Local dynamic range compensation for scanning electron microscope imaging system by sub‐blocking multiple peak HE with convolution

Sim, K. S.; Teh, V.; Tey, Yi Chin; Kho, T.K.

doi:10.1002/sca.21285

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Next, entropy is used along with specific statistical measures to redistribute the image intensities and obtain the output. Likewise, a histogram sub-blocking-based algorithm is developed by (Sim, Teh, Tey & Kho, 2016), which starts by normalizing the input image to get its correct confined information. Then, the normalized image is broken into different sub-blocks and each subblock is enhanced using a generalized HE technique.…”

Section: Related Workmentioning

confidence: 99%

Contrast Enhancement of Scanning Electron Microscopy Images Using a Noncomplex Multiphase Algorithm

ALSAYGH

Al-Ameen

2022

acs

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Microscopic technology has recently flourished, allowing unparalleled viewing of microscopic elements invisible to the normal eye. Still, the existence of unavoidable constraints led on many occasions to have low contrast scanning electron microscopic (SEM) images. Thus, a noncomplex multiphase (NM) algorithm is proposed in this study to provide better contrast for various SEM images. The developed algorithm contains the following stages: first, the intensities of the degraded image are modified using a two-step regularization procedure. Next, a gamma-corrected cumulative distribution function of the logarithmic uniform distribution approach is applied for contrast enhancement. Finally, an automated histogram expansion technique is used to redistribute the pixels of the image properly. The NM algorithm is applied to natural-contrast distorted SEM images, as well as its results are compared with six algorithms with different processing notions. To assess the quality of images, three modern metrics are utilized, in that each metric measures the quality based on unique aspects. Extensive appraisals revealed the adequate processing abilities of the NM algorithm, as it can process many images suitably and its performances outperformed many available contrast enhancement algorithms in different aspects.

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Section: Related Workmentioning

confidence: 99%

Contrast Enhancement of Scanning Electron Microscopy Images Using a Noncomplex Multiphase Algorithm

ALSAYGH

Al-Ameen

2022

acs

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“…Furthermore, Sim and Huang () introduced an improved version of a dynamic range histogram modification technique to stretch the dynamic range devoid of altering its histogram. Afterwards, Sim, Teh, Tey, and Kho () proposed a subblocking multiple‐peak histogram equalization technique, which works by sampling the input image then normalizing it to get the local information. The input image is then divided into subblocks and the generalized histogram is determined for each block to equalize it in a piecewise way.…”

Section: Related Workmentioning

confidence: 99%

An improved contrast equalization technique for contrast enhancement in scanning electron microscopy images

Al-Ameen

2018

Microscopy Res & Technique

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In scanning electron microscopy (SEM), an intensive beam of electrons is utilized to generate high-quality images that can be used across scientific disciplines including biology and medicine. However, the images produced by different SEM devices are often degraded by a low-contrast effect as a result of various existing limitations. Hence, an improved contrast equalization technique is proposed in this article to ameliorate the contrast of SEM images and provide better visual quality results. The proposed technique consists of two major phases; the first includes a two-step regularization process used to regularize the intensities of the input image, while the second includes a two-step mapping process used to further improve the contrast and to remap image intensities to their natural dynamic range. The proposed technique is tested with many real-degraded and synthetic-degraded SEM images, and its results are compared with six well-known contrast enhancement techniques. Accordingly, the comparison results are evaluated using four advanced image quality assessment metrics. Intensive experiments and comparisons exhibited the favorability of the proposed technique, in that it provided visually pleasing results with no visible flaws and outperformed the comparison techniques in terms of perceived quality and scored accuracy.

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“…However, there are some drawbacks from this HE technique. HE tends to be over enhanced in certain situation and causes unwanted noise existed (Sim et al ., ). Noise on CT brain images can affect the ischemic stroke diagnosis process and may also lead to wrong interpretation.…”

Section: Introductionmentioning

confidence: 97%

“…DSIHE has the same concept as BBHE by separating the input image into two sub‐images. The only difference is that DSIHE uses Shannon's entropy value for histogram division instead of the mean value (Wang et al ., ; Sim et al ., ). However, these two techniques may produce some artifacts depending on the difference of gray level distribution.…”

Section: Introductionmentioning

confidence: 97%

Brain early infarct detection using gamma correction extreme‐level eliminating with weighting distribution

2016

Self Cite

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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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Local dynamic range compensation for scanning electron microscope imaging system by sub‐blocking multiple peak HE with convolution

Cited by 4 publications

References 11 publications

Contrast Enhancement of Scanning Electron Microscopy Images Using a Noncomplex Multiphase Algorithm

Contrast Enhancement of Scanning Electron Microscopy Images Using a Noncomplex Multiphase Algorithm

An improved contrast equalization technique for contrast enhancement in scanning electron microscopy images

Brain early infarct detection using gamma correction extreme‐level eliminating with weighting distribution

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