Image feature index: A novel metric for quantifying chest radiographic image quality

Abstract Background Radiographic X‐ray imaging is a common clinical examination. Current objective methods for quantifying image quality for radiographs struggle to capture the combined impact of factors throughout the imaging chain on the perceived image quality. Therefore, there is a need to further develop metrics that correlate with image quality as perceived by the observer. Objectives We proposed the image feature index (IFI) to comprehensively quantify radiographic X‐ray image quality. We also aimed to study the correlation between IFI and observer‐perceived image quality for chest radiographs. Materials and methods The IFI algorithm was developed, which measured the amount of information, textural features, and noise in the image. A total of 70 chest phantom radiographs were generated under 60–120 kV and 0.2–80 mAs. A vendor‐proprietary exposure index (EI) and dose area product (DAP) were extracted from the DICOM header in addition to calculating IFI for each image to investigate the relationships between IFI, EI and DAP. The quality of the images was rated by three observers, and the correlation between IFI and subjective score of image quality was tested. Next, a retrospective study using a random sample of 50 clinical chest radiographs was performed, and the correlation between IFI and subjective score was tested. The correlation was determined by the Spearman test. Results The curves of IFI versus DAP and IFI versus EI both demonstrated a similar three‐stage form where IFI is above zero: in the first stage, IFI increases rapidly with increased DAP or EI, whereas in the second stage, the slope of the curves decreased towards an asymptote, that is, minimal gain in IFI with increased DAP or EI—until they hit the inflection point and then descended sharply in the third stage. For both phantom and clinical chest images, IFI demonstrated good correlation with subjective score ( r = 0.9084 for phantom images, r = 0.8153 for clinical images). Conclusions IFI is a feasible and efficient descriptor for image quality for chest radiographs. Future studies with larger sample sizes and sample types are needed to confirm the feasibility of IFI for other exam types and anatomical views, thus fulfilling and extending the potential applications of IFI in quality control and radiation dose reduction.