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.

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 body's 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.

Microstructure core photonic crystal fiber for gas sensing applications.

In this paper, a highly sensitive gas sensor based on the microstructure core and cladding photonic crystal fiber (PCF) is presented over the wavelength range from 1.3 to 2.2 µm, which is advantageous for sensor fabrication. The guiding properties of the proposed structure are dependent on geometrical parameters and wavelengths, which are numerically investigated by using a finite element method (FEM). Introducing the microstructure core makes it possible to obtain higher relative sensitivity and achieves low confinement loss. Moreover, it can be shown that increasing the diameter of the air holes in the microstructure core and decreasing the size of hole to hole space (pitch), the relative sensitivity is enhanced. In addition, the confinement loss is reduced by increasing the value of the diameter of the air holes in the cladding. Simulation results reveal that for the optimum design of the proposed PCF it is possible to obtain the highest relative sensitivity of about 42.27% at the wavelength λ=1.33 µm for the absorption line of methane (CH4) and hydrogen fluoride (HF) gases. In this case, the confinement loss of the fiber is 4.78345×10-6 dB/m.

Mitigation technique for receiver performance variation of multi-color channels in visible light communication.

"Green" and energy-efficient wireless communication schemes have recently experienced rapid development and garnered much interest. One such scheme is visible light communication (VLC) which is being touted as one of the next generation wireless communication systems. VLC allows communication using multi-color channels that provide high data rates and illumination simultaneously. Even though VLC has many advantageous features compared with RF technologies, including visibility, ubiquitousness, high speed, high security, harmlessness for the human body and freedom of RF interference, it suffers from some problems on the receiver side, one of them being photo sensitivity dissimilarity of the receiver. The photo sensitivity characteristics of a VLC receiver such as Si photo-detector depend on the wavelength variation. The performance of the VLC receiver is not uniform towards all channel colors, but it is desirable for receivers to have the same performance on each color channel. In this paper, we propose a mitigation technique for reducing the performance variation of the receiver on multi-color channels. We show received power, SNR, BER, output current, and outage probability in our simulation for different color channels. Simulation results show that, the proposed scheme can reduce the performance variation of the VLC receiver on multi-color channels.

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.