Color Threshold Research Articles

Color Dominance-Based Polynomial Optimization Segmentation for Identifying Tomato Leaves and Fruits

Optimization processes or methods play an essential role in the continuous improvement of various human activities, particularly in agriculture, given its vital role in food production. In precision agriculture, which utilizes technology to optimize food production, a primary goal is to minimize the consumption of resources like water, fertilizers, and the detection of pests and diseases. In the fertilization process, it is essential to identify any deficiencies or excesses of chemical elements. Nutrient deficiencies, which are essential for plant development, are typically detected in the leaves of crops. This paper proposes a methodology for optimizing the color threshold dominance factors employed in the segmentation process for tomato crop leaves and fruits. The optimization is performed using an interpolation method to find the values that maximize the segmentation of leaves and fruits used by the color dominance segmentation method. A comparison of the interpolation method results with those obtained using a greedy algorithm, which iteratively finds the optimal segmentation values, shows nearly identical outcomes. Similarly, a UNetmodel is used for semantic segmentation, the results of which are inferior to those obtained by the proposed interpolation optimization method. The most significant contribution of the interpolation method is that it requires only a single iteration to generate the initial data, in contrast to the iterative search required by the greedy algorithm and the lengthy training process and video card dependency of the UNet model. This results in an 80% reduction in computation time.

Automated image transcription for perinatal blood pressure monitoring using mobile health technology.

This paper introduces a novel approach to address the challenges associated with transferring blood pressure (BP) data obtained from oscillometric devices used in self-measured BP monitoring systems to integrate this data into medical health records or a proxy database accessible by clinicians, particularly in low literacy populations. To this end, we developed an automated image transcription technique to effectively transcribe readings from BP devices, ultimately enhancing the accessibility and usability of BP data for monitoring and managing BP during pregnancy and the postpartum period, particularly in low-resource settings and low-literate populations. In the designed study, the photos of the BP devices were captured as part of perinatal mobile health (mHealth) monitoring programs, conducted in four studies across two countries. The Guatemala Set 1 and Guatemala Set 2 datasets include the data captured by a cohort of 49 lay midwives from 1697 and 584 pregnant women carrying singletons in the second and third trimesters in rural Guatemala during routine screening. Additionally, we designed an mHealth system in Georgia for postpartum women to monitor and report their BP at home with 23 and 49 African American participants contributing to the Georgia I3 and Georgia IMPROVE projects, respectively. We developed a deep learning-based model which operates in two steps: LCD localization using the You Only Look Once (YOLO) object detection model and digit recognition using a convolutional neural network-based model capable of recognizing multiple digits. We applied color correction and thresholding techniques to minimize the impact of reflection and artifacts. Three experiments were conducted based on the devices used for training the digit recognition model. Overall, our results demonstrate that the device-specific model with transfer learning and the device independent model outperformed the device-specific model without transfer learning. The mean absolute error (MAE) of image transcription on held-out test datasets using the device-independent digit recognition were 1.2 and 0.8 mmHg for systolic and diastolic BP in the Georgia IMPROVE and 0.9 and 0.5 mmHg in Guatemala Set 2 datasets. The MAE, far below the FDA recommendation of 5 mmHg, makes the proposed automatic image transcription model suitable for general use when used with appropriate low-error BP devices.

Vision-based mixed color detection of plastic particles.

In the production process of high-end PP-R pipes, mixing different colored raw material particles can result in uneven color in the final product, affecting its appearance quality. in addition, color mixing can reduce the physical properties of the pipes, impacting their durability and safety. To address this issue, we propose a visual, non-destructive inspection solution based on image processing technology. The solution aims to enhance detection efficiency and accuracy by reducing background interference and enabling adaptive adjustments in various environments. Initially, the K-Means image segmentation algorithm is employed to eliminate complex background factors from the original image, significantly improving image segmentation accuracy. Subsequently, the Gaussian mixture model algorithm is utilized to automatically extract the color threshold of the foreground image after background removal, facilitating adaptive algorithm adjustments. Finally, the mean value algorithm is introduced to swiftly and accurately identify plastic particles of different colors using the automatically obtained color thresholds. Experimental results demonstrate that this method can quickly and accurately identify different color particles and effectively support the rejection of impurity particles. Through this approach, the algorithm achieves an average detection accuracy of 99.3%.

