Adaptive Rician denoising with edge preservation for MR images of the articular cartilage

Aarya, Isshaa; Jiang, Danchi; Gale, Timothy J.

doi:10.1080/21681163.2014.922036

Cited by 2 publications

(2 citation statements)

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“…This operation converts the linear additive Gaussian noise into signal‐dependent Rician noise [5]. 2D magnitude image function in MRI is given as follows:

M false(x, y false) = \sqrt{{false[A_{r} false(x, y false) + n_{r} false(x, y false) false]}^{2} + n_{i}^{2} (x, y)}

where M ( x , y ) is the observed magnitude image function, A r ( x , y ) is the real component of the true signal function, n r ( x , y ) and n i ( x , y ) are the real and imaginary components of the noise function, respectively, and

N false(x, y false) = \sqrt{n_{r}^{2} (x, y) + n_{i}^{2} (x, y)}

is the magnitude of noise, x and y are coordinate positions [6]. It is not possible to remove noise completely from an image despite several Rician denoising method [5, 6].…”

Section: Introductionmentioning

confidence: 99%

“…where M(x, y) is the observed magnitude image function, A r (x, y) is the real component of the true signal function, n r (x, y) and n i (x, y) are the real and imaginary components of the noise function, respectively, and N (x, y) = n 2 r (x, y) + n 2 i (x, y) is the magnitude of noise, x and y are coordinate positions [6]. It is not possible to remove noise completely from an image despite several Rician denoising method [5,6]. Fig.…”

mentioning

confidence: 99%

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Edge localisation in MRI for images with signal‐dependent noise

2015

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A new edge localisation technique for step edges in images with signaldependent Rician noise in MRI is proposed. Dependent noise can not only affect the detection of true edges in an image but also their position. Inaccurate localisation of an edge can lead to insufficient segmentation and reduce the overall accuracy of any edge detection method. The proposed technique uses higher-order moments of the noise function to determine the correction factor for localisation and thus reduce the overall mean square error for true edge localisation due to noise. The proposed technique offers significant improvement in edge localisation for an image as compared with Canny and Sobel methods.

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“…This operation converts the linear additive Gaussian noise into signal‐dependent Rician noise [5]. 2D magnitude image function in MRI is given as follows:

M false(x, y false) = \sqrt{{false[A_{r} false(x, y false) + n_{r} false(x, y false) false]}^{2} + n_{i}^{2} (x, y)}

N false(x, y false) = \sqrt{n_{r}^{2} (x, y) + n_{i}^{2} (x, y)}

is the magnitude of noise, x and y are coordinate positions [6]. It is not possible to remove noise completely from an image despite several Rician denoising method [5, 6].…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%