Modelagem baseada em grafo para detecção de regiões anômalas em termografias da mama
Resumo
O tipo de câncer mais comum entre as mulheres ocorre nas mamas, sendo este o segundo tipo de câncer que mais faz vítimas no mundo. Por isso são dedicados muitos esforços para aprimorar os métodos de diagnósticos, melhorando a qualidade, precisão e custo dos exames de diagnóstico por imagem. Esse trabalho tem como objetivo oferecer uma metodologia de deteção de região de anomalias em termografia de mama. Nesta metodologia são combinadas segmentação de superpixel, construção do grafo e análise de medidas de redes complexas. A avaliação da deteção realizada nos experimentos da metodologia forneceu resultados que ainda podem ser aprimorados, em relação à detecção da exata região das anomalias, com percentual de 36,2%. Contudo a mesma propiciou resultados expressivos, no sentido de indicar em qual das mamas há anomalia, com percentual de acerto em 68,4%.Referências
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Süsstrunk, S. (2011). Finding objects of interest in images using saliency and superpixels. Technical report, EPFL, Lausanne.
Aidossov, N., Zarikas, V., Zhao, Y., Mashekova, A., et al. (2023). An integrated intelligent system for breast cancer detection at early stages using ir images and machine learning methods with explainability. SN Computer Science, 4(153).
Bezerra, L., Oliveira, M., Araújo, M., Viana, M. J., Santos, L. C., Santos, F., Rolim, T., Lyra, P., Lima, R. C., Borchartt, T. B., Resmini, R., and Conci, A. (2013). Infrared imaging for breast cancer detection with proper selection of properties: From acquisition protocol to numerical simulation. In Ng, E., Acharya, U., Rangayyan, R., and Suri, J., editors, Multimodality Breast Imaging: Diagnosis and Treatment, pages 285–322. Washington, USA.
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2018). Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6):394–424.
Gonzalez, R. C. and Woods, R. E. (2012). Digital Image Processing. Pearson, London, England.
Jorge-Hernandez, F., Chimeno, Y. G., Garcia-Zapirain, B., Zubizarreta, A. C., Beldarrain, M. A. G., and Fernandez-Ruanova, B. (2014). Graph theory for feature extraction and classification: A migraine pathology case study. Bio-Medical Materials and Engineering, 24(6):2979–2986.
Luna, J. and Delgado, F. (2010). Feasibility of new-generation infrared imaging screening for breast cancer in rural communities. US Obstetrics & Gynecology, 1:52–55.
Parshionikar, S. et al. (2025). Breast cancer detection using optimized hyperbolic graph attention with bidirectional convolutional neural network. Discover Oncology, 16.
Pizer, S. M., Johnston, R. E., Ericksen, J. P., Yankaskas, B. C., and Muller, K. E. (1990). Contrast-limited adaptive histogram equalization: speed and effectiveness. In Proceedings of the First Conference on Visualization in Biomedical Computing, pages 337–345, Nova Jersey, EUA. IEEE.
Sattarov, A., McIntyre, P., and Motowidlo, L. (2015). High-field open mri for breast cancer screening. IEEE Transactions on Applied Superconductivity, 25(3):1–5.
Silva, L. F., Saade, D. C. M., Sequeiros, G. O., Silva, A. C., Paiva, A. C., Bravo, R. S., and Conci, A. (2014). A new database for breast research with infrared image. Journal of Medical Imaging and Health Informatics, 4(1):92–100.
Usuki, H., Ikeda, T., Igarashi, Y., Takahashi, I., Fukami, A., Yokoe, T., Sonoo, H., and Asaishi, K. (1998). What kinds of non-palpable breast cancer can be detected by thermography? Biomed Thermology, 18(4):8–12.
Veerlapalli, P. and Das, K. (2024). Roisegnet: A deep learning framework for automatic segmentation of region of interest from breast thermogram imagery. International Journal of Intelligent Systems and Applications in Engineering, 12(3):2248–2261.
Veerlapalli, P. and Dutta, S. R. (2025). A hybrid gan-based deep learning framework for thermogram-based breast cancer detection. Scientific Reports, 15(19665).
Wang, L., Bohler, T., Zohrer, F., Georgii, J., Rauh, C., Fasching, P., Brehm, B., Schulz-Wendtland, R., Beckmann, M., Uder, M., and Hahn, H. (2014). A hybrid method towards automated nipple detection in 3d breast ultrasound images. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 2869–2872, Chicago, Illinois, EUA. IEEE EMBC.
Wishart, G., Campisi, M., Boswell, M., Chapman, D., Shackleton, V., Iddles, S., Hallett, A., and Britton, P. (2010). The accuracy of digital infrared imaging for breast cancer detection in women undergoing breast biopsy. European Journal of Surgical Oncology, 36(6):535–540.
