multi-atlas-library/
Article Image segmentation evaluation: a survey of methods
An evaluation metric for image segmentation of multiple objects - ScienceDirect https://www.sciencedirect.com/science/article/abs/pii/S0262885608001984
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