Gray Value Research Articles

Research on image scaling algorithm for granular image detection

AbstractIn order to improve the accuracy of granular image detection during image scaling, an image scaling algorithm combining the interpolation algorithm of protective features and Kalman filtering of neurons is proposed. Firstly, the interpolation algorithm of protective features is de‐designed according to the phenomenon of sudden change of edge grey values, and the difference and trend of grey values in horizontal and vertical directions are used to obtain the pre‐scaled image with maximum contrast. Then the grey value data input of the pre‐scaled image is introduced into the Kalman filter of neurons, and the filtering process is optimized by using the black‐box thinking of neurons, and the pre‐scaled image is smoothed. Finally, the scale transformation is implemented to obtain the final grey value to complete the final scaling, so that the image has both visual experience and features. Comparison with mainstream image scaling algorithms shows that the proposed algorithm can effectively overcome the degradation of granularity image detection accuracy due to image scaling, with point acutance value (PAV) improved by 10%–41%, and the granularity detection error caused by image scaling algorithms reduced from about 0.7% to about 0.1%, with a relative improvement of 85%.

Integrated Short-TE and Hadamard-edited Multi-Sequence (ISTHMUS) for advanced MRS

BackgroundTo examine data quality and reproducibility using ISTHMUS, which has been implemented as the standardized MR spectroscopy sequence for the multi-site Healthy Brain and Child Development (HBCD) study. MethodsISTHMUS is the consecutive acquisition of short-TE PRESS (32 transients) and long-TE HERCULES (224 transients) data with dual-TE water reference scans. Voxels were positioned in the centrum semiovale, dorsal anterior cingulate cortex, posterior cingulate cortex and bilateral thalamus regions. After acquisition, ISTHMUS data were separated into the PRESS and HERCULES portions for analysis and modeled separately using Osprey. In vivo experiments were performed in 10 healthy volunteers (6 female; 29.5±6.6 years). Each volunteer underwent two scans on the same day. Differences in metabolite measurements were examined. T2 correction based on the dual-TE water integrals were compared with: 1) T2 correction based on the default white matter and gray matter T2 reference values in Osprey and 2) shorter WM and GM T2 values from recent literature. ResultsNo significant difference in linewidth was observed between PRESS and HERCULES. Bilateral thalamus spectra had produced significantly higher (p<0.001) linewidth compared to the other three regions. Linewidth measurements were similar between scans, with scan-to-scan differences under 1 Hz for most subjects. Paired t-tests indicated a significant difference only in PRESS NAAG between the two thalamus scans (p=0.002). T2 correction based on shorter T2 values showed better agreement to the dual-TE water integral ratio. ConclusionsISTHMUS facilitated data acquisition and post-processing and reduced operator workload to eliminate potential human error.

Exploration on OCT biomarker candidate related to macular edema caused by diabetic retinopathy and retinal vein occlusion in SD-OCT images

To improve the understanding of potential pathological mechanisms of macular edema (ME), we try to discover biomarker candidates related to ME caused by diabetic retinopathy (DR) and retinal vein occlusion (RVO) in spectral-domain optical coherence tomography images by means of deep learning (DL). 32 eyes of 26 subjects with non-proliferative DR (NPDR), 77 eyes of 61 subjects with proliferative DR (PDR), 120 eyes of 116 subjects with branch RVO (BRVO), and 17 eyes of 15 subjects with central RVO (CRVO) were collected. A DL model was implemented to guide biomarker candidate discovery. The disorganization of the retinal outer layers (DROL), i.e., the gray value of the retinal tissues between the external limiting membrane (ELM) and retinal pigment epithelium (RPE), the disrupted and obscured rate of the ELM, ellipsoid zone (EZ), and RPE, was measured. In addition, the occurrence, number, volume, and projected area of hyperreflective foci (HRF) were recorded. ELM, EZ, and RPE are more likely to be obscured in RVO group and HRFs are observed more frequently in DR group (all P ≤ 0.001). In conclusion, the features of DROL and HRF can be possible biomarkers related to ME caused by DR and RVO in OCT modality.

