Denoised CT Images Quality Assessment Through COVID-19 Pneumonia Detection Task

Abstract

Medical images largely contribute to the diagnosis of lung diseases, especially pneumonia, an inflammation of lungs tissue. Since the emergence of COVID-19 in late 2019, medical imaging systems, notably computed tomography (CT) scans, have considerably helped in its diagnosis as well as revealing its infection severity. Serving as such an important role in clinical practice, the quality of medical images is therefore crucial for an accurate diagnosis. Denoising techniques, as a common image processing method, are being more and more used in medical imaging. However, how image denoising technique influences medical images' quality in terms of diagnostic performance still remains to be answered. In this paper, a primary study was carried out thanks to a detection task-based image quality assessment experiment, where we explored the performance of COVID-19 classifiers on both original and denoised chest CT scans. Two different denoising methods, i.e., anisotropic diffusion (AD) and total variation (TV) filters, were used. Results showed that the TV denoised model performed better than both baseline and AD denoised model, despite its less favorable mathematical image quality metrics.