Displaying publications 1 - 20 of 23 in total

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  1. Teh V, Sim KS, Wong EK
    Scanning, 2016 Nov;38(6):842-856.
    PMID: 27302216 DOI: 10.1002/sca.21334
    According to the statistic from World Health Organization (WHO), stroke is one of the major causes of death globally. Computed tomography (CT) scan is one of the main medical diagnosis system used for diagnosis of ischemic stroke. CT scan provides brain images in Digital Imaging and Communication in Medicine (DICOM) format. The presentation of CT brain images is mainly relied on the window setting (window center and window width), which converts an image from DICOM format into normal grayscale format. Nevertheless, the ordinary window parameter could not deliver a proper contrast on CT brain images for ischemic stroke detection. In this paper, a new proposed method namely gamma correction extreme-level eliminating with weighting distribution (GCELEWD) is implemented to improve the contrast on CT brain images. GCELEWD is capable of highlighting the hypodense region for diagnosis of ischemic stroke. The performance of this new proposed technique, GCELEWD, is compared with four of the existing contrast enhancement technique such as brightness preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), extreme-level eliminating histogram equalization (ELEHE), and adaptive gamma correction with weighting distribution (AGCWD). GCELEWD shows better visualization for ischemic stroke detection and higher values with image quality assessment (IQA) module. SCANNING 38:842-856, 2016. © 2016 Wiley Periodicals, Inc.
  2. Sim KS, Nia ME, Tso CP
    Scanning, 2013 May-Jun;35(3):205-12.
    PMID: 22961698 DOI: 10.1002/sca.21055
    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.
    Matched MeSH terms: Microscopy, Electron, Scanning
  3. Lau CK, Sim KS, Tso CP
    Scanning, 2011 Jan-Feb;33(1):13-20.
    PMID: 21462221 DOI: 10.1002/sca.20216
    This article focuses on the localization of burn mark in MOSFET and the scanning electron microscope (SEM) inspection on the defect location. When a suspect abnormal topography is shown on the die surface, further methods to pin-point the defect location is necessary. Fault localization analysis becomes important because an abnormal spot on the chip surface may and may not have a defect underneath it. The chip surface topography can change due to the catastrophic damage occurred at layers under the chip surface, but it could also be due to inconsistency during metal deposition in the wafer fabrication process. Two localization techniques, liquid crystal thermography and emission microscopy, were performed to confirm that the abnormal topography spot is the actual defect location. The tiny burn mark was surfaced by performing a surface decoration at the defect location using hot hydrochloric acid. SEM imaging, which has the high magnification and three-dimensional capabilities, was used to capture the images of the burn mark.
    Matched MeSH terms: Microscopy, Electron, Scanning
  4. Sim KS, Nia ME, Tso CP
    Scanning, 2011 Mar-Apr;33(2):82-93.
    PMID: 21381045 DOI: 10.1002/sca.20223
    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.
    Matched MeSH terms: Microscopy, Electron, Scanning
  5. Sim KS, Yeap ZX, Tso CP
    Scanning, 2016 Nov;38(6):502-514.
    PMID: 26618491 DOI: 10.1002/sca.21286
    An improvement to the existing technique of quantifying signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images using piecewise cubic Hermite interpolation (PCHIP) technique is proposed. The new technique uses an adaptive tuning onto the PCHIP, and is thus named as ATPCHIP. To test its accuracy, 70 images are corrupted with noise and their autocorrelation functions are then plotted. The ATPCHIP technique is applied to estimate the uncorrupted noise-free zero offset point from a corrupted image. Three existing methods, the nearest neighborhood, first order interpolation and original PCHIP, are used to compare with the performance of the proposed ATPCHIP method, with respect to their calculated SNR values. Results show that ATPCHIP is an accurate and reliable method to estimate SNR values from SEM images. SCANNING 38:502-514, 2016. © 2015 Wiley Periodicals, Inc.
  6. Sim KS, Kho YY, Tso CP, Nia ME, Ting HY
    Scanning, 2013 Mar-Apr;35(2):75-87.
    PMID: 22777599 DOI: 10.1002/sca.21037
    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.
  7. Wan Ismail WZ, Sim KS, Tso CP, Ting HY
    Scanning, 2011 Jul-Aug;33(4):233-51.
    PMID: 21611953 DOI: 10.1002/sca.20237
    To reduce undesirable charging effects in scanning electron microscope images, Rayleigh contrast stretching is developed and employed. First, re-scaling is performed on the input image histograms with Rayleigh algorithm. Then, contrast stretching or contrast adjustment is implemented to improve the images while reducing the contrast charging artifacts. This technique has been compared to some existing histogram equalization (HE) extension techniques: recursive sub-image HE, contrast stretching dynamic HE, multipeak HE and recursive mean separate HE. Other post processing methods, such as wavelet approach, spatial filtering, and exponential contrast stretching, are compared as well. Overall, the proposed method produces better image compensation in reducing charging artifacts.
