HqRF: Novos Descritores de Forma para Auxiliar no Diagnóstico de Câncer de Mama
Resumo
Tumores malignos e lesões benignas aparecem em exames radiológicos com diferentes características: os primeiros geralmente apresentam contornos grosseiros, espiculados ou microlobulados, enquanto os últimos comumente têm contornos suaves, arredondados, ovalados ou macrolobulados. Características visuais que descrevem a aspereza da forma podem auxiliar na distinção entre os tipos de tumor. Neste trabalho são propostos novos métodos para descrever formas 2D e 3D, baseados na curva de Hilbert. Os descritores são aplicados para a recuperação de imagens/volumes por conteúdo. O método foi avaliado usando uma base de dados de contornos de mama e uma base de dados sintética com objetos 3D. A avaliação dos experimentos mostram uma precisão média de 1.00 (para formas 2D) e 0.99 (para formas 3D).Referências
Alto, H., Rangayyan, R., and Desautels, J. (2005). Content-based retrieval and analysis of mammographic masses. Journal of Electronic Imaging.
American College of Radiology, Reston, VA (1998). Illustrated breast imaging reporting and data system BI-RADSTM ).
Armstrong, J., Ahmed, M., and Chau, S.-C. (2009). A rotation-invariant approach to 2d shape representation using the Hilbert curve. Image Analysis and Recognition, 5627:594–603.
Barcelos, C., Ribeiro, E., and Batista, M. (2008). Image characterization via multilayer neural networks. International Conference on Tools with Artificial Intelligence, pages 325–332.
Chen, W., Giger, M., and Bick, U. (2006). A fuzzy c-means (fcm)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced mr images. Acad. Radiol., 13(1):63–72.
Croft, W., Metzler, D., and Strohman, T. (2009). Search Engines: Information Retrieval in Practice. Addison Wesley.
Ebrahim, Y., Ahmed, M., Abdelsalam, W., and Chau, S.-C. (2008). Shape representation and description using Hilbert curve. Pattern Recognition Letters.
Guliato, D., de Oliveira, W., and Jr., C. T. (2010). A new feature descriptor derived from Hilbert space-filling curve to assist breast cancer classification. 1:303–308.
Guliato, D., Rangayyan, R., Carvalho, J., and Santiago, S. (2008a). Polygonal modeling of contours of breast tumors with the preservation of spicules. IEEE Trans Biomed Eng.
Guliato, D., Rangayyan, R., de Carvalho, J., and Santiago, S. (2008b). Feature extraction from the turning angle function for the classification of contours of breast tumors. J. Digit Imaging.
Gupta, S., P.F., C., and Markey, M. (2006). Breast cancer cadx based on BI RADSTM descriptors from two mammographic views. Med. Phys., 33(6):1810–1817.
Heath, M., Bowyer, K., and Kopans, D. (1998). Current status of the digital database for screening mammography.
Manning, C., Raghavan, P., and Schütze, H. (2009). An Introduction to Information Retrieval. Cambridge University Press.
Prusinkiewicz, P. and Lindenmayer, A. (2004). The Algorithmic Beauty of Plants. Springer-Verlag.
Rangayyan, R., El-Faramawy, N., Desautels, J., and Alim, O. (1997). Measures of acutance and shape for classification of breast tumors. IEEE Transactions on Medical Imaging.
Rangayyan, R., Mudigonda, N., and Desautels, J. (2000). Boundary modelling and shape analysis methods for classification of mammographic masses. Medical and Biological Engineering and Computing.
R.G.-Caballero, C.J.G.-Orellana, H.M.G.-Velasco, and M.M.-Macías (2007). Independent component analysis applied to detection of early breast cancer signs. Computational and Ambient Intelligence.
Shilane, P., Min, P., Kazhdan, M., and Funkhouser, T. (2004). The princeton shape benchmark. Shape Modeling International.
Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., Taylor, P., Betal, D., and Savage, J. (1994). The mammographic image analysis society digital mammogram database.
