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Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

Yakno M, Mohamad-Saleh J, Ibrahim MZ

Sensors (Basel), 2021 Sep 27;21(19).
PMID: 34640769 DOI: 10.3390/s21196445

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique's impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.

Matched MeSH terms: Image Enhancement*
Fulltext Perbandingan dos glandular purata payudara berdasarkan kombinasi anod/penuras tungsten/rhodium dan tungsten/argentum pada mamografi digital

Chelliah, Kanaga Kumari, Lumin, Felecia

Jurnal Sains Kesihatan Malaysia, 2012;10(1):53-56.
MyJurnal

Mamografi adalah cara paling efektif untuk mengesan keabnormalan payudara di kalangan wanita. Namun, mamografi dipercayai boleh menyebabkan karsinogenesis aruhan sinaran. Justeru pengukuran dos adalah penting untuk menganggar risiko dan mengawal kualiti imej. Kajian ini dijalankan untuk membandingkan dos glandular purata (AGD) yang diperolehi fantom payudara berdasarkan dua kombinasi anod/penuras yang berbeza iaitu tungsten/rhodium (W/Rh) dan tungsten/argentum (W/Ag). Fantom payudara CIRS 012A didedahkan pada projeksi kraniokaudal (CC) menggunakan sistem mamografi digital Hologic Selenia. Kerma udara kemasukan permukaan (ESAK) diukur menggunakan dosimeter pendar cahaya terma (TLD). AGD diperolehi daripada pengiraan asas ESAK dengan faktor penukaran berdasarkan formula Euref. Ujian t tak bersandar menunjukkan perbezaan bererti dalam purata AGD yang diperolehi. Purata AGD W/Rh adalah lebih tinggi berbanding purata AGD W/Ag (p = 0.002, 95% CI: 0.22, 0.53). Kesimpulannya, penggunaan W/Ag menyumbang kepada pengurangan dos semasa pemeriksaan mamografi.

Matched MeSH terms: Radiographic Image Enhancement
Fulltext Optimization of exposure parameters in full field digital mammography using figure of merit. [Pengoptimuman parameter dedahan dalam mamografi digital medan penuh menggunakan rajah merit]

Laila Suryani Elias, Eng M, Ang W, Chelliah K, Abd Aziz Tajuddin, Arasaratnam S, et al.

Sains Malaysiana, 2010;39:299-303.

Mammography is used as a screening tool for early detection of breast cancer. However, the radiation dose used should be as low as possible to minimize any effects on asymptomatic woman while maintaining the diagnostic value of the image as mammography is done annually. This study was done to determine the optimum exposure parameter in exposure control mode (AEC) for two combinations of anode/filter which were molybdenum/molybdenum (Mo/Mo) and molybdenum/rhodium (Mo/Rh) using the Lorad Selenia digital mammography system at the Women’s Breast Clinic, National Cancer Society (NCS). A CIRS012A mammography research of phantom 4 cm thickness with 50% glandularity was exposed in the cranio-caudal projection. TLD 100H was used to measure the entrance surface air kerma (ESAK). The AGD values were then calculated from the ESAK values, incorporating three correction factors (g, c and s) according to Euref protocol. Image quality was evaluated using signal to noise ratio (SNR). Figure of Merit (FOM) which is the ratio of the square of SNR to the AGD shows that 30 kVp is the optimum exposure parameter for a 4 cm thickness phantom with the use of Mo/Rh and Mo/Mo anode/filter combination. Non-parametric Spearman correlation test showed a negative linear relationship between AGD and SNR with increasing tube voltage for both anode/filters.
Keywords: Average glandular dose; CIRS012A phantom; exposure; full field digital mammography
Study site: Women’s Breast Clinic, National Cancer Society (NCS), Kuala Lumpur, Malaysia

Matched MeSH terms: Radiographic Image Enhancement
Fulltext Local convexity-preserving C2 rational cubic spline for convex data

Abbas M, Abd Majid A, Ali JM

ScientificWorldJournal, 2014;2014:391568.
PMID: 24757421 DOI: 10.1155/2014/391568

We present the smooth and visually pleasant display of 2D data when it is convex, which is contribution towards the improvements over existing methods. This improvement can be used to get the more accurate results. An attempt has been made in order to develop the local convexity-preserving interpolant for convex data using C(2) rational cubic spline. It involves three families of shape parameters in its representation. Data dependent sufficient constraints are imposed on single shape parameter to conserve the inherited shape feature of data. Remaining two of these shape parameters are used for the modification of convex curve to get a visually pleasing curve according to industrial demand. The scheme is tested through several numerical examples, showing that the scheme is local, computationally economical, and visually pleasing.

