A robust detail preserving anisotropic diffusion for speckle reduction in ultrasound images

Liu, Xiaoming; Liu, Jun; Xu, Xin; Chun, Lei; Tang, Jinshan; Deng, Youping

doi:10.1186/1471-2164-12-s5-s14

Cited by 24 publications

(9 citation statements)

References 27 publications

(45 reference statements)

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“…To adopt correctly the SRAD for multiplicative noise, Aja-Fernán-dez and Alberola-López [61] suggested a detail preserving anisotropic diffusion (DPAD) filter, which estimated the noise using the mode of the distributions of local statistics of the whole image [61,62]. In the recent years, other studies have been developed on new well posed equations such as [63][64][65][66][67][68]. In [63], the authors implemented a ramp preserving PMAD model based on an edge indicator, a difference curvature, which can distinguish edges from flat and ramp regions.…”

Section: Related Workmentioning

confidence: 99%

Adaptive noise-reducing anisotropic diffusion filter

Abdallah

Malek

Azar

et al. 2015

Neural Comput & Applic

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In image processing and computer vision, the denoising process is an important step before several processing tasks. This paper presents a new adaptive noisereducing anisotropic diffusion (ANRAD) method to improve the image quality, which can be considered as a modified version of a speckle-reducing anisotropic diffusion (SRAD) filter. The SRAD works very well for monochrome images with speckle noise. However, in the case of images corrupted with other types of noise, it cannot provide optimal image quality due to the inaccurate noise model. The ANRAD method introduces an automatic RGB noise model estimator in a partial differential equation system similar to the SRAD diffusion, which estimates at each iteration an upper bound of the real noise level function by fitting a lower envelope to the standard deviations of pre-segment image variances. Compared to the conventional SRAD filter, the proposed filter has the advantage of being adapted to the color noise produced by today's CCD digital camera. The simulation results show that the ANRAD filter can reduce the noise while preserving image edges and fine details very well. Also, it is favorably compared to the fast non-local means filter, showing an improvement in the quality of the restored image. A quantitative comparison measure is given by the parameters like the mean structural similarity index and the peak signal-to-noise ratio.

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Section: Related Workmentioning

confidence: 99%

Adaptive noise-reducing anisotropic diffusion filter

Abdallah

Malek

Azar

et al. 2015

Neural Comput & Applic

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“…Flores et al [ 18 ] extended the SRAD to a Log-Gabor guided anisotropic diffusion (ADLG), handling the trade-off between smoothing level and preservation of lesion contour details. Thereafter, a lot of work has been done with anisotropic diffusion equations in such a way that the important structural information can be retained in the denoised images [ 19 – 21 ]. But these SRAD based methods often produce a visually disappointing outputs when they are applied to filter the primary noise contained in ultrasound images, which is subject to Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement

Miao

et al. 2017

BioMed Eng OnLine

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BackgroundUltrasound imaging plays an important role in computer diagnosis since it is non-invasive and cost-effective. However, ultrasound images are inevitably contaminated by noise and speckle during acquisition. Noise and speckle directly impact the physician to interpret the images and decrease the accuracy in clinical diagnosis. Denoising method is an important component to enhance the quality of ultrasound images; however, several limitations discourage the results because current denoising methods can remove noise while ignoring the statistical characteristics of speckle and thus undermining the effectiveness of despeckling, or vice versa. In addition, most existing algorithms do not identify noise, speckle or edge before removing noise or speckle, and thus they reduce noise and speckle while blurring edge details. Therefore, it is a challenging issue for the traditional methods to effectively remove noise and speckle in ultrasound images while preserving edge details.MethodsTo overcome the above-mentioned limitations, a novel method, called Rayleigh-maximum-likelihood switching bilateral filter (RSBF) is proposed to enhance ultrasound images by two steps: noise, speckle and edge detection followed by filtering. Firstly, a sorted quadrant median vector scheme is utilized to calculate the reference median in a filtering window in comparison with the central pixel to classify the target pixel as noise, speckle or noise-free. Subsequently, the noise is removed by a bilateral filter and the speckle is suppressed by a Rayleigh-maximum-likelihood filter while the noise-free pixels are kept unchanged. To quantitatively evaluate the performance of the proposed method, synthetic ultrasound images contaminated by speckle are simulated by using the speckle model that is subjected to Rayleigh distribution. Thereafter, the corrupted synthetic images are generated by the original image multiplied with the Rayleigh distributed speckle of various signal to noise ratio (SNR) levels and added with Gaussian distributed noise. Meanwhile clinical breast ultrasound images are used to visually evaluate the effectiveness of the method. To examine the performance, comparison tests between the proposed RSBF and six state-of-the-art methods for ultrasound speckle removal are performed on simulated ultrasound images with various noise and speckle levels.ResultsThe results of the proposed RSBF are satisfying since the Gaussian noise and the Rayleigh speckle are greatly suppressed. The proposed method can improve the SNRs of the enhanced images to nearly 15 and 13 dB compared with images corrupted by speckle as well as images contaminated by speckle and noise under various SNR levels, respectively. The RSBF is effective in enhancing edge while smoothing the speckle and noise in clinical ultrasound images. In the comparison experiments, the proposed method demonstrates its superiority in accuracy and robustness for denoising and edge preserving under various levels of noise and speckle in terms of visual quality as well as numeric metr...

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“…The most popular methods include Lee filter [14], Kuan et al method [15] and anisotropic diffusion [6]. A lot of work has been done with anisotropic diffusion equations in such a way that the important structural information can be retained in the filtered image [8,[16][17][18]. Buades et al proposed a new filtering technique, named non-local means algorithm [19] which works well in Gaussian noise reduction with edge preservation because of using the region comparison instead of pixel comparison.…”

Section: Introductionmentioning

confidence: 99%

“…Filtering is useful alternative for the speckle reduction in most clinical applications. Many algorithms have been developed to reduce the speckles in ultrasound images, including single‐scale spatial filtering such as linear [3, 4], non‐linear [3, 5], adaptive methods [4, 5], multiscale spatial filtering such as diffusion‐based methods [2, 6–8] and other multiscale methods in different transform‐based techniques such as pyramid [9], wavelet [10, 11], ridgelet [12] and curvelet [13]. Simple mathematical linear filters, such as mean filter, degrade the sharp transitions of the images [4].…”

Section: Introductionmentioning

confidence: 99%

Speckle filtering of ultrasound images using a modified non‐linear diffusion model in non‐subsampled shearlet domain

Gupta

Anand

Tyagi

2015

IET Image Processing

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Speckle filtering is of great interest for the ultrasound medical images in which various noises and artefacts are introduced because of various limitations of the acquisition systems and techniques. Speckle is a prime factor to degrade the quality and most importantly, texture information present in the ultrasound images. This study presents a despeckling method based on a modified non-linear diffusion model and non-subsampled shearlet transform (NSST). As a new image representation method with the different features of localisation, directionality and multiscale, the NSST is utilised to provide the effective representation of the image coefficients. The modified anisotropic diffusion is applied to the noisy coarser NSST coefficients to improve the denoising efficiency and preserve the edge features effectively. In the diffusion process, the nonlocal pixel information is incorporated to evaluate the gradient of eight connected neighbouring pixels with an adaptive grey variance. The performance of the proposed method is evaluated for both the standard test and real ultrasound images. Experimental results show that the proposed method produces better results of noise suppression with the preservation of more edges compared with several existing methods.

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A robust detail preserving anisotropic diffusion for speckle reduction in ultrasound images

Cited by 24 publications

References 27 publications

Adaptive noise-reducing anisotropic diffusion filter

Adaptive noise-reducing anisotropic diffusion filter

Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement

Speckle filtering of ultrasound images using a modified non‐linear diffusion model in non‐subsampled shearlet domain

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