Muhammad Shahin Uddin

Speckle reduction for ultrasound images using nonlinear multi-scale complex wavelet diffusion

Speckle noise is a major shortcoming of any type of ultrasound imaging. Hence, speckle reduction is vital in providing a better clinical diagnosis. The key objective of any speckle reduction algorithm is to attain a speckle free image, whilst preserving the important anatomical features. In this paper, we introduce a nonlinear multi-scale complex wavelet diffusion based algorithm for speckle reduction and sharp edge preservation of 2D ultrasound images. The proposed method exploits some useful features of the dual tree complex wavelet transform and nonlinear diffusion. Simulated experimental results demonstrate that our proposed algorithm significantly reduces speckle noise while preserving sharp edges without discernible distortions. The proposed approach performs better than the previous existing approaches in both qualitative and quantitative measures.

Depression and Quality of Life among Postmenopausal Women in Bangladesh: A Cross-sectional Study

Objectives The aims of the research are to examine the problems of abnormal menopausal women: the relationship between depression and menopausal-specific quality of life (MENQOL)-symptoms among postmenopausal women; the association of MENQOL-symptoms between pre- and postmenopausal female society in Bangladeshi real community. Methods This cross sectional study was conducted on 435 women of Tangail, aged (≥ 17) years, using a structured questionnaire where is inaacluded the information of MENQOL and one of the main outcomes “depression” is measured by beck depression inventory. Results Menopausal status and MENQOL symptoms (except pain) are significantly (P < 0.05) associated. By using post-hoc analysis, the proportion of menopausal women, classified as having a depressive mood of early menopause is significantly higher than natural-menopause. Among postmenopausal women, there is a significant correlation between “concentration problem” and “depression”. Here mean depression score (29.40 ± 6.42) of menopausal women who have any difficulty in concentrating is higher than mean depression score (20.89 ± 6.64) of menopausal women who have no difficulty in concentrating. Another six factors (osteoporosis, heart-beating, fatigue, pressure, tingling, headaches) of MENQOL-symptoms were significantly correlated with depression and P-values are 0.000, 0.000, 0.000, 0.033, 0.006, and 0.002, respectively. Finally the presence of “difficulty in concentrating” and “fatigue” are strongly associated factors with depression score (P < 0.001). Conclusions The early postmenopausal women have to face more psychological problems (e.g., depression) compare to others. Among postmenopausal women, there is no significant relation between depression and vasomotor symptom (e.g., hot-flashes) perspective to menopausal female society of Bangladesh.

Speckle-reduction algorithm for ultrasound images in complex wavelet domain using genetic algorithm-based mixture model.

Compared with other medical-imaging modalities, ultrasound (US) imaging is a valuable way to examine the bodys internal organs, and two-dimensional (2D) imaging is currently the most common technique used in clinical diagnoses. Conventional 2D US imaging systems are highly flexible cost-effective imaging tools that permit operators to observe and record images of a large variety of thin anatomical sections in real time. Recently, 3D US imaging has also been gaining popularity due to its considerable advantages over 2D US imaging. It reduces dependency on the operator and provides better qualitative and quantitative information for an effective diagnosis. Furthermore, it provides a 3D view, which allows the observation of volume information. The major shortcoming of any type of US imaging is the presence of speckle noise. Hence, speckle reduction is vital in providing a better clinical diagnosis. The key objective of any speckle-reduction algorithm is to attain a speckle-free image while preserving the important anatomical features. In this paper we introduce a nonlinear multi-scale complex wavelet-diffusion based algorithm for speckle reduction and sharp-edge preservation of 2D and 3D US images. In the proposed method we use a Rayleigh and Maxwell-mixture model for 2D and 3D US images, respectively, where a genetic algorithm is used in combination with an expectation maximization method to estimate mixture parameters. Experimental results using both 2D and 3D synthetic, physical phantom, and clinical data demonstrate that our proposed algorithm significantly reduces speckle noise while preserving sharp edges without discernible distortions. The proposed approach performs better than the state-of-the-art approaches in both qualitative and quantitative measures.

Speckle reduction and deblurring of ultrasound images using artificial neural network

Ultrasound (US) imaging is widely used in clinical diagnostics as it is an economical, portable, painless, comparatively safe, and non-invasive real-time tool. However, the image quality of US imaging is severely affected by the presence of speckle noise during the acquisition process. It is essential to achieve speckle-free high resolution US imaging for better clinical diagnosis. In this paper, we propose a speckle and blur reduction algorithm for US imaging based on artificial neural networks (ANNs). Here, speckle noise is modelled as a multiplicative noise following a Rayleigh distribution, whereas blur is modelled as a Gaussian blur function. The noise and blur variances are estimated by a cascade-forward back propagation (CFBP) neural network using a set of intensity and wavelet features of the US image. The estimated noise and blur variances are then used for speckle reduction by solving the inverse Rayleigh function, and for de-blurring, using the Lucy-Richardson algorithm. The proposed approach gives improved results for both qualitative and quantitative measures.

