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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
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 Weld defect inspection uses X-radiography testing, which is now mostly outdated.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
Fulltext Subspace-based technique for speckle noise reduction in ultrasound images

Yahya N, Kamel NS, Malik AS

Biomed Eng Online, 2014;13(1):154.
PMID: 25421914 DOI: 10.1186/1475-925X-13-154

Ultrasound imaging is a very essential technique in medical diagnosis due to its being safe, economical and non-invasive nature. Despite its popularity, the US images, however, are corrupted with speckle noise, which reduces US images qualities, hampering image interpretation and processing stage. Hence, there are many efforts made by researches to formulate various despeckling methods for speckle reduction in US images.

Matched MeSH terms: Image Enhancement/methods*
A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram

Meselhy Eltoukhy M, Faye I, Belhaouari Samir B

Comput Biol Med, 2010 Apr;40(4):384-91.
PMID: 20163793 DOI: 10.1016/j.compbiomed.2010.02.002

This paper presents a comparative study between wavelet and curvelet transform for breast cancer diagnosis in digital mammogram. Using multiresolution analysis, mammogram images are decomposed into different resolution levels, which are sensitive to different frequency bands. A set of the biggest coefficients from each decomposition level is extracted. Then a supervised classifier system based on Euclidian distance is constructed. The performance of the classifier is evaluated using a 2 x 5-fold cross validation followed by a statistical analysis. The experimental results suggest that curvelet transform outperforms wavelet transform and the difference is statistically significant.

Matched MeSH terms: Image Enhancement/methods*
Time-domain reconstruction using sensitivity coefficients for limited view ultrawide band tomography

Abdullah MZ, Yin W, Bilal M, Armitage DW, Mackin R, Peyton AJ

Rev Sci Instrum, 2007 Aug;78(8):084703.
PMID: 17764343

This article addresses time-domain ultrawide band (UWB) electromagnetic tomography for reconstructing the unknown spatial characteristic of an object from observations of the arrivals of short electromagnetic (EM) pulses. Here, the determination of the first peak arrival of the EM traces constitutes the forward problem, and the inverse problem aims to reconstruct the EM property distribution of the media. In this article, the finite-difference time-domain method implementing a perfectly matched layer is used to solve the forward problem from which the system sensitivity maps are determined. Image reconstruction is based on the combination of a linearized update and regularized Landweber minimization algorithm. Experimental data from a laboratory UWB system using targets of different contrasts, sizes, and shapes in an aqueous media are presented. The results show that this technique can accurately detect and locate unknown targets in spite of the presence of significant levels of noise in the data.

Matched MeSH terms: Image Enhancement/methods*
Magnetic resonance angiographic demonstration of carotid-cavernous fistula using elliptical centric time resolved imaging of contrast kinetics (EC-TRICKS)

Vattoth S, Cherian J, Pandey T

Magn Reson Imaging, 2007 Oct;25(8):1227-31.
PMID: 17442526

Magnetic resonance angiographic evaluation of the intracranial vasculature has been predominantly carried out using conventional angiographic techniques such as time of flight and phase contrast sequences. These techniques have good spatial resolution but lack temporal resolution. Newer faster angiographic techniques have been developed to circumvent this limitation. Elliptical centric time-resolved imaging of contrast kinetics (EC-TRICKS) is one such technique which has combined the use of elliptical centric ordering of the k-space with multiphase 3D digital subtraction MR angiogram (MRA) to achieve excellent temporal resolution of the arterial and venous circulations. Its applications have been mainly in the peripheral vasculature. We report the use of this technique in a case of a high-flow, direct carotid-cavernous fistula to demonstrate its potential in intracranial MR angiography.

Matched MeSH terms: Image Enhancement/methods*
Neural networks aided stone detection in thick slab MRCP images

Logeswaran R

Med Biol Eng Comput, 2006 Aug;44(8):711-9.
PMID: 16937213

This paper proposes a detection scheme for identifying stones in the biliary tract of the body, which is examined using magnetic resonance cholangiopancreatography (MRCP), a sequence of magnetic resonance imaging targeted at the pancreatobiliary region of the abdomen. The scheme enhances the raw 2D thick slab MRCP images and extracts the biliary structure in the images using a segment-based region-growing approach. Detection of stones is scoped within this extracted structure, by highlighting possible stones. A trained feedforward artificial neural network uses selected features of size and average segment intensity as its input to detect possible stones in MRCP images and eliminate false stone-like objects. The proposed scheme achieved satisfactory results in tests of clinical MRCP thick slab images, indicating potential for implementation in computer-aided diagnosis systems for the liver.

