The chitosan has been used as the primary excipient in transdermal particulate dosage form design. Its distribution pattern across the epidermis and dermis is not easily accessible through chemical assay and limited to radiolabelled molecules via quantitative autoradiography. This study explored Fourier-transform infrared spectroscopy imaging technique with built-in microscope as the means to examine chitosan molecular distribution over epidermis and dermis with the aid of histology operation. Fourier-transform infrared spectroscopy skin imaging was conducted using chitosan of varying molecular weights, deacetylation degrees, particle sizes and zeta potentials, obtained via microwave ligation of polymer chains at solution state. Both skin permeation and retention characteristics of chitosan increased with the use of smaller chitosan molecules with reduced acetyl content and size, and increased positive charge density. The ratio of epidermal to dermal chitosan content decreased with the use of these chitosan molecules as their accumulation in dermis (3.90% to 18.22%) was raised to a greater extent than epidermis (0.62% to 1.92%). A larger dermal chitosan accumulation nonetheless did not promote the transdermal polymer passage more than the epidermal chitosan. A small increase in epidermal chitosan content apparently could fluidize the stratum corneum and was more essential to dictate molecular permeation into dermis and systemic circulation. The histology technique aided Fourier-transform infrared spectroscopy imaging approach introduces a new dimension to the mechanistic aspect of chitosan in transdermal delivery.
A new technique based on cubic spline interpolation with Savitzky-Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real-time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky-Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal-to-noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation-based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time.
A new technique based on cubic spline interpolation with Savitzky-Golay noise reduction filtering is designed to estimate signal-to-noise ratio of scanning electron microscopy (SEM) images. This approach is found to present better result when compared with two existing techniques: nearest neighbourhood and first-order interpolation. When applied to evaluate the quality of SEM images, noise can be eliminated efficiently with optimal choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time.
A new technique for estimation of signal-to-noise ratio in scanning electron microscope images is reported. The method is based on the image noise cross-correlation estimation model recently developed. We derive the basic performance limits on a single image signal-to-noise ratio estimation using the Cramer-Rao inequality. The results are compared with those from existing estimation methods including the nearest neighbourhood (the simple method), the first order linear interpolator, and the autoregressive based estimator. The comparisons were made using several tests involving different images within the performance bounds. From the results obtained, the efficiency and accuracy of image noise cross-correlation estimation technique is considerably better than the other three methods.
An exponential contrast stretching (ECS) technique is developed to reduce the charging effects on scanning electron microscope images. Compared to some of the conventional histogram equalization methods, such as bi-histogram equalization and recursive mean-separate histogram equalization, the proposed ECS method yields better image compensation. Diode sample chips with insulating and conductive surfaces are used as test samples to evaluate the efficiency of the developed algorithm. The algorithm is implemented in software with a frame grabber card, forming the front-end video capture element.
An improvement to the previously proposed Canny optimization technique for scanning electron microscope image colorization is reported. The additional process is adaptive tuning, where colour tuning is performed adaptively, based on comparing the original luminance values with calculated luminance values. The complete adaptive Canny optimization technique gives significantly better mechanical contrast on scanning electron microscope grey-scale images than do existing methods.
Interpolation techniques that are used for image magnification to obtain more useful details of the surface such as morphology and mechanical contrast usually rely on the signal information distributed around edges and areas of sharp changes and these signal information can also be used to predict missing details from the sample image. However, many of these interpolation methods tend to smooth or blur out image details around the edges. In the present study, a Lagrange time delay estimation interpolator method is proposed and this method only requires a small filter order and has no noticeable estimation bias. Comparing results with the original scanning electron microscope magnification and results of various other interpolation methods, the Lagrange time delay estimation interpolator is found to be more efficient, more robust and easier to execute.
A new and robust parameter estimation technique, named Gaussian-Taylor interpolation, 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 piecewise cubic Hermite interpolation, quadratic spline interpolation, autoregressive moving average and moving average. Overall, the proposed estimations for noise-free peak and SNR are most consistent and accurate to within a certain acceptable degree compared with the others.
