Modelagem 3D de imagens tomográficas de nódulos pulmonares para auxílio ao diagnóstico
Abstract
The lung cancer is the world cancer death leader, also, is the leader in malignant form appearance. The computer assisted diagnosis (CAD) is a powerful tool for lung images medical analysis that can support the early detection, essential for diagnosis process. The computed tomography (CT) obtains two and three-dimensional images with high quality that can be used in lung cancer diagnosis. Nevertheless, the lung nodule segmentation is a complex step in the CAD process due to the lung’s or the nodule structure. In this work we present different approaches for lung tomographic image segmentation using simple characteristics as area and grayscale. For testing our methodology, the Lung Image Database (LIDC-IDRI) was used and we obtain good results.
References
Ferreira Junior, J. R., Oliveira, M. C., and de Azevedo-Marques, P. M. (2016). Cloud- Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research. Journal of Digital Imaging, 29(6):716–729.
Fricke, T. (2004). The hoshen-kopelman algorithm. Acesso: 2018-06-26.
Gallagher, J. (2017). Novo tratamento contra câncer de pulm˜ao pode dobrar sobrevida de pacientes , diz estudo. pages 1–6.
Gonzales, R. and Woods, R. (2010). Processamento Digitais de Imagens.
Jayanti, S. and Tarjan, R. (2016). A randomized concurrent algorithm for disjoint set union.
Kittler, J. and Illingworth, J. (1986). Minimum error thresholding. Pattern Recognition, 19(1):41–47.
Li, C. H. and Tam, P. K. (1998). An iterative algorithm for minimum cross entropy thresholding. Pattern Recognition Letters, 19(8):771–776.
MENDONC¸ A, R. S. (2016). Caraterizac¸ ˜ao de Argamassas Leves Usando Processamento Tridimensional de Imagens e Processamento Paralelo. Master’s thesis, UNIVERSIDADE ESTADUAL DE SANTA CRUZ.
Nithila, E. E. and Kumar, S. S. (2016). Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clustering. Alexandria Engineering Journal, 55(3):2583–2588.
Smith, P., Reid, D. B., Environment, C., Palo, L., Alto, P., and Smith, P. L. (1979). A Threshold Selection Method from Gray-Level Histograms. C(1):62–66.
