Comparison of PDE based and other techniques for speckle reduction from digitally reconstructed holographic images

Srivastava, Rajeev; Gupta, J. R. P.; Parthasarthy, Harish

doi:10.1016/j.optlaseng.2009.09.012

Cited by 32 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the spatial average, the speckle contrast improves at the cost of a resolution worsening. In [6,7] a homomorphic approach is followed, involving a logarithmic transform of the image intensity to force the speckle to get additive. Then classical processing techniques are applied that are commonly used to filter additive noise (e.g., Wiener, Lee filters).…”

mentioning

confidence: 99%

Clear coherent imaging in turbid microfluidics by multiple holographic acquisitions

Bianco¹,

Paturzo²,

Fińizio³

et al. 2012

Opt. Lett.

View full text Add to dashboard Cite

Recently it has been demonstrated that digital holography is a powerful means allowing imaging of both amplitude and phase objects in turbid flowing media. However, in quasi-static turbid microfluidics, multiple scattering contributions through the colloids superimpose coherently to the recording device, resulting in speckle noise and hindering a clear vision of the objects. In this Letter we exploit the Brownian motion of the colloidal particles to get multiple uncorrelated holograms, and we combine them to reduce the speckle contrast. In this way we get a multi-look gain without losing image resolution.

show abstract

mentioning

confidence: 99%

Clear coherent imaging in turbid microfluidics by multiple holographic acquisitions

Bianco¹,

Paturzo²,

Fińizio³

et al. 2012

Opt. Lett.

View full text Add to dashboard Cite

show abstract

“…In other words, the HD between I and I 0 is the smallest value d such that every point of I has a point of I 0 within distance d and every point of I 0 has a point of I within distance d [33]. We also further analyzed segmentation performance in terms of the mean square error (MSE) [34], which is an average square difference between the original and predicted values and computed as (4).…”

Section: Plos Onementioning

confidence: 99%

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture

2020

View full text Add to dashboard Cite

Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in quantifying the changes in brain structure. Deep learning in recent years has been extensively used for brain image segmentation with highly promising performance. In particular, the U-net architecture has been widely used for segmentation in various biomedical related fields. In this paper, we propose a patch-wise U-net architecture for the automatic segmentation of brain structures in structural MRI. In the proposed brain segmentation method, the non-overlapping patch-wise U-net is used to overcome the drawbacks of conventional U-net with more retention of local information. In our proposed method, the slices from an MRI scan are divided into non-overlapping patches that are fed into the U-net model along with their corresponding patches of ground truth so as to train the network. The experimental results show that the proposed patch-wise U-net model achieves a Dice similarity coefficient (DSC) score of 0.93 in average and outperforms the conventional U-net and the SegNetbased methods by 3% and 10%, respectively, for on Open Access Series of Imaging Studies (OASIS) and Internet Brain Segmentation Repository (IBSR) dataset.

show abstract

“…To quantify the performance of the Despeckle filter algorithm in terms of the efficiency of speckle noise reduction and enhancing the useful image information, the following established performance metrics found in the literatures [27] [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46] are calculated in this study and their mathematical expression, significance, etc are given in Table I…”

Section: Parameters/metrics For Analyzing Despeckle Filter Perfomentioning

confidence: 99%

Speckle Filtering of Ultrasound B-Scan Images- A Comparative Study of Single Scale Spatial Adaptive Filters, Multiscale Filter and Diffusion Filters

Sivakumar¹,

Gayathri²,

Nedumaran³

2010

IJET

View full text Add to dashboard Cite

This paper presents a comparative study of among one Multi Scale filter (wavelet), nine single scale spatial adaptive filters (viz., Frost, Enhanced Frost, Median, Lee, Enhanced Lee, Kuan, Gamma Map, Wiener, Homomorphic speckle filters) and two Diffusion filters (viz., Speckle Reduction Anisotropic Diffusion (SRAD), Perona Malik Anistropic Diffusion (PMAD)) that are widely used for speckle noise reduction in biomedical ultrasound B-Scan images. The main objective of this study is to identify the efficient and optimum speckle filter in terms of preserving the edge details of the images with effective denoising. The performance of the filters are best estimated by calculating twenty-one established performance metrics along with execution time in order to determine the effective and optimum-despeckling algorithm for real time implementation. To do this, we have developed a cumulative speckle reduction (CSR) algorithm in the MATLAB environment, which performs all despeckle filtering functions as well as performance metrics calculation in a single trial. In the case of diffusion filter implementation, provision is given to execute the diffusion filter for several trials to identify the best iteration in terms of denoising the speckle and preserving the diagnostic information found in the B-scan images. The algorithm has been experimented with more than 200 digital ultrasound B-scan images of kidney, abdomen, liver and choroids. Based on the visual inspection of the despeckled images and the calculation of the performance metrics, it is found that SRAD and Wavelet despeckling filters are exhibiting fairly well performance over the other standard spatial filters.

show abstract

Comparison of PDE based and other techniques for speckle reduction from digitally reconstructed holographic images

Cited by 32 publications

References 18 publications

Clear coherent imaging in turbid microfluidics by multiple holographic acquisitions

Clear coherent imaging in turbid microfluidics by multiple holographic acquisitions

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture

Speckle Filtering of Ultrasound B-Scan Images- A Comparative Study of Single Scale Spatial Adaptive Filters, Multiscale Filter and Diffusion Filters

Contact Info

Product

Resources

About