Images of scanning electron microscope are usually in the monochrome mode. A simple and user-friendly approach is proposed to improve the mechanical contrast of the scanning electron microscope grey images. Also, most colourization techniques involve image segmentation or region tracking, which tend to degrade the image with fuzzy or complex region boundaries. A technique is proposed, which is a hybrid between the Canny edge detection technique and the optimization technique. Compared with existing methods, the new Canny optimization technique gives satisfactory results for scanning electron microscope images.
A proposal to assess the quality of scanning electron microscope images using mixed Lagrange time delay estimation technique is presented. With optimal scanning electron microscope scan rate information, online images can be quantified and improved. The online quality assessment technique is embedded onto a scanning electron microscope frame grabber card for real-time image processing. Different images are captured using scanning electron microscope and a database is built to optimally choose filter parameters. An optimum choice of filter parameters is obtained. With the optimum choice of scan rate, noise can be removed from real-time scanning electron microscope images without causing any sample contamination or increasing scanning time.
The quality of an image generated by a scanning electron microscope is dependent on secondary emission, which is a strong function of surface condition. Thus, empirical formulae and available databases are unable to take into account actual metrology conditions. This paper introduces a simple and reliable measurement technique to measure secondary electron yield (delta) and backscattered electron yield (eta) that is suitable for in-situ measurements on a specimen immediately prior to imaging. The reliability of this technique is validated on a number of homogenous surfaces. The measured electron yields are shown to be within the range of published data and the calculated signal-to-noise ratio compares favourably with that estimated from the image.
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.
Nonlinear optical microscopy (NLOM) was used as a noninvasive and label-free tool to detect and quantify the extent of the cartilage recovery. Two cartilage injury models were established in the outer ears of rabbits that created a different extent of cartilage recovery based on the presence or absence of the perichondrium. High-resolution NLOM images were used to measure cartilage repair, specifically through spectral analysis and image texture. In contrast to a wound lacking a perichondrium, wounds with intact perichondria demonstrated significantly larger TPEF signals from cells and matrix, coarser texture indicating the more deposition of type I collagen. Spectral analysis of cells and matrix can reveal the matrix properties and cell growth. In addition, texture analysis of NLOM images showed significant differences in the distribution of cells and matrix of repaired tissues with or without perichondrium. Specifically, the decay length of autocorrelation coefficient based on TPEF images is 11.2 ± 1.1 in Wound 2 (with perichondrium) and 7.5 ± 2.0 in Wound 1 (without perichondrium), indicating coarser image texture and faster growth of cells in repaired tissues with perichondrium (p < 0.05). Moreover, the decay length of autocorrelation coefficient based on collagen SHG images also showed significant difference between Wound 2 and 1 (16.2 ± 1.2 vs. 12.2 ± 2.1, p < 0.05), indicating coarser image texture and faster deposition of collagen in repaired tissues with perichondrium (Wound 2). These findings suggest that NLOM is an ideal tool for studying cartilage repair, with potential applications in clinical medicine. NLOM can capture macromolecular details and distinguish between different extents of cartilage repair without the need for labelling agents.
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.
Pontamine fast scarlet 4B is a red paper and textiles dye that has recently been introduced as a fluorescent probe for plant cell walls. Pontamine exhibits bifluorescence, or fluorescence dependent on the polarization of the excitation light: Because cellulose is aligned within the cell wall, pontamine-labelled cell walls exhibit variable fluorescence as the excitation polarization is modulated. Thus, bifluorescence measurements require polarized excitation that can be directly or indirectly modulated. In our confocal microscopy observations of various cellulose samples labelled with pontamine, we modulated excitation polarization either through sample rotation or by the confocal's scanfield rotation function. This variably rotated laser polarizations on Leica confocal microscopes, but not those from other makers. Beginning with samples with directly observable microfibril orientations, such as purified bacterial cellulose, the velamen of orchid roots and the inner S2 layer of radiata pine compression wood, we demonstrate that modelling the variations in pontamine fluorescence with a sine curve can be used to measure the known microfibril angles. We then measured average local microfibril angles in radiata pine samples, and showed similar microfibril angles in compression and normal (opposite) wood. Significantly, bifluorescence measurements might also be used to understand the degree of local cellulose alignment within the cell wall, as opposed to variations in the overall cellulose angle.