Complex wavelet based speckle reduction using multiple ultrasound images

Ultrasound imaging is a dominant tool for diagnosis and evaluation in medical imaging systems. However, as its major limitation is that the images it produces suffer from low quality due to the presence of speckle noise, to provide better clinical diagnoses, reducing this noise is essential. The key purpose of a speckle reduction algorithm is to obtain a speckle-free high-quality image whilst preserving important anatomical features, such as sharp edges. As this can be better achieved using multiple ultrasound images rather than a single image, we introduce a complex wavelet-based algorithm for the speckle reduction and sharp edge preservation of two-dimensional (2D) ultrasound images using multiple ultrasound images. The proposed algorithm does not rely on straightforward averaging of multiple images but, rather, in each scale, overlapped wavelet detail coefficients are weighted using dynamic threshold values and then reconstructed by averaging. Validation of the proposed algorithm is carried out using simulated and real images with synthetic speckle noise and phantom data consisting of multiple ultrasound images, with the experimental results demonstrating that speckle noise is significantly reduced whilst sharp edges without discernible distortions are preserved. The proposed approach performs better both qualitatively and quantitatively than previous existing approaches.

Nonlinear multi-scale complex wavelet diffusion based speckle reduction approach for 3D ultrasound images

3D ultrasound imaging has advantages as a non-invasive and a faster examination procedure capable of displaying volume information in real time. However, its resolution is affected by speckle noise. Speckle reduction and feature preservation are seemingly opposing goals. In this paper, a nonlinear multi-scale complex wavelet diffusion based algorithm for 3D ultrasound imaging is introduced. Speckle is suppressed and sharp edges are preserved by applying iterative multi-scale diffusion on the complex wavelet coefficients. The proposed method is validated using synthetic, real phantom, and clinical 3D images, and it is found to outperform other methods in both qualitative and quantitative measures.

Proposed Square Lattice Photonic Crystal Fiber for Extremely High Nonlinearity, Birefringence and Ultra-High Negative Dispersion Compensation

Abstract A photonic crystal fiber in square lattice architecture is numerically investigated and proposed for broadband dispersion compensation in optical transmission system. Simulation results reveal that it is possible to obtain an ultra-high negative dispersion of about −571.7 to −1889.7 (ps/nm.km) in the wavelength range of 1340 nm to 1640 nm. Experimentally it is demonstrated that the design fiber covers a high birefringence of order 4.74×10‒3 at the wavelength of 1550 nm. Here, numerical investigation of guiding properties and geometrical properties of the proposed PCF are conducted using the finite element method (FEM) with perfectly match layers. Moreover, it is established more firmly that the proposed fiber successfully compensates the chromatic dispersion of standard single mode in entire band of interest. Our result is attractive due to successfully achieve ultra-high negative dispersion that is more promisor than the prior best results.

Restoration approach of 3D ultrasound images using complex wavelet-based Laplacian-TV mixture prior

3D ultrasound imaging has advantages over other medical imaging modalities, as it does not involve invasive measurements, reduces study time and operator dependency and can display volume information in real time. It is a valuable way of examining many of the body’s internal organs; however the visibility of details of the organs are usually degraded by speckle noise and blurring during the image acquisition process. A number of image restoration methods have been used to reduce the effects of blurring and noise. Most of the restoration methods involve the use of a prior model as a regularisation term to solve an ill-posed inverse problem. In this paper, we present a wavelet-based restoration method for 3D ultrasound image restoration where a Laplacian scale mixture model combined with a total variation constraint is used as a regularisation prior. Our results show that the restored 3D ultrasound volumes produced by this approach have improved quality over those restored using other recently proposed approaches.

Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images.

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function. Hereto, we propose an intelligent estimator based on artificial neural networks (ANNs) to estimate the variances of noise and blur, which, in turn, are used to obtain an image without discernible distortions. A set of statistical features computed from the image and its complex wavelet sub-bands are used as input to the ANN. In the proposed method, we solve the inverse Rayleigh function numerically for speckle reduction and use the Richardson-Lucy algorithm for de-blurring. The performance of this method is compared with that of the traditional methods by applying them to a synthetic, physical phantom and clinical data, which confirms better restoration results by the proposed method.

A new combined prior based reconstruction method for compressed sensing in 3D ultrasound imaging

Ultrasound (US) imaging is one of the most popular medical imaging modalities, with 3D US imaging gaining popularity recently due to its considerable advantages over 2D US imaging. However, as it is limited by long acquisition times and the huge amount of data processing it requires, methods for reducing these factors have attracted considerable research interest. Compressed sensing (CS) is one of the best candidates for accelerating the acquisition rate and reducing the data processing time without degrading image quality. However, CS is prone to introduce noise-like artefacts due to random under-sampling. To address this issue, we propose a combined prior-based reconstruction method for 3D US imaging. A Laplacian mixture model (LMM) constraint in the wavelet domain is combined with a total variation (TV) constraint to create a new regularization regularization prior. An experimental evaluation conducted to validate our method using synthetic 3D US images shows that it performs better than other approaches in terms of both qualitative and quantitative measures.