Use Of Digital Image Analysis Research Articles

Differential QTL underlie wheat grain physical quality when measured using image‐based versus traditional laboratory methods

AbstractBackgroundThe marketing value of wheat (Triticum aestivum L.) is determined, in parts, by the grain's physical characteristics, owing to which they directly (or indirectly) influence milling performance and baking quality. These characteristics have been manually measured in the past, but now, digital image analysis (DIA) is being increasingly used to replace the slow phenotyping system. Here, we asked whether this could lead to the identification of the same or different genes when compared to the traditional phenotyping methods.ResultsWe measured grain physical quality on 142 wheat doubled haploids grown in the field over 2 years, and in using the quantitative trait locus (QTL) mapping approach we found that (1) for wheat grain weight, the use of DIA provided genetic information that mostly conformed to those obtained using the traditional phenotyping methods, with heritability estimates that were identical across both methods. Majority of the QTL detected were consistent between the traditional versus digital phenotyping methods; (2) a more complex architecture, however, arose from QTL analyses of hectoliter mass (HLM) and percentage of shriveled grains (SCR). The estimates for heritability varied by as much as 0.24 across methods and, more significantly, many of the detected QTL for both traits were method‐specific; (3) though method‐specific, identified QTL was mapped to genomic regions known to harbor genes for grain physical traits.ConclusionsThousand‐grain weight (TGW) is robust to a phenotyping method, but a different genetic system underlies HLM and SCR, when these were measured using traditional versus digital image analysis. For these traits, heritability estimates were larger when phenotyped using traditional methods relative to digital image analysis, suggesting that further refinements are required to better correlate digital image analysis with the traditional phenotyping methods.

Comparison of hypospadias phenotype pixel segmentation to GMS score

Hypospadias phenotype assessment determines if the anatomy is favorable for reconstruction. Glans-Urethral Meatus-Shaft (GMS) has been adopted in an effort to standardize hypospadias classification. While extremely subjective, GMS has been widely used to classify the severity of the phenotype to predict surgical outcomes. The use of digital image analysis has proven to be feasible and prior efforts by our team have demonstrated that machine learning algorithms can emulate an expert's assessment of the phenotype. Nonetheless, the creation of these image recognition algorithms is highly subjective. In order to reduce a subjective input in the evaluation of the phenotype, we propose a novel approach to analyze the anatomy using digital image pixel analysis and to compare the results using the GMS score. Our hypothesis is that pixel cluster segmentation can discriminate between favorable and unfavorable anatomy. To evaluate whether image segmentation and digital pixel analysis are able to analyze favorable vs unfavorable hypospadias anatomy in a less subjective manner than GMS score. A total of 148 patients with different types of hypospadias were classified by 1 of 5 independent experts following the GMS score into "favorable" (GG), "moderately favorable" (GM) and "unfavorable" (GP) glans. From there, 592 images were generated using digital image segmentation. 584 were included for final analysis due to certain images being excluded for poor image quality or inadequate capture of target anatomy. For each image, the region of interest was segmented separately by two evaluators into "glans," "urethral plate," "foreskin" and "periurethral plate". The values obtained for each segmented region using machine-learning statistical pixel k-means cluster analysis were analyzed and compared to the GMS score given to that image using an ANOVA analysis. Analysis of image segmentation demonstrated that k-means pixel cluster analysis discriminated "favorable" vs "unfavorable" urethral plates. There was a significant difference between scores when comparing the GG and GM groups (p=0.03) and GG and GP groups (p=0.05). Pixel cluster analysis could not discriminate between "moderately favorable" and "unfavorable" urethral plates. Through our analysis, we found significant pairwise difference for different tissue qualities. Digital image segmentation and statistical k-means cluster analysis can discriminate anatomical features in a similar way to the GMS score. Future research can target discerning between different tissue qualities in an effort to predict surgical outcomes for hypospadias repair.

Predictive biometrics of hair sheep through digital imaging

Direct collection of biometric measurements (BM) from sheep is an expensive and stressful procedure for animals; instead, indirect and novel methods have recently been used. The objective of this study was to use digital image analysis (DIA) to predict biometric measurements of Pelibuey sheep as a non-invasive approach under on-farm conditions. Withers height (WH), body length (BL), body diagonal length (BDL) and rib depth (RD) were predicted in Pelibuey ewes using DIA. Images were taken from the left flank from 65 non-pregnