Investigation of medical image noise reduction methods for clinical applications based on subjective evaluation

Abstract

In order to properly interpret medical images, a great deal of experience is required in addition to specialized knowledge. However, the noise generated by the limitations of examination equipment and other factors has made their interpretation difficult. Although various denoising methods using deep learning have been proposed, it is not always clear which denoising method is appropriate for medical image interpretation by a specialist. In this study, we investigated denoising methods suitable for medical image interpretation through evaluation experiments on four kinds of movies: echocardiography (gray scale and color), coronary angiography (gray scale), and in-vehicle videos in a city (gray scale).The videos using DnCNN, PPN2V, and Real ESRGAN, which are denoising methods based on deep learning, and the original videos were ranked by five cardiologists. Real ESRGAN was stably rated higher than the original images except coronary angiography. The other methods showed equal or slightly inferior results when compared to the original movie. This suggests that as for medical images a combination of Real ESRGAN and a denoising method to preserve the edges and structure of the objects will enable better interpretation support.