Improved automatic exposure control using morphology-based disturbance recognition

In medical X-ray imaging, the detector intensity heavily influences the signal-to-noise ratio, and thus the image quality [1]. Consequently, image quality and patient dose are dependent on the performance of the Automatic Exposure Control. Introducing large opaque objects to the image, which can be considered disturbances for the dose control, leads to a loss of image quality (overexposed tissue) as well as an increased patient dose. The effect of scatter-radiation makes it difficult to exclude these disturbances from the image using simple thresholding. In this work, a morphology-based filter is proposed as a pre-processing step for the Automatic Exposure Control leading to a superior disturbance exclusion. The algorithm has been verified in a real-time environment and it is shown to be robust against large disturbances in the X-ray images.