Citrus recognition in orchard scene based on modified HSV-morphology method

The orchard has always been an important scene for citrus pickers, and the existence of factors such as leaf occlusion and color similarity always lead to the difficulty of robot recognition. Based on a citrus image dataset collected from actual orchard scenes, we extract image features and build a mathematical model to identify and count the number of oranges in each image, and show the distribution of apples in the entire dataset. Firstly, this paper establishes a model based on HSV method, focusing on recognizing yellow citrus. Secondly, with the further analysis of the data set, it is found that the cyan citrus exists. Therefore, this paper defines the empirical HSV range of two kinds of citrus at the same time and introduces contour processing to optimize the model. In order to accurately distinguish the immature citrus from the leaves with similar colors and the citrus occluded by leaves, this paper uses more detailed color threshold setting and morphological operation, and makes the eccentricity of the contour less than 0.85. At the same time, considering the size and overlap of citrus, morphological operations such as dilation and erosion and contour detection techniques are applied to further improve the recognition ability of the model. Finally, the experimental results show that the average accuracy of the established model reaches 92.1%, which has a good recognition effect on citrus.

Bornean Orangutan Nest Classification using Image Enhancement with Convolutional Neural Network and Kernel Multi Support Vector Machine Classifier

Preserving wildlife habitats is crucial in mitigating climate change. Species like orangutans and monkeys contribute to fruiting and planting in forests. The World Wide Fund Sabah Malaysia faces challenges in manually identifying and classifying orangutan nests for studying their behaviour and conserving their habitats. To address this, we propose automating the classification of captured images using machine learning algorithms. This research involves three key components: image processing, feature extraction, and image classification. Our proposed image processing includes several steps, such as image pre-processing and enhancement techniques like local contrast enhancement, sharpening, intensity adjustment, histogram equalization, and colour thresholding. We applied four different Convolutional Neural Networks (CNNs) to extract and identify orangutan nests’ features. Subsequently, we utilize Support Vector Machine (SVM) for image classification. The results reveal that the Inception Residual Network Version 2 (ResNet-v2) achieves the best performance. This architecture is then combined with a kernel SVM to classify Bornean orangutan nests. Our approach demonstrates impressive results, boasting an accuracy of 96.60%, an F1-score of 96.60%, a precision of 96.59%, and a recall of 96.58%. These metrics underscore the high accuracy and effectiveness of our proposed methodology for classifying Bornean orangutan nests. By reducing the need for extensive human intervention in image analysis, our method presents a valuable tool for conservationists and researchers committed to studying and safeguarding these endangered orangutans and their habitats. In future work, we aim to develop orangutan nest detector, contributing to wildlife conservation research.

Fabrication of black wolfberry anthocyanin-based hydrogels for monitoring freshness and extending shelf-life of Dolang lamb

In this study, a black wolfberry anthocyanin-based indication label (BWIL) was developed using black wolfberry pigment (BWP) in combination with polyvinyl alcohol (PVA) and carboxymethyl cellulose (CMC) (PVA:CMC = 4:3). The potential use of BWIL for monitoring the freshness of Dorang lamb was further investigated. As revealed, physical cross-linking occurred between PVA, CMC and BWP during the preparation of BWIL. The addition of BWP promoted the internal cross-linking, porosity, and thermal stability of BWIL significantly (p < 0.05). Specifically, BWIL showed a distinct color change when exposed to the refrigerated conditions of Dorang lamb. After 6 days, 12 days and 16 days of lamb refrigeration, the ΔE of BWIL was 26.3, 28.6 and 30.7, respectively, which far exceeded the human eyes' color threshold discernible (ΔE = 3.5). Besides, the ΔE of BWIL was significantly correlated with pH, fat oxidation, and TVB-N content of Dorang lamb (p < 0.05). This result indicated that BWIL could be used for identifying the freshness of lamb accurately. Importantly, the shelf-life of lamb with BWIL was extended from 6 days to 16 days, which suggests that BWIL would be an effective tool for real-time freshness monitoring and shelf-life extending of Dorang lamb.

