Image Segmentation Quality Scores

Let you evaluate the image segmentation quality scores, such TP, FP, TN, FN, Accuracy, Sensitivity, Precision, MCC, Dice, Jaccard

Image Segmentation Quality Scores

Let you evaluate the image segmentation quality scores, such TP, FP, TN, FN, Accuracy, Sensitivity, Precision, MCC, Dice, Jaccard

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Copyright 2019 by Dang N. H. Thanh. Email: [e-mail]

Website: https://sites.google.com/view/crx/sdm

You need to install the statistics and Image Processing toolboxes

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Please cite the following papers:

Thanh, Dang N. H., et al. (2020), “Melanoma Skin Cancer Detection Method Based on Adaptive Principal Curvature, Colour Normalisation and Feature Extraction with the ABCD Rule.” Journal of Digital Imaging, vol. 33, no. 3, pp. 574–85, doi:10.1007/s10278-019-00316-x.

Thanh, Dang N. H., et al. (2021), “Skin Lesion Segmentation Method for Dermoscopic Images with Convolutional Neural Networks and Semantic Segmentation.” Computer Optics, vol. 45, no. 1, pp. 122–29, doi:10.18287/2412-6179-co-748.

Thanh, Dang N. H., et al. (2021), “Dermoscopic Image Segmentation Method Based on Convolutional Neural Networks.” International Journal of Computer Applications in Technology, vol. 66, no. 2, p. 89, doi:10.1504/ijcat.2021.10043447.

HOW TO USE:

Launch two images: a ground truth segmentation image A and an acquired segmented image B:

A = imread('groundtruth.png');

B = imread('segmented.png');

And call the function - EvaluateImageSegmentationScores:

[Accuracy, Sensitivity, Fmeasure, Precision, MCC, Dice, Jaccard, Specitivity] = EvaluateImageSegmentationScores(A, B)