Segmentação de Núcleos Celulares Baseada em Agrupamento e Características de Forma
Resumo
Dentre os tipos de câncer, o de colo de útero é a quarto mais comum em todo o mundo em mulheres. O diagnóstico é feito, principalmente por meio do exame de papanicolau, que oferece uma boa taxa de detecção em estágios primários da doença, oferecendo mais chances de cura. Desse modo, métodos de avaliação automatizados de exames constituem uma ferramenta muito valiosa no combate a essa doença. Este trabalho avalia os parâmetros para a segmentação de núcleos em imagens de papanicolau na base CRIC. Com a variação e estudo dos parâmetros obtivemos um resultado médio de dice em 0.7023 com a melhor combinação de parâmetros.
Referências
Bandyopadhyay, H. and Nasipuri, M. (2020). Segmentation of pap smear images for cervical cancer detection. In 2020 IEEE Calcutta Conference (CALCON), pages 30–33. IEEE.
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6):679–698.
Cheng, Y. (1995). Mean shift, mode seeking, and clustering. IEEE transactions on pattern analysis and machine intelligence, 17(8):790–799.
Comaniciu, D. and Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, 24(5):603– 619.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273– 297.
Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, volume 1, pages 278–282. IEEE.
Jantzen, J., Norup, J., Dounias, G., and Bjerregaard, B. (2005a). Pap-smear benchmark data for pattern classication. Nature inspired Smart Information Systems (NiSIS 2005), pages 1–9.
Jantzen, J., Norup, J., Dounias, G., and Bjerregaard, B. (2005b). Pap-smear benchmark data for pattern classication. Nature inspired Smart Information Systems (NiSIS 2005), pages 1–9.
Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
Rezende, M. T., Silva, R., Tobias, A. H. G., Oliveira, P., Sombra de Medeiros, F. N., Ushizima, D., Carneiro, C. M., and Bianchi, A. G. C. (2020). Cric cervix classication.
Silva, R. R., Araujo, F. H., Ushizima, D. M., Bianchi, A. G., Carneiro, C. M., and Medeiros, F. N. (2019). Radial feature descriptors for cell classication and recommendation. Journal of Visual Communication and Image Representation, 62:105–116.
Talukdar, J., Nath, C. K., and Talukdar, P. (2013). Fuzzy clustering based image segmentation of pap smear images of cervical cancer cell using fcm algorithm. markers, 3(1):460–2.
Wang, P., Wang, L., Li, Y., Song, Q., Lv, S., and Hu, X. (2019). Automatic cell nuclei segmentation and classication of cervical pap smear images. Biomedical Signal Processing and Control, 48:93–103.
WHO (2021). Cervical cancer. https://www.who.int/health-topics/cervical-cancer\#tab=tab\_1. Accessed on 2021-06-11.
William, W., Ware, A., Basaza-Ejiri, A. H., and Obungoloch, J. (2019). Cervical cancer classication from pap-smears using an enhanced fuzzy c-means algorithm. Informatics in Medicine Unlocked, 14:23–33.
Xiang, Y., Sun, W., Pan, C., Yan, M., Yin, Z., and Liang, Y. (2020). A novel automationassisted cervical cancer reading method based on convolutional neural network. Biocybernetics and Biomedical Engineering, 40(2):611–623.
Zhao, M., Wang, H., Han, Y., Wang, X., Dai, H.-N., Sun, X., Zhang, J., and Pedersen, M. (2021). Seens: Nuclei segmentation in pap smear images with selective edge enhancement. Future Generation Computer Systems, 114:185–194.
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6):679–698.
Cheng, Y. (1995). Mean shift, mode seeking, and clustering. IEEE transactions on pattern analysis and machine intelligence, 17(8):790–799.
Comaniciu, D. and Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, 24(5):603– 619.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273– 297.
Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, volume 1, pages 278–282. IEEE.
Jantzen, J., Norup, J., Dounias, G., and Bjerregaard, B. (2005a). Pap-smear benchmark data for pattern classication. Nature inspired Smart Information Systems (NiSIS 2005), pages 1–9.
Jantzen, J., Norup, J., Dounias, G., and Bjerregaard, B. (2005b). Pap-smear benchmark data for pattern classication. Nature inspired Smart Information Systems (NiSIS 2005), pages 1–9.
Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
Rezende, M. T., Silva, R., Tobias, A. H. G., Oliveira, P., Sombra de Medeiros, F. N., Ushizima, D., Carneiro, C. M., and Bianchi, A. G. C. (2020). Cric cervix classication.
Silva, R. R., Araujo, F. H., Ushizima, D. M., Bianchi, A. G., Carneiro, C. M., and Medeiros, F. N. (2019). Radial feature descriptors for cell classication and recommendation. Journal of Visual Communication and Image Representation, 62:105–116.
Talukdar, J., Nath, C. K., and Talukdar, P. (2013). Fuzzy clustering based image segmentation of pap smear images of cervical cancer cell using fcm algorithm. markers, 3(1):460–2.
Wang, P., Wang, L., Li, Y., Song, Q., Lv, S., and Hu, X. (2019). Automatic cell nuclei segmentation and classication of cervical pap smear images. Biomedical Signal Processing and Control, 48:93–103.
WHO (2021). Cervical cancer. https://www.who.int/health-topics/cervical-cancer\#tab=tab\_1. Accessed on 2021-06-11.
William, W., Ware, A., Basaza-Ejiri, A. H., and Obungoloch, J. (2019). Cervical cancer classication from pap-smears using an enhanced fuzzy c-means algorithm. Informatics in Medicine Unlocked, 14:23–33.
Xiang, Y., Sun, W., Pan, C., Yan, M., Yin, Z., and Liang, Y. (2020). A novel automationassisted cervical cancer reading method based on convolutional neural network. Biocybernetics and Biomedical Engineering, 40(2):611–623.
Zhao, M., Wang, H., Han, Y., Wang, X., Dai, H.-N., Sun, X., Zhang, J., and Pedersen, M. (2021). Seens: Nuclei segmentation in pap smear images with selective edge enhancement. Future Generation Computer Systems, 114:185–194.
Publicado
23/11/2021
Como Citar
LÉLIS, Samuel P. B. D.; SOUZA, Lucas B. M. de; SILVA, Romuere R. V..
Segmentação de Núcleos Celulares Baseada em Agrupamento e Características de Forma. In: ENCONTRO UNIFICADO DE COMPUTAÇÃO DO PIAUÍ (ENUCOMPI), 14. , 2021, Picos.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 33-40.
DOI: https://doi.org/10.5753/enucompi.2021.17751.
