Robust online orientation correction for radiographs in PACS environments

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