期刊
JOURNAL OF DIGITAL IMAGING
卷 34, 期 5, 页码 1134-1145出版社
SPRINGER
DOI: 10.1007/s10278-021-00512-8
关键词
Denoising; Image processing; Radiomics; Oncologic imaging biomarkers
资金
- Horizon 2020 project (RIA) [SC1-DTH-07-2018]
The study evaluated the performance of 5 denoising filters on T2-weighted MR images, eventually selecting ADF and UNLM filters, and studied their preservation of 107 radiomics features under different noise levels.
Several noise sources, such as the Johnson-Nyquist noise, affect MR images disturbing the visualization of structures and affecting the subsequent extraction of radiomic data. We evaluate the performance of 5 denoising filters (anisotropic diffusion filter (ADF), curvature flow filter (CFF), Gaussian filter (GF), non-local means filter (NLMF), and unbiased non-local means (UNLMF)), with 33 different settings, in T2-weighted MR images of phantoms (N = 112) and neuroblastoma patients (N = 25). Filters were discarded until the most optimal solutions were obtained according to 3 image quality metrics: peak signal-to-noise ratio (PSNR), edge-strength similarity-based image quality metric (ESSIM), and noise (standard deviation of the signal intensity of a region in the background area). The selected filters were ADFs and UNLMs. From them, 107 radiomics features preservation at 4 progressively added noise levels were studied. The ADF with a conductance of 1 and 2 iterations standardized the radiomic features, improving reproducibility and quality metrics.
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