P-380 Improvement of follicle growth and health during dynamic culture of bovine ovarian cortical tissue (BOCT) is associated to a higher remodeling of the extracellular matrix

Abstract Study question Is improvement of follicle growth and health during dynamic culture in Perifusion Bioreactor (PB) associated to a healthier condition and remodeling of extracellular matrix? Summary answer The dynamic culture in PB allows the extracellular matrix (ECM) network rearrangement improving follicle growth and viability compared to static culture What is known already Over the past two decades, in vitro folliculogenesis has achieved moderate success in large mammals, emerging as a distinctive and reliable method for obtaining secondary follicles. Recently, although dynamic culture in bioreactors has been demonstrated to enhance follicle progression, quality, and viability compared to static culture, less emphasis has been devoted to ECM remodeling. Ovarian tissue ECM plays a crucial role in cell–cell, and cell-ECM interactions involved in follicle activation and growth. The comprehension of ECM function deepens our understanding of follicular development and holds significant promise for future reproductive technologies Study design, size, duration Bovine ovaries (age 8-24 months) were collected at slaughterhouse. BOCT strips (1x1x0.5mm) from the same ovary were cultured in groups of 10 for 14 days in PB (dynamic culture) and conventional dishes (CD, static culture). At the end of culture, collagen thickness and packing density was assessed through PicroSirius red (PSR) staining, follicle stages and quality through histology, and follicle viability through live-dead far-red and propidium iodide labeling under confocal microscopy Participants/materials, setting, methods Neosynthesis and remodeling of collagen fibers was analyzed on PSR-stained samples under polarized light measuring the color of each pixel. A color threshold was set to isolate the four colors seen in PSR-stained samples under polarized light: green, thin fibers (neosynthesized); yellow, mid-sized fibers (low assembly); orange, mid-sized fibers (medium assembly); red, mature thick fibers (high assembly). The color threshold was set as follows: red 2–9, yellow 39-51, orange 10–38, green 52–128 Main results and the role of chance In fresh tissue, analysis of PSR-stained samples showed the following values: Red 1.35; Orange 4.93; Yellow 0.18; Green 0.15. At Day 14, compared to the static culture, culture in PB, exhibits a significantly higher production of green (PB 0.22 vs CD 0.02 P &amp;lt; 0.01) and yellow (PB 0.56 vs CD 0.26 P &amp;lt; 0.05) fibers indicating an improved neosynthesis and remodeling of collagen and ECM health condition. Such higher ECM remodeling after dynamic culture is associated to a significantly higher percentage of secondary follicles (PB 15.4– CD 4.1%, P &amp;lt; 0.01 ), superior follicle quality (grade I/II, PB 70,5 - CD 28.4%, P &amp;lt; 0.01) and viability (PB 70 – CD 41.1%, P &amp;lt; 0.01). PSR analysis suggests that the continuous nutrients and oxygen supply, catabolites removal, and biomechanical stimulation during dynamic PB culture, stimulates the deposition of newly synthesized collagen which in turn, promotes follicle growth Limitations, reasons for caution Although the bovine is a suitable reproductive model for human, such findings should be replicated on human ovarian tissue Wider implications of the findings The aim of ovarian tissue culture is to promote the growth of healthy secondary follicles that can be isolated for further culture to generate MII oocytes. The present findings suggest that adoption of dynamic ovarian tissue culture could enhance the outcome of folliculogenesis in vitro Trial registration number not applicable

