Fourier analysis of signal dependent noise images
Abstract An archetype signal dependent noise (SDN) model is a component used in analyzing images or signals acquired from different technologies. This model-component may share properties with stationary normal white noise (WN). Measurements from WN images were used as standards for making compariso...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78299-1 |
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| author | John Heine Erin Fowler Matthew B. Schabath |
| author_facet | John Heine Erin Fowler Matthew B. Schabath |
| author_sort | John Heine |
| collection | DOAJ |
| description | Abstract An archetype signal dependent noise (SDN) model is a component used in analyzing images or signals acquired from different technologies. This model-component may share properties with stationary normal white noise (WN). Measurements from WN images were used as standards for making comparisons with SDN in both the image domain (ID) and Fourier domain (FD). The ID wavelet expansion was applied to WN images (n = 1000). Orthogonality conditions were used to parametrically model the variance decomposition, as described in both domains. FD components were investigated with probability density function modeling and summarized measures. SDN images were constructed by multiplying both simulated and clinical mammograms (both with n = 1000) by WN. The variance decomposition for both WN and SDN decreases exponentially as a parametric function of the ID expansion level; expansion image variances for both types of noise were captured similarly in the Fourier plane corresponding with the ID parametric model. The Fourier transform of WN has a uniform power spectrum distributed exponentially; SDN has similar attributes. Fourier inversion of the lag-autocorrelation performed in the FD produced a statistical estimation of the SDN’s image factor. These findings are counterintuitive as SDN can be nonstationary in the ID but have stationary attributes in the FD. |
| format | Article |
| id | doaj-art-de5dd41d2c914d5f9e16484ccc5e13fe |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-de5dd41d2c914d5f9e16484ccc5e13fe2025-08-20T02:39:35ZengNature PortfolioScientific Reports2045-23222024-12-0114112410.1038/s41598-024-78299-1Fourier analysis of signal dependent noise imagesJohn Heine0Erin Fowler1Matthew B. Schabath2Cancer Epidemiology Department, H. Lee Moffitt Cancer Center and Research InstituteCancer Epidemiology Department, H. Lee Moffitt Cancer Center and Research InstituteCancer Epidemiology Department, H. Lee Moffitt Cancer Center and Research InstituteAbstract An archetype signal dependent noise (SDN) model is a component used in analyzing images or signals acquired from different technologies. This model-component may share properties with stationary normal white noise (WN). Measurements from WN images were used as standards for making comparisons with SDN in both the image domain (ID) and Fourier domain (FD). The ID wavelet expansion was applied to WN images (n = 1000). Orthogonality conditions were used to parametrically model the variance decomposition, as described in both domains. FD components were investigated with probability density function modeling and summarized measures. SDN images were constructed by multiplying both simulated and clinical mammograms (both with n = 1000) by WN. The variance decomposition for both WN and SDN decreases exponentially as a parametric function of the ID expansion level; expansion image variances for both types of noise were captured similarly in the Fourier plane corresponding with the ID parametric model. The Fourier transform of WN has a uniform power spectrum distributed exponentially; SDN has similar attributes. Fourier inversion of the lag-autocorrelation performed in the FD produced a statistical estimation of the SDN’s image factor. These findings are counterintuitive as SDN can be nonstationary in the ID but have stationary attributes in the FD.https://doi.org/10.1038/s41598-024-78299-1Signal dependent noiseMammographyMammographic simulationsWavelet expansionFourier analysis |
| spellingShingle | John Heine Erin Fowler Matthew B. Schabath Fourier analysis of signal dependent noise images Scientific Reports Signal dependent noise Mammography Mammographic simulations Wavelet expansion Fourier analysis |
| title | Fourier analysis of signal dependent noise images |
| title_full | Fourier analysis of signal dependent noise images |
| title_fullStr | Fourier analysis of signal dependent noise images |
| title_full_unstemmed | Fourier analysis of signal dependent noise images |
| title_short | Fourier analysis of signal dependent noise images |
| title_sort | fourier analysis of signal dependent noise images |
| topic | Signal dependent noise Mammography Mammographic simulations Wavelet expansion Fourier analysis |
| url | https://doi.org/10.1038/s41598-024-78299-1 |
| work_keys_str_mv | AT johnheine fourieranalysisofsignaldependentnoiseimages AT erinfowler fourieranalysisofsignaldependentnoiseimages AT matthewbschabath fourieranalysisofsignaldependentnoiseimages |