Before a radiographic image is sent to a picture archiving and communications system (PACS), its projection
information needs to be correctly identified at capture modalities to facilitate image archive and retrieval. Currently,
annotating radiographic images is manually performed by technologists. It is labor intensive and cost ineffective.
Moreover, man-made annotation errors occur frequently during image acquisition. To address this issue, an automatic
image recognition method is developed. It first extracts a set of visual features from the most indicative region in a
radiograph for image recognition, and then uses a family of classifiers, each of which is trained for a specific projection
to determine the most appropriate projection for the image. The method has been tested on a large number of clinical
images and has shown excellent robustness and efficiency.