ArticleTechnology in cancer research & treatment2017
Sensitivity of Image Features to Noise in Conventional and Respiratory-Gated PET/CT Images of Lung Cancer: Uncorrelated Noise Effects.
Article in Technology in cancer research & treatment, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 23 citations in OpenAlex.
- Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes.EJNMMI physics · 2025Article
- Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods.Abdominal radiology (New York) · 2024Article
- Robustness of radiomic features in 123I-ioflupane-dopamine transporter single-photon emission computer tomography scan.PloS one · 2024Article
- Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy.Frontiers in oncology · 2023Article
- Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study.Tomography (Ann Arbor, Mich.) · 2022Article
- Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy?World journal of hepatology · 2022Article
- Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy.Physics in medicine and biology · 2021Article
- A Systematic Review of PET Textural Analysis and Radiomics in Cancer.Diagnostics (Basel, Switzerland) · 2021Review
- Article
- Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis.European journal of nuclear medicine and molecular imaging · 2019Review
- Systematic analysis of bias and variability of texture measurements in computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features.Scientific reports · 2019Article
- Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra.Journal of medical imaging (Bellingham, Wash.) · 2018Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The effect of noise on image features has yet to be studied in depth. Our objective was to explore how significantly image features are affected by the addition of uncorrelated noise to an image. The signal-to-noise ratio and noise power spectrum were calculated for a positron emission tomography/computed tomography scanner using a Ge-68 phantom. The conventional and respiratory-gated positron emission tomography/computed tomography images of 31 patients with lung cancer were retrospectively examined. Multiple sets of noise images were created for each original image by adding Gaussian noise of varying standard deviation equal to 2.5%, 4.0%, and 6.0% of the maximum intensity for positron emission tomography images and 10, 20, 50, 80, and 120 Hounsfield units for computed tomography images. Image features were extracted from all images, and percentage differences between the original image and the noise image feature values were calculated. These features were then categorized according to the noise sensitivity. The contour-dependent shape descriptors averaged below 4% difference in positron emission tomography and below 13% difference in computed tomography between noise and original images. Gray level size zone matrix features were the most sensitive to uncorrelated noise exhibiting average differences >200% for conventional and respiratory-gated images in computed tomography and 90% in positron emission tomography. Image feature differences increased as the noise level increased for shape, intensity, and gray-level co-occurrence matrix features in positron emission tomography and for gray-level co-occurrence matrix and gray-level size zone matrix features in conventional computed tomography. Investigators should be aware of the noise effects on image features.
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