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Signal, Noise, Resolution, and Image Contrast
Published in Christakis Constantinides, Magnetic Resonance Imaging, 2016
Equally important in image evaluation is the contrast-to-noise ratio (CNR). Such an estimate quantifies the observer’s ability to distinguish and resolve neighboring tissue structures. It is defined as () CNR=IROI1−IROI2(IROI1+IROI22) where Imax, Imin are the maximum and minimum signal intensities within the ROI, Ibkg the background ROI intensity, and IROI1, IROI2 the signal intensities of neighboring tissue structures. Decreased spatial resolution often results in poor contrast. In effect, the modulation transfer function (MTF) represents the relationship between contrast and spatial resolution, alternatively defined as the magnitude of the Fourier transfer of the system’s impulse response (or equivalently, the point spread function), for positive spatial frequencies.
Radiographic imaging
Published in Ross I. Berbeco, Beam’s Eye View Imaging in Radiation Oncology, 2017
Contrast-to-noise ratio (CNR) provides a measure of an imaging system’s low-contrast resolution, and hence the system’s ability to distinguish soft tissue features on a radiograph. Relatively poor CNR is a limitation of BEV imaging due to the reasons described in Section 4.2.1, and is a key advantage of kV in-room imaging systems. BEV radiographic imaging is used primarily for registering bony anatomy, implanted fiducial markers, or lung and bronchial boundaries where contrast is sufficiently high to overcome the CNR limitations. Commercial image quality phantoms provide convenient tools for measuring CNR for QA and detector comparisons (Menon and Sloboda 2004, Das et al. 2011, Blake et al. 2013).
Microcomputed Tomography
Published in George C. Kagadis, Nancy L. Ford, Dimitrios N. Karnabatidis, George K. Loudos, Handbook of Small Animal Imaging, 2018
Contrast refers to the ability of an observer to visualize the desired object in the image. Contrast arises from differences in the x-ray attenuation properties of the different materials in the sample and is related to chemical composition. Materials with a high effective atomic number (Zeff) will absorb x-rays more strongly. For biomedical imaging, this implies that denser materials like bone or metal will attenuate the x-ray beam more effectively than soft tissues like muscle or fat, as shown by the x-ray attenuation curves in Figure 5.2. The larger the separation between the curves at a given energy, the larger the difference in x-ray attenuation and, therefore, the image contrast. In the 3D volume, contrast is the difference in the signal intensity between the desired object and the background. However all micro-CT images have some inherent image noise, which degrades the contrast or the conspicuity of the desired object. The contrast-to-noise ratio (CNR) is a commonly used metric to describe the ability to visualize objects within the image, taking into account the differences in signal intensities and the image noise. The easiest method to increase the CNR is to decrease the image noise, as described earlier. Altering the contrast itself is more difficult, as the Zeff for the target object must be altered to artificially increase the difference in the signal intensity compared to the background material. For biomedical studies, a contrast agent can be introduced that will increase the attenuation by the target organ, while the surrounding tissues remain unaltered. Contrast agents have high atomic numbers, and with a sufficient concentration, serve to increase the Zeff of the mixture (contrast media plus target object).
On the use of pulsed thermography signal reconstruction based on linear support vector regression for carbon fiber reinforced polymer inspection
Published in Quantitative InfraRed Thermography Journal, 2023
J. Fleuret, S. Ebrahimi, C. Ibarra Castanedo, X. Maldague
The Signal-to-noise ratio (SNR) is a metric that measures image quality by estimating the signal level with respect to the background noise. The Contrast-over-noise ratio (CNR) is similar to SNR although based on the difference (i.e. the contrast) between two features in an image. This contrast can be calculated, for instance, for a defect area with respect to a sound area. This is interesting since it provides a tool to assess the defect detection capabilities of a given method quantitatively. Several CNR definitions can be found in the literature, as summarised by Usamentiaga et al. [31]. This study also proposes to use the following definition, as it is the most robust against noise and image enhancement operations (e.g. Gamma correction):
Latent Low Rank Representation Applied to Pulsed Thermography Data For Carbon Fibre Reinforced Polymer Inspection
Published in Quantitative InfraRed Thermography Journal, 2022
J. Fleuret, S. Ebrahimi, C. Ibarra-Castanedo, X. Maldague
In order to assess the performance of the proposed approach when compared to state-of-the-art methods, one metric has been selected. The signal-to-noise ratio (SNR) is a metric that measures image quality by estimating the signal level with respect to the background noise. The contrast-to-noise ratio (CNR) is similar to SNR although based on the difference (i.e. the contrast) between two features in an image. This contrast can be calculated, for instance, for a defect area with respect to a sound area. This is interesting since it provides a tool to quantitatively assess the defect detection capabilities of a given method. Several CNR definitions can be found in the literature, as summarised by Usamentiaga et al. [28]. This study also proposes to use the following definition, as it has been shown to be the most robust against noise and image enhancement operations (e.g. Gamma correction):
Application of blind image quality assessment metrics to pulsed thermography
Published in Quantitative InfraRed Thermography Journal, 2022
J. Fleuret, S. Ebrahimi, C. Ibarra-Castanedo, X. Maldague
In order to assess the interest of the proposed approach and compare the state-of-the-art methods, one metric has been selected. The Signal-to-noise ratio (SNR) is a metric that measures image quality by estimating the signal level with respect to the background noise. The Contrast-to-noise ratio (CNR) is similar to SNR although based on the difference (i.e. the contrast) between two features in an image. This contrast can be calculated, for instance, for a defect area with respect to a sound area. This is interesting since it provides a tool to quantitatively assess the defect detection capabilities of a given method. Several CNR definitions can be found in the literature, as summarised by Usamentiaga et al. [28]. This study also proposes to use the following definition, as it has been shown to be the most robust against noise and image enhancement operations (e.g. Gamma correction):