OBJECTIVE: We developed a digital image database (www.macnet.or.jp/jsrt2/cdrom_nodules.html ) of 247 chest radiographs with and without a lung nodule. The aim of this study was to investigate the characteristics of image databases for potential use in various digital image research projects. Radiologists' detection of solitary pulmonary nodules included in the database was evaluated using a receiver operating characteristic (ROC) analysis. MATERIALS AND METHODS: One hundred and fifty-four conventional chest radiographs with a lung nodule and 93 radiographs without a nodule were selected from 14 medical centers and were digitized by a laser digitizer with a 2048 x 2048 matrix size (0.175-mm pixels) and a 12-bit gray scale. Lung nodule images were classified into five groups according to the degrees of subtlety shown. The observations of 20 participating radiologists were subjected to ROC analysis for detecting solitary pulmonary nodules. Experimental results (areas under the curve, Az) obtained from observer studies were used for characterization of five groups of lung nodules with different degrees of subtlety. RESULTS: ROC analysis showed that the database included a wide range of various nodules yielding Az values from 0.574 to 0.991 for the five categories of cases for different degrees of subtlety. CONCLUSION: This database can be useful for many purposes, including research, education, quality assurance, and other demonstrations.
OBJECTIVE: We developed a digital image database (www.macnet.or.jp/jsrt2/cdrom_nodules.html ) of 247 chest radiographs with and without a lung nodule. The aim of this study was to investigate the characteristics of image databases for potential use in various digital image research projects. Radiologists' detection of solitary pulmonary nodules included in the database was evaluated using a receiver operating characteristic (ROC) analysis. MATERIALS AND METHODS: One hundred and fifty-four conventional chest radiographs with a lung nodule and 93 radiographs without a nodule were selected from 14 medical centers and were digitized by a laser digitizer with a 2048 x 2048 matrix size (0.175-mm pixels) and a 12-bit gray scale. Lung nodule images were classified into five groups according to the degrees of subtlety shown. The observations of 20 participating radiologists were subjected to ROC analysis for detecting solitary pulmonary nodules. Experimental results (areas under the curve, Az) obtained from observer studies were used for characterization of five groups of lung nodules with different degrees of subtlety. RESULTS: ROC analysis showed that the database included a wide range of various nodules yielding Az values from 0.574 to 0.991 for the five categories of cases for different degrees of subtlety. CONCLUSION: This database can be useful for many purposes, including research, education, quality assurance, and other demonstrations.
Authors: Jens von Berg; Stewart Young; Heike Carolus; Robin Wolz; Axel Saalbach; Alberto Hidalgo; Ana Giménez; Tomás Franquet Journal: Int J Comput Assist Radiol Surg Date: 2015-09-04 Impact factor: 2.924