Applicability research and experimental verification based on the coupling of turbulence model and mesh types to capture jet characteristics

Through the experimental verification, the accuracy of the cavitation jet feature captured by the coupling numerical calculation with different mesh types and different turbulence models is evaluated. Firstly, we built a high-speed jet experimental platform, and a high-speed camera and a paperless recorder are used to record the pressure, flow rate, and jet cloud oscillation of the jet pump in different states. The oscillation period and length of the jet cloud under different cavitation numbers are analyzed by the gray value method. The numerical results are compared with the experimental results. It is found that after the numerical calculation converges, the states of the jet cloud calculated by K–O and LES coupled with different meshes are periodically oscillating, while K-E does not. However, the maximum jet cloud lengths calculated by K-E and K–O turbulence models under different mesh types are nearly the same. Overall, the oscillation period calculated by the LES model is more in line with the experimental results, but its amplitude and maximum length are more different from the experimental results. In terms of capturing the jet cloud, critical pressure ratio, turbulent state and vapor volume fraction, the accuracy order of numerical calculation results are P–H (polyhedron-hexahedron mesh) ≈ P (polyhedron mesh) > H-T (hexahedral-tetrahedral mesh) > T (tetrahedral mesh), P–H ≈ P > T > H-T, P–H ≈ P > H-T > T and P–H > P > T > H-T, respectively. This research can provide more appropriate solutions for different concerns, as well as guidance for improving the accuracy and efficiency of simulations, and provides a basis for optimizing and developing specialized mesh types and turbulence models.

A voxel-based magnetic resonance study of cortical gray matter structures in intermittent exotropia

Objective: To explore the changes in gray matter volume of the cerebral cortex in patients with intermittent exotropia (IXT) using the voxel-based analysis and to analyze the correlation between these changes and clinical manifestations. Methods: This was a cross-sectional study. A collection of 15 consecutive patients diagnosed with IXT at Tianjin Eye Hospital from March 2021 to May 2022 formed the exotropia group, which comprised 8 males and 7 females, with an average age of (23.5±5.2) years. Ten healthy individuals, 3 males and 7 females, with an average age of (27.0±7.5) years, were selected as the control group. All participants underwent assessments of exotropia severity and Titmus stereoacuity. Three-dimensional high-resolution brain images were obtained through MRI scans. Voxel-based morphometry was employed to preprocess the MRI data, and the SPM toolbox in MATLAB was utilized to analyze differences of images between the two groups. Regions of interest (ROI) with structural abnormalities in the gray matter volume analysis were selected, and the ratio of gray matter voxel values in the ROI to the mean gray matter voxel values of the whole brain for each participant was calculated using the MarsBaR software. The correlation between this ratio and exotropia severity as well as the common logarithm of Titmus stereoacuity was analyzed. Results: The differences in age, gender distribution, and refractive error between the two groups were not statistically significant (all P>0.05). However, there were statistically significant differences in the degree of strabismus and Titmus stereoacuity (both P<0.001). Compared to the control group, patients in the strabismus group exhibited decreased gray matter volume in several brain regions, including the wedges of the medial surface of the cerebral hemisphere (decreased by 89 voxels), the left lingual gyrus (decreased by 176 voxels), the left calcarine sulcus V3 area (decreased by 30 voxels), the central anterior gyrus of the right frontal lobe (decreased by 192 voxels), the gray matter of the left hippocampal gyrus (decreased by 20 voxels), and the bilateral lateral geniculate nuclei (decreased by 100 and 40 voxels on the left and right sides, respectively). These differences were all statistically significant (all P<0.001). Additionally, there was an increase in gray matter volume in several brain regions, including the bilateral caudate nuclei (increased by 60 and 76 voxels on the left and right sides, respectively) and the left precentral gyrus (increased by 36 voxels). These differences were also statistically significant (all P<0.001). A group-level analysis identified 10 brain regions with structural differences between the two groups, which were used as ROI. The gray matter volume ratio was negatively correlated with the degree of exotropia (all P<0.05) in the ROI of the left wedges (r=-0.670), left calcarine sulcus V3 area (r=-0.610), and left lingual gyrus (r=-0.684). The gray matter volume ratio was negatively correlated with lgTS (all P<0.05) in the ROI of the left wedges (r=-0.568) and the central anterior gyrus of the right frontal lobe (r=-0.563). Conclusions: Patients with IXT exhibit decreased gray matter volume in the horizontal connection areas between the primary visual cortices V1 and V2. The reduction in gray matter volume of the lingual gyrus and the dorsal visual pathway V3 area becomes more pronounced with increasing exotropia severity, while the gray matter volume of the precentral gyrus (BA6 area) decreases with worsening stereoacuity.

