The lower on the plane, the smaller the scale and so the lower the multiplicand of the coordinate, and so the lower the offset, and so the lower of the moving texture speed. Going back to the example where the Scale depends on Y coordinate: at the bottom Y=0, and so bottom color doesn't change at all at the top Y=1, and so the color changes as with Scale = 1. This is why setting Scale to 0 will output a solid color everywhere, because each coordinate will be multiplied with 0, resulting with x:0, y:0, z:0, and so only a single noise coordinate will be sampled for all pixels/lightrays. It no longer moves, it squeezes! Why? Because the noise texture isn't generated by first moving to a given location, and then generating some noise there, with the size of pockets of similar color based on Scale - no, instead it simply multiplies the given coordinate by the scale, and then generates noise for that coordinate: Now, let's look what happens when you offset the Y coordinate passed to the 2nd noise texture, with the first noise removed:īut what if we base the scale on the Y coordinate (before offsetting it, simply the Y coordinate of the rendered pixel):
J.I replaced your Mapping nodes with Vector Math > Add as it achieves the same thing. This work focuses on the effect of electronic, uncorrelated, noise and future work shall examine the influence of changes in quantum noise on the features.
We speculate that image features will be more difficult to detect in the presence of electronic noise (an uncorrelated noise contributor) or, for that matter, any other highly correlated image noise. Conclusion: Image features are sensitive to acquisition factors (simulated by adding uncorrelated Gaussian noise). However, it did affect the image features and textures significantly as demonstrated by GLCM differences. The dramatic increase in noise texture did not affect image structure/contours significantly for patient images. Results: Adding the electronic noise to the images modified the quality of the NPS, shifting the noise from mostly correlated to mostly uncorrelated voxels. RLM feature calculation was performed in 13 directions with grey levels binning into 128 levels. GLCM (size 128x128) was calculated with a step size of 1 voxel in 13 directions and averaged. These features provide the underlying structural information of the images.
Concurrently, on patient images (original and noise-added images), image features were calculated: 14 shape, 19 intensity (1st order statistics from intensity volume histograms), 18 GLCM features (2nd order statistics from grey level co-occurrence matrices) and 11 RLM features (2nd order statistics from run-length matrices). We calculated the noise-power spectrum (NPS) of the original CT images of the phantom, and of the phantom images with added Gaussian noise with means of 50, 80, and 120 HU. Methods: Three levels of uncorrelated Gaussian noise were added to CT images of phantom and patients (20) acquired in static mode and respiratory tracking mode. electronic noise) in clinical Computed Tomography (CT) using the standardized American College of Radiology (ACR) CT accreditation phantom and patient images. radiomics) and statistical fluctuations (i.e. Purpose: To investigate the relationship between quantitative image features (i.e.
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