Matched MeSH terms: Image Enhancement/methods
A comparative study of computed radiography-based mammography using digital phosphor storage plate and full field digital mammography

Chelliah KK, Tamanang S, Bt Elias LS, Ying KY

Indian J Med Sci, 2013 11 2;67(1-2):23-8.
PMID: 24178338

BACKGROUND: Two digital mammography systems, based on different physical concepts, have been introduced in the last few years namely the full-field digital mammography (FFDM) system and computed radiography-based mammography using digital storage phosphor plate (DSPM).
AIMS: The objective of this study was to compare the image quality for DSPM and FFDM using a grading scale based on previously published articles.
MATERIALS AND METHODS: This comparative diagnostic study was done for 5-month duration at the Breast Clinic. The system used was the Lorad Selenia FFDM system and the Mammomat 3000 Nova DSPM system. The craniocaudal and mediolateral oblique projections were done on both breast on 58 asymptomatic women using both DSPM and FFDM. The mammograms were evaluated for eight criteria of image quality: Tissue coverage, compression, exposure, contrast, resolution, noise, artifact, and sharpness by two independent radiologists.
STATISTICAL ANALYSIS: Wilcoxon Signed Rank Test and Weighted Kappa.
RESULTS: FFDM was rated significantly better (P < 0.05) for five aspects: Tissue coverage, compression, contrast, exposure, and resolution and equal to DSPM for sharpness, noise, and artifact.
CONCLUSION: FFDM was superior in five aspects and equal to DSPM for three aspects of image quality.

Matched MeSH terms: Radiographic Image Enhancement/instrumentation; Radiographic Image Enhancement/methods*
Assessment of local diagnostic reference levels (LDRLS) for full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) mammography in Tamil Nadu, India

Adhimoolam SK, Kumar S, Manojkumar T, Devanand BL, Elango N, Govindarajan N, et al.

Radiat Prot Dosimetry, 2024 Nov 18;200(19):1926-1932.
PMID: 39375206 DOI: 10.1093/rpd/ncae199

The purpose of this study was to assess local diagnostic reference levels (LDRLs) for full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) mammography in India. Data from 1500 women were collected from five different mammography facilities in major cities in Tamil Nadu, India. The mean of mean glandular dose were used to arrive at an LDRL. The noted mean compressed breast thickness was 55.26 ± 3.4. The recorded mean MGDs for the five centres were 3.1 ± 0.1 and 3.8 ± 0.2 mGy for FFDM and DBT, respectively. The 75th percentile value for all five centers is 3.3 and 4.0 mGy for FFDM and DBT, respectively. The LDRLs found in the current study were also compared with those from earlier studies conducted in other nations, such as the United Kingdom, Malaysia, Morocco, and Ghana. The present study is the first of its kind to determine the LDRL for the FFDM and DBT scanners operating in the Tamil Nadu region, India, and is proposed as a starting point that will allow professionals to evaluate and optimize their practice. Furthermore, similar studies in other regions of India are necessary in order to establish National DRLs.

Matched MeSH terms: Radiographic Image Enhancement/methods; Radiographic Image Enhancement/standards
Fulltext A critical comparison of three full field digital mammography systems using figure of merit

