Proposta de uma solução computacional para detecção de nódulos pulmonares
Resumo
O câncer ainda é um dos principais motivos de óbitos em todo o mundo. Só o câncer de pulmão causou mais de 1 milhão de mortes recentemente. A detecção precoce aumenta a probabilidade de cura, tentando auxiliar o diagnóstico é apresentada uma proposta de solução computacional para detecção de nódulos pulmonares em imagens de Tomografia Computadorizada de tórax. A base de dados de imagens utilizada foi Lung Imaging Database Consortium. A solução proposta utiliza a segmentação da área pulmonar, segmentação e rotulação de objetos candidatos a nódulos pulmonares, extração de características empregando descritores de textura e forma e classificação dos objetos candidatos a nódulos pulmonares. Na etapa de segmentação menos de 2% dos nódulos foram perdidos e a classificação ficou com 78,44% de sensibilidade e 88,59% de acurácia média. Os resultados indicam que com alguns ajustes a técnica pode ser promissora.
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Awai, K., Murao, K., Ozawa, A., Komi, M., Hayakawa, H., Hori, S., and Nishimura, Y. (2004). Pulmonary nodules at chest ct: Effect of computer-aided diagnosis on radiologists’ detection performance. Radiology, 230(2):347–352.
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Buzug, T. (2008). From photon statistics to modern cone-beam ct. Springer-Verlag Berlin Heidelberg.
Ciompi, F., Chung, K., Riel, S., Setio, A., Gerke, P., Jacobs, C., Scholten, E., Schaefer-Prokop, C., Wille, M., Marchianò, A., Pastorino, U., Prokop, M., and Ginneken, B. (2017). Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Scientific Reports, 7(46479).
Cover, T. and Hart, P. (2006). Nearest neighbor pattern classification. IEEE Trans. Inf. Theor., 13(1):21–27.
Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. pages 886–893.
Deep, G., Kaur, L., and Gupta, S. (2016). Lung nodule retrieval by integrating local binary pattern with template matching. American Journal of Computer Science and Engineering Survey, 4(2). ACSES (033-047).
Firmino, M., Angelo, G., Morais, H., Dantas, M., and Valentim, R. (2016). Computer-aided detection (cade) and diagnosis (cadx) system for lung cancer with likelihood of malignancy. BioMedical Engineering OnLine, 15(1):2.
Gonzalez, R. and Woods, R. (2006). Digital image processing (3rd edition).
Ojala, T., Pietikäinen, M., and Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell., 24(7):971–987.
Reeves, A. and Biancardi, A. (2011). The lung image database consortium (lidc) nodule size report.
Russell, S. J. and Norvig, P. (2009). Artificial intelligence: a modern approach (3rd edition). Prentice Hall.
Stéfan van der Walt, Johannes Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu, and the scikit-image contributors (2014). scikit-image: image processing in Python. PeerJ, 2:e453.
Topi, M., Timo, O., Matti, P., and Maricor, S. (2000). Robust texture classification by subsets of local binary patterns. Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 3:935–938 vol.3.
Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc., New York, NY, USA.
WHO (2017). Cancer. [link], Acesso em 06/12/2017.
Publicado
22/07/2018
Como Citar
PEREIRA, Fernando Roberto; DE OLIVEIRA, Lucas Ferrari.
Proposta de uma solução computacional para detecção de nódulos pulmonares. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 47-57.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2018.3672.
