Recasted nonlinear complex diffusion method for removal of Rician noise from breast MRI images

The evolution of magnetic resonance imaging (MRI) leads to the study of the internal anatomy of the breast. It maps the physical features along with functional characteristics of selected regions. However, its mapping accuracy is affected by the presence of Rician noise. This noise limits the qualitative and quantitative measures of breast image. This paper proposes recasted nonlinear complex diffusion filter for sharpening the details and removal of Rician noise. It follows maximum likelihood estimation along with optimal parameter selection of complex diffusion where the overall functionality is balanced by regularization parameters. To make recasted nonlinear complex diffusion, the edge threshold constraint “k” of diffusion coefficient is reformed. It is replaced by the standard deviation of the image. It offers a wide range of threshold as variability present in the image with respect to edge. It also provides an automatic selection of “k” instead of user-based value. A series of evaluation has been conducted with respect to different noise ratios further quality improvement of MRI. The qualitative and quantitative assessments of evaluations are tested for the Reference Image Database to Evaluate Therapy Response (RIDER) Breast database. The proposed method is also compared with other existing methods. The quantitative assessment includes the parameters of the full-reference image, human visual system, and no-reference image. It is observed that the proposed method is capable of preserving edges, sharpening the details, and removal of Rician noise.

Restoration and enhancement of breast ultrasound images using extended complex diffusion based unsharp masking

Ultrasound is a well-known imaging modality for the interpretation of breast cancer. It is playing very important role for breast cancer detection that are missed by mammograms. The image acquisition is usually affected by the presence of noise, artifacts, and distortion. To overcome such type of issues, there is a need of image restoration and enhancement to improve the quality of image. This paper proposes a single framework for denoising and enhancement of ultrasound images, where a smoothing filter is replaced with an extended complex diffusion-based filter in an unsharp masking technique. The performance evaluation of the proposed method is tested on real ultrasound breast cancer images database and synthetic ultrasound image. The performance evaluation comprises qualitative and quantitative evaluation along with comparative analysis of pre-existing and proposed method. The quantitative evaluation metrics are mean squared error, peak-signal-to-noise ratio, correlation parameter, normalized absolute error, universal quality index, similarity structure index, edge preservation index, a measure of enhancement, a measure of enhancement by entropy, and second derivative like measurement. The result specifies that the proposed method is better suited approach for the removal of speckle noise which follows Rayleigh distribution, restoration of information, enhancement of abnormalities, and proper edge preservation.

Diffusion of CH4 and N2 in Barium-Exchanged Reduced Pore Zorite (Ba-RPZ) and Zeolite 4A

<pre><p>Barium-exchanged reduced pore zorite (Ba-RPZ) is a titanosilicate molecular sieve that is able to separate CH4 from N2 based on their relative molecular sizes. A detailed study of N2 and CH4 adsorption equilibrium and diffusion on Ba-RPZ was completed using low and high-pressure volumetry. Adsorption equilibrium data for Ba-RPZ from limiting vacuum to 1.2 bar were measured at 30, 40, and 50° C for CH4 and at 30, 50, and 70° C for N2. Constant volume uptake experiments were conducted to estimate the diffusivities of CH4 at 30, 40, and 50° C and N2 -20, -10, and 0° C. Similar experiments were carried out with zeolite 4A to validate the methods used in this study. On the one hand, the transport of N2 in Ba-RPZ was found to be controlled by diffusion in the micropores. On the other hand, the transport of CH4 in Ba-RPZ was described by a dual-resistance model, including a barrier resistance and micropore diffusional resistance. Both the barrier and micropore diffusion coefficients demonstrated concentration dependence. While the micropore diffusion constant followed Darken's relationship, the barrier resistance did not. A concentration-dependent dual-resistance diffusion model for methane was constructed and validated using experimental data across a range of pressures and temperatures. The concentration-dependent dual-resistance model was able to describe the complex diffusion behaviour methane displays as it progressed from the dual-resistance controlled region to the micropore-controlled region of the isotherm. The calculated CH4/N2 kinetic selectivity of Ba-RPZ was shown to be significantly larger than the current benchmark material for CH4/N2 separation.</p></pre>

Diffusion of CH4 and N2 in Barium-Exchanged Reduced Pore Zorite (Ba-RPZ) and Zeolite 4A