Visual function and retinal thickness in children with type 1 diabetes mellitus

ABSTRACT Clinical Relevance The possibility that changes in blue–yellow visual thresholds and some retinal thickness measures in children with diabetes mellitus may be observed before any visible fundus changes points to the possibility of these measures being a useful predictor that the risks of diabetic retinopathy are higher in some children than in others. Introduction Previous studies showed mixed results on chromatic and achromatic contrast sensitivity early in the course of diabetes mellitus, and the findings of these studies may have been influenced by a lack of experimental sensitivity to visual deficits, a bias towards tritan-like errors or the cognitive demands of the tests and variations in sample composition. The purpose of this study was to evaluate colour and contrast thresholds and retinal thickness in children with type 1 diabetes mellitus compared with age-matched controls. Methods A prospective case–control study was carried out on 9–14-year-old children with type 1 diabetes mellitus (49 cases) and age matched controls (49) in which isoluminant red–green and blue–yellow and achromatic luminance contrast thresholds were measured. Fundus photography was used to grade diabetic retinopathy. Retinal thickness parameters were measured using optical coherence tomography. Data on the duration of diabetes mellitus, glycaemic control (HbA1c), blood glucose level, body mass index, blood pressure and blood oxygenation at the time of testing were obtained. Results The cases mostly had poorly controlled diabetes, HbA1c 8.6% (6.4–12.8%), for an average (range) duration of 5 (0.4–12) years. The cases had significantly higher blue–yellow thresholds (p = 0.02) and greater total retinal and inner retinal thickness (p < 0.05) than controls. No cases had diabetic retinopathy. Within the cases, poorer visual function and systemic health measures were associated with thinner retinal structures and greater global loss volume percentage in the ganglion cell complex. Conclusion Blue–yellow thresholds of cases were raised compared to normal. Within the cases, higher luminance contrast thresholds were also associated with, mostly, ganglion cell complex reductions.

Bluish veil detection and lesion classification using custom deep learnable layers with explainable artificial intelligence (XAI)

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm (color threshold techniques) on lesion patches based on color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to the conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71 % on the augmented PH2 dataset, 95.00 % on the augmented ISIC archive dataset, 95.05 % on the combined augmented (PH2+ISIC archive) dataset, and 90.00 % on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN's decision-making process about the BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.

ScAnalyzer: an image processing tool to monitor plant disease symptoms and pathogen spread in Arabidopsis thaliana leaves

BackgroundPlants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in a quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, computational image processing provides additional information on the spatial localization of pathogenic microbes in different plant tissues.ResultsHere we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Thereto, detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms.ConclusionTaken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software (https://github.com/MolPlantPathology/ScAnalyzer) has the potential to standardize the analysis of disease assays between different groups.

Research on High Performance Apple Recognition Model Construction Strategy Based on Deep Learning

In order to improve the working efficiency and environmental adaptability of apple picking robots, which can realize accurate recognition and localization, ripening assessment, and quality assessment under complex situations such as "leaf occlusion", "branch occlusion", "fruit occlusion", "mixed occlusion", etc., this paper proposes a Faster R-CNN-based apple recognition method. "mixed occlusion" and other complex situations to achieve accurate recognition and localization, maturity assessment, quality assessment, the paper proposes an apple recognition method based on Faster R-CNN. The method combines the cv2.inRange and cv2.fitEllipse functions in OpenCV to optimize the model, and performs color segmentation and shape fitting by setting up color and shape thresholds to recognize apples. The trained model was tested to have a recall of 88.44% under the validation set, an accuracy of 90.35%, an F1 value of 89.38%, and a recognition time of about 0.3 s per image.The experimental results showed that the number of apples in the dataset showed a normal distribution, with the number distributed more in the range of 0 to 10. Candidate frames are screened by RPN, and apple coordinates are derived based on the coordinate transformation formula, and the experimental results show that the apples are more densely distributed near the range of x=100, y=100. The image color distribution was extracted through color histogram, and the entropy value of GLCM, contrast as the texture feature of apples, and the classification prediction of apple ripeness was carried out by using Random Forest Model, and the accuracy of the model prediction was 83.2%, the precision rate was 84.1%, and the recall rate was 83.5%, and the experimental results showed that 88.7% of the apples were ripe. Image edges were extracted by the Canny edge detection algorithm and the area values of pixel points within the edges were calculated. The ratio of pixel area converted to apple mass was estimated to be 0.007 based on the image resolution and apple density, and the test results indicated that the mass of apples was mostly concentrated in the range of 0~100g.

Tracing the green valley with entropic thresholding

ABSTRACT The green valley represents the population of galaxies that are transitioning from the actively star-forming blue cloud to the passively evolving red sequence. Studying the properties of the green valley galaxies is crucial for our understanding of the exact mechanisms and processes that drive this transition. The green valley does not have a universally accepted definition. The boundaries of the green valley are often determined by empirical lines that are subjective and vary across studies. We present an unambiguous definition of the green valley in the colour–stellar mass plane using the entropic thresholding. We first divide the galaxy population into the blue cloud and the red sequence based on a colour threshold that minimizes the intraclass variance and maximizes the interclass variance. Our method splits the region between the mean colours of the blue cloud and the red sequence into three parts by maximizing the total entropy of that region. We repeat our analysis in a number of independent stellar mass bins to define the boundaries of the green valley in the colour–mass diagram. Our method provides a robust and natural definition of the green valley.

A high-capacity Coverless Image Steganography Based on OMR and Mapping Rules

Securing private information during its transmission over the internet is of paramount importance, as it safeguards against unauthorized access and guarantees the integrity of data. Steganography serves as a technique for enhancing security by concealing sensitive information within digital content such as images, videos, audio, and textual carriers, thus thwarting unauthorized interception. Current methodologies involve manipulating pixels to create stego-images, effectively embedding confidential messages within the image structure. However, this approach has led to steganalysis experts uncovering these hidden messages. To address this issue, an innovative strategy for covert data concealment is proposed. This paper proposes a robust, secure, and embedding capacity coverless image steganography method using OMR and Mapping Rules. The secret information is represented by a binary string, and the bubble sheet is used to represent binary fragments. This approach ensures the confidentiality of sensitive data communicated over the internet. Notably, this approach surpasses previous alternatives in terms of embedding capacity, as substantiated by result analysis. Furthermore, it is adaptable for refinement as needed. Significantly, it exhibits remarkable resilience, safeguarding against a spectrum of attacks including scaling, color space conversion, JPEG compression, thresholding, "salt and pepper" noise, file format conversion, and steganalysis tools, among others.

Enhancing bighead carp cutting: Chilled storage insights and machine vision-based segmentation algorithm development

To enhance market demand and fish utilization, cutting processing is essential for fish. Bighead carp were cut into four primary cuts: head, dorsal, belly, and tail, collectively accounting for 77.03% of the fish's total weight. These cuts were refrigerated at 4 °C for 10 days, during which the muscle from each cut was analyzed. Pseudomonas.fragi proliferated most rapidly and was most abundant in eye muscle (EM), while Aeromonas.sobria showed similar growth patterns in tail muscle (TM). Notably, EM exhibited the highest rate of fat oxidation. TM experienced the most rapid protein degradation. Furthermore, to facilitate the cutting applied in mechanical processing, a machine vision-based algorithm was developed. This algorithm utilized color threshold and morphological parameters to segment image background and divide bighead carp region. Consequently, each cut of bighead carp had a different storage quality and the machine vision-based algorithm proved effective for processing bighead carp.

