scholarly journals Edge Preserved Speckle Noise Reduction Using Integrated Fuzzy Filters

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Nagashettappa Biradar ◽  
M. L. Dewal ◽  
Manoj Kumar Rohit
Keyword(s):  
Moving Average ◽  
Wiener Filter ◽  
Iteration Process ◽  
Speckle Noise ◽  
Speckle Reduction ◽  
Nonlocal Means ◽  
Anisotropic Diffusion Filters ◽  
Echocardiographic Images ◽  
Visual Reading ◽  
Fuzzy Filters

Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are studied and analyzed along with 26 denoising techniques. It is observed that geometric filter retains noise and, to address this issue, wiener filter is embedded into the geometric filter during iteration process. The performance of geometric-wiener filter is further enhanced using fuzzy filters and the proposed despeckling techniques are called integrated fuzzy filters. Fuzzy filters based on moving average and median value are employed in the integrated fuzzy filters. The performances of integrated fuzzy filters are tested on echocardiographic images and synthetic images in terms of image quality metrics. It is observed that the performance parameters are highest in case of integrated fuzzy filters in comparison to fuzzy and geometric-fuzzy filters. The clinical validation reveals that the output images obtained using geometric-wiener, integrated fuzzy, nonlocal means, and details preserving anisotropic diffusion filters are acceptable. The necessary finer details are retained in the denoised echocardiographic images.

De-Speckling of Ultrasound Images Using Local Statistics-Based Trilateral Filter

2019 ◽  
Vol 28 (09) ◽  
pp. 1950150 ◽  
Author(s):  
S. Jayanthi Sree ◽  
C. Vasanthanayaki
Keyword(s):  
Adaptive Filters ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Speckle Reduction ◽  
Destructive Interference ◽  
Ultrasound Images ◽  
Edge Preserving ◽  
Local Statistics ◽  
Trilateral Filter ◽  
Anisotropic Diffusion Filters

Speckle noise in ultrasound images is a major hindrance for the automation of segmentation, detection, classification and measurements of region of interest, to assist clinician for diagnosing pathologies. Speckle noise occurs due to constructive and destructive interference of the echo signals reflected from the target and has a granular appearance. Various techniques have been devised for speckle reduction. Most of these techniques are based on adaptive filters, wavelet transform and anisotropic diffusion filters. In this paper, a new speckle reduction technique based on the trilateral filter and local statistics of the image has been developed. The local speckle content of the image influences the trilateral filtering. The trilateral filter is a robust edge preserving filter which considers the similarity of neighboring regions in terms of adjacency, intensity and edge details. Hence, the new method preserves the finer details of the ultrasound images in the process of filtering speckle noise. The proposed technique is validated using synthetic, simulated and real-time clinical ultrasound images. Comparison of the proposed technique with the existing speckle removal algorithms in terms of quality metrics such as MSE, PSNR, UQI, SSI, FoM has been made and best results are obtained for the proposed technique.

scholarly journals A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter

2020 ◽  
Vol 12 (15) ◽  
pp. 2371 ◽  
Author(s):  
Hadi Salehi ◽  
Javad Vahidi ◽  
Thabet Abdeljawad ◽  
Aziz Khan ◽  
Seyed Yaser Bozorgi Rad
Keyword(s):  
Additive Noise ◽  
Weighted Least Squares ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Input Image ◽  
Logarithmic Transformation ◽  
Guided Filter ◽  
Sar Image ◽  
Edge Information ◽  
Image Despeckling

The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.

A Nonlocal Means for Speckle Reduction of SAR Image With Multiscale-Fusion-Based Steerable Kernel Function

2016 ◽  
Vol 13 (11) ◽  
pp. 1646-1650 ◽  
Author(s):  
Jie Wu ◽  
Fang Liu ◽  
Hongxia Hao ◽  
Lingling Li ◽  
Licheng Jiao ◽  
...  
Keyword(s):  
Kernel Function ◽  
Speckle Reduction ◽  
Sar Image ◽  
Nonlocal Means

Speckle noise reduction in echocardiographic images of aortic valve and cardiac chambers

Optik ◽  
2015 ◽  
Vol 126 (2) ◽  
pp. 153-163 ◽  
Author(s):  
Nagashettappa Biradar ◽  
M.L. Dewal ◽  
Manoj Kumar Rohit
Keyword(s):  
Aortic Valve ◽  
Noise Reduction ◽  
Speckle Noise ◽  
Echocardiographic Images ◽  
Speckle Noise Reduction

scholarly journals GPU-Based Block-Wise Nonlocal Means Denoising for 3D Ultrasound Images

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Liu Li ◽  
Wenguang Hou ◽  
Xuming Zhang ◽  
Mingyue Ding
Keyword(s):  
3D Ultrasound ◽  
Noise Model ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Volume Data ◽  
Nonlocal Means ◽  
Data Parallel Computing ◽  
Processor Unit ◽  
Work Done ◽  
Speckle Suppression

Speckle suppression plays an important role in improving ultrasound (US) image quality. While lots of algorithms have been proposed for 2D US image denoising with remarkable filtering quality, there is relatively less work done on 3D ultrasound speckle suppression, where the whole volume data rather than just one frame needs to be considered. Then, the most crucial problem with 3D US denoising is that the computational complexity increases tremendously. The nonlocal means (NLM) provides an effective method for speckle suppression in US images. In this paper, a programmable graphic-processor-unit- (GPU-) based fast NLM filter is proposed for 3D ultrasound speckle reduction. A Gamma distribution noise model, which is able to reliably capture image statistics for Log-compressed ultrasound images, was used for the 3D block-wise NLM filter on basis of Bayesian framework. The most significant aspect of our method was the adopting of powerful data-parallel computing capability of GPU to improve the overall efficiency. Experimental results demonstrate that the proposed method can enormously accelerate the algorithm.

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