Displaying publications 21 - 23 of 23 in total

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  1. Image noise cross-correlation for signal-to-noise ratio estimation in scanning electron microscope images
    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
  2. Signal-to-noise ratio estimation using adaptive tuning on the piecewise cubic Hermite interpolation model for images
    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.
  3. Brain early infarct detection using gamma correction extreme-level eliminating with weighting distribution
    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.
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