title: Effect of multiscale processing in digital chest radiography on automated detection of lung nodule with a computer assistance system.
abstract: The aim of this study is to evaluate the effect of multiscale processing in digital chest radiography on automated detection of lung nodule with a computer-aided diagnosis (CAD) system. The study involved 58 small-nodule patient cases and 58 normal cases. The 58 patient cases included a total of 64 noncalcified lung nodules up to 15 mm in diameter. Each case underwent an examination with a digital radiography system (Digital Diagnost, Philips Medical Systems), and the acquired image was processed by the following three types of multiscale processing (Unique Image Processing Package, Philips Medical Systems) respectively: (1) standard image from the default processing parameter (structure preference, 0.0), (2) high-pass image with structure preference of 0.4, (3) low-pass image with structure preference of -0.4. The CAD output images were produced with a real-time computer assistance system (IQQAtrade mark-Chest, EDDA Technology). Two experienced chest radiologists established the nodule gold standard by consensus reading according to computed tomography results, and analyzed and recorded the detection of lung nodules and false-positive detections of these CAD output images. For the entire cases involved (each case with three types of different processing), a total of 348 observations were evaluated by the receiver operating characteristic (ROC) analysis. The mean area under the ROC curve (A ( z )) value was 0.700 for the standard images, 0.587 for the high-pass images, and 0.783 for the low-pass images. There were statistically significant A (z) values among these three types of processed images (p < 0.01). Multiscale processing in digital chest radiography can affect the automated detection of lung nodule by CAD, which is consistent with effects from visual inspection.
authors.affiliation: Department of Radiology, Beijing Friendship Hospital-Affiliated Capital Medical University, XuanWu District, Beijing, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: He
authors.forename: Wen
authors.initials: W
authors.lastname: He
authors.forename: Keyang
authors.initials: K
authors.lastname: Wang
authors.forename: Daqing
authors.initials: D
authors.lastname: Ma
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D011856:Radiographic Image Enhancement; D013902:Radiography, Thoracic / Q000379:methods; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*