Segmentação Automática de Candidatos a Nódulos Pulmonares em Imagens de Tomografia Computadorizada
Abstract
This paper presents an algorithm for automatic segmentation of pulmonary nodules candidates in chest computed tomography images. The methodology includes acquisition images, noise elimination, segmentation of pulmonary parenchyma and segmentation pulmonary nodules candidates. The use of the filter wiener and the application of ideal threshold ensures to the algorithm a significant improvement in results, allowing to detect a greater number of nodules on the images. The tests were conducted using a set of images of the base LIDC-IDRI, containing 708 nodules. The test results showed that the algorithm located 93.08% of the nodules considered.
References
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Lee, S. L. A.; Kousani, A. Z.; Hu, E. J. (2012) “Automated detection of lung nodules in computed tomography images: review”, Mach.Vis.Appl.23 (1), p. 151–163.
Ma. L.; Liu, X.; Song, L.; Zhou, C.; Zhao, X., Zhao, Y. (2015) “A new classifier fusion method based on historical and on-line classification reliability for recognizing common CT imaging signs of lung diseases”, Computerized Medical Imaging and Graphics 40, p. 39-48.
Messay, T.; Hardie, R. C.; Tuinstra, T. R. (2015) “Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset”, Medical Image Analysis, p.48-62.
Netto, S. M. B; Silva, A. C; Nunes, R. A; Gattass, M. (2012) “Automatic segmentation of lung nodules with growing neural gas and support vector machine”, Computers in Biology and Medicine 42, p. 1110–1121.
Ramalho, G. L. B.; Filho, P. P. R.; Medeiros, F. N. S.; Cortez, P. C. (2014) “Lung disease detection using feature extraction and extreme learning machine”, Revista Brasileira de Engenharia Biomédica, p. 207-214.
Santos, A. M.; Filho, A. O. C.; Silva, A. C.; Paiva, A. C.; Nunes, R. A.; Gattass, M. (2014) “Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM”, Engineering Applications of Artificial Intelligence 36, p. 27-39.
Shao, H.; Cao, L.; Liu, Y. (2012) “A detection approach for solitary pulmonary nodules based on CT images”, 2nd International Conference on Computer Science and Network Technology (ICCSNT).
Shen, S.; Bui, A. A. T.; Cong, J.; Hsu, W. (2015) “An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy”, Computers in Biology Medicine 57, p. 139-149.
Vivanti, R.; Joskowicz, L.; Karaaslan, O. A.; Sosna, J. (2015) “Automatic lung tumor segmentation with leaks removal in follow-up CT studies”, Int J CARS.
Zhou, S.; Cheng, Y.; Tamura, S. (2014) “Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonar vessels on chest CT images”, Biomedical Signal Processing and Control, p. 62-70.
Carvalho Filho, A. O. de. (2013) “Detecção automática de nódulos pulmonares solitários usando quality threshold clustering e mvr”, Dissertação de Mestrado na área de Ciência da Computação, (Programa de Pós-Graduação em Engenharia de Eletricidade), Universidade Federal do Maranhão, São Luís.
Instituto Nacional do Câncer – INCA (2015), Mistério da Saúde, Câncer de pulmão, http://www2.inca.gov.br/wps/wcm/connect/tiposdecancer/site/home/pulmao/definicao, abril.
Jacobs, C.; Rikxoort, E.M.V.; Twellmann, T.; Scholten, E. Th.; Jong, P. A.; De. Kuhnigk, J. M.; Oudkerk, M.; Koning, H. J.; De Prokop, M.; Prokop, C. S.; Ginneken, B. V.(2013) “Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images”, Medical Image Analysis, p. 374-384.
Lee, S. L. A.; Kousani, A. Z.; Hu, E. J. (2012) “Automated detection of lung nodules in computed tomography images: review”, Mach.Vis.Appl.23 (1), p. 151–163.
Ma. L.; Liu, X.; Song, L.; Zhou, C.; Zhao, X., Zhao, Y. (2015) “A new classifier fusion method based on historical and on-line classification reliability for recognizing common CT imaging signs of lung diseases”, Computerized Medical Imaging and Graphics 40, p. 39-48.
Messay, T.; Hardie, R. C.; Tuinstra, T. R. (2015) “Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset”, Medical Image Analysis, p.48-62.
Netto, S. M. B; Silva, A. C; Nunes, R. A; Gattass, M. (2012) “Automatic segmentation of lung nodules with growing neural gas and support vector machine”, Computers in Biology and Medicine 42, p. 1110–1121.
Ramalho, G. L. B.; Filho, P. P. R.; Medeiros, F. N. S.; Cortez, P. C. (2014) “Lung disease detection using feature extraction and extreme learning machine”, Revista Brasileira de Engenharia Biomédica, p. 207-214.
Santos, A. M.; Filho, A. O. C.; Silva, A. C.; Paiva, A. C.; Nunes, R. A.; Gattass, M. (2014) “Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM”, Engineering Applications of Artificial Intelligence 36, p. 27-39.
Shao, H.; Cao, L.; Liu, Y. (2012) “A detection approach for solitary pulmonary nodules based on CT images”, 2nd International Conference on Computer Science and Network Technology (ICCSNT).
Shen, S.; Bui, A. A. T.; Cong, J.; Hsu, W. (2015) “An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy”, Computers in Biology Medicine 57, p. 139-149.
Vivanti, R.; Joskowicz, L.; Karaaslan, O. A.; Sosna, J. (2015) “Automatic lung tumor segmentation with leaks removal in follow-up CT studies”, Int J CARS.
Zhou, S.; Cheng, Y.; Tamura, S. (2014) “Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonar vessels on chest CT images”, Biomedical Signal Processing and Control, p. 62-70.
Published
2015-07-20
How to Cite
DE MOURA, Maria; DE SOUSA, Alcilene; DE OLIVEIRA, Ivo; MESQUITA, Laércio; DRUMOND, Patrícia.
Segmentação Automática de Candidatos a Nódulos Pulmonares em Imagens de Tomografia Computadorizada. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 15. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 131-140.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2015.10373.
