title: Robust online orientation correction for radiographs in PACS environments.
abstract: In picture archiving and communications systems (PACS), images need to be displayed in standardized ways for radiologists' interpretations. However, for most radiographs acquired via computed radiography (CR), digital radiography (DR), or digitized films, the image orientation is undetermined because of the variations in examination conditions and patients' situations. To address this problem, an automatic orientation correction method is developed. It first detects the most indicative region in a radiograph for image orientation, and then extracts a set of low-level visual features from the region. Based on these features, a well-trained classifier, using support vector machines, is employed to recognize the correct orientation of the radiograph and reorient it to the desired position. A large-scale experiment was conducted on more than 12 000 radiographs, which covered a wide variety of exam types, to validate the method. The overall success rate of orientation correction was 96.1%. A workflow study on the method also demonstrated a significant improvement in efficiency for image display. To our knowledge, this work represents the first robust system designed to handle all radiographic exam types using a unified framework instead of using dedicated strategies for different exam types.
authors.affiliation: Kodak Health Imaging Research Laboratory, Rochester, NY 14650, USA. hui.luo@kodak.com
authors.forename: Hui
authors.initials: H
authors.lastname: Luo
authors.forename: Jiebo
authors.initials: J
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011859:Radiography / Q000379:methods*; D011873:Radiology Information Systems; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity