Good morning everyone. As a part of my research work, I designed a network that extracts the leaf region from the real field images. when I search about performance evaluation metrics for the segmentation I found a lot of metrics. Here I provided the list
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. Fmeasure = 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);
suggest to me which performance evaluation metrics are best suited for my work. thank you.
https://www.quora.com/What-are-the-best-performance-evaluation-metrics-for-image-segmentation
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Dear Srinivas Talasila ,
Look the link, maybe helpful.
https://core.ac.uk/download/pdf/153403985.pdf
Regards,
Shafagat
Hi Dear
Even I faced the same problem since there is no particular standard parameter that has been used in order to measure the performance of the work. For my work, I used accuracy and specificity, as these were best suited for my work, and were maximum used by most of the papers that I studied, thus giving a standard way for comparison.
In spite of significant advances in image segmentation techniques, evaluation of these methods thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images that are evaluated by some method, or it is otherwise left to subjective evaluation by the reader. ... approach for evaluation of segmentation that takes into account not only the accuracy of the boundary localization of the created segments but also the under-segmentation and over-segmentation effects, regardless to the number of regions in each partition. In addition, it takes into account the way humans perceive visual information. This metric can be applied both to automatically provide a ranking among different segmentation algorithms and to find an optimal set of input parameters of a given algorithm. https://www.researchgate.net/publication/221472498_Performance_Evaluation_of_Image_Segmentation
Pixel Accuracy and mIoU are the most common two ways used to evaluate how well an image segmentation model performs. While pixel accuracy is an extremely easy method to code, it also is strongly biased by classes that take a large portion of the image. https://towardsdatascience.com/evaluating-image-segmentation-models-1e9bb89a001b
...researchers have proposed many effective metrics for evaluating image segmentation, among which the common methods include three categories: pixel-based evaluation, region-based evaluation and distance-based evaluation. https://link.springer.com/article/10.1007%2Fs10462-020-09830-9
https://core.ac.uk/download/pdf/153403985.pdf
https://www.hindawi.com/journals/am/2018/4976372/
http://zoi.utia.cas.cz/files/perfsegm_benes.pdf
Hi Srinivas,
I think it will be great to show the model's performance in the area under the curve (AUC) or accuracy with sensitivity and specificity.
Cheers
Dear Dr. Srinivas Talasila
Thank you for this contribution.
The common metrics are accuracy, specificity, and sensitivity
https://www.quora.com/What-are-the-best-performance-evaluation-metrics-for-image-segmentation
You can follow this question.
Article
Performance Evaluation of Image Segmentation
Auteur
Fernando C. Monteiro, and Aurlio C. Campilho
In spite of significant advances in image segmentation techniques, evaluation of these methods thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images that are evaluated by some method, or it is otherwise left to subjective evaluation by the reader. We propose a new approach for evaluation of segmentation that takes into account not only the accuracy of the boundary localization of the created segments but also the under-segmentation and over-segmentation effects, regardless to the number of regions in each partition. In addition, it takes into account the way humans perceive visual information. This new metric can be applied both to automatically provide a ranking among different segmentation algorithms and to find an optimal set of input parameters of a given algorithm.
Regards
Article
Performance Evaluation of Image Segmentation
Auteur
Fernando C. Monteiro, and Aurlio C. Campilho
An evaluation metric is desired to take into account the following effects: – Over-segmentation. A region of the reference is represented by two or more regions in the examined segmentation. – Under-segmentation. Two or more regions of the reference are represented by a single region in the examined segmentation. – Inaccurate boundary localization. Ground truth is usually produced by humans that segment at different granularities. – Different number of segments. We need to be able to compare two segmentations when they have different numbers of segments.
Article
Performance Evaluation of Image Segmentation
Auteur
Fernando C. Monteiro, and Aurlio C. Campilho
Under-segmentation is considered to be as a much more serious problem as it is easier to recover true segments through a merging process after oversegmentation rather than trying to split an heterogeneous region. One desirable property of a good evaluation measure is to accommodate refinement only in regions that human segmenters could find ambiguous and to penalize differences in refinements elsewhere. In addition to being tolerant to refinement, any evaluation measure should also be robust to noise along region boundaries and tolerant to different number of segments in each partition.
