Atsushi Teramoto1, Hiroshi Fujita. 1. Faculty of Radiological Technology, School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake-city, Aichi 470-1192, Japan. teramoto@fujita-hu.ac.jp
Abstract
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.
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.
Authors: Samuel G Armato; Rachael Y Roberts; Michael F McNitt-Gray; Charles R Meyer; Anthony P Reeves; Geoffrey McLennan; Roger M Engelmann; Peyton H Bland; Denise R Aberle; Ella A Kazerooni; Heber MacMahon; Edwin J R van Beek; David Yankelevitz; Barbara Y Croft; Laurence P Clarke Journal: Acad Radiol Date: 2007-12 Impact factor: 3.173
Authors: Michael F McNitt-Gray; Samuel G Armato; Charles R Meyer; Anthony P Reeves; Geoffrey McLennan; Richie C Pais; John Freymann; Matthew S Brown; Roger M Engelmann; Peyton H Bland; Gary E Laderach; Chris Piker; Junfeng Guo; Zaid Towfic; David P-Y Qing; David F Yankelevitz; Denise R Aberle; Edwin J R van Beek; Heber MacMahon; Ella A Kazerooni; Barbara Y Croft; Laurence P Clarke Journal: Acad Radiol Date: 2007-12 Impact factor: 3.173