Plot Multiple Segmented Boundaries

Version 1.0.2 (1,72 ko) par Dang N. H. Thanh

Draw multiple boundaries of segmented results on the original image

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Mise à jour 23 août 2020

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Copyright 2019 by Dang N. H. Thanh. Email: thanh.dnh.cs@gmail.com
Visit my site: https://sites.google.com/view/crx/sdm
You need to install the image processing toolbox
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Draw multiple boundaries of segmented results on the original image, where, inputimagepath - The path to input image, bordersize - The border width measured in pixel, segoption - This is 2D array, including the path of segmented images. The segoptions should be:
segoptions = {{path0; color0}, {path1; color1}, ...};
Note that: comma (,) and semicolon (;).

For example:
segoptions = {{'seg1.png'; 'r--'}, {'seg2.png'; 'b-.'}};
PlotMultipleSegmentedBoundaries('orin.png', 3, segoptions);

Note: result will be stored in the __boundaries folder of current path

Citation pour cette source

Thanh, Dang N. H., et al. “Automatic Initial Boundary Generation Methods Based on Edge Detectors for the Level Set Function of the Chan-Vese Segmentation Model and Applications in Biomedical Image Processing.” Frontiers in Intelligent Computing: Theory and Applications, Springer Singapore, 2019, pp. 171–81, doi:10.1007/978-981-13-9920-6_18.

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Thanh, Dang N. H., et al. “A Skin Lesion Segmentation Method for Dermoscopic Images Based on Adaptive Thresholding with Normalization of Color Models.” 2019 6th International Conference on Electrical and Electronics Engineering (ICEEE), IEEE, 2019, doi:10.1109/iceee2019.2019.00030.

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Thanh, Dang N. H., et al. “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, Springer Science and Business Media LLC, Dec. 2019, pp. 574–85, doi:10.1007/s10278-019-00316-x.

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Compatibilité avec les versions de MATLAB

Créé avec R2019a

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PlotMultipleSegmentedBoundaries

Version	Publié le	Notes de version
1.0.2	23 août 2020	Update info	Télécharger
1.0.1	13 fév. 2020	Update references	Télécharger
1.0.0	18 avr. 2019		Télécharger

MLA	Thanh, Dang N. H., et al. “Automatic Initial Boundary Generation Methods Based on Edge Detectors for the Level Set Function of the Chan-Vese Segmentation Model and Applications in Biomedical Image Processing.” Frontiers in Intelligent Computing: Theory and Applications, Springer Singapore, 2019, pp. 171–81, doi:10.1007/978-981-13-9920-6_18.
APA	Thanh, D. N. H., Hien, N. N., Prasath, V. B. S., Thanh, L. T., & Hai, N. H. (2019). Automatic Initial Boundary Generation Methods Based on Edge Detectors for the Level Set Function of the Chan-Vese Segmentation Model and Applications in Biomedical Image Processing. In Frontiers in Intelligent Computing: Theory and Applications (pp. 171–181). Springer Singapore. Retrieved from https://doi.org/10.1007%2F978-981-13-9920-6_18
BibTeX	@incollection{Thanh_2019, doi = {10.1007/978-981-13-9920-6_18}, url = {https://doi.org/10.1007%2F978-981-13-9920-6_18}, year = 2019, month = {oct}, publisher = {Springer Singapore}, pages = {171--181}, author = {Dang N. H. Thanh and Nguyen Ngoc Hien and V. B. Surya Prasath and Le Thi Thanh and Nguyen Hoang Hai}, title = {Automatic Initial Boundary Generation Methods Based on Edge Detectors for the Level Set Function of the Chan-Vese Segmentation Model and Applications in Biomedical Image Processing}, booktitle = {Frontiers in Intelligent Computing: Theory and Applications} }

MLA	Thanh, Dang N. H., et al. “A Skin Lesion Segmentation Method for Dermoscopic Images Based on Adaptive Thresholding with Normalization of Color Models.” 2019 6th International Conference on Electrical and Electronics Engineering (ICEEE), IEEE, 2019, doi:10.1109/iceee2019.2019.00030.
APA	Thanh, D. N. H., Erkan, U., Prasath, V. B. S., Kumar, V., & Hien, N. N. (2019). A Skin Lesion Segmentation Method for Dermoscopic Images Based on Adaptive Thresholding with Normalization of Color Models. In 2019 6th International Conference on Electrical and Electronics Engineering (ICEEE). IEEE. Retrieved from https://doi.org/10.1109%2Ficeee2019.2019.00030
BibTeX	@inproceedings{Thanh_2019, doi = {10.1109/iceee2019.2019.00030}, url = {https://doi.org/10.1109%2Ficeee2019.2019.00030}, year = 2019, month = {apr}, publisher = {{IEEE}}, author = {Dang N.H. Thanh and Ugur Erkan and V.B. Surya Prasath and Vivek Kumar and Nguyen Ngoc Hien}, title = {A Skin Lesion Segmentation Method for Dermoscopic Images Based on Adaptive Thresholding with Normalization of Color Models}, booktitle = {2019 6th International Conference on Electrical and Electronics Engineering ({ICEEE})} }

MLA	Thanh, Dang N. H., et al. “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, Springer Science and Business Media LLC, Dec. 2019, pp. 574–85, doi:10.1007/s10278-019-00316-x.
APA	Thanh, D. N. H., Prasath, V. B. S., Hieu, L. M., & Hien, N. N. (2019). Melanoma Skin Cancer Detection Method Based on Adaptive Principal Curvature, Colour Normalisation and Feature Extraction with the ABCD Rule. Journal of Digital Imaging, 33(3), 574–585. Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1007%2Fs10278-019-00316-x
BibTeX	@article{Thanh_2019, doi = {10.1007/s10278-019-00316-x}, url = {https://doi.org/10.1007%2Fs10278-019-00316-x}, year = 2019, month = {dec}, publisher = {Springer Science and Business Media {LLC}}, volume = {33}, number = {3}, pages = {574--585}, author = {Dang N. H. Thanh and V. B. Surya Prasath and Le Minh Hieu and Nguyen Ngoc Hien}, title = {Melanoma Skin Cancer Detection Method Based on Adaptive Principal Curvature, Colour Normalisation and Feature Extraction with the {ABCD} Rule}, journal = {Journal of Digital Imaging} }