Thesis
Élaboration de méthodes d'évaluation d'algorithmes de segmentation d'images January 2006
Zianou Ahmed Seghir
Regards
Article
Region-based spatial and temporal image segmentation
March 2008 Fernando C. Monteiro
Regards
Article
Distance Measures for Image Segmentation Evaluation
- September 2012
- Fernando C. Monteiro Aurélio C. Campilho
Article
Maximizing a Sum Related to Image Segmentation Evaluation
- June 2011
- Journal of Mathematical Modelling and Algorithms
- Johannes Hatzl Toufik Mansour Mark Shattuck
Conference Paper
Watershed framework to region-based image segmentation
- January 2009
- Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
- Fernando C. Monteiro Aurélio C. Campilho
Image Segmentation is the process of partitioning a digital image into different image classes or objects. This is a very important part of self-driving cars as correctly detecting what objects are in front of the car and where they are located are critical inputs to the self-driving algorithm.
https://towardsdatascience.com/
The image segmentation model is created through a neural network that takes in a given Ground Truth. The ground truth is a correctly labeled image that tells the neural network what the expected output is.Ground Truth vs. Prediction of the Person image class
After the image segmentation is trained and outputs a prediction, we need to evaluate how well the model performed. This is usually done through two ways, Pixel Accuracy and Intersection over Union (or IoU).
https://towardsdatascience.com/
Article
Image segmentation evaluation: a survey of methods
· Zhaobin Wang, E. Wang Ying Zhu
Artificial Intelligence Review volume 53, pages5637–5674(2020)
Image segmentation is a prerequisite for image processing. There are many methods for image segmentation, and as a result, a great number of methods for evaluating segmentation results have also been proposed. How to effectively evaluate the quality of image segmentation is very important. In this paper, the existing image segmentation quality evaluation methods are summarized, mainly including unsupervised methods and supervised methods. Based on hot issues, the application of metrics in natural, medical and remote sensing image evaluation is further outlined. In addition, an experimental comparison for some methods were carried out and the effectiveness of these methods was ranked. At the same time, the effectiveness of classical metrics for remote sensing and medical image evaluation is also verified.
Check these link
Check these link
Berezsky O, Melnyk G, Batko Y, Pitsun O (2016) Regions matching algorithms analysis to quantify the image segmentation results. In: 2016 XITH international scientific and technical conference computer sciences and information technologies (CSIT), pp 33–36
Bernard O, Bosch JG, Heyde B (2016) Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography. IEEE Trans Med Imaging 35(4):967–977. https://doi.org/10.1109/TMI.2015.2503890
Boeck S, Immitzer M, Atzberger C (2017) On the objectivity of the objective function-problems with unsupervised segmentation evaluation based on global score and a possible remedy. Remote Sens 9(8):2017. https://doi.org/10.3390/rs9080769
Borsotti M, Campadelli P, Schettini R (1998) Quantitative evaluation of color image segmentation results. Pattern Recognit Lett 19(8):741–747. https://doi.org/10.1016/S0167-8655(98)00052-X
Check these link
Cai Z, Liang Y, Huang H (2017) Unsupervised segmentation evaluation: an edge-based method. Multimed Tools Appl 76(8):11097–11110. https://doi.org/10.1007/s11042-016-3542-8
Cappabianco FAM, de Miranda PAV, Udupa JK (2017) A critical analysis of the methods of evaluating MRI brain segmentation algorithms. In: 2017 IEEE international conference on image processing (ICIP), pp 3894–3898
Cappabianco FAM, Ribeiro PFO, de Miranda PAV, Udupa JK (2019) A general and balanced region-based metric for evaluating medical image segmentation algorithms. In: 2019 IEEE international conference on image processing (ICIP), pp 1525–1529
Cardoso J, Corte-Real L (2005) Toward a generic evaluation of image segmentation. IEEE Trans Image Process 14(11):1773–1782. https://doi.org/10.1109/TIP.2005.854491
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