World Health Organization (2026). Global health observatory database: Cancer. disponível em: [link]. Acesso em: 25 maio 2026.
Yasmin, M., Sharif, M., and Mohsin, S. (2013). Survey paper on diagnosis of breast cancer using image processing techniques. Research Journal of Recent Sciences, 2(10):88–98.
Aidossov, N., Zarikas, V., Zhao, Y., Mashekova, A., et al. (2023). An integrated intelligent system for breast cancer detection at early stages using ir images and machine learning methods with explainability. SN Computer Science, 4(153).
Bezerra, L., Oliveira, M., Araújo, M., Viana, M. J., Santos, L. C., Santos, F., Rolim, T., Lyra, P., Lima, R. C., Borchartt, T. B., Resmini, R., and Conci, A. (2013). Infrared imaging for breast cancer detection with proper selection of properties: From acquisition protocol to numerical simulation. In Ng, E., Acharya, U., Rangayyan, R., and Suri, J., editors, Multimodality Breast Imaging: Diagnosis and Treatment, pages 285–322. Washington, USA.
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., and Jemal, A. (2018). Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6):394–424.
Gonzalez, R. C. and Woods, R. E. (2012). Digital Image Processing. Pearson, London, England.
Jorge-Hernandez, F., Chimeno, Y. G., Garcia-Zapirain, B., Zubizarreta, A. C., Beldarrain, M. A. G., and Fernandez-Ruanova, B. (2014). Graph theory for feature extraction and classification: A migraine pathology case study. Bio-Medical Materials and Engineering, 24(6):2979–2986.
Luna, J. and Delgado, F. (2010). Feasibility of new-generation infrared imaging screening for breast cancer in rural communities. US Obstetrics & Gynecology, 1:52–55.
Parshionikar, S. et al. (2025). Breast cancer detection using optimized hyperbolic graph attention with bidirectional convolutional neural network. Discover Oncology, 16.
Pizer, S. M., Johnston, R. E., Ericksen, J. P., Yankaskas, B. C., and Muller, K. E. (1990). Contrast-limited adaptive histogram equalization: speed and effectiveness. In Proceedings of the First Conference on Visualization in Biomedical Computing, pages 337–345, Nova Jersey, EUA. IEEE.
Sattarov, A., McIntyre, P., and Motowidlo, L. (2015). High-field open mri for breast cancer screening. IEEE Transactions on Applied Superconductivity, 25(3):1–5.
Silva, L. F., Saade, D. C. M., Sequeiros, G. O., Silva, A. C., Paiva, A. C., Bravo, R. S., and Conci, A. (2014). A new database for breast research with infrared image. Journal of Medical Imaging and Health Informatics, 4(1):92–100.
Usuki, H., Ikeda, T., Igarashi, Y., Takahashi, I., Fukami, A., Yokoe, T., Sonoo, H., and Asaishi, K. (1998). What kinds of non-palpable breast cancer can be detected by thermography? Biomed Thermology, 18(4):8–12.
Veerlapalli, P. and Das, K. (2024). Roisegnet: A deep learning framework for automatic segmentation of region of interest from breast thermogram imagery. International Journal of Intelligent Systems and Applications in Engineering, 12(3):2248–2261.
Veerlapalli, P. and Dutta, S. R. (2025). A hybrid gan-based deep learning framework for thermogram-based breast cancer detection. Scientific Reports, 15(19665).
Wang, L., Bohler, T., Zohrer, F., Georgii, J., Rauh, C., Fasching, P., Brehm, B., Schulz-Wendtland, R., Beckmann, M., Uder, M., and Hahn, H. (2014). A hybrid method towards automated nipple detection in 3d breast ultrasound images. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 2869–2872, Chicago, Illinois, EUA. IEEE EMBC.
Wishart, G., Campisi, M., Boswell, M., Chapman, D., Shackleton, V., Iddles, S., Hallett, A., and Britton, P. (2010). The accuracy of digital infrared imaging for breast cancer detection in women undergoing breast biopsy. European Journal of Surgical Oncology, 36(6):535–540.
World Health Organization (2026). Global health observatory database: Cancer. disponível em: [link]. Acesso em: 25 maio 2026.
Yasmin, M., Sharif, M., and Mohsin, S. (2013). Survey paper on diagnosis of breast cancer using image processing techniques. Research Journal of Recent Sciences, 2(10):88–98.
Publicado
01/06/2026
Como Citar
PONTES, Stefano W. P.; BORCHARTT, Tiago B.; CLÍMACO, Francisco G. N.; QUINTANILHA, Darlan B. P..
Modelagem baseada em grafo para detecção de regiões anômalas em termografias da mama. In: WORKSHOP DE TERMOGRAFIA COMPUTACIONAL E IA PARA APLICAÇÕES MÉDICAS - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 534-545.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2026.26544.
