An image quality assessment index based on image features and keypoints for X-ray CT images.

ABSTRACT: Optimization tasks in diagnostic radiological imaging require objective quantitative metrics that correlate with the subjective perception of observers. However, although one such metric, the structural similarity index (SSIM), is popular, it has limitations across various aspects in its application to medical images. In this study, we introduce a novel image quality evaluation approach based on keypoints and their associated unique image feature values, focusing on developing a framework to address the need for robustness and interpretability that are lacking in conventional methodologies. The proposed index quantifies and visualizes the distance between feature vectors associated with keypoints, which varies depending on changes in the image quality. This metric was validated on images w

SUBMITTER: Maruyama S 

PROVIDER: S-EPMC11238976 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature