Nodule Detection In A Lung Region That's Segmented With Using Genetic Cellular Neural Networks And 3D Template Matching With Fuzzy Rule Based Thresholding

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dc.authoridTR29371en_US
dc.authoridTR13219en_US
dc.authoridTR26113en_US
dc.contributor.authorÖzekes, Serhat
dc.contributor.authorOsman, Onur
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2015-09-14T14:12:38Z
dc.date.available2015-09-14T14:12:38Z
dc.date.issued2008en_US
dc.departmentFakülteler, Mühendislik ve Tasarım Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractObjective: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods: Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. Results: The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Conclusion: Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computeraided detection of lung nodules.en_US
dc.identifier.doi10.3348/kjr.2008.9.1.1en_US
dc.identifier.endpage9en_US
dc.identifier.issn1229-6929
dc.identifier.issue1en_US
dc.identifier.pmid18253070en_US
dc.identifier.scopus2-s2.0-39449121298en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/11467/1188
dc.identifier.urihttp://dx.doi.org/10.3348/kjr.2008.9.1.1
dc.identifier.volume9en_US
dc.identifier.wosWOS:000253367600001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherKorean Radiological Soc.en_US
dc.relation.ispartofKorean Journal Of Radiologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectComputer Aided Lung Nodule Detection; ROI Specification; Genetic Algorithm; Cellular Neural Networks Fuzzy Logic; 3D Template Matching.en_US
dc.titleNodule Detection In A Lung Region That's Segmented With Using Genetic Cellular Neural Networks And 3D Template Matching With Fuzzy Rule Based Thresholdingen_US
dc.typeArticleen_US

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