Quantitative study of fast non-local means-based denoising filter in chest X-ray imaging with lung nodule using three-dimensional printing

Abstract

In chest radiography, a solitary pulmonary nodule, which may be a precursor of lung cancer, is a frequently detected finding. However, as the image quality is deteriorated owing to the increase in the noise, lung cancer screening studies revealed that the likelihood of finding a nodule is lower than those of other modalities. This study quantitatively evaluates three widely used filters (median, Wiener, and total variation) and a newly proposed filter (fast non-local means (FNLM)), which reduce image noise. Images of a phantom with lung nodules, obtained from a patient using the 3D printing technology, were acquired at the chest anterior posterior, lateral, and posterior-anterior positions. To evaluate their denoising performance, normalized noise power spectrum, contrast to noise ratio and coefficient of variation were used. In the quantitative evaluation of the overall image, the proposed FNLM filter exhibited the best image performance. In the quantitative evaluation of the nodule image, the FNLM filter, which exhibits outstanding denoising performance and time efficiency, can be employed. Therefore, with the use of the FNLM filter in chest radiography, the detection probability of a nodule, which can be a precursor of lung cancer, is increased, and the cancer can be prevented even with a lower dose.