Automation of green roof plant cover measurements using machine learning and a comparison of digital and thermal imaging techniques

AbstractAimsPost‐analyses of digital red, green, blue (RGB) and thermal images have become increasingly popular as modern approaches to plant cover analysis. Image analyses are often coupled with semi‐automated or automated workflows to reduce the amount of human labor input compared with traditional manual procedures. This study aims to evaluate and compare different image segmentation methods for plant cover analysis using digital RGB and thermal images, focusing on the effectiveness of semi‐automated and manual segmentation techniques in monitoring plant cover on green roofs.LocationAn Extensive green roof in the City of Toronto.MethodsWe surveyed the plant cover of an extensive green roof using digital and thermal imagery. The plant cover values were obtained using three methods: traditional manual segmentation based on a visual examination (MS), ImageJ Color Threshold (CT) and Trainable Weka Segmentation (TWS), all performed within FIJI (a distribution of ImageJ). Manual segmentation based on visual examination was used as a reference standard.ResultsSignificant correlation was found between the cover estimation using the CT and TWS methods relative to MS, and between cover estimation using the thermal image and the RGB image. TWS overestimated plant cover on thermal images while producing an underestimation on RGB images. CT demonstrated a performance closer to MS than TWS, indicating that manually customized methods produced results more aligned with MS. The estimated cover values by MS were not significantly affected by the image type (digital RGB or thermal).ConclusionsResults suggest that RGB and thermal imaging techniques may provide complementary results and reveal unique information regarding the functioning of green roofs. The accuracy of supervised machine‐learning methods could be enhanced with site‐specific data to provide a more accurate and efficient estimation of plant cover, which might be beneficial for long‐term studies on green roofs and ecological sites in remote locations.

A Multi-Target Identification and Positioning System Method for Tomato Plants Based on VGG16-UNet Model

The axillary buds that grow between the main and lateral branches of tomato plants waste nutrients and lead to a decrease in yield, necessitating regular removal. Currently, these buds are removed manually, which requires substantial manpower and incurs high production costs, particularly on a large scale. Replacing manual labor with robots can lead to cost reduction. However, a critical challenge is the accurate multi-target identification of tomato plants and precise positioning for axillary bud removal. Therefore, this paper proposes a multi-target identification and localization method for tomato plants based on the VGG16-UNet model. The average intersection and pixel accuracies of the VGG16-UNet model after introducing the pretrained weights were 85.33% and 92.47%, respectively, which were 5.02% and 4.08% higher than those of the VGG16-UNet without pretrained weights, achieving the identification of main branches, side branches, and axillary bud regions. Then, based on the multi-objective segmentation of the tomato plants in the VGG16-UNet model, the regions of the axillary buds in the tomato plants were identified by HSV color space conversion and color threshold range selection. Morphological dilation and erosion operations were used to remove noise and connect adjacent regions of the same target. The endpoints and centroids of the axillary buds were identified using the feature point extraction algorithm. The left and right positions of the axillary buds were judged by the relationship between the position of the axillary bud centroid and the position of the main branch. Finally, the coordinate parameters of the axillary bud removal points were calculated using the feature points to determine the relationship between the position of the axillary bud and the position of the branch. Experimental results showed that the average accuracy of the axillary bud pruning point recognition was 85.5%.

Feasibility of Tear Meniscus Height Measurements Obtained with a Smartphone-Attachable Portable Device and Agreement of the Results with Standard Slit Lamp Examination

Purpose: We aimed to evaluate the feasibility of using a novel device, the Smart Eye Camera (SEC), for assessing tear meniscus height (TMH) after fluorescein staining and the agreement of the results with measurements obtained using standard slit lamp examination. Methods: TMH was assessed using both SEC and conventional slit lamp examination. The images were analyzed using the software ImageJ 1.53t (National Institutes of Health, Bethesda, MD, USA). A common measurement unit scale was established based on a paper strip, which was used as a calibration marker to convert pixels into metric scale. A color threshold was applied using uniform parameters for brightness, saturation, and hue. The images were then binarized to black and white to enhance the representation of the tear menisci. A 2 mm area around the upper and lower meniscus in the central eye lid zone was selected and magnified 3200 times to facilitate manual measurement. The values obtained using SEC were compared with those obtained with a slit lamp. Results: The upper and lower TMH values measured using the SEC were not statistically different from those obtained with a slit lamp (0.209 ± 0.073 mm vs. 0.235 ± 0.085, p = 0.073, and 0.297 ± 0.168 vs. 0.260 ± 0.173, p = 0.275, respectively). The results of Bland–Altman analysis demonstrated strong agreement between the two instruments, with a mean bias of −0.016 mm (agreement limits: −0.117 to 0.145 mm) for upper TMH and 0.031 mm (agreement limits: −0.306 to 0.368 mm) for lower TMH. Conclusions: The SEC demonstrated sufficient validity and reliability for assessing TMH in healthy eyes in a clinical setting, demonstrating concordance with the conventional slit lamp examination.

Effects of post-polymerization conditions on color properties, surface roughness, and flexural strength of 3D-printed permanent resin material after thermal aging.

To evaluate the color, surface properties, and flexural strength of 3D-printed permanent crown resin subjected to different post-polymerization conditions after artificial aging. Ninety (10×2mm) disc-shaped specimens were printed by using permanent crown resin with SLA technology. Specimens were divided into nine different groups, subject to post-polymerization conditions at three different times (15, 20, and 30min) and three different temperatures (40, 60, and 80°C) (n = 10). Color and surface roughness measurements were repeated pre-post thermal aging (5.000 cycles, 5-55°C) and a flexural strength test was carried out. Data were analyzed with Shapiro-Wilk, Kruskal-Wallis, ANOVA, Tukey HSD, and Dunn tests (α<0.05). ΔE00 values showed results below the acceptable color threshold, except for the 30min 40°C group (ΔE00 <1.8). No difference was found between the relative translucency parameter and surface roughness values of the 20min 60°C group recommended by the manufacturer and the other groups. A significant difference was found between the flexural strength values of the groups (p<0.001). The color properties, surface topography, and mechanical properties of the printed permanent crown material were affected by different post-polymerization conditions: polymerized at different times and temperatures. Although the flexural strength and color change values showed promising results, more studies are required to evaluate its suitability for clinical use.

Effect of macular pigment optical density on Yellow‐Blue and Red‐Green colour discrimination

The macular pigment (MP) absorbs preferentially short‐wavelength light and its narrow spectral absorption curve overlaps mostly the spectral responsivity function of S‐cones, although the M‐ and L‐cones are also affected. The MP is restricted to the centre ~14° disc region of the retina with a peak optical density (OD) at the centre in the range 0 to 1 log units. Although the macular pigment optical density (MPOD) decreases rapidly with increasing eccentricity, carotenoid supplementation studies reveal a large uptake of lutein and zeaxanthin, particularly in the parafoveal region and covering the centre ~6° to 8° of the fovea (doi: 10.1111/j.1475‐1313.2006.00386.x).Since the MP contributes to the reduced excitation of S‐cones, it is of interest to examine the effects MP may have on colour vision, particularly when ‘blue‐blocking’ lenses are also involved. We studied the relationship between MPOD and red/green (RG) and yellow/blue (YB) colour thresholds by measuring the spatial distribution of the MP in normal trichromats using the Macular Assessment Profile (MAP) test (doi: 10.1111/j.1475‐1313.2010.00748.x) and the corresponding RG and YB colour thresholds using the CAD test (doi:10.1093/bmb/ldx007).The results show negligible correlation between peak MPOD and YB thresholds and a small, but significant negative correlation with RG thresholds. This observation is of interest since increased MPOD values result in lower RG thresholds. The same experiment was also carried out in subjects with significantly larger amounts of MP following 3 months of supplementation with carotenoids. When averaged over the centre disc region of 2.5° and then over a parafoveal annulus between 2.5° and 4°, the corresponding, mean MP densities show only negligible correlations with RG and YB colour thresholds. To explain these findings, we calculated the expected cone‐contrasts for S‐, M‐ and L‐cones for chromatic displacements defined by the threshold ellipse. The results show that MP absorption of short‐wavelength light has a negligible effect on S‐cone contrast over the whole range of displacement directions associated with the colour threshold ellipse. Repeated calculations with simulated MPOD values up to 1 log unit, produced the same result. More recently, we modelled the effects ‘blue‐blocking’ lenses and pre‐receptor filters in the eye to explore changes to the orientation and the size of the colour threshold ellipse. The model predicts well the measured RG and YB thresholds and confirms the absence of significant threshold changes for MPOD values in the range 0 to 1 log unit. The model does, however, predict systematic changes in the orientation of the major axis (i.e., the tritan axis) and MPOD. These observations are of interest since the orientation of the major axis of the best‐fit ellipse appears to decrease monotonically from ~75° to 55°, as MPOD increases from very small values up to a peak OD of 1 log unit. More work is needed to investigate how YB and RG chromatic sensitivity is affected by combined absorption of short‐wavelength light by MPOD, the lens of the eye and ‘blue‐blocking’ spectacle lenses.

