Abstract
The quality of a chest X-ray image or radiograph, which is widely used in clinics, is a very important factor affects doctors’ clinical decision making. Since there is no chest X-ray image quality database so far, we conduct the first study of perceptual quality assessment of chest X-ray images by introducing a Chest X-ray Image Quality Database, which contains 2,160 chest X-ray images obtained from 60 reference images. In order to simulate the real noise of X-ray images, we add different levels of Gaussian noise and Poisson noise, which are most commonly found in X-ray images. Mean opinion scores (MOS) have been collected by performing user experiments with 74 subjects (25 professional doctors and 49 non-doctors). The availability of MOS allows us to design more effective image quality metrics. We use the database to train a blind image quality assessment model based on deep neural networks, which attains better performances than conventional approaches in terms of Spearman rank-order correlation coefficient and Pearson linear correlation coefficient. The database and the deep learning models are available at https://github.com/ICT-MIRACLE-lab/CXIQ.
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Guan, M., Lyu, Y., Cao, W., Wu, X., Lu, J., Zhou, S.K. (2021). Perceptual Quality Assessment of Chest Radiograph. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_30
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