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

## TL;DR

This paper introduces a new image quality assessment method for X-ray CT images using keypoints and features, offering better robustness and interpretability than existing metrics.

## Contribution

A novel image quality index based on keypoint features that improves robustness and interpretability for medical imaging.

## Key findings

- The proposed index showed strong correlation with SSIM while outperforming conventional metrics in robustness.
- Feature descriptor distances increased with image quality degradation in X-ray CT phantom tests.
- The method effectively visualizes lost feature information due to image quality changes.

## 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 with varying noise levels and resolution characteristics, and its applicability and effectiveness were examined by evaluating images subjected to various affine transformations. In the verification of X-ray computed tomography imaging using a head phantom, the distances between feature descriptors for each keypoint increased as the image quality degraded, exhibiting a strong correlation with the changes in the SSIM. Notably, the proposed index outperformed conventional full-reference metrics in terms of robustness to various transformations which are without changes in the image quality. Overall, the results suggested that image analysis performed using the proposed framework could effectively visualize the corresponding feature points, potentially harnessing lost feature information owing to changes in the image quality. These findings demonstrate the feasibility of applying the novel index to analyze changes in the image quality. This method may overcome limitations inherent in conventional evaluation methodologies and contribute to medical image analysis in the broader domain.