<pre><p>Barium-exchanged reduced pore zorite (Ba-RPZ) is a titanosilicate molecular sieve that is able to separate CH4 from N2 based on their relative molecular sizes. A detailed study of N2 and CH4 adsorption equilibrium and diffusion on Ba-RPZ was completed using low and high-pressure volumetry. Adsorption equilibrium data for Ba-RPZ from limiting vacuum to 1.2 bar were measured at 30, 40, and 50° C for CH4 and at 30, 50, and 70° C for N2. Constant volume uptake experiments were conducted to estimate the diffusivities of CH4 at 30, 40, and 50° C and N2 -20, -10, and 0° C. Similar experiments were carried out with zeolite 4A to validate the methods used in this study. On the one hand, the transport of N2 in Ba-RPZ was found to be controlled by diffusion in the micropores. On the other hand, the transport of CH4 in Ba-RPZ was described by a dual-resistance model, including a barrier resistance and micropore diffusional resistance. Both the barrier and micropore diffusion coefficients demonstrated concentration dependence. While the micropore diffusion constant followed Darken's relationship, the barrier resistance did not. A concentration-dependent dual-resistance diffusion model for methane was constructed and validated using experimental data across a range of pressures and temperatures. The concentration-dependent dual-resistance model was able to describe the complex diffusion behaviour methane displays as it progressed from the dual-resistance controlled region to the micropore-controlled region of the isotherm. The calculated CH4/N2 kinetic selectivity of Ba-RPZ was shown to be significantly larger than the current benchmark material for CH4/N2 separation.</p></pre>

Diffusion of CH4 and N2 in Barium-Exchanged Reduced Pore Zorite (Ba-RPZ) and Zeolite 4A

<pre><p>Barium-exchanged reduced pore zorite (Ba-RPZ) is a titanosilicate molecular sieve that is able to separate CH4 from N2 based on their relative molecular sizes. A detailed study of N2 and CH4 adsorption equilibrium and diffusion on Ba-RPZ was completed using low and high-pressure volumetry. Adsorption equilibrium data for Ba-RPZ from limiting vacuum to 1.2 bar were measured at 30, 40, and 50° C for CH4 and at 30, 50, and 70° C for N2. Constant volume uptake experiments were conducted to estimate the diffusivities of CH4 at 30, 40, and 50° C and N2 -20, -10, and 0° C. Similar experiments were carried out with zeolite 4A to validate the methods used in this study. On the one hand, the transport of N2 in Ba-RPZ was found to be controlled by diffusion in the micropores. On the other hand, the transport of CH4 in Ba-RPZ was described by a dual-resistance model, including a barrier resistance and micropore diffusional resistance. Both the barrier and micropore diffusion coefficients demonstrated concentration dependence. While the micropore diffusion constant followed Darken's relationship, the barrier resistance did not. A concentration-dependent dual-resistance diffusion model for methane was constructed and validated using experimental data across a range of pressures and temperatures. The concentration-dependent dual-resistance model was able to describe the complex diffusion behaviour methane displays as it progressed from the dual-resistance controlled region to the micropore-controlled region of the isotherm. The calculated CH4/N2 kinetic selectivity of Ba-RPZ was shown to be significantly larger than the current benchmark material for CH4/N2 separation.</p></pre>

Experimental and Theoretical Analysis of Metal Complex Diffusion through Cell Monolayer

The study of drugs diffusion through different biological membranes constitutes an essential step in the development of new pharmaceuticals. In this study, the method based on the monolayer cell culture of CHO-K1 cells has been developed in order to emulate the epithelial cells barrier in permeability studies by laser interferometry. Laser interferometry was employed for the experimental analysis of nickel(II) and cobalt(II) complexes with 1-allylimidazole or their chlorides’ diffusion through eukaryotic cell monolayers. The amount (mol) of nickel(II) and cobalt(II) chlorides transported through the monolayer was greater than that of metals complexed with 1-allylimidazole by 4.34-fold and 1.45-fold, respectively, after 60 min. Thus, laser interferometry can be used for the quantitative analysis of the transport of compounds through eukaryotic cell monolayers, and the resulting parameters can be used to formulate a mathematical description of this process.

CT Image Reconstruction using NLMfuzzyCD Regularization Method

Aims and scope: Computed Tomography (CT) is one of the most efficient clinical diagnostic tools. The main goal of CT is to reproduce an acceptable reconstructed image of an object (either anatomical or functional behaviour) with the help of a limited set of its projections at different angles. Background: To achieve this goal, one of the most commonly iterative reconstruction algorithm called Maximum Likelihood Expectation Maximization (MLEM) is used. Objective: Although the conventional Maximum Likelihood (ML) algorithm can achieve quality images in CT. However, it still suffers from the optimal smoothing as the number of iterations increase. Methods: For solving this problem, in this paper present a novel statistical image reconstruction algorithm for CT, which utilizes a nonlocal means fuzzy complex diffusion as a regularization term for noise reduction and edge preservation. Results: The proposed model was evaluated on four test cases phantoms. Conclusion: Qualitative and quantitative analyses indicate that the proposed technique has higher efficiency for computed tomography. The proposed method yields significant improvements when compare with the state-of-the-art techniques.