Kanaga KC, Yap HH, Laila SE, Sulaiman T, Zaharah M, Shantini AA

Med J Malaysia, 2010 Jun;65(2):119-22.
PMID: 23756795 MyJurnal

Full field digital mammography (FFDM) has been progressively introduced in medical centers in recent years. However, it is questionable which exposure parameters are suitable in order to reduce the glandular breast doses as they are related to induced carcinogenesis. The goal of this study was to compare the average glandular doses (AGD) and image quality of three FFDM systems namely Siemens Mammomat NovationDR, Hologic Lorad Selenia and General Electric Senographe Essential using a Figure of Merit. A Computerized Imaging Reference Systems (CIRS) tissue equivalent breast phantom which consists of phototimer compensation plate with different thickness and glandularity was exposed in fully automatic exposure control mode in the cranio-caudal projection similar to clinical settings. Thermoluminescent dosimeter 100H (TLD- 100H) was used to measure the entrance surface air kerma (ESAK), the AGD was calculated using European protocol whilst the image quality was assessed quantitatively by measuring the contrast to noise ratio (CNR) value. The obtained values were used to calculate the Figure of Merit (FOM) to analyze the effectiveness of the system. Repeated Measures ANOVA analysis showed that there is a significant difference (p<0.05) in the mean value of AGD and CNR between the three FFDM systems. Hologic Lorad Selenia system contrbuted the highest AGD value while General Electric Senographe Essential had the highest CNR and FOM value. In conclusion, this study may provide an objective criterion during the selection of a mammography unit by using the figure of merit for screening or diagnostic purpose.

Matched MeSH terms: Radiographic Image Enhancement*
Fulltext Significant effect of image contrast enhancement on weld defect detection

Mustafa WA, Yazid H, Alquran H, Al-Issa Y, Junaini S

PLoS One, 2024;19(6):e0306010.
PMID: 38941319 DOI: 10.1371/journal.pone.0306010

Weld defect inspection is an essential aspect of testing in industries field. From a human viewpoint, a manual inspection can make appropriate justification more difficult and lead to incorrect identification during weld defect detection. Weld defect inspection uses X-radiography testing, which is now mostly outdated. Recently, numerous researchers have utilized X-radiography digital images to inspect the defect. As a result, for error-free inspection, an autonomous weld detection and classification system are required. One of the most difficult issues in the field of image processing, particularly for enhancing image quality, is the issue of contrast variation and luminosity. Enhancement is carried out by adjusting the brightness of the dark or bright intensity to boost segmentation performance and image quality. To equalize contrast variation and luminosity, many different approaches have recently been put forth. In this research, a novel approach called Hybrid Statistical Enhancement (HSE), which is based on a direct strategy using statistical data, is proposed. The HSE method divided each pixel into three groups, the foreground, border, and problematic region, using the mean and standard deviation of a global and local neighborhood (luminosity and contrast). To illustrate the impact of the HSE method on the segmentation or detection stage, the datasets, specifically the weld defect image, were used. Bernsen and Otsu's methods are the two segmentation techniques utilized. The findings from the objective and visual elements demonstrated that the HSE approach might automatically improve segmentation output while effectively enhancing contrast variation and normalizing luminosity. In comparison to the Homomorphic Filter (HF) and Difference of Gaussian (DoG) approaches, the segmentation results for HSE images had the lowest result according to Misclassification Error (ME). After being applied to the HSE images during the segmentation stage, every quantitative result showed an increase. For example, accuracy increased from 64.171 to 84.964. In summary, the application of the HSE method has resulted in an effective and efficient outcome for background correction as well as improving the quality of images.

Matched MeSH terms: Radiographic Image Enhancement/methods
A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI

Al-Masni MA, Lee S, Al-Shamiri AK, Gho SM, Choi YH, Kim DH

Comput Biol Med, 2023 Feb;153:106553.
PMID: 36641933 DOI: 10.1016/j.compbiomed.2023.106553

Patient movement during Magnetic Resonance Imaging (MRI) scan can cause severe degradation of image quality. In Susceptibility Weighted Imaging (SWI), several echoes are typically measured during a single repetition period, where the earliest echoes show less contrast between various tissues, while the later echoes are more susceptible to artifacts and signal dropout. In this paper, we propose a knowledge interaction paradigm that jointly learns feature details from multiple distorted echoes by sharing their knowledge with unified training parameters, thereby simultaneously reducing motion artifacts of all echoes. This is accomplished by developing a new scheme that boosts a Single Encoder with Multiple Decoders (SEMD), which assures that the generated features not only get fused but also learned together. We called the proposed method Knowledge Interaction Learning between Multi-Echo data (KIL-ME-based SEMD). The proposed KIL-ME-based SEMD allows to share information and gain an understanding of the correlations between the multiple echoes. The main purpose of this work is to correct the motion artifacts and maintain image quality and structure details of all motion-corrupted echoes towards generating high-resolution susceptibility enhanced contrast images, i.e., SWI, using a weighted average of multi-echo motion-corrected acquisitions. We also compare various potential strategies that might be used to address the problem of reducing artifacts in multi-echoes data. The experimental results demonstrate the feasibility and effectiveness of the proposed method, reducing the severity of motion artifacts and improving the overall clinical image quality of all echoes with their associated SWI maps. Significant improvement of image quality is observed using both motion-simulated test data and actual volunteer data with various motion severity strengths. Eventually, by enhancing the overall image quality, the proposed network can increase the effectiveness of the physicians' capability to evaluate and correctly diagnose brain MR images.