Concepts for compensation of wave effects when measuring through water surfaces in photogrammetric applications

Abstract. A common problem when imaging and measuring through moving water surfaces is the quasi-random refraction caused by waves. The article presents two strategies to overcome this problem by lowering the complexity down to a planer air/water interface problem. In general, the methods assume that the shape of the water surface changes randomly over time and that the water surface moves around an idle-state (calm planar water surface). Thus, moments at which the surface normal is orientated vertically should occur more frequently than others should. By analysing a sequence of images taken from a stable camera position these moments could be identified – this can be done in the image or object space. It will be shown, that a simple median filtering of grey values in each pixel position can provide a corrected image freed from wave and glint effects. This should have the geometry of an image taken through calm water surface. However, in case of multi camera setups, the problem can be analysed in object space. By tracking homological underwater features, sets of image rays hitting accidently horizontal orientated water surface areas can be identified. Both methods are described in depth and evaluated on real and simulated data.

Combined application of artificial bone powders and concentrated growth factor membranes on the autotransplantation of mature third molars: A 5‐year retrospective case series

AbstractAimThis study aimed to observe the efficacy and outcomes of the combined application of artificial bone powder and concentrated growth factor (CGF) membranes for tooth transplantation in cases with wide recipient sockets and small donor teeth.Material and MethodsA total of 36 teeth from 36 patients with wide recipient sockets and small donor teeth were enrolled. Autogenous tooth transplantation was performed using bone powders and CGF membranes. After transplantation, the visual analog scale (VAS) score, Landry Wound Healing Index, probing depth (PD), mobility, and gray value of the alveolar bone around the transplanted teeth were measured, and a patient satisfaction questionnaire was administered. All patients underwent clinical and radiographic examinations during follow‐up.ResultsThe VAS score of 16 (44.4%) cases after 1 week was 0, and 26 (72.2%) patients showed excellent gingival healing after 2–4 weeks. The PD of a few cases was deeper than 3 mm during the first month; however, returned to normal after 3 months. Although the majority of the transplanted teeth possessed mobility greater than grade I during the first month, the mobility gradually improved within 3 months. The gray value of the alveolar bone around the transplanted teeth, 1 year postoperatively showed no difference with pre‐operation (p &gt; .05). During the mean follow‐up period of 42.7 months (range 20–72 months), 33 of the 36 transplanted teeth remained in situ without clinical or radiographic complications, with an overall success rate of 91.7%.ConclusionsAlthough the PD and mobility of the transplanted teeth were not ideal during the early stages of healing, most of the transplanted teeth had good clinical outcomes. In cases with large recipient sites accompanied by small donor teeth, autotransplantation of teeth using artificial bone powder combined with CGF membranes is a viable option and can lead to optimistic results with favorable success rates.

Evaluation of CHD5, H3K9me3, and H4K12ac in Human Testes with Spermatogenic Maturation Arrest: A Cross-Sectional Study.

Spermatogenic maturation arrest is thought to be caused by epigenetic defects, specifically in chromatin remodeling and histone modification. This study evaluated the status of chromatin remodeling chromodomain helicase DNA binding protein 5 (CHD5) and histone modifications histone 4 lys-12 acetylation (H4K12ac) and histone 3 lys-9 trimethylation (H3K9me3) in human testicular biopsies, based on maturation arrest type. The cross-sectional study utilized 18 Bouin-fixed paraffin-embedded (BFPE) specimens prepared from residual tissue from routine laboratory tests of infertile patients. The expression of CHD5, H4K12ac, and H3K9me3 was examined through immunohistochemistry (IHC). The intensity was measured using ImageJ with IHC Profiler and StarDist plugins. Statistical analysis was performed using Python with Scipy.Stats module. The data were tested with Shapiro- Wilk for normality and Levene test for homogeneity. The differences in the intensity of spermatogenic cells were assessed using Kruskal-Wallis and Mann-Whitney tests. A difference was considered statistically significant if P<0.05. We found three types of maturation arrest, including Sertoli cell only (n=5), spermatocyte arrest (n=4), and spermatid arrest (n=9). CHD5 was positive in spermatogonia and round spermatids but absent in spermatocytes. The mean grey value (MGV) of CHD5 in spermatogonia was generally weak in spermatocyte arrest (157.4 ± 16.6) and spermatid arrest (155.3 ± 16.8), and there was no significant difference between them [P=0.49, 95% confidence interval (CI): (-4.3, 6), effect size (r): 0.02]. Although there was a significant difference in the expression of H3K9me3 and H4K12ac (P<0.001), both histone modifications were found in all observed spermatogenic cells. The expressions of CHD5, H3K9me3, and H4K12ac in different spermatogenic cell types produce similar results, indicating that they cannot be used as markers to determine the type of spermatogenic maturation arrest in humans. The significant finding in this research is the expression of CHD5 in human spermatogonia cells, which requires further study for elaboration.

Measurement-based Detector Characteristics for Digital Twins in aRTist

Various software products for the simulation of industrial X-ray radiography have been developed in recent years (e.g., aRTist 2, CIVA CT, Scorpius XLab, SimCT, Wilcore) and their application potential has been shown in numerous works. However, full systematic approaches to characterise a specific CT system for these simulation software products to obtain a truthful digital twin are still missing. In this contribution, we want to present two approaches to obtain realistic grey values in X-ray projections in aRTist 2 simulations based on measured projections. In aRTist 2, the displayed grey value of a pixel is based on the energy density incident on that pixel. The energy density is calculated based on the X-ray tube spectrum, the attenuation between source and detector as well as an energy-dependent sensitivity curve of the detector. The first approach presented in this contribution uses the sensitivity curve as a free modelling parameter. We measured the signal response at different thicknesses of Al EN-AW6082 at different tube voltages (i.e., different tube spectra). We then regarded the grey values displayed by these projections as a data regression respectively an optimisation problem and obtained the sensitivity curve that is best able to reproduce the measured behaviour in aRTist 2. The resulting sensitivity curve does not necessarily hold physical meaning but is able to simulate the real system behaviour in the simulation software. The second approach presented in this contribution is to estimate the sensitivity curve based on assumptions about the characteristics of the scintillation detector (e.g., scintillator material, scintillator thickness and signal processing characteristics). For this approach, a linear response function (linear relationship between the deposited energy per pixel and the resulting grey value) is assumed. If the detector characteristics, which affect the simulated deposited energy, are properly modelled, the slope and offset of the response function to match the measured grey values should be the same for different tube spectra. As the offset is constant and given by the grey values measured at no incident radiation, the slope is the remaining parameter to evaluate the success of the detector modelling. We therefore adapted the detector characteristics by changing the detector setup until the slope was nearly the same for all measured tube spectra. We are aware that the resulting parameters of the scintillator material and thickness might not be the real ones, but with those modelling parameters we are able to simulate realistic grey values in aRTist 2. Both of those approaches could potentially be a step forward to a full systematic approach for a digital twin of a real CT system in aRTist 2.