Matched MeSH terms: Radiographic Image Enhancement/methods
Enhancement of seminal stains using background correction algorithm with colour filters

Lee WC, Khoo BE, Abdullah AFL

Forensic Sci Int, 2016 06;263:1-9.
PMID: 27061146 DOI: 10.1016/j.forsciint.2016.03.046

Evidence in crime scenes available in the form of biological stains which cannot be visualized during naked eye examination can be detected by imaging their fluorescence using a combination of excitation lights and suitable filters. These combinations selectively allow the passage of fluorescence light emitted from the targeted stains. However, interference from the fluorescence generated by many of the surface materials bearing the stains often renders it difficult to visualize the stains during forensic photography. This report describes the use of background correction algorithm (BCA) to enhance the visibility of seminal stain, a biological evidence that fluoresces. While earlier reports described the use of narrow band-pass filters for other fluorescing evidences, here, we utilize BCA to enhance images captured using commonly available colour filters, yellow, orange and red. Mean-based contrast adjustment was incorporated into BCA to adjust the background brightness for achieving similarity of images' background appearance, a crucial step for ensuring success while implementing BCA. Experiment results demonstrated the effectiveness of our proposed colour filters' approach using the improved BCA in enhancing the visibility of seminal stains in varying dilutions on selected surfaces.

Matched MeSH terms: Image Enhancement/methods*
Fulltext Hybrid Pixel-Based Method for Cardiac Ultrasound Fusion Based on Integration of PCA and DWT

Mazaheri S, Sulaiman PS, Wirza R, Dimon MZ, Khalid F, Moosavi Tayebi R

Comput Math Methods Med, 2015;2015:486532.
PMID: 26089965 DOI: 10.1155/2015/486532

Medical image fusion is the procedure of combining several images from one or multiple imaging modalities. In spite of numerous attempts in direction of automation ventricle segmentation and tracking in echocardiography, due to low quality images with missing anatomical details or speckle noises and restricted field of view, this problem is a challenging task. This paper presents a fusion method which particularly intends to increase the segment-ability of echocardiography features such as endocardial and improving the image contrast. In addition, it tries to expand the field of view, decreasing impact of noise and artifacts and enhancing the signal to noise ratio of the echo images. The proposed algorithm weights the image information regarding an integration feature between all the overlapping images, by using a combination of principal component analysis and discrete wavelet transform. For evaluation, a comparison has been done between results of some well-known techniques and the proposed method. Also, different metrics are implemented to evaluate the performance of proposed algorithm. It has been concluded that the presented pixel-based method based on the integration of PCA and DWT has the best result for the segment-ability of cardiac ultrasound images and better performance in all metrics.

Matched MeSH terms: Image Enhancement/methods*
Evaluating the quality of images produced by soft X-ray units

Bradley DA, Wong CS, Ng KH

Appl Radiat Isot, 2000 9 26;53(4-5):691-7.
PMID: 11003508

For broad-beam soft X-ray sources, assessment of the quality of image produced by such units is made complex by the low penetration capabilities of the radiation. In the present study we have tested the utility of several types of test tool, some of which have been fabricated by us, as part of an effort to evaluate several key image defining parameters. These include the film characteristic, focal-spot size, image resolution and detail detectability. The two sources of X-rays used in present studies were the University of Malaya flash X-ray device (UMFX1) and a more conventional soft X-ray tube (Softex, Tokyo), the latter operating at peak accelerating potentials of 20 kVp. We have established, for thin objects, that both systems produce images of comparable quality and, in particular, objects can be resolved down to better than 45 microm.

Matched MeSH terms: Radiographic Image Enhancement/methods*
A fuzzy-based texture analysis for tissue characterization of diffused liver diseases on B-scan images

Gangeh MJ, Hanmandlu M, Bister M

Biomed Sci Instrum, 2002;38:369-74.
PMID: 12085634

The specific texture on B-scan images is believed to be related to both ultrasound machine characteristics and tissue properties, i.e., the pathological states of the soft tissue. Therefore, for classification, features can be extracted with the use of image texture analysis techniques. In this paper a novel fuzzy approach for texture characterization is used for classification of normal liver and diffused liver diseases, here fatty liver, liver cirrhosis, and hepatitis are emphasized. The texture analysis techniques are diversified by the existence of several approaches. We propose fuzzy features for the analysis of the texture image. For this, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors: maximum, entropy, and energy as used in co-occurrence method, for each window.