Turnbull, L., Brown, S., Harvey, I., Olivier, C., Drew, P., Napp, V., Hanby, A., and Brown, J. (2010). Comparative effectiveness of mri in breast cancer (comice) trial: a randomised controlled trial. The Lancet, 375(9714):563–571.
Weiqiang, Z., Xiangmin, X., and Wei, H. (2008). Shape and boundary analysis for classification of breast masses. International Symposium on Computational Intelligence and Design.
WHO (2010). Breast cancer: prevention and control.
American College of Radiology, Reston, VA (1998). Illustrated breast imaging reporting and data system BI-RADSTM ).
Armstrong, J., Ahmed, M., and Chau, S.-C. (2009). A rotation-invariant approach to 2d shape representation using the Hilbert curve. Image Analysis and Recognition, 5627:594–603.
Barcelos, C., Ribeiro, E., and Batista, M. (2008). Image characterization via multilayer neural networks. International Conference on Tools with Artificial Intelligence, pages 325–332.
Chen, W., Giger, M., and Bick, U. (2006). A fuzzy c-means (fcm)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced mr images. Acad. Radiol., 13(1):63–72.
Croft, W., Metzler, D., and Strohman, T. (2009). Search Engines: Information Retrieval in Practice. Addison Wesley.
Ebrahim, Y., Ahmed, M., Abdelsalam, W., and Chau, S.-C. (2008). Shape representation and description using Hilbert curve. Pattern Recognition Letters.
Guliato, D., de Oliveira, W., and Jr., C. T. (2010). A new feature descriptor derived from Hilbert space-filling curve to assist breast cancer classification. 1:303–308.
Guliato, D., Rangayyan, R., Carvalho, J., and Santiago, S. (2008a). Polygonal modeling of contours of breast tumors with the preservation of spicules. IEEE Trans Biomed Eng.
Guliato, D., Rangayyan, R., de Carvalho, J., and Santiago, S. (2008b). Feature extraction from the turning angle function for the classification of contours of breast tumors. J. Digit Imaging.
Gupta, S., P.F., C., and Markey, M. (2006). Breast cancer cadx based on BI RADSTM descriptors from two mammographic views. Med. Phys., 33(6):1810–1817.
Heath, M., Bowyer, K., and Kopans, D. (1998). Current status of the digital database for screening mammography.
Manning, C., Raghavan, P., and Schütze, H. (2009). An Introduction to Information Retrieval. Cambridge University Press.
Prusinkiewicz, P. and Lindenmayer, A. (2004). The Algorithmic Beauty of Plants. Springer-Verlag.
Rangayyan, R., El-Faramawy, N., Desautels, J., and Alim, O. (1997). Measures of acutance and shape for classification of breast tumors. IEEE Transactions on Medical Imaging.
Rangayyan, R., Mudigonda, N., and Desautels, J. (2000). Boundary modelling and shape analysis methods for classification of mammographic masses. Medical and Biological Engineering and Computing.
R.G.-Caballero, C.J.G.-Orellana, H.M.G.-Velasco, and M.M.-Macías (2007). Independent component analysis applied to detection of early breast cancer signs. Computational and Ambient Intelligence.
Shilane, P., Min, P., Kazhdan, M., and Funkhouser, T. (2004). The princeton shape benchmark. Shape Modeling International.
Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., Taylor, P., Betal, D., and Savage, J. (1994). The mammographic image analysis society digital mammogram database.
Turnbull, L., Brown, S., Harvey, I., Olivier, C., Drew, P., Napp, V., Hanby, A., and Brown, J. (2010). Comparative effectiveness of mri in breast cancer (comice) trial: a randomised controlled trial. The Lancet, 375(9714):563–571.
Weiqiang, Z., Xiangmin, X., and Wei, H. (2008). Shape and boundary analysis for classification of breast masses. International Symposium on Computational Intelligence and Design.
WHO (2010). Breast cancer: prevention and control.
Publicado
19/07/2011
Como Citar
OLIVEIRA, Walter Alexandre A. de; GULIATO, Denise.
HqRF: Novos Descritores de Forma para Auxiliar no Diagnóstico de Câncer de Mama. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 11. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 1840-1849.
ISSN 2763-8952.