A comparative radiographic study of bone density changes around titanium implants in the posterior mandible, preoperative, and postoperative

Background: Implant success and the state of the surrounding bone require multiple measures, especially in humans, and this study aimed to identify the development of the state of the latter by means of radiographic examination performed during the period of osseointegration as well as investigate the changes in bone density that occur after implant installation and 2 months after functional loading. Implant success rates are affected by bone density at the implant site. Therefore, understanding changes in bone density after dental implant placement is essential, as it correlates with subsequent implant success. Materials and methods: Digital radiographs of 28 implants were taken and evaluated at four intervals: preoperatively, 1 and 3 months postoperatively, and 2 months following placement of the permanent prosthesis. Gray values were measured in different areas around the implants through analyzing X-ray images and measuring bone density around the implants using EzDent – 2D software. The aim of this study was to investigate changes in bone density around implants in three regions: apex, neck, and body, as well as to record average density values during the observation period by measuring digital image gray levels (the gray values of the digital radiographs). This was conducted to determine local bone densities in dental implant recipient sites and to study changes in local bone densities at different intervals, preoperatively and postoperatively and after placement of the prosthesis. Results: A decrease was observed in gray values proportional to reference values 1-month after implant insertion, but these increased at 3 months after insertion and continued to rise 2 months after placement of the prosthesis in the apical, body, and neck regions of the implant. Conclusion: Sensor-tuned radiography can be used as an effective method to support clinical follow-ups as well as measure changes in bone densities around implants in critical cases.

Exposure Factors for Flexible Digital Detector Arrays

Exposure factors (R factors) provide guidance in gamma radiography applications. The exposure time is calculated for a specific source type, activity, thickness of steel, desired exposure factor for a target film density or pixel value, and source-to-detector distance (SDD). R factors to produce target film densities of 2.0, 2.5, 3.0, and 3.5 were published for M100, MX125, T200, AA400, and HS800 films. Digital R factors are now published for Carestream rigid and flexible digital detector array (DDA) products. The flexible DDA’s are bent repeatedly around curved surfaces for single wall viewing, as radiography codes and standards require that the detector be in direct contact with the weld whenever practical. The R factor tables are published along with the supporting data used to calculate them, which are provided in graphical format for each condition. Iridium, selenium, and cobalt radioisotope sources were utilized for the generation of the data. Radiographers utilize the tables provided to enter the R factor into a commercially available phone-based calculator to determine the exposure time required to obtain the target optical density or grey value through the base metal. R factors are meant to be a starting point only, as other factors related to the part geometry and technique impact the required exposure.

78‐3: A Tristimulus Electro‐optical Model Describing Interactions of a RGB Backlight Unit and an LC Panel

Describing the behavior of an RGB backlight is complex but crucial in applications as local dimming with a variable backlight color. A color model to describe the interaction of the spectral backlight composition and the LC panel is the scope of this paper. The output of the model extends the commonly used one‐dimensional luminance to three‐dimensional tristimulus. Such a model requires that the physical characteristics of the RGB BLU as well as the LC‐panel are considered. The input variables include beyond the RGB subpixel gray values also the three duty ratios for the RGB‐LEDs. The calculation of the XYZ coordinates of an LC pixel, while an RGB backlight is varied, may get feasible. Also, a method for parameter extraction will be shown.

Exploring contrast-enhancing staining agents for studying adipose tissue through contrast-enhanced computed tomography

Contrast-enhanced computed tomography offers a nondestructive approach to studying adipose tissue in 3D. Several contrast-enhancing staining agents (CESAs) have been explored, whereof osmium tetroxide (OsO4) is the most popular nowadays. However, due to the toxicity and volatility of the conventional OsO4, alternative CESAs with similar staining properties were desired. Hf-WD 1:2 POM and Hexabrix have proven effective for structural analysis of adipocytes using contrast-enhanced computed tomography but fail to provide chemical information. This study introduces isotonic Lugol’s iodine (IL) as an alternative CESA for adipose tissue analysis, comparing its staining potential with Hf-WD 1:2 POM and Hexabrix in murine caudal vertebrae and bovine muscle tissue strips. Single and sequential staining protocols were compared to assess the maximization of information extraction from each sample. The study investigated interactions, distribution, and reactivity of iodine species towards biomolecules using simplified model systems and assesses the potential of the CESA to provide chemical information. (Bio)chemical analyses on whole tissues revealed that differences in adipocyte gray values post-IL staining were associated with chemical distinctions between bovine muscle tissue and murine caudal vertebrae. More specific, a difference in the degree of unsaturation of fatty acids was identified as a likely contributor, though not the sole determinant of gray value differences. This research sheds light on the potential of IL as a CESA, offering both structural and chemical insights into adipose tissue composition.

