naa a22 4500 510808298 CHVBK 20180411083428.0 cr unu---uuuuu 180411e20130301xx s 000 0 eng 10.1007/s11548-012-0767-5 doi (NATIONALLICENCE)springer-10.1007/s11548-012-0767-5 Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter [Elektronische Daten] [Atsushi Teramoto, Hiroshi Fujita] Purpose: Existing computer-aided detection schemes for lung nodule detection require a large number of calculations and tens of minutes per case; there is a large gap between image acquisition time and nodule detection time. In this study, we propose a fast detection scheme of lung nodule in chest CT images using cylindrical nodule-enhancement filter with the aim of improving the workflow for diagnosis in CT examinations. Methods: Proposed detection scheme involves segmentation of the lung region, preprocessing, nodule enhancement, further segmentation, and false-positive (FP) reduction. As a nodule enhancement, our method employs a cylindrical shape filter to reduce the number of calculations. False positives (FPs) in nodule candidates are reduced using support vector machine and seven types of characteristic parameters. Results: The detection performance and speed were evaluated experimentally using Lung Image Database Consortium publicly available image database. A 5-fold cross-validation result demonstrates that our method correctly detects 80% of nodules with 4.2 FPs per case, and detection speed of proposed method is also 4-36 times faster than existing methods. Conclusion: Detection performance and speed indicate that our method may be useful for fast detection of lung nodules in CT images. CARS, 2012 Computer-aided detection (CAD) nationallicence Lung nationallicence Nodule nationallicence Computed tomography (CT) nationallicence Image processing nationallicence Fast detection nationallicence CAD : Computer-aided detection nationallicence CNEF : Cylindrical nodule-enhancement filter nationallicence C-SVC : C-support vector classification nationallicence CT : Computed tomography nationallicence FROC : Free-response receiver operating characteristic nationallicence GGO : Ground glass opacity nationallicence LIDC : Lung image database consortium nationallicence MIP : Maximum intensity projection nationallicence PET : Positron emission tomography nationallicence SVM : Support vector machine nationallicence Teramoto Atsushi Faculty of Radiological Technology, School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, 470-1192, Toyoake-city, Aichi, Japan aut Fujita Hiroshi Department of Intelligent Image Information, Graduate School of Medicine Gifu University, 1-1 Yanagido, 501-1194, Gifu-city, Gifu, Japan aut International Journal of Computer Assisted Radiology and Surgery Springer-Verlag 8/2(2013-03-01), 193-205 1861-6410 8:2<193 2013 8 11548 https://doi.org/10.1007/s11548-012-0767-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-012-0767-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Teramoto Atsushi Faculty of Radiological Technology, School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, 470-1192, Toyoake-city, Aichi, Japan aut NATIONALLICENCE 700 1- Fujita Hiroshi Department of Intelligent Image Information, Graduate School of Medicine Gifu University, 1-1 Yanagido, 501-1194, Gifu-city, Gifu, Japan aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer-Verlag 8/2(2013-03-01), 193-205 1861-6410 8:2<193 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer