Digital Color Research Articles

The assessment of color adjustment potentials for monoshade universal composites

Abstract Objective To evaluate the color adjustment potentials of monoshade universal composites in different shades of class I cavities. Methodology About 132 restorations were performed using monoshade universal composites (Omnichroma, OMNI; Essentia Universal; EU; Charisma Diamond One, CDO), and one simplyshade composite (NeoSpectra-ST, NEO) as control. A transparent silicone mold was used to provide standardized restorations on prefabricated class I cavities of acrylic dentures in B1, A2, and C4 shades. The assessment of color adjustment was performed immediately after, at 24 h, and at 2 weeks, through the cross-polarized dental photographs. The quantitative assessments were performed using a software (Digital Color Meter, Macintosh AC). The collected L*, a*, and b* coordinates from the restorations and the adjacent denture surfaces were used to provide Δ E 00 ⁎ \text{Δ}{E}_{00}^{\ast } values. Shapiro–Wilk, three-way Anova and Tukey HSD Test were used for the statistical analyses (p < 0.05). Results Among all composites and shades, the Δ E 00 ⁎ \text{Δ}{E}_{00}^{\ast } values were significantly the highest (1.27 ± 0.49a) for the immediately after assessments compared to the assessments at 1 day (1.01 ± 0.41b) and 2 weeks (0.97 ± 0.4c)(p < 0.05). At 24 h and 2 weeks of evaluations, the Δ E 00 ⁎ \text{Δ}{E}_{00}^{\ast } values for the control (0.42 ± 0.16H) were significantly the lowest (p < 0.05). Only the control presented imperceptible color changes for all the cavity shades (PT ≤ 0.8). After 2 weeks, EU (1.06 ± 0.32F) and OMNI (1.12 ± 0.18EF) presented significantly lower Δ E 00 ⁎ \text{Δ}{E}_{00}^{\ast } values compared to the CDO (1.29 ± 0.2CD) (p < 0.05). Restorations in lighter cavities presented significantly lower Δ E 00 ⁎ \text{Δ}{E}_{00}^{\ast } values. Conclusion Monoshade universal composites presented acceptable but perceptible color adjustment potentials in class I cavities without the shade selection procedure. Simplyshade composites following the shade selection might still be the best options regarding the level of color adjustment. Monoshade universal composites might provide better color adjustments in cavities with lighter shades.

Preferred skin reproduction of different skin groups

To accurately reproduce preferred skin colour is an important goal in digital image colour reproduction. A psychophysical experiment was conducted to specify the preferred skin colour for different skin types. Ten original facial images were captured to cover different skin types, including Caucasian, Chinese, South Asian, African, different genders and different ages. For each original image, 49 rendered images, uniformly sampled within the skin colour ellipsoid in CIELAB colour space, were used to morph the skin colours. Thirty observers from each of three ethnic backgrounds, Caucasian, Chinese and South Asian, participated in the experiment to investigate ethnic differences. Ellipsoid models were developed to specify preferred skin colour regions and centres for each original image. These results can be used to improve skin colour reproduction of colour imaging products, for example on mobile phones, for different skin types.

Dermal Nipple-Areola Complex Perfusion through Circumareolar Scars: A Delay Model in Two-Stage Nipple-Sparing Mastectomy.

Nipple-sparing mastectomy (NSM) has evolved as a standard surgical option. The NSM complication rate remains high in large breasts. To reduce the risk of necrosis, several authors have proposed delayed procedures to enhance blood supply to the nipple-areola complex (NAC). The purpose of this study in a porcine model was to show adequate redirection of NAC perfusion by neoangiogenesis through circumareolar scars. Delayed two-staged NSM was simulated in 52 nipples (six pigs) with a 60-day interval. The nipples underwent a full-thickness, circumareolar incision onto the muscular fascia, with preservation of underlying glandular perforators. After 60 days, NSM was performed through a radial incision. A silicone sheet was introduced in the mastectomy plane to prevent NAC revascularization by wound bed imbibition. Digital color imaging was used to assess necrosis. Near-infrared fluorescence with indocyanine green was used to assess perfusion patterns and perfusion in real time. No NAC necrosis was seen after 60 days' delay in any nipples. In all nipples, indocyanine green angiography showed complete alteration of the NAC vascular perfusion pattern from subjacent gland to a capillary fill following devascularization, exhibiting a predominant arteriolar capillary blush without distinct larger vessels. NAC delay reverses glandular perfusion to adequate dermal neovascularization. Neovascularization through full-thickness scars provides sufficient dermal perfusion after 60 days' delay. Identical staged delay in humans may be a surgically safe NSM option and could broaden therapeutic NSM indications in difficult breasts. Large clinical trials are necessary to provide identical results in human breasts. NAC delay reverses glandular perfusion to adequate dermal neovascularization. Neovascularization through full-thickness scars provides sufficient dermal perfusion after 60 days of delay. Identical staged delay in humans may be a surgically safe NSM option.

Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person's growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.

Microscopic cracking behaviors of rocks under uniaxial compression with microscopic multiphase heterogeneity by deep learning

Cracking behaviors of rocks significantly affect the safety and stability of the explorations of underground space and deep resources. To understand deeply the microscopic cracking process and mechanical property of rocks, X-ray micro-computed tomography (X-μCT) is applied to capture the rock microstructures. The digital color difference UNet (DCD-UNet)-based deep learning algorithm with 3D reconstruction is proposed to reconstruct the multiphase heterogeneity microstructure models of rocks. The microscopic cracking and mechanical properties are studied based on the proposed microstructure-based peridynamic model. Results show that the DCD-UNet algorithm is more effective to recognize and to represent the microscopic multiphase heterogeneity of rocks. As damage characteristic index of multiphase rocks increases, transgranular cracks in the same grain phase, transgranular and intergranular cracks of pore-grain phase, intergranular and secondary transgranular cracks and transgranular crack between different grains propagate. The ultimate microscopic failure modes of rocks are mainly controlled by the transgranular cracks-based T1-shear, T3-shear, T1-tension, T2-tension and T3-tension failures, and the intergranular cracks-based T1-tension, T1-shear and T3-shear failures under uniaxial compression.

Differentiating Glaucomatous Optic Neuropathy From Non-glaucomatous Optic Neuropathies Using Deep Learning Algorithms

A deep learning framework to differentiate glaucomatous optic disc changes due to glaucomatous optic neuropathy (GON) from non-glaucomatous optic disc changes due to non-glaucomatous optic neuropathies (NGONs). Cross-sectional study. A deep-learning system was trained, validated, and externally tested to classify optic discs as normal, GON, or NGON, using 2183 digital color fundus photographs. A Single-Center data set of 1822 images (660 images of NGON, 676 images of GON, and 486 images of normal optic discs) was used for training and validation, whereas 361 photographs from 4 different data sets were used for external testing. Our algorithm removed the redundant information from the images using an optic disc segmentation (OD-SEG) network, after which we performed transfer learning with various pre-trained networks. Finally, we calculated sensitivity, specificity, F1-score, and precision to show the performance of the discrimination network in the validation and independent external data set. For classification, the algorithm with the best performance for the Single-Center data set was DenseNet121, with a sensitivity of 95.36%, precision of 95.35%, specificity of 92.19%, and F1 score of 95.40%. For the external validation data, the sensitivity and specificity of our network for differentiating GON from NGON were 85.53% and 89.02%, respectively. The glaucoma specialist who diagnosed those cases in masked fashion had a sensitivity of 71.05% and a specificity of 82.21%. The proposed algorithm for the differentiation of GON from NGON yields results that have a higher sensitivity than those of a glaucoma specialist, and its application for unseen data thus is extremely promising.

Accurate luminance and chromaticity controls of digital colors using CIE-based RGBW algorithms.

Emerging high brightness of color displays and high signal-to-noise ratio of camera sensors require an addition of white (W) subpixels to ordinary red, green, and blue (RGB) subpixels. Conventional algorithms converting RGB signals to RGBW signals suffer from reduced chroma of highly saturated colors and complicated coordinate transformationsbetween RGB color spaces and color spaces defined by the Commission internationale de l'éclairage (CIE). In this work, we developed a complete set of RGBW algorithms to digitally code a color in the CIE-based color spaces, making complicated processes including color space transformations and white balancing become largely unnecessary. The analytic three-dimensional gamut can be obtained so that the maximal hue and luminance of a digital frame could be simultaneously obtained. Exemplary applications in adaptive controls of the colors of an RGB display in accordance with the W component of background light validate our theory. The algorithm opens an avenue toward accurate manipulations of digital colors for RGBW sensors and displays.

An Optimized Deep Fusion Convolutional Neural Network-Based Digital Color Image Watermarking Scheme for Copyright Protection

An Optimized Deep Fusion Convolutional Neural Network-Based Digital Color Image Watermarking Scheme for Copyright Protection

Comparison between digital and paper urine color to assess hydration status.

The purpose of this study was to investigate associations between digital urine color and paper urine color with other urine indices to assess hydration status. Twelve male subjects (mean ± standard deviation; age, 26 ± 8years; body mass, 57.8 ± 5.3kg; height, 177.5 ± 8.9cm; VO2max, 57.8 ± 5.8ml·kg-1·min-1) performed four exercise trials in the heat. Before and following exercise trials, subjects provide urine samples. Urine samples were measured using a digital urine color chart on a portable device screen. Urine samples were also assessed with urine specific gravity (USG), urine osmolality (UOsmo), and a validated paper urine color chart. There were extremely large associations found between digital urine color and paper urine color (r = 0.926, p < 0.001). Correlation coefficients showing associations with USG and UOsmo were similar between digital urine color (USG, r = 0.695, p < 0.001; UOsmo, r = 0.555, p < 0.001) and paper urine color (USG, r = 0.713, p < 0.001; UOsmo, r = 0.570, p < 0.001). Bland-Altman analysis indicated that no proportional bias was observed between digital and paper urine colors (bias, -0.148; SD of bias, 0.492; 95% LOA, -1.11, 0.817; p = 0.094). Strong associations were found between digital and paper urine colors with no proportional bias. Furthermore, the degree of associations with USG and UOsmo was similar between digital and paper urine color. These results indicate that digital urine color is a useful tool to assess hydration status and this method could be used as an alternative method to using paper urine color.

New automated digital colour workflow for accurate 3D printed models.

New automated digital colour workflow for accurate 3D printed models.

The Online Construction of Digital Spatial Representation in Alphanumeric Mixed Situations Is Not Automaticity

SNARC effect is called the joint effect of spatial digital response coding, which is the proof of the connection between number and space. There are mainly theoretical explanations, such as the mental number line explanation of long-term memory, the working memory explanation of short-term memory, and the dual path pattern explanation. Recent studies have demonstrated that SNARC effect is the online construction of digits in working memory during task execution, and the online construction of digits SNARC effect is interfered by letter processing in the mixed situation of digits and letters, which further supports the hypothesis of working memory. Although online encoding of SNARC effect has been proved, it is not clear whether online encoding of digital spatial representation in the mixed alphanumeric context has automaticity, and if so, whether it will be interfered by letter processing. In this study, three digit color judgment experiments were conducted, and the ratio of digits to letters was 1:1. 1:6. The paper explores whether the online construction of digital space representation is automaticity in the mixed interference situation of 6:1. To investigate the interference of letter processing on digital SNARC effect, explore whether the interference is regulated by the proportion of number letters, and further explore the automatic mechanism of the interference of letter processing on digital space coding. The results showed that SNARC effect was not observed in the mixed numbers and letters at different proportions. It shows that the online construction of digital space representation in the mixed alphanumeric situation does not have automaticity. When the task does not directly activate the size order information of the number letters, the letters will interfere with the online construction of the number SNARC effect, which is not proportional adjustment.

Neuronal network algorithm towards color model of full color gamut yarn and its color prediction research

This article builds a gridded color matching model based on digital color mixing of four primary colored fibers in the context of digital spinning, which can separately perform regulation of hue, saturation, and lightness within the full color gamut. Combining the requirements of a neuronal network prediction algorithm, the mixing ratios of primary colored fibers and spectral reflectance were used as input and output parameters for each other, and 41 grid point mixing samples were selected from the constructed gridded color mixing model, 31 of which were chosen as training samples to establish a neuronal network model containing input, hidden, and output layers. The remaining 10 mixed samples were then selected as predicting samples to perform validation of the model's capability to predict the color or the mixing ratio of the four primary colored fibers. The final results showed that the color difference of the mixed samples used for color prediction was 0.90 at the minimum, 2.31 at the maximum, and 1.45 at the average; the mean absolute error for proportional forecasting was 0.19%, and the root mean square error was 0.97%. The findings indicate that the forecasting method has excellent prediction accuracy, and the constructed color matching model of four primary colors and neuronal network algorithm can be applied to the prediction of mixed colors of multiple primary colored fibers, providing technical support for the spinning of colored yarns.

ФУНКЦІОНУВАННЯ ІНФОГРАФІЧНИХ РЕСУРСІВ У ТЕКСТАХ НОВИН (НА МАТЕРІАЛІ ОНЛАЙН-НОВИН СЛУЖБ BBC І CNN)

The article highlights the use of infographics to effectively present the news material at BBC and CNN web pages. Infographic is a visual representation of information, data and knowledge. The infographic types that are widely used in news texts include maps, different kinds of graphs and charts such as bar/line/pie/column/bubble/gauge/stock/waterfall/flow charts; area/ hierarchy/circuit/tree diagrams; pictographs, timelines. Also, the symbiosis of infographic resources and additional graphic components (images, icons, geometric shapes, colour, numbers, and headers) was taken into account within the scopes of the current research. All of these allow controlling the infographics flow. Both verbal and nonverbal elements are considered to be crucially significant in creating the eye-catching and attention-grabbing infographic picture. Besides, the digital mode and colour play a significant role in presenting the information infographically. Numbers convey large amounts of information briefly and accurately and colour denotes emotionally valuable metaphorical meaning, stylistic and associative properties. The visual and the verbal can be integrated into a single syntagmatic unit. Verbal elements elucidate and decipher the nonverbal symbols, restate the message of the infographic image in a more precise way, distilling just one from the many possible meanings the image might have. Verbal items are represented by means of a single lexeme, a word combination or a simple sentence. Nonverbal components provide the verbal ones with the essential and highly informative visuals by showing the data, presenting many numbers in a small space, making large data sets coherent. They serve a reasonably clear purpose of description, exploration, tabulation and decoration. The visual elements serve as syntax identifying the participants by nonverbal means. Infographic is an effective and powerful tool to explain complex issues in a way that can quickly lead to insight and better understanding by making information easy to digest, educational, and engaging.

Construction of a canine model with acute type B aortic dissection using a self-made pressure-driven flow device.

A reproducible canine aortic dissection (AD) model would be useful for evaluating the performance of novel endovascular treatment devices. Therefore, we attempted to create a reproducible canine model of Stanford type B AD (TBAB) by a surgical method. Computed tomography angiography was performed 2 h after the procedure to determine if a false lumen was present, and follow-up imaging was performed 10 d after the procedure using digital subtraction angiography, intravascular ultrasound (IVUS), and color Doppler flow imaging (CDFI) to confirm stable persistence of the false lumen. The success rate of model construction was 88.8% (16/18). All surviving dogs had distal re-entries (16/16). The number of re-entries in the dogs was 1.50 ± 0.52, and the mean length of the false lumen was 175.37 ± 16.98 mm. IVUS showed the area of the false lumen at the narrowest part of the arterial lumen was 84.88 ± 1.27%. The CDFI showed that the peak systolic velocity in the false lumen (10.89 ± 0.74 cm/s) was significantly slower than that in the true lumen (25.31 ± 1.72 cm/s; P<0.001). Moreover, the direction of blood flow in the true lumen was consistent, whereas that in the false lumen was disordered. We optimized the traditional surgical method to construct a canine model of TBAD to improve the success rate of model construction, and designed a novel device to lengthen the false lumen. The proposed model has wide implications in evaluating the performance of novel endovascular treatment devices and studying the AD-related hemodynamics.

Development and Application of a Standardized Testset for an Artificial Intelligence Medical Device Intended for the Computer-Aided Diagnosis of Diabetic Retinopathy.

To explore a centralized approach to build test sets and assess the performance of an artificial intelligence medical device (AIMD) which is intended for computer-aided diagnosis of diabetic retinopathy (DR). A framework was proposed to conduct data collection, data curation, and annotation. Deidentified colour fundus photographs were collected from 11 partner hospitals with raw labels. Photographs with sensitive information or authenticity issues were excluded during vetting. A team of annotators was recruited through qualification examinations and trained. The annotation process included three steps: initial annotation, review, and arbitration. The annotated data then composed a standardized test set, which was further imported to algorithms under test (AUT) from different developers. The algorithm outputs were compared with the final annotation results (reference standard). The test set consists of 6327 digital colour fundus photographs. The final labels include 5 stages of DR and non-DR, as well as other ocular diseases and photographs with unacceptable quality. The Fleiss Kappa was 0.75 among the annotators. The Cohen's kappa between raw labels and final labels is 0.5. Using this test set, five AUTs were tested and compared quantitatively. The metrics include accuracy, sensitivity, and specificity. The AUTs showed inhomogeneous capabilities to classify different types of fundus photographs. This article demonstrated a workflow to build standardized test sets and conduct algorithm testing of the AIMD for computer-aided diagnosis of diabetic retinopathy. It may provide a reference to develop technical standards that promote product verification and quality control, improving the comparability of products.

Construction of a Digital Color Grading Laboratory Based on DaVinci Resolve

Construction of a Digital Color Grading Laboratory Based on DaVinci Resolve

Designing Digital Color Library as Inventory Media for The Natural Dye of Indonesian Traditional Textile (Study Case: Classical Batik of Yogyakarta)

Textile coloring using natural colorant has its own unique value on each color results as well as in the color-making creation process itself. In addition to the decorative elements, Indonesian traditional textile also has distinct signature textile culture on its natural dye, for example Batik Klasik Yogyakarta which is well known for its brown soga color, a brownish color obtained from the colorant’s combination of jambal bark (Pelthophorum ferrugineum), tingi bark (Ceriops candolleana), and tegeran wood (Cudrania javanensis). Along with those ingredients, Batik Klasik Yogyakarta also uses indigo plant (Indigofera tinctoria) as its blue colorant and kapur tohor (Calcium Oxide) for pre and post mordant treatment. The designing of “Manékawarna” digital content output as content of mobile application would be a recommendation as it would be a data inventory media of Batik Klasik Yogyakarta natural dye. In order to make the color visual data suitable for digital device, the research requires fabrics dyed with Batik Klasik Yogyakarta natural dye ingredients and technique as samples for color identification study by digitalization method to obtain RGB color value. The natural dye color digital format data is designed to facilitate data preservation and its acquisition by individuals whom have interest on textile natural dye.

Multifunctional intelligent film integrated with purple sweet potato anthocyanin and quercetin-loaded chitosan nanoparticles for monitoring and maintaining freshness of shrimp

Herein, an antioxidant, antibacterial and pH-sensitive multifunctional film is fabricated by incorporating anthocyanins-rich purple sweet potato extract and quercetin-loaded chitosan nanoparticles in agar /sodium alginate polymer matrix for monitoring and maintaining the freshness of shrimp. The film shows distinguishable color changes as pH changes and is highly sensitive to volatile ammonia. Moreover, the digital color information of the film is obtained by smartphone combined with RGB (red/green/blue) analysis. The G/R values are successfully used for the evaluation of shrimp freshness. More importantly, the film exhibits strong UV-blocking, antioxidant, antibacterial properties, and excellent mechanical, water vapor barrier properties, which enables the film to be used as a food packaging film. Notably, shrimp packed with the film can extend shelf life to about 36 h in refrigerated storage. Consequently, the multifunctional film can be applied to intelligent packaging for not only real-time indicating but also high-efficiently preserving the freshness of animal-derived foods.

Classification of coffee beans roast maturity levels based on digital image processing color using the KNN and PCA method

Classification of coffee beans roast maturity levels based on digital image processing color using the KNN and PCA method

The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality

The objective of this study was to reveal the usefulness of image processing and machine learning for the non-destructive evaluation of the changes in mint leaves caused by two natural drying techniques. The effects of shade drying and open-air sun drying on the ventral side (upper surface) and dorsal side (lower surface) of leaves were compared. Texture parameters were extracted from the digital color images converted to color channels R, G, B, L, a, b, X, Y, and Z. Models based on image features selected for individual color channels were built for distinguishing mint leaves in terms of drying techniques and leaf side using machine learning algorithms from groups of Lazy, Rules, and Trees. In the case of classification of the images of the ventral side of fresh and shade-dried mint leaves, an average accuracy of 100% and values of Precision, Recall, F-Measure, and MCC of 1.000 were obtained for color channels B (KStar and J48 machine learning algorithms), a (KStar and J48), b (KStar), and Y (KStar). The effect of open-air sun drying was greater. Images of the ventral side of fresh and open-air sun-dried mint leaves were completely correctly distinguished (100% correctness) for more color channels and algorithms, such as color channels R and G (J48), B, a and b (KStar, JRip, and J48), and X and Y (KStar). The classification of the images of the dorsal side of fresh and shade-dried mint leaves provided 100% accuracy in the case of color channel B (KStar) and a (KStar, JRip, and J48). The fresh and open-air sun-dried mint leaves imaged on the dorsal side were correctly classified at an accuracy of 100% for selected textures from color channels a (KStar, JRip, J48), b (J48), and Z (J48). The developed approach may be used in practice to monitor the changes in the structure of mint leaves caused by drying in a non-destructive, objective, cost-effective, and fast manner without the need to damage the leaves.