Comparison of quantitative radiomorphometric predictors of healthy and MRONJ-affected bone using panoramic radiography and cone-beam CT.

To determine the most distinctive quantitative radiomorphometric parameter(s) for the detection of MRONJ-affected bone changes in panoramic radiography (PR) and cone-beam CT (CBCT). PR and sagittal CBCT slices of 24 MRONJ patients and 22 healthy controls were used for the measurements of mandibular cortical thickness (MCT), fractal dimension (FD), lacunarity, mean gray value (MGV), bone area fraction (BA/TA), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N). MCT was measured in the mental foramen region. While FD and lacunarity were measured on mandibular trabecular and cortical regions-of-interest (ROIs), the remaining parameters were measured on trabecular ROIs. The independent samples t-test was used to compare the measurements between the MRONJ and control groups for both imaging modalities (P = .05). MCT was the only parameter that differentiated MRONJ-affected bone in both PR and CBCT (P < .05). None of the remaining parameters revealed any difference for MRONJ-affected bone in CBCT (P > .05). FD, lacunarity, MGV, BA/TA, and Tb.Sp could distinguish MRONJ-affected trabecular bone in PR (P < .05). The correspondent ROI for both imaging methods that was reliable for detecting MRONJ-affected bone was the trabecular bone distal to the mental foramen above the inferior alveolar canal (ROI-3). MCT is a reliable parameter for the discrimination of MRONJ-affected bone in both PR and CBCT images. PR may be used to detect MRONJ-affected trabecular bone using FD, lacunarity, MGV, BA/TA, and Tb.Sp measurements as well.

Serum (-2)proPSA/freePSAratio, (-2)proPSA/freePSA density, prostate health index, and prostate health index density as clues to reveal postoperative clinically significant prostate cancer in men with prostate-specific antigen 2-10 ng/mL.

There is a strong clinical need to fill the gap of identifying clinically significant prostate cancer (csPCa) in men with prostate-specific antigen(PSA) gray zone values. Promising, but not definitive results have been obtained using PSA derivatives such as prostate health index (PHI) and PHI density (PHID) and the percentage (-2)proPSA/free PSA(%p2PSA/fPSA). Thus, this study aimed to compare the diagnostic value of PHI, PHID, %proPSA/fPSA, and (-2)proPSA/freePSA density (-2pPSA/fPSAD) for csPCa in the patients with PSAwithin 2-10 ng/mL. Serum samples and clinicopathological features were prospectively collected from 142 patients who underwent robot-assisted radical prostatectomy between September 2021 and December 2023. According to the inclusion criteria, the patients with total PSA within 2 and 10 ng/mL and negative or suspicious digital rectal examination were enrolled. We used two different classifications for csPCa: 1) patients with Gleason score (GS) ≥ 7(4 + 3) and 2) patients with GS ≥ 7(3 + 4). The receiver operating characteristic curves and the area under the curve (AUC)values were used to assess the diagnostic performance. Of the 142 men included, 116 (82%) patients were diagnosed with csPCa as GS ≥ 3 + 4 and 107 (75%) defined as csPCa as GS ≥ 7(4 + 3), respectively. We found that p2PSA/fPSA, p2PSA/fPSAD, PHI, and PHID were significantly higher in csPCa classified as GS ≥ 7(3 + 4) as well as GS ≥ 7(4 + 3), with p-values 0.027, 0.054, 0.0016, and 0.0027, respectively. AUCs of the analyzed variables were higher when used to predict csPCa as GS ≥ 6 compared to csPCa as GS ≥7(4 + 3), with an AUC equal, respectively, to 0.679 (95% CI: 0.571-0.786), 0.685 (95% CI: 0.571-0.799), 0.737 (95% CI: 0.639-0.836), and 0.736 (95% CI: 0.630-0.841) in the first subgroup and with an AUC equal, respectively, to 0.653 (95% CI: 0.552-0.754), 0.665 (95% CI: 0.560-0.770), 0.668 (95% CI: 0.568-0.769), and 0.670 (95% CI: 0.567-0.773) in the second, respectively. Both PHID and p2PSA/fPSADallowed improvement in the diagnostic accuracy with respect to PHI and p2PSA/fPSA ratio, however the differences were not statistically significant (p = 0.409, 0.180 for csPCa as G ≥ Gleason grade (GG) 2 and 0.558 and 0.087 for csPCa as G ≥ GG3, respectively). We found that PHI, PHID, p2PSA/fPSA ratio, and p2PSA/fPSADshowed higher sensitivity, specificity, and positive predictive valuewhen used to predict csPCa as GG ≥ 2, whereas negative predictive valueof all four parameters was higher when used to predict GG ≥ 3. In men with a PSA level between 2 and 10 ng/mL, PHI and PHID, p2PSA/fPSA, and p2PSA/fPSAD showed good diagnostic performance for postoperative csPCa. However, PHID and p2PSA/fPSAD had a small advantage over PHI which needs to be further investigated for the reduction of unnecessary surgical interventions. This finding suggests that it could be a promising biomarker for making the treatment-decision strategy.