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/methods*
A robust background correction algorithm for forensic bloodstain imaging using mean-based contrast adjustment

Lee WC, Khoo BE, Abdullah AFL

Sci Justice, 2016 May;56(3):201-209.
PMID: 27162018 DOI: 10.1016/j.scijus.2016.01.001

Background correction algorithm (BCA) is useful in enhancing the visibility of images captured in crime scenes especially those of untreated bloodstains. Successful implementation of BCA requires all the images to have similar brightness which often proves a problem when using automatic exposure setting in a camera. This paper presents an improved background correction algorithm (BCA) that applies mean-based contrast adjustment as a pre-correction step to adjust the mean brightness of images to be similar before implementing BCA. The proposed modification, namely mean-based adaptive BCA (mABCA) was tested on various image samples captured under different illuminations such as 385 nm, 415 nm and 458 nm. We also evaluated mABCA of two wavelengths (415 nm and 458 nm) and three wavelengths (415 nm, 380 nm and 458 nm) in enhancing untreated bloodstains on different surfaces. The proposed mABCA is found to be more robust in processing images captured in different brightness and thus overcomes the main issue faced in the original BCA.

Matched MeSH terms: Image Enhancement/methods*
Automated pterygium detection method of anterior segment photographed images

Wan Zaki WMD, Mat Daud M, Abdani SR, Hussain A, Mutalib HA

Comput Methods Programs Biomed, 2018 Feb;154:71-78.
PMID: 29249348 DOI: 10.1016/j.cmpb.2017.10.026

BACKGROUND AND BJECTIVE: Pterygium is an ocular disease caused by fibrovascular tissue encroachment onto the corneal region. The tissue may cause vision blurring if it grows into the pupil region. In this study, we propose an automatic detection method to differentiate pterygium from non-pterygium (normal) cases on the basis of frontal eye photographed images, also known as anterior segment photographed images.
METHODS: The pterygium screening system was tested on two normal eye databases (UBIRIS and MILES) and two pterygium databases (Australia Pterygium and Brazil Pterygium). This system comprises four modules: (i) a preprocessing module to enhance the pterygium tissue using HSV-Sigmoid; (ii) a segmentation module to differentiate the corneal region and the pterygium tissue; (iii) a feature extraction module to extract corneal features using circularity ratio, Haralick's circularity, eccentricity, and solidity; and (iv) a classification module to identify the presence or absence of pterygium. System performance was evaluated using support vector machine (SVM) and artificial neural network.
RESULTS: The three-step frame differencing technique was introduced in the corneal segmentation module. The output image successfully covered the region of interest with an average accuracy of 0.9127. The performance of the proposed system using SVM provided the most promising results of 88.7%, 88.3%, and 95.6% for sensitivity, specificity, and area under the curve, respectively.
CONCLUSION: A basic platform for computer-aided pterygium screening was successfully developed using the proposed modules. The proposed system can classify pterygium and non-pterygium cases reasonably well. In our future work, a standard grading system will be developed to identify the severity of pterygium cases. This system is expected to increase the awareness of communities in rural areas on pterygium.

Matched MeSH terms: Image Enhancement/methods*
Local gray level S-curve transformation - A generalized contrast enhancement technique for medical images

Gandhamal A, Talbar S, Gajre S, Hani AF, Kumar D

Comput Biol Med, 2017 04 01;83:120-133.
PMID: 28279861 DOI: 10.1016/j.compbiomed.2017.03.001

Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images.

Matched MeSH terms: Image Enhancement/methods*
Fulltext An efficient post-processing adaptive filtering technique to rectifying the flickering effects

Gandam A, Sidhu JS, Verma S, Jhanjhi NZ, Nayyar A, Abouhawwash M, et al.

PLoS One, 2021;16(5):e0250959.
PMID: 33970949 DOI: 10.1371/journal.pone.0250959

Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7-1.9db) while (35.83-47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.

Matched MeSH terms: Image Enhancement/methods*

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