A patient image-based technique to assess the image quality of clinical chest radiographs

Current clinical image quality assessment techniques mainly analyze image quality for the imaging system in terms of factors such as the capture system DQE and MTF, the exposure technique, and the particular image processing method and processing parameters. However, when assessing a clinical image, radiologists seldom refer to these factors, but rather examine several specific regions of the image to see whether the image is suitable for diagnosis. In this work, we developed a new strategy to learn and simulate radiologists' evaluation process on actual clinical chest images. Based on this strategy, a preliminary study was conducted on 254 digital chest radiographs (38 AP without grids, 35 AP with 6:1 ratio grids and 151 PA with 10:1 ratio grids). First, ten regional based perceptual qualities were summarized through an observer study. Each quality was characterized in terms of a physical quantity measured from the image, and as a first step, the three physical quantities in lung region were then implemented algorithmically. A pilot observer study was performed to verify the correlation between image perceptual qualities and physical quantitative qualities. The results demonstrated that our regional based metrics have promising performance for grading perceptual properties of chest radiographs.