Effect of pre‐receptor filters on chromatic sensitivity and other visual functions in the eye

Spatial and spectral information carried by light emitted from visual displays is filtered as it passes through spectacle lenses in front of the eye as well as by filters within the eye. The image forming and the spectral transmittance properties of the elements involved in image formation in the eye cause changes in both the luminance and colour contrast in retinal images. In addition, the amount of light that reaches the retina is always reduced by pre‐receptor filters and this reduction in retinal illuminance can cause diminished retinal sensitivity to both luminance and colour contrast. Of particular interest are the use of coloured lenses in front of the eye to reduce the short‐wavelength content of the light that reaches the retina and the use of ‘notch’ filters to absorb light selectively in certain wavebands, with inevitable consequences on both the luminance and chromatic contrast of retinal images. The presence of variant cone‐pigments in some subjects or the complete absence of one class of cone pigment in others, can also produce large deviations in both luminance and colour contrast signals that can either enhance or diminish the conspicuity of coloured objects (doi: 10.1364/josaa.22.000017).The purpose of this study was to develop and validate a model of red/green (RG) and yellow/blue (YB) colour discrimination that can also predict how chromatic sensitivity is affected by the use of spectrally‐selective, spectacle lenses or by filters internal to the eye, when viewing visual displays. We examined how RG and YB colour thresholds relate to the corresponding cone contrasts needed for detection of colour differences. The results reveal an invariance of cone contrasts at threshold for different states of chromatic adaptation. In other words, colour contrast signals at threshold follow Weber's law (doi: 10.1111/j.1475‐1313.2010.00773.x). A combination of RG and YB colour signals is needed to predict the colour threshold ellipse. The model relies on these findings and predicts how coloured filters external to the eye as well as pre‐receptor filters within the eye alter the measured RG and YB colour thresholds. Model predictions have been compared against measured colour thresholds using the CAD test (doi: 10.1093/bmb/ldx007). A number of experiments were carried out when the visual display was viewed with no lens in front of the eye or through a number of tinted lenses designed to absorb progressively more blue light.The effects of pre‐receptor filters within the eye such as the lens and the macular pigment have also been investigated. The results show that ‘blue‐blocking’ filters employed in typical spectacle lenses used with daylight illumination cause only small shifts, approximately along the daylight locus, without any significant loss of either RG or YB chromatic sensitivity. ‘Orange’ lenses that absorb greater amounts of short‐wavelength light cause a significant increase in YB thresholds, but have little or no effect on RG thresholds. More surprisingly, the model also predicts that the optical density of the macular pigment has little or no effect on the size of the colour threshold ellipse.Keywords: colour, cone contrasts, blue light, CAD test.