A novel technique based on the statistical autoregressive (AR) model has recently been developed as a solution to estimate the signal-to-noise ratio (SNR) in scanning electron microscope (SEM) images. In another research study, the authors also developed an algorithm by cascading the AR model with the Lagrange time delay (LTD) estimator. This technique is named the mixed Lagrange time delay estimation autoregressive (MLTDEAR) model. In this paper, the fundamental performance limits for the problem of single-image SNR estimation as derived from the Cramer-Rao inequality is presented. We compared the experimental performances of several existing methods--the simple method, the first-order linear interpolator, the AR-based estimator as well as the MLTDEAR method--with respect to this performance bound. In a few test cases involving different images, the efficiency of the MLTDEAR single-image estimation technique proved to be significantly better than that of the other three methods. Study of the effect of different SEM setting conditions that affect the autocorrelation function curve is also discussed.
A new technique based on nearest neighbourhood method is proposed. In this paper, considering the noise as Gaussian additive white noise, new technique single-image-based estimator is proposed. The performance of this new technique such as adaptive slope nearest neighbourhood is compared with three of the existing method which are original nearest neighbourhood (simple method), first-order interpolation method and shape-preserving piecewise cubic hermite autoregressive moving average. In a few cases involving images with different brightness and edges, this adaptive slope nearest neighbourhood is found to deliver an optimum solution for signal-to-noise ratio estimation problems. For different values of noise variance, the adaptive slope nearest neighbourhood has highest accuracy and less percentage estimation error. Being more robust with white noise, the new proposed technique estimator has efficiency that is significantly greater than those of the three methods.
A new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first-order interpolation and the combination of both nearest neighbourhood and first-order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods.
A new technique to quantify signal-to-noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson-Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first-order linear interpolation and nearest neighbourhood combined with first-order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation.
This is the extended project by introducing the modified dynamic range histogram modification (MDRHM) and is presented in this paper. This technique is used to enhance the scanning electron microscope (SEM) imaging system. By comparing with the conventional histogram modification compensators, this technique utilizes histogram profiling by extending the dynamic range of each tile of an image to the limit of 0-255 range while retains its histogram shape. The proposed technique yields better image compensation compared to conventional methods.
A number of techniques have been proposed during the last three decades for noise variance and signal-to-noise ratio (SNR) estimation in digital images. While some methods have shown reliability and accuracy in SNR and noise variance estimations, other methods are dependent on the nature of the images and perform well on a limited number of image types. In this article, we prove the accuracy and the efficiency of the image noise cross-correlation estimation model, vs. other existing estimators, when applied to different types of scanning electron microscope images.
Detection of cracks from stainless steel pipe images is done using contrast stretching technique. The technique is based on an image filter technique through mathematical morphology that can expose the cracks. The cracks are highlighted and noise removal is done efficiently while still retaining the edges. An automated crack detection system with a camera platform has been successfully implemented. We compare crack extraction in terms of quality measures with those of Otsu's threshold technique and the another technique (Iyer and Sinha, 2005). The algorithm shown is able to achieve good results and perform better than these other techniques.
A new and robust parameter estimation technique, named image noise cross-correlation, is proposed to predict the signal-to-noise ratio (SNR) of scanning electron microscope images. The results of SNR and variance estimation values are tested and compared with nearest neighborhood and first-order interpolation. Overall, the proposed method is best as its estimations for the noise-free peak and SNR are most consistent and accurate to within a certain acceptable degree, compared with the others.
In this paper, we propose to use the autoregressive (AR)-based interpolator with Wiener filter and apply the idea to scanning electron microscope (SEM) images. The concept for combining the AR-based interpolator with Wiener filtering comes from the essential requirement of Wiener filtering for accurate and consistent estimation of the power of the noise in images prior to filter implementation. The resultant filter is called AR-Wiener filter. The proposed filter is embedded onto the frame grabber card of the scanning electron microscope (SEM) for real-time image processing. Different images are captured using SEM and used to compare the performances of the conventional Wiener and the proposed AR-Wiener technique.
A new technique based on the statistical autoregressive (AR) model has recently been developed as a solution to signal-to-noise (SNR) estimation in scanning electron microscope (SEM) images. In the present study, we propose to cascade the Lagrange time delay (LTD) estimator with the AR model. We call this technique the mixed Lagrange time delay estimation autoregressive (MLTDEAR) model. In a few test cases involving different images, this model is found to present an optimum solution for SNR estimation problems under different noise environments. In addition, it requires only a small filter order and has no noticeable estimation bias. The performance of the proposed estimator is compared with three existing methods: simple method, first-order linear interpolator, and AR-based estimator over several images. The efficiency of the MLTDEAR estimator, being more robust with noise, is significantly greater than that of the other three methods.
