In this paper, a Hessian-based strategy, based on the central medialness adaptive principle, was adapted and proposed in a multiscale approach for the 3D segmentation of pulmonary nodules in chest CT scans. This proposal is compared with another well stated Hessian based strategy of the literature, for nodule extraction, in order to demonstrate its accuracy.
Several scans from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were employed in the test and validation procedure. The scans include a large and heterogeneous set of 569 solid and mostly solid nodules with a large variability in the nodule characteristics and image conditions. The results demonstrated that the proposal offers correct results, similar to the performance of the radiologists, providing accurate nodule segmentations that perform the desirable scenario for a posterior analysis and the eventual lung cancer diagnosis.
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This work is financed by project NORTE-01-0145-FEDER-000016 by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF); and through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the grant contract SFRH/BPD/85663/2012 (J. Novo).
Greenlee, R.T., Murray, T., Bolden, S., Wingo, P.A.: Cancer statistics. CA Cancer J. Clin. 2000(50), 7–33 (2000)CrossRefGoogle Scholar
Setio, A.A.A., Jacobs, C., Gelderblom, J., van Ginneken, B.: Automatic detection of large pulmonary solid nodules in thoracic CT images. Med. Phys. 42(10), 5642–5653 (2015)CrossRefGoogle Scholar
Gu, Y., Kumar, V., Hall, L.O., Goldgof, D.B., et al.: Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Pattern Recogn. 46(3), 692–702 (2013)CrossRefGoogle Scholar
Zhang, X., Zhang, C., Tang, W., Wei, Z.: Medical image segmentation using improved fcm. Sci. China Inf. Sci. 55(5), 1052–1061 (2012)MathSciNetCrossRefGoogle Scholar
Qiang, Y., Zhang, X., Ji, G., Zhao, J.: Measuring agreement in method comparison studies. J. Comput. Theoret. Nanosci. 12, 1972–1976 (2015)CrossRefGoogle Scholar
Chen, B., Kitasaka, T., Honma, H., Takabatake, H., et al.: Automatic segmentation of pulmonary blood vessels and nodules basedon local intensity structure analysis and surface propagation in 3D chest CT images. Int. J. Comput. Assist. Radiol. Surg. 2010, 465–482 (2012)CrossRefGoogle Scholar
Zhao, J., Ji, G., Xia, Y., Zhang, X.: Cavitary nodule segmentation in computed tomography images based on self-generating neural networks and particle swarm optimisation. Int. J. Bio-Inspired Comput. 7(1), 62–67 (2015)CrossRefGoogle Scholar
Murphy, K., van Ginneken, B., Schilham, A.M.R., et al.: A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med. Image Anal. 13, 757–770 (2009)CrossRefGoogle Scholar
Novo, J., Gonçalves, L., Mendonça, A.M., Campilho, A.: 3D lung nodule candidates detection in multiple scales. In: MVA 2015-IAPR International Conference on Machine Vision Applications, pp. 61–64 (2015)Google Scholar
Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model-based detection of tubular structures in 3d images. Comput. Vis. Image Underst. 80(2), 130–171 (2000)CrossRefMATHGoogle Scholar
Rudyanto, R.D., Kerkstra, S., van Rikxoort, E.M., et al.: Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Med. Image Anal. 18(7), 1217–1232 (2014)CrossRefGoogle Scholar
Armato, S.G., McLennan, G., Bidaut, L., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38, 915–931 (2011)CrossRefGoogle Scholar