Matched MeSH terms: Image Enhancement/methods
Comparison study of the MRI shoulder PROPELLER technique with differential image quality by variation of shoulder coils

Khairi M, Zakaria F, Supar R, Mohd Z

Med J Malaysia, 2024 Mar;79(Suppl 1):74-81.
PMID: 38555889

INTRODUCTION: Motion and pulsation artifacts are the most prominent types of artifacts in Magnetic Resonance Imaging (MRI) of the shoulder. Therefore, this study examined the Periodically Rotating Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) technique with small flex coil (SFC) and dedicated shoulder coil (DSC) for the reduction of motion and pulsation artifacts. The signalto- noise ratio (SNR) and contrast-to-noise ratio (CNR) of the standard proton density fat saturation (PDFS) pulse sequence and the PROPELLER proton density fat saturation (PROPELLER PDFS) pulse sequence were also evaluated.
MATERIALS AND METHODS: Eighteen (18) participants who met the inclusion and exclusion criteria were scanned using a standard non-contrast MRI shoulder protocol including the PDFS pulse sequence and the PROPELLER PDFS pulse sequence using a small flex coil and a dedicated shoulder coil. Two experienced musculoskeletal (MSK) radiologists evaluated and graded the presence of artifacts on the MR images and the SNR and CNR were measured quantitatively.
RESULTS: The non-parametric Wilcoxon Signed Rank test revealed a significant reduction in motion and pulsation artifacts between the PROPELLER PDFS pulse sequence and the standard PDFS pulse sequence. In addition, the nonparametric Mann-Whitney U test revealed that the mean rank of SNR for the standard sequence was statistically significant when compared to the PROPELLER sequence for both coil types. The CNR of the PROPELLER sequence was statistically significant between fat-fluid, bone-fluid, bonetendon, bone-muscle, and muscle-fluid when using SFC and DSC.
CONCLUSION: This study proved that the PROPELLER-PDFS pulse sequence effectively eliminates motion and pulsation artifacts, regardless of the coils utilised. The PROPELLERPDFS pulse sequence can therefore be implemented into the standard MRI shoulder procedure.

Matched MeSH terms: Image Enhancement/methods
Fulltext Optimized exposer region-based modified adaptive histogram equalization method for contrast enhancement in CXR imaging

Gangwar S, Devi R, Mat Isa NA

Sci Rep, 2025 Feb 25;15(1):6693.
PMID: 40000697 DOI: 10.1038/s41598-025-90876-6

In medical imaging, low-contrast chest X-ray (CXR) images may fail to provide adequate information for accurate visual interpretation and disease diagnosis. Conventional contrast enhancement techniques, such as histogram equalization, often introduce intensity shifts and loss of fine details. This study presents an advanced Exposure Region-Based Modified Adaptive Histogram Equalization (ERBMAHE) method, further optimized using Particle Swarm Optimization (PSO) to enhance contrast, preserve brightness, and strengthen fine details. The ERBMAHE method segments CXR images into underexposed, well-exposed, and overexposed regions using the 9IEC algorithm. The well-exposed region is further divided, generating five histograms. Each region undergoes adaptive contrast enhancement via a novel weighted probability density function (PDF) and power-law transformation to ensure balanced enhancement across different exposure levels. The PSO algorithm is then employed to optimize power-law parameters, further refining contrast enhancement and illumination uniformity while maintaining the natural appearance of medical images. The PSO-ERBMAHE method was tested on 600 Kaggle CXR images and compared against six state-of-the-art techniques. It achieved a superior peak signal-to-noise ratio (PSNR = 31.10 dB), entropy (7.48), feature similarity index (FSIM = 0.98), tenengrad function (TEN = 0.19), quality-aware relative contrast measure (QRCM = 0.10), and contrast ratio, while maintaining a low absolute mean brightness error (AMBE = 0.10). The method effectively enhanced image contrast while preserving brightness and visual quality, as confirmed by medical expert evaluations. The proposed PSO-ERBMAHE method delivers high-quality contrast enhancement in medical imaging, ensuring better visibility of critical anatomical features. By strengthening fine details, maintaining mean brightness, and improving computational efficiency, this technique enhances disease examination and diagnosis, reducing misinterpretation risks and improving clinical decision-making.