Color-to-Gray Conversion Method Based on Chroma Difference

Most of the images encountered in daily life are color images, yet grayscale remains prevalent in many fields due to its reduced data size and simplified operations. When reducing the dimensions of a three-channel color image to a single-channel grayscale, a portion of the original color information is inevitably lost. To yield high-quality grayscale images, this study introduces a color-to-gray conversion method based on chroma difference. This method defines a novel color distance metric for color-to-grayscale conversion, incorporating pixel chromaticity differences alongside brightness variations. The discrepancy in gray pixel values in the output grayscale image effectively mirrors the overall differences among input color image pixels. Optimization is conducted using the conjugate gradient method, ensuring appropriate reflection of luminance information from the input image and chromaticity data from the original color image within the grayscale rendering. Experimental validation confirms the efficacy of this approach. However, as the method accounts for all pixel pairs, it occasionally considers unnecessary pairs, leading to potential distortion in color differences between pixels and consequent inadequacies in chromaticity variation. To address this issue, a weighting factor is introduced, prioritizing color combinations with similar color differences. Experimental findings demonstrate that grayscale images produced using the proposed method outperform those generated by alternative approaches in preserving color differentials and retaining detailed features of the original image. The resulting output exhibits clear and natural outlines, as supported by both subjective and objective assessments.

Detection and classification of invisible weld defects by magneto-optical imaging under alternating magnetic field excitation

Aiming at the difficult to detect invisible weld defects, a magneto-optical (MO) imaging non-destructive testing (NDT) system excited by an alternating magnetic field is proposed for feature extraction and detection classification of invisible weld defects. The relationship between the MO image features and the magnetic field intensity is analyzed based on the Faraday MO effect. A magnetic dipole model is proposed to study the magnetic field distribution on weld defect. A three-dimensional finite element model of invisible weld defects (with a width of 0.01 mm) is established, and the distribution of leakage magnetic field for different types of invisible defects is analyzed. In order to detect different invisible weld defects under the excitation of alternating magnetic field, MO imaging NDT experiments are carried out. The effectiveness of the finite element model is verified by the experiment. The gray value of the MO image can match the corresponding leakage magnetic field intensity. The Tamura method and gray-level co-occurrence matrix (GLCM) method are used to extract the texture features of natural invisible weld defect’s MO image, and these texture features of MO images are used as input data for the defect classification model established using back propagation (BP) neural network. Experimental results show that the classification accuracy of the GLCM + Tamura-BP model is higher than that of the GLCM-BP model, and its overall classification accuracy reaches 91.1%, indicating that this model can effectively and accurately classify natural invisible weld defects.

AI-Driven Synthetization Pipeline of Realistic 3D-CT Data for Industrial Defect Segmentation

Training data is crucial for any artificial intelligence model. Previous research has shown that various methods can be used to enhance and improve AI training data. Taking a step beyond previous research, this paper presents a method that uses AI techniques to generate CT training data, especially realistic, artificial, industrial 3D voxel data. This includes that material as well as realistic internal defects, like pores, are artificially generated. To automate the processes, the creation of the data is implemented in a 3D Data Generation, called SPARC (Synthetized Process Artificial Realistic CT data). The SPARC is built as a pipeline consisting of several steps where different types of AI fulfill different tasks in the process of generating synthetic data. One AI generates geometrically realistic internal defects. Another AI is used to generate a realistic 3D voxel representation. This involves a conversion from STL to voxel data and generating the gray values accordingly. By combining the different AI methods, the SPARC pipeline can generate realistic 3D voxel data with internal defects, addressing the lack of data for various applications. The data generated by SPARC achieved a structural similarity of 98% compared to the real data. Realistic 3D voxel training data can thus be generated. For future AI applications, annotations of various features can be created to be used in both supervised and unsupervised training.

Upconversion-based hydrogel kit with Python-assisted analysis platform for sample-to-result detection of organophosphorus pesticide

On-site quantitative analysis of pesticide residues is crucial for monitoring environmental quality and ensuring food safety. Herein, we have developed a reliable hydrogel portable kit using NaYbF4@NaYF4: Yb, Tm upconversion nanoparticles (UCNPs) combined with MnO2 nanoflakes. This portable kit is integrated with a smartphone reader and Python-assisted analysis platform to enable sample-to-result analysis for chlorpyrifos. The novel UCNPs maximizes energy donation to MnO2 acceptor by employing 100 % of activator Yb3+ in the nucleus for NIR excitation energy collection and confining emitter Tm3+ to the surface layer to shorten energy transfer distance. Under NIR excitation, efficient quenching of upconversion blue-violet emission by MnO2 nanoflakes occurs, and the quenched emission is recovered with acetylcholinesterase-mediated reactions. This process allows for the determination of chlorpyrifos by inhibiting enzymatic activity. The UCNPs/MnO2 were embedded to fabricate a hydrogel portable kit, the blue-violet emission images captured by smartphone were converted into corresponding gray values by Python-assisted superiority chart algorithm which achieves a real-time rapid quantitative analysis of chlorpyrifos with a detection limit of 0.17 ng mL−1. At the same time, pseudo-color images were also added by Python in “one run” to distinguish images clearly. This sensor detection with Python-assisted analysis platform provides a new perspective on pesticide monitoring and broadens the application prospects in bioanalysis.

Newtonian Physics Informed Neural Network (NwPiNN) for Spatio-Temporal Forecast of Visual Data

Machine intelligence is at great height these days and has been evident with its effective provenance in almost all domains of science and technology. This work will focus on one handy and profound application of machine intelligence-time series forecast, and that too on visual data points, i.e., our objective is to predict future visual data points, given a subtle lag to work on. For the same, we would propose a deep learner, Newtonian physics informed neural network (NwPiNN) with the critical modelling capabilities of the physics informed neural networks, modelled on the laws of Newtonian physics. For computational efficacy, we would work on the gray-scale values of pixels. Since the variation in data pixel values is not only provoked by the pixel gray values but also by the velocity component of each pixel, the final prediction of the model would be a weighted average of the gray value forecast and the kinematics of each pixel, as modelled by the PINN. Besides its’ proposal, NwPiNN is subjected to benchmark visual dataset, and compared with existing models for visual time series forecast, like ConvLSTM, and CNN-LSTM, and in most of the occasions, NwPiNN is found to outperform its preliminaries.