title: Automated recognition of lateral from PA chest radiographs: saving seconds in a PACS environment.
abstract: Images acquired in a two-view digital chest examination are frequently not electronically distinguishable. As a result the lateral and posterioanterio (PA) images are often improperly positioned on a PACS work station. A series of 1998 chest radiographs (999 lateral, 999 PA or AP) were used to develop a neural network classifier. The images were down-sampled to 16 x 16 matrices, and a feed-forward neural network was trained and tested using the "leave-one-out" method. Using five nodes in the hidden layer, the neural network correctly identified 987 of the 999 test cases (98.8%) (average of six runs). The simple architecture and speed of this technique suggests that it would be a useful addition to PACS work station software. The accumulated time saved by correctly positioning the lateral and PA chest images on the work station monitors in accordance with each radiologist's hanging protocols was estimated to be about 1 week of radiologist time per year.
authors.affiliation: Department of Radiology, University of California-Davis Medical Center, Sacramento, CA 95817, USA. jmboone@ucdavis.edu
authors.forename: John M
authors.initials: JM
authors.lastname: Boone
authors.forename: Greg S
authors.initials: GS
authors.lastname: Hurlock
authors.forename: J Anthony
authors.initials: JA
authors.lastname: Seibert
authors.forename: Richard L
authors.initials: RL
authors.lastname: Kennedy
mesh_terms: D001330:Electronic Data Processing; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008482:Mediastinum / Q000000981:diagnostic imaging; D009415:Nerve Net; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D013902:Radiography, Thoracic; D011873:Radiology Information Systems; D012680:Sensitivity and Specificity; D013995:Time