Matched MeSH terms: Radiographic Image Enhancement/methods
Fulltext Color correction methods for underwater image enhancement: A systematic literature review

Lai YL, Ang TF, Bhatti UA, Ku CS, Han Q, Por LY

PLoS One, 2025;20(3):e0317306.
PMID: 40063649 DOI: 10.1371/journal.pone.0317306

Underwater vision is essential in numerous applications, such as marine resource surveying, autonomous navigation, objective detection, and target monitoring. However, raw underwater images often suffer from significant color deviations due to light attenuation, presenting challenges for practical use. This systematic literature review examines the latest advancements in color correction methods for underwater image enhancement. The core objectives of the review are to identify and critically analyze existing approaches, highlighting their strengths, limitations, and areas for future research. A comprehensive search across eight scholarly databases resulted in the identification of 67 relevant studies published between 2010 and 2024. These studies introduce 13 distinct methods for enhancing underwater images, which can be categorized into three groups: physical models, non-physical models, and deep learning-based methods. Physical model-based methods aim to reverse the effects of underwater image degradation by simulating the physical processes of light attenuation and scattering. In contrast, non-physical model-based methods focus on manipulating pixel values without modeling these underlying degradation processes. Deep learning-based methods, by leveraging data-driven approaches, aim to learn mappings between degraded and enhanced images through large datasets. However, challenges persist across all categories, including algorithmic limitations, data dependency, computational complexity, and performance variability across diverse underwater environments. This review consolidates the current knowledge, providing a taxonomy of methods while identifying critical research gaps. It emphasizes the need to improve adaptability across diverse underwater conditions and reduce computational complexity for real-time applications. The review findings serve as a guide for future research to overcome these challenges and advance the field of underwater image enhancement.

Matched MeSH terms: Image Enhancement/methods
Improvement of digital mammogram images using histogram equalization, histogram stretching and median filter

Langarizadeh M, Mahmud R, Ramli AR, Napis S, Beikzadeh MR, Rahman WE

J Med Eng Technol, 2011 Feb;35(2):103-8.
PMID: 21204610 DOI: 10.3109/03091902.2010.542271

Breast cancer is one of the most important diseases in females worldwide. According to the Malaysian Oncological Society, about 4% of women who are 40 years old and above are involved have breast cancer. Masses and microcalcifications are two important signs of breast cancer diagnosis on mammography. Enhancement techniques, i.e. histogram equalization, histogram stretching and median filters, were used to provide better visualization for radiologists in order to help early detection of breast abnormalities. In this research 60 digital mammogram images which includes 20 normal and 40 confirmed diagnosed cancerous cases were selected and manipulated using the mentioned techniques. The original and manipulated images were scored by three expert radiologists. Results showed that the selected methods have a positive significant effect on image quality.

Matched MeSH terms: Radiographic Image Enhancement
Fulltext A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement

Al-Shamasneh AR, Jalab HA, Palaiahnakote S, Obaidellah UH, Ibrahim RW, El-Melegy MT

Entropy (Basel), 2018 May 05;20(5).
PMID: 33265434 DOI: 10.3390/e20050344

Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. The proposed model estimates the probability of pixels that represent edges based on the entropy of the neighboring pixels, which results in local fractional entropy. When there is a small change in the intensity values (indicating the presence of edge in the image), the local fractional entropy gives fine image details. Similarly, when no change in intensity values is present (indicating smooth texture), the LFE does not provide fine details, based on the fact that there is no edge information. Tests were conducted on a large dataset of different, poor-quality kidney images to show that the proposed model is useful and effective. A comparative study with the classical methods, coupled with the latest enhancement methods, shows that the proposed model outperforms the existing methods.

Matched MeSH terms: Image Enhancement
Fulltext Radiographic imaging of steel oval lock carabiner by using computed radiography: a pilot study

Nadzirah Mohamad Radzi, Zafri Azran Abdul Majid

International Journal of Allied Health Sciences, 2018;2(2):310-318.
MyJurnal

Carabiner is one of Personal Protective Equipment (PPE), which is used to protect the users from hazards by reducing any chance of serious injury. Thus, it is very important to detect even a small
defect on the component before it becomes worse that could give harm to the users. The aim of this paper is to find out the appropriate imaging technical factors of steel carabiner by using computed radiography (CR). Methods: Radiographic images of carabiner were obtained by manipulating the values of kVp and mAs with respect to contrast and density. A preliminary study was carried out to determine the exposure factor combination in order to produce perceptible visual quality of radiographic images. Positioning techniques applied in this study were whole view (open-gate and close-gate position) and screw view (open-gate and close-gate position). An assessor was invited to evaluate the radiographs by using Image Quality Criteria Scoring (ICS) adapted from European Guidelines on Quality Criteria for Diagnostic Radiographic Images. Results: Findings showed that the optimum values of kVp and mAs in imaging whole view (open-gate and close-gate) carabiner were 133 kVp and 28 mAs while, for screw view (opengate and close-gate) the range of kVp and mAs preferred were 121 kVp to 133 kVp and 28 mAs to 36 mAs respectively. Conclusion: This study has found that the use of medical CR to expose metal steel such as
carabiner is accepted. By manipulating the imaging parameters, CR can produce a good quality image of carabiner.

Matched MeSH terms: Radiographic Image Enhancement
Autonomous Vision-Based Primary Distribution Systems Porcelain Insulators Inspection Using UAVs

Rahman EU, Zhang Y, Ahmad S, Ahmad HI, Jobaer S

Sensors (Basel), 2021 Feb 02;21(3).
PMID: 33540500 DOI: 10.3390/s21030974

The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. Unmanned aerial vehicles (UAVs) present a safer, autonomous, and efficient way to examine the power system components without closing the power distribution system. In this work, a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. A deep Laplacian pyramid-based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low-light images, a low-light image enhancement technique is used for the robust exposure correction of the training images. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. Several flight path strategies are proposed to overcome the shuttering effect of insulators, along with providing a less complex and time- and energy-efficient approach for capturing a video stream of the power system components. The performance of different object detection models is presented for selecting the most suitable one for fine-tuning on the specific faulty insulator dataset. For the detection of damaged insulators, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets and presents a simple and more efficient flight strategy. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust fault recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat, Pakistan.

Matched MeSH terms: Image Enhancement
Digital radiographic image enhancement for weld defect detection using smoothing and morphological transformations

Suhaila Abdul Halim, Arsmah Ibrahim, Yupiter Harangan Prasada Manurung

Scientific Research Journal, 2012;9(1):15-27.
MyJurnal

Accurate inspection of welded materials is important in relation to achieve acceptable standards. Radiography, a non-destructive test method, is commonly used to evaluate the internal condition of a material with respect to defect detection. The presence of noise in low resolution of radiographic images significantly complicates analysis; therefore attaining higher quality radiographic images makes defect detection more readily achievable. This paper presents a study pertaining to the quality enhancement of radiographic images with respect to different types of defects. A series of digital radiographic weld flaw images were smoothed using multiple smoothing techniques to remove inherent noise followed by top and bottom hat morphological transformations. Image quality was evaluated quantitatively with respect to SNR, PSNR and MAE. The results indicate that smoothing enhances the quality of radiographic images, thereby promoting defect detection with the respect to original radiographic images.

Matched MeSH terms: Radiographic Image Enhancement
Improvement to the scanning electron microscope image adaptive Canny optimization colorization by pseudo-mapping

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: Image Enhancement
A contrast stretching bilateral closing top-hat Otsu threshold technique for crack detection in images

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.

Matched MeSH terms: Image Enhancement
Fulltext Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Abul Kashem SB, et al.

Comput Biol Med, 2021 May;132:104319.
PMID: 33799220 DOI: 10.1016/j.compbiomed.2021.104319

Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.

Matched MeSH terms: Image Enhancement

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