Segmentação Baseada Em Superpixels Para Avaliar Os Impactos Da Supervisão Na Segmentação De Lesões Melanocíticas Em Imagens Macroscópicas
Abstract
Melanoma is a skin cancer that can be extremely aggressive in its final stages and can, in some cases, develop metastasis. Therefore, proposals to improve the use of computational systems, in this context, have been widely investigated. In the present work, two variations, one supervised and the other unsupervised, of a method for the segmentation of macroscopic images of melanocytic lesions were investigated. Before the actual segmentation, the input image is pre-processed and then represented by superpixels. Subsequently, a clustering algorithm is fed with the extracted data and partitions it into two groups: lesion regions (foreground) and non-lesion regions (background).
References
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Glaister, J., Wong, A., and Clausi, D. A. (2014). Segmentation of skin lesions from digital images using joint statistical texture distinctiveness. IEEE Transactions on Biomedical Engineering, 61(4):1220–1230.
Hasan, M. K., Dahal, L., Samarakoon, P. N., Tushar, F. I., and Martí, R. (2020). DSNet: Automatic dermoscopic skin lesion segmentation. Computers in Biology and Medicine, 120:103738.
Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Jafari, M. H., Ward, K., and Najarian, K. (2016). Melanoma detection by analysis of clinical images using convolutional neural network. In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 1373–1376.
University of Waterloo (2013). Skin cancer detection. Disponível em: [link]. Último acesso em: 13 mar. 2023.
Zortea, M., Flores, E., and Scharcanski, J. (2017). A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images. Pattern Recognition, 64:92–104.
Al-Masni, M. A., Al-Antari, M. A., Choi, M.-T., Han, S.-M., and Kim, T.-S. (2018). Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Computer Methods and Programs in Biomedicine, 162:221–231.
Bernart, E., Flores, E. S., and Scharcanski, J. (2019). Macroscopic pigmented skin lesion prescreening. In Narayan, R., editor, Encyclopedia of Biomedical Engineering, volume 2, pages 561–573. Elsevier.
Bissoto, A., Fornaciali, M., Valle, E., and Avila, S. (2019). (de) constructing bias on skin lesion datasets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 2766–2774.
Cavalcanti, P. G., Scharcanski, J., and Lopes, C. B. (2010). Shading attenuation in human skin color images. In Proceedings of the International Symposium on Visual Computing (ISVC), pages 190–198.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639):115–118.
Flores, E. and Scharcanski, J. (2016). Segmentation of melanocytic skin lesions using feature learning and dictionaries. Expert Systems with Applications, 56:300–309.
Glaister, J., Wong, A., and Clausi, D. A. (2014). Segmentation of skin lesions from digital images using joint statistical texture distinctiveness. IEEE Transactions on Biomedical Engineering, 61(4):1220–1230.
Hasan, M. K., Dahal, L., Samarakoon, P. N., Tushar, F. I., and Martí, R. (2020). DSNet: Automatic dermoscopic skin lesion segmentation. Computers in Biology and Medicine, 120:103738.
Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Jafari, M. H., Ward, K., and Najarian, K. (2016). Melanoma detection by analysis of clinical images using convolutional neural network. In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 1373–1376.
University of Waterloo (2013). Skin cancer detection. Disponível em: [link]. Último acesso em: 13 mar. 2023.
Zortea, M., Flores, E., and Scharcanski, J. (2017). A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images. Pattern Recognition, 64:92–104.
Published
2023-06-27
How to Cite
FERREIRA, Mauricio S.; FLORES, Eliezer Soares.
Segmentação Baseada Em Superpixels Para Avaliar Os Impactos Da Supervisão Na Segmentação De Lesões Melanocíticas Em Imagens Macroscópicas. In: UNDERGRADUATE RESEARCH WORKS CONTEST - BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTHCARE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 43-47.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2023.229706.