    Matched MeSH terms: Microscopy, Electron, Scanning/methods*
  8. Al-Ameen Z, Sulong G
    Scanning, 2015 Mar-Apr;37(2):116-25.
    PMID: 25663630 DOI: 10.1002/sca.21187
    Contrast is a distinctive visual attribute that indicates the quality of an image. Computed Tomography (CT) images are often characterized as poor quality due to their low-contrast nature. Although many innovative ideas have been proposed to overcome this problem, the outcomes, especially in terms of accuracy, visual quality and speed, are falling short and there remains considerable room for improvement. Therefore, an improved version of the single-scale Retinex algorithm is proposed to enhance the contrast while preserving the standard brightness and natural appearance, with low implementation time and without accentuating the noise for CT images. The novelties of the proposed algorithm consist of tuning the standard single-scale Retinex, adding a normalized-ameliorated Sigmoid function and adapting some parameters to improve its enhancement ability. The proposed algorithm is tested with synthetically and naturally degraded low-contrast CT images, and its performance is also verified with contemporary enhancement techniques using two prevalent quality evaluation metrics-SSIM and UIQI. The results obtained from intensive experiments exhibited significant improvement not only in enhancing the contrast but also in increasing the visual quality of the processed images. Finally, the proposed low-complexity algorithm provided satisfactory results with no apparent errors and outperformed all the comparative methods.
  9. Kamel NS, Sim KS
    Scanning, 2004 12 23;26(6):277-81.
    PMID: 15612204
    During the last three decades, several techniques have been proposed for signal-to-noise ratio (SNR) and noise variance estimation in images, with different degrees of success. Recently, a novel technique based on the statistical autoregressive model (AR) was developed and proposed as a solution to SNR estimation in scanning electron microscope (SEM) image. In this paper, the efficiency of the developed technique with different imaging systems is proven and presented as an optimum solution to image noise variance and SNR estimation problems. Simulation results are carried out with images like Lena, remote sensing, and SEM. The two image parameters, SNR and noise variance, are estimated using different techniques and are compared with the AR-based estimator.
    Matched MeSH terms: Microscopy, Electron, Scanning
  10. Zhang Y, Liu X, Yusoff M, Razali MH
    Scanning, 2021;2021:3839235.
    PMID: 34630820 DOI: 10.1155/2021/3839235
    Flower-like titanium dioxide (TiO2) nanostructures are successfully synthesized using a hybrid sol-gel and a simple hydrothermal method. The sample was characterized using various techniques to study their physicochemical properties and was tested as a photocatalyst for methyl orange degradation and as an antibacterial material. Raman spectrum and X-ray diffraction (XRD) pattern show that the phase structure of the synthesized TiO2 is anatase with 80-100 nm in diameter and 150-200 nm in length of flower-like nanostructures as proved by field emission scanning electron microscope (FESEM). The energy-dispersive X-ray spectroscopy (EDS) analysis of flower-like anatase TiO2 nanostructure found that only titanium and oxygen elements are present in the sample. The anatase phase was confirmed further by a high-resolution transmission electron microscope (HRTEM) and selected area electron diffraction (SAED) pattern analysis. The Brunauer-Emmett-Teller (BET) result shows that the sample had a large surface area (108.24 m2/g) and large band gap energy (3.26 eV) due to their nanosize. X-ray photoelectron spectroscopy (XPS) analysis revealed the formation of Ti4+ and Ti3+ species which could prevent the recombination of the photogenerated electron, thus increased the electron transportation and photocatalytic activity of flower-like anatase TiO2 nanostructure to degrade the methyl orange (83.03%) in a short time (60 minutes). These properties also support the good performance of flower-like titanium dioxide (TiO2) nanostructure as an antibacterial material which is comparable with penicillin which is 13.00 ± 0.02 mm inhibition zone against Staphylococcus aureus.
  11. Sim KS, NorHisham S
    Scanning, 2016 Nov;38(6):771-782.
    PMID: 27253888 DOI: 10.1002/sca.21327
    A technique based on linear Least Squares Regression (LSR) model is applied to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. In order to test the accuracy of this technique on SNR estimation, a number of SEM images are initially corrupted with white noise. The autocorrelation function (ACF) of the original and the corrupted SEM images are formed to serve as the reference point to estimate the SNR value of the corrupted image. The LSR technique is then compared with the previous three existing techniques known as nearest neighbourhood, first-order interpolation, and the combination of both nearest neighborhood and first-order interpolation. The actual and the estimated SNR values of all these techniques are then calculated for comparison purpose. It is shown that the LSR technique is able to attain the highest accuracy compared to the other three existing techniques as the absolute difference between the actual and the estimated SNR value is relatively small. SCANNING 38:771-782, 2016. © 2016 Wiley Periodicals, Inc.
