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dc.contributor.authorTulum, Gökalp
dc.contributor.authorBolat, Bülent
dc.contributor.authorOsman, Onur
dc.date.accessioned2019-07-08T11:45:04Z
dc.date.available2019-07-08T11:45:04Z
dc.date.issued2017en_US
dc.identifier.citationTulum, G., Bolat, B., & Osman, O. (2017). A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans. International Journal of Computer Assisted Radiology and Surgery, 12(4), 627-644. doi:10.1007/s11548-017-1521-9en_US
dc.identifier.issn1861-6410
dc.identifier.issn1861-6429
dc.identifier.urihttps://hdl.handle.net/20.500.12294/1558
dc.descriptionOsman, Onur (Arel Author)en_US
dc.description.abstractComputer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives. The proposed CAD system is a multistage implementation whose main components are: automatic colon segmentation, candidate detection, feature extraction and classification. The first element of the algorithm includes a discrete segmentation for both air and fluid regions. Colon-air regions were determined based on adaptive thresholding, and the volume/length measure was used to detect air regions. To extract the colon-fluid regions, a rule-based connectivity test was used to detect the regions belong to the colon. Potential polyp candidates were detected based on the 3D Laplacian of Gaussian filter. The geometrical features were used to reduce false-positive detections. A 2D projection image was generated to extract discriminative features as the inputs of an artificial neural network classifier. Our CAD system performs at 100% sensitivity for polyps larger than 9 mm, 95.83% sensitivity for polyps 6-10 mm and 85.71% sensitivity for polyps smaller than 6 mm with 5.3 false positives per dataset. Also, clinically relevant polyps (6 mm) were identified with 96.67% sensitivity at 1.12 FP/dataset.To the best of our knowledge, the novel polyp candidate detection system which determines polyp candidates with LoG filters is one of the main contributions. We also propose a new 2D projection image calculation scheme to determine the distinctive features. We believe that our CAD system is highly effective for assisting radiologist interpreting CT.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.isversionof10.1007/s11548-017-1521-9en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPolyp Detectionen_US
dc.subjectPolyp Detectionen_US
dc.subjectComputer-Aided Detectionen_US
dc.subjectComputer-Aided Detectionen_US
dc.subjectColon Segmentationen_US
dc.subjectColon Segmentationen_US
dc.subjectComputed Tomography Imagesen_US
dc.subjectComputed Tomography Imagesen_US
dc.titleA CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scansen_US
dc.typearticleen_US
dc.relation.journalInternational Journal of Computer Assisted Radiology and Surgeryen_US
dc.contributor.departmentMühendislik ve Mimarlık Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0001-7675-7999en_US
dc.identifier.volume12en_US
dc.identifier.issue4en_US
dc.identifier.startpage627en_US
dc.identifier.endpage644en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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