In the last two decades, a variety of techniques for signal-to-noise ratio (SNR) estimation in scanning electron microscope (SEM) images have been proposed. However, these techniques can be divided into two groups: first, SNR estimators of good accuracy, but based on impractical assumptions; second, estimators based on realistic assumptions but of poor accuracy. In this paper we propose the implementation of autoregressive (AR)-model interpolation as a solution to the problem. Unlike others, the proposed technique is based on a single SEM image and offers the required accuracy and robustness in estimating SNR values.
Data analysis based on breast cancer risk factors such as age, race, breastfeeding, hormone replacement therapy, family history, and obesity was conducted on breast cancer patients using a new enhanced computerized database management system. My Structural Query Language (MySQL) is selected as the application for database management system to store the patient data collected from hospitals in Malaysia. An automatic calculation tool is embedded in this system to assist the data analysis. The results are plotted automatically and a user-friendly graphical user interface is developed that can control the MySQL database. Case studies show breast cancer incidence rate is highest among Malay women, followed by Chinese and Indian. The peak age for breast cancer incidence is from 50 to 59 years old. Results suggest that the chance of developing breast cancer is increased in older women, and reduced with breastfeeding practice. The weight status might affect the breast cancer risk differently. Additional studies are needed to confirm these findings.
Seven new indole alkaloids (1-7) comprising four vobasine, two tacaman, and one corynanthe-tryptamine bisindole alkaloid were isolated from the stem-bark extract of a Malayan Tabernaemontana. Two of the new vobasine alkaloids (1, 3), as well as 16-epivobasine (15) and 16-epivobasenal (17), showed appreciable cytotoxicity toward KB cells (IC50 ca. 5 μg/mL). The structure of the known Tabernaemontana alkaloid tronoharine (8) was revised based on newly acquired NMR data, as well as X-ray diffraction analysis.
Six new bisindole alkaloids of the iboga-vobasine type, vobatensines A-F (1-6), in addition to four known bisindoles (8-11), were isolated from a stem bark extract of a Malayan Tabernaemontana corymbosa. The structures of these alkaloids were determined based on analysis of the spectroscopic data and in the case of vobatensines A (1), B (2), and 16'-decarbomethoxyvoacamine (8) also confirmed by partial syntheses. Nine of these alkaloids (1-5, 8-11) showed pronounced in vitro growth inhibitory activity against human KB, PC-3, LNCaP, HCT 116, HT-29, MCF7, MDA-MB-231, and A549 cancer cells.
Examination of the EtOH extract of the leaves of the Malayan Tabernaemontana corymbosa resulted in the isolation of four new (1-4) and two known bisindole alkaloids (5, 6) of the Aspidosperma- Aspidosperma type. The structures of these alkaloids were determined based on analysis of the spectroscopic data (NMR and HRESIMS). X-ray diffraction analyses of the related bisindole alkaloids conophylline (5) and conophyllinine (6) established the absolute configurations. Treatment of the bisindole alkaloid conophylline (5) with benzeneselenic anhydride gave, in addition to the known bisindole polyervinine (7) previously isolated from another Malayan Tabernaemontana, another bisindole product, 8, an isolable tautomer of 7. X-ray diffraction analyses yielded the absolute configurations of both bisindoles and in addition showed that polyervinine (7) exists primarily as the neutral dione structure. The bisindoles (1-8) and the related conophylline-type bisindoles (9-13) showed pronounced in vitro growth inhibitory activity against an array of human cancer cell lines, including KB, vincristine-resistant KB, PC-3, LNCaP, MCF7, MDA-MB-231, A549, HT-29, and HCT 116 cells, with IC50 values for the active compounds in the 0.01-5 μM range.
Physiochemical changes, including size, are known to affect gold nanoparticle cellular internalization and treatment efficacy. Here, we report the effect of four sizes of cystine/citric acid-coated confeito-like gold nanoparticles (confeito-AuNPs) (30, 60, 80 and 100nm) on cellular uptake, intracellular localization and photothermal anticancer treatment efficiency in MDA-MB231 breast cancer cells. Cellular uptake is size dependent with the smallest size of confeito-AuNPs (30nm) having the highest cellular internalization via clathrin- and caveolae-mediated endocytosis. However, the other three sizes (60, 80 and 100nm) utilize clathrin-mediated endocytosis for cellular uptake. The intracellular localization of confeito-AuNPs is related to their endocytosis mechanism, where all sizes of confeito-AuNPs were localized highly in the lysosome and mitochondria, while confeito-AuNPs (30nm) gave the highest localization in the endoplasmic reticulum. Similarly, a size-dependent trend was also observed in in vitro photothermal treatment experiments, with the smallest confeito-AuNPs (30nm) giving the highest cell killing rate, whereas the largest size of confeito-AuNPs (100nm) displayed the lowest photothermal efficacy. Its desirable physicochemical characteristics, biocompatible nature and better photothermal efficacy will form the basis for further development of multifunctional confeito-AuNP-based nanotherapeutic applications.