  12. Lo TY, Sim KS, Tso CP, Nia ME
    Scanning, 2014 Sep-Oct;36(5):530-9.
    PMID: 25139061 DOI: 10.1002/sca.21152
    An improvement to the previously proposed adaptive Canny optimization technique for scanning electron microscope image colorization is reported. The additional feature, called pseudo-mapping technique, is that the grayscale markings are temporarily mapped to a set of pre-defined pseudo-color map as a mean to instill color information for grayscale colors in chrominance channels. This allows the presence of grayscale markings to be identified; hence optimization colorization of grayscale colors is made possible. This additional feature enhances the flexibility of scanning electron microscope image colorization by providing wider range of possible color enhancement. Furthermore, the nature of this technique also allows users to adjust the luminance intensities of selected region from the original image within certain extent.
    Matched MeSH terms: Microscopy, Electron, Scanning
  13. Sim KS, Teh V, Nia ME
    Scanning, 2016 Mar;38(2):148-63.
    PMID: 26235517 DOI: 10.1002/sca.21250
    Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments. SCANNING 38:148-163, 2016. © 2015 Wiley Periodicals, Inc.
  14. Sim KS, Teh V, Tey YC, Kho TK
    Scanning, 2016 Nov;38(6):492-501.
    PMID: 26618303 DOI: 10.1002/sca.21285
    This paper introduces new development technique to improve the Scanning Electron Microscope (SEM) image quality and we name it as sub-blocking multiple peak histogram equalization (SUB-B-MPHE) with convolution operator. By using this new proposed technique, it shows that the new modified MPHE performs better than original MPHE. In addition, the sub-blocking method consists of convolution operator which can help to remove the blocking effect for SEM images after applying this new developed technique. Hence, by using the convolution operator, it effectively removes the blocking effect by properly distributing the suitable pixel value for the whole image. Overall, the SUB-B-MPHE with convolution outperforms the rest of methods. SCANNING 38:492-501, 2016. © 2015 Wiley Periodicals, Inc.
  15. Sim KS, Kamel NS
    Scanning, 2004 7 31;26(3):135-9.
    PMID: 15283250
    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.
    Matched MeSH terms: Microscopy, Electron, Scanning
  16. Sim KS, Huang YH
    Scanning, 2015 Nov-Dec;37(6):381-8.
    PMID: 25969945 DOI: 10.1002/sca.21226
    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.
    Matched MeSH terms: Microscopy, Electron, Scanning
  17. Ahmed HM, Khamis MF, Gutmann JL
    Scanning, 2016 Nov;38(6):554-557.
    PMID: 26751249 DOI: 10.1002/sca.21299
    The root and root canal morphology of deciduous molars shows considerable variations. Consequently, a comprehensive understanding of the normal and unusual root and root canal configuration types in deciduous teeth is of prime importance. The purpose of this report is to describe a rare anatomical variation in a double-rooted maxillary deciduous molar examined by the dental operating microscope and micro-computed tomography. SCANNING 38:554-557, 2016. © 2016 Wiley Periodicals, Inc.
  18. Rad MA, Ahmad MR, Nakajima M, Kojima S, Homma M, Fukuda T
    Scanning, 2017;2017:8393578.
    PMID: 29109826 DOI: 10.1155/2017/8393578
    The preparation and observations of spheroplast W303 cells are described with Environmental Scanning Electron Microscope (ESEM). The spheroplasting conversion was successfully confirmed qualitatively, by the evaluation of the morphological change between the normal W303 cells and the spheroplast W303 cells, and quantitatively, by determining the spheroplast conversion percentage based on the OD800 absorbance data. From the optical microscope observations as expected, the normal cells had an oval shape whereas spheroplast cells resemble a spherical shape. This was also confirmed under four different mediums, that is, yeast peptone-dextrose (YPD), sterile water, sorbitol-EDTA-sodium citrate buffer (SCE), and sorbitol-Tris-Hcl-CaCl2 (CaS). It was also observed that the SCE and CaS mediums had a higher number of spheroplast cells as compared to the YPD and sterile water mediums. The OD800 absorbance data also showed that the whole W303 cells were fully converted to the spheroplast cells after about 15 minutes. The observations of the normal and the spheroplast W303 cells were then performed under an environmental scanning electron microscope (ESEM). The normal cells showed a smooth cell surface whereas the spheroplast cells had a bleb-like surface after the loss of its integrity when removing the cell wall.
    Matched MeSH terms: Microscopy, Electron, Scanning*
  19. Sim KS, Kamel NS, Chuah HT
    Scanning, 2005 6 7;27(3):147-53.
    PMID: 15934507
    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.
    Matched MeSH terms: Microscopy, Electron, Scanning
  20. Sim KS, Cheng Z, Chuah HT
    Scanning, 2004 12 23;26(6):287-95.
    PMID: 15612206
    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.
    Matched MeSH terms: Microscopy, Electron, Scanning
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