3D lung nodule candidate detection in multiple scales

dc.contributor.author Novo,J en
dc.contributor.author Goncalves,L en
dc.contributor.author Ana Maria Mendonça en
dc.contributor.author Aurélio Campilho en
dc.date.accessioned 2018-01-06T17:00:23Z
dc.date.available 2018-01-06T17:00:23Z
dc.date.issued 2015 en
dc.description.abstract Lung cancer is mainly diagnosed by the identification of malignant nodules in the lung parenchyma. For that purpose, the identification of all the possible structures that could be suspicious of lung nodules became a crucial task in any lung cancer computer aided diagnosis (CAD) system. In this paper, a new approach for lung nodule candidate identification is proposed. This method uses a 3D medialness Hessian-based filtering to identify round shape structures that could be identified as nodules. This technique, that demonstrated its accuracy in lung vesselness extraction, provides clearer candidates than other approaches, providing less response in the presence of noise artifacts and returns a better continuity in vessels, mostly responsible for false positives. That way, they will be better distinguishable from the nodules in posterior analysis. This approach was validated in 120 scans from the LIDC/IDRI image database. They include 212 nodules with diameters in the range 3 mm to 30 mm. The results demonstrate that our approach is capable of identifying most of the nodules and include less false positives than other approaches, facilitating a posterior task for false positive removal. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/5664
dc.identifier.uri http://dx.doi.org/10.1109/MVA.2015.7153133 en
dc.language eng en
dc.relation 6381 en
dc.relation 6071 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title 3D lung nodule candidate detection in multiple scales en
dc.type conferenceObject en
dc.type Publication en
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