Displaying publications 1 - 20 of 89 in total

Abstract:
Sort:
  1. Sim KS, Teh V
    J Microsc, 2015 Dec;260(3):352-62.
    PMID: 26292081 DOI: 10.1111/jmi.12302
    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.
  2. Sim KS, Norhisham S
    J Microsc, 2016 11;264(2):159-174.
    PMID: 27238911 DOI: 10.1111/jmi.12425
    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.
  3. 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.
  4. Sim KS, White JD
    J Microsc, 2005 Mar;217(Pt 3):235-40.
    PMID: 15725127
    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.
  5. 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.
  6. 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.
  7. 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.
  8. Ranjit S, Sim K, Besar R, Tso C
    Biomed Imaging Interv J, 2009 Jul;5(3):e32.
    PMID: 21611059 MyJurnal DOI: 10.2349/biij.5.3.e32
    By applying a hexagon-diamond search (HDS) method to an ultrasound image, the path of an object is able to be monitored by extracting images into macro-blocks, thereby achieving image redundancy is reduced from one frame to another, and also ascertaining the motion vector within the parameters searched. The HDS algorithm uses six search points to form the six sides of the hexagon pattern, a centre point, and a further four search points to create diamond pattern within the hexagon that clarifies the focus of the subject area.
  9. Sim KS, Wee MY, Lim WK
    Microsc Res Tech, 2008 Oct;71(10):710-20.
    PMID: 18615490 DOI: 10.1002/jemt.20610
    We propose to cascade the Shape-Preserving Piecewise Cubic Hermite model with the Autoregressive Moving Average (ARMA) interpolator; we call this technique the Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average (SP2CHARMA) model. In a few test cases involving different images, this model is found to deliver an optimum solution for signal to noise ratio (SNR) estimation problems under different noise environments. The performance of the proposed estimator is compared with two existing methods: the autoregressive-based and autoregressive moving average estimators. Being more robust with noise, the SP2CHARMA estimator has efficiency that is significantly greater than those of the two methods.
  10. Sim KS, Tso CP, Ting HY
    J Microsc, 2008 Nov;232(2):313-34.
    PMID: 19017231 DOI: 10.1111/j.1365-2818.2008.02103.x
    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.
  11. Sim KS, Tso CP, Law KK
    Microsc Res Tech, 2008 Apr;71(4):315-24.
    PMID: 18172898 DOI: 10.1002/jemt.20558
    The mixed Lagrange time-delay estimation autoregressive (MLTDEAR) model is proposed as a solution to estimate image noise variance. The only information available to the proposed estimator is a corrupted image and the nature of additive white noise. The image autocorrelation function is calculated and used to obtain the MLTDEAR model coefficients; the relationship between the MLTDEAR and linear prediction models is utilized to estimate the model coefficients. The forward-backward prediction is then used to obtain the predictor coefficients; the MLTDEAR model coefficients and prior samples of zero-offset autocorrelation values are next used to predict the power of the noise-free image. Furthermore, the fundamental performance limit of the signal and noise estimation, as derived from the Cramer-Rao inequality, is presented.
  12. Sim KS, Law KK, Tso CP
    Microsc Res Tech, 2007 Nov;70(11):919-27.
    PMID: 17661362
    A new filter is developed for the enhancement of scanning electron microscope (SEM) images. A mixed Lagrange time delay estimation auto-regression (MLTDEAR)-based interpolator is used to provide an estimate of noise variance to a standard Wiener filter. A variety of images are captured and the performance of the filter is shown to surpass the conventional noise filters. As all the information required for processing is extracted from a single image, this method is not constrained by image registration requirements and thus can be applied in real-time in cases where specimen drift is presented in the SEM image.
  13. Sim KS, Chuah HT, Zheng C
    J Microsc, 2005 Jul;219(Pt 1):1-17.
    PMID: 15998361
    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.
  14. Sim KS, Lim MS, Yeap ZX
    J Microsc, 2016 07;263(1):64-77.
    PMID: 26871742 DOI: 10.1111/jmi.12376
    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.
  15. Heng MP, Sim KS, Tan KW
    J Inorg Biochem, 2020 07;208:111097.
    PMID: 32438269 DOI: 10.1016/j.jinorgbio.2020.111097
    Two new Schiff base ligands (TE and TF) were prepared from conjugation of testosterone with 4-(4-ethylphenyl)-3-thiosemicarbazide and 4-(4-fluorophenyl)-3-thiosemicarbazide, respectively. Their nickel (NE and NF) and zinc (ZE and ZF) complexes were reported. X-ray crystallography revealed a distorted square planar geometry was adopted by NE. The compounds demonstrated excellent selectivity towards the colorectal carcinoma cell line HCT 116 despite their weak preferences towards the prostate cancer cell lines (PC-3 and LNCaP). Against HCT 116, all these compounds were able to arrest cell cycle at G0/G1 phase and induce apoptosis via mitochondria-dependent (TE, NE, and TF) and extrinsic apoptotic pathway (ZE, NF, and ZF). Moreover, only ZE was able to act as topoisomease I poison and halt its enzymatic reactions although all compounds presented excellent affinity towards DNA.
  16. Yeap ZX, Sim KS, Tso CP
    Microsc Res Tech, 2019 Apr;82(4):402-414.
    PMID: 30575192 DOI: 10.1002/jemt.23181
    Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two-dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub-band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub-band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.
  17. Saad HM, Sim KS, Tan YS
    Int J Med Mushrooms, 2018;20(2):141-153.
    PMID: 29773006 DOI: 10.1615/IntJMedMushrooms.2018025463
    Five culinary-medicinal mushrooms are commonly available in the Malaysian market: Agaricus bisporus (white and brown), Ganoderma lucidum, Hypsizygus marmoreus, Pleurotus floridanus, and P. pulmonarius. These species were selected for use in the current study, the aim of which was to investigate the antimelanogenesis and anti-inflammatory activity of these mushrooms in an attempt to evaluate their potential use in cosmeceuticals. Mushroom fruiting bodies were extracted with hot water, and the extracts were freeze-dried before testing. The antimelanogenesis activity of the extracts was determined by cell viability assay, measurement of intracellular melanin content, and cellular tyrosinase assay with B16F10 melanoma cells. The anti-inflammatory activity of the mushroom extracts was tested by measuring the levels of nitric oxide (NO), tumor necrosis factor (TNF)-α, and interleukin-10 excreted by RAW264.7 macrophages. Brown A. bisporus reduced intracellular melanin content to the largest extent-up to 57.05 ± 3.90%-without a cytotoxic effect on B16F10 melanoma cells. This extract also reduced cellular tyrosinase activity to 17.93 ± 2.65%, performing better than kojic acid, the positive control. In parallel, the extract from brown A. bisporus, at the highest concentration tested, has appreciable anti-inflammatory activity through reductions of NO and TNF-α levels. The other 5 extracts showed moderate antimelanogenesis and anti-inflammatory activities. In summary, our findings show that A. bisporus (brown) extract has the potential to be used as an ingredient in whitening skincare products and to sooth the inflammatory response on the skin.
  18. Teoh WY, Wahab NA, Sim KS
    Nucleosides Nucleotides Nucleic Acids, 2017 Apr 03;36(4):243-255.
    PMID: 28323520 DOI: 10.1080/15257770.2016.1268693
    This study aims to investigate the mechanisms associated with the antiproliferation effect of guanosine on human colon carcinoma HCT 116 cells. In this study, guanosine induced more drastic cell cycle arrest effect than cell death effect on HCT 116 cells. The cell cycle arrest effect of guanosine on HCT 116 cells appeared to be associated with the increased activation of mitogen-activated protein kinases (MAPK) such as ERK1/2, p38 and JNK. The decrease of AMP-activated protein kinase (AMPK) activation and cyclin D1 expression was also involved. Thus, the antiproliferation of colon cancer cells of guanosine could be mediated by the disruption of MAPK and AMPK pathways.
  19. Ting FF, Sim KS, Lim CP
    Comput Med Imaging Graph, 2018 11;69:82-95.
    PMID: 30219737 DOI: 10.1016/j.compmedimag.2018.08.011
    Computed Tomography (CT) images are widely used for the identification of abnormal brain tissues following infarct and hemorrhage of a stroke. The treatment of this medical condition mainly depends on doctors' experience. While manual lesion delineation by medical doctors is currently considered as the standard approach, it is time-consuming and dependent on each doctor's expertise and experience. In this study, a case-control comparison brain lesion segmentation (CCBLS) method is proposed to segment the region pertaining to brain injury by comparing the voxel intensity of CT images between control subjects and stroke patients. The method is able to segment the brain lesion from the stacked CT images automatically without prior knowledge of the location or the presence of the lesion. The aim is to reduce medical doctors' burden and assist them in making an accurate diagnosis. A case study with 300 sets of CT images from control subjects and stroke patients is conducted. Comparing with other existing methods, the outcome ascertains the effectiveness of the proposed method in detecting brain infarct of stroke patients.
  20. 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.
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links