Single Image Signal-to-Noise Ratio Estimation for Magnetic Resonance Images

Sim, K. S.; Lai, M.A.; Tso, C.P.; Teo, Chuan Chin

doi:10.1007/s10916-009-9339-9

Cited by 11 publications

(9 citation statements)

References 15 publications

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“…The noise level of the conductivity and permittivity map was quantified using a method based on a correlation function . The correlation of a pixel with neighboring pixels is defined by the cross‐correlation between the original image and shifted images.…”

Section: Methodsmentioning

confidence: 99%

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Electrical Properties Tomography in the Human Brain at 1.5, 3, and 7T: A Comparison Study

Lier

Raaijmakers

Voigt

et al. 2013

Magnetic Resonance in Med

129

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The transceive phase assumption is most accurate for low-field strengths and low permittivity and in symmetric objects. The phase-only conductivity reconstruction is only applicable at 1.5 and 3T for the investigated geometries. The measurement precision was found to benefit from a higher field strength, which is related to increased signal-to-noise ratio (SNR) and increased curvature of the B1 (+) field.

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Section: Methodsmentioning

confidence: 99%

“…function (25,26). The correlation of a pixel with neighboring pixels is defined by the cross-correlation between the original image and shifted images.…”

Section: Noise Levelmentioning

confidence: 99%

Electrical Properties Tomography in the Human Brain at 1.5, 3, and 7T: A Comparison Study

Lier

Raaijmakers

Voigt

et al. 2013

Magnetic Resonance in Med

129

View full text Add to dashboard Cite

show abstract

“…We can measure the total variance from the time series, but we also need the background variance to compute the SW variance. Some proposals (6–8) suggest measuring the variance in the actual background, e.g., in the corners of the image or in other areas away from locations were EPI artifacts are suspected. There are several problems with this approach.…”

Section: Theorymentioning

confidence: 99%

A novel method for quantifying scanner instability in fMRI

Greve

Mueller

Liu

et al. 2010

Magnetic Resonance in Med

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A method was developed to quantify the effect of scanner instability on functional MRI data by comparing the instability noise to endogenous noise present when scanning a human. The instability noise was computed from agar phantom data collected with two flip angles, allowing for a separation of the instability from the background noise. This method was used on human data collected at four 3 T scanners, allowing the physiological noise level to be extracted from the data. In a ''well-operating'' scanner, the instability noise is generally less than 10% of physiological noise in white matter and only about 2% of physiological noise in cortex. This indicates that instability in a well-operating scanner adds very little noise to functional MRI results. This new method allows researchers to make informed decisions about the maximum instability level a scanner can have before it is taken off line for maintenance or rejected from a multisite consortium. This method also provides information about the background noise, which is generally larger in magnitude than the instability noise. Magn Reson Med 65:1053-1061,

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“…An autocorrelation based method can be applied for the purpose. In the present work we obtain the estimate of the SNR for a region following the method developed in [22]. It includes computation of mean and autocorrelation peak at origin of the image region which is contaminated by zero-mean Gaussian noise.…”

Section: Estimation Of Snr From Single Imagementioning

confidence: 99%

“…This method only needs an estimate of the Signal-to-Noise ratio (SNR) of the images to compensate for the effect of noise. The SNR is estimated using the method proposed by Sim et al [22]. The compensated feature image is passed through non-minimum suppression (as local energy is inversely proportional to edge strength here) within 55 window and a universal thresholding processes to produce the final edge map.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Edge Extraction in Images using Local Form Factors

Gupta¹,

Routray²,

Mukherjee³

2011

IJCA

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This article proposes a new spatial domain measure of local energy to extract the image features like edges. We define the measure as the local form factor (FF). It is the ratio of RMS to average of the pixel values in a region. Inverse square of the local FF around a center pixel is defined as an index of edge strength at that pixel. The proposed method could be applied directly on any image without smoothing for noise removal. It only needs an estimate of the Signal-to-Noise ratio (SNR) of the images to compensate the effect of noise. The compensated feature image is passed through non-minimum suppression and universal thresholding processes to produce the final edge map. The performance of the method is assessed using Baddeley Error Metric (BEM) and compared with those resulted from the popular Canny edge detector with different scales. The experimental results are encouraging the application of the method to extract edges and hence can be used as a potential candidate for general feature extraction. General TermsImage feature extraction, Low-level image processing

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Single Image Signal-to-Noise Ratio Estimation for Magnetic Resonance Images

Cited by 11 publications

References 15 publications

Electrical Properties Tomography in the Human Brain at 1.5, 3, and 7T: A Comparison Study

Electrical Properties Tomography in the Human Brain at 1.5, 3, and 7T: A Comparison Study

A novel method for quantifying scanner instability in fMRI

A New Method for Edge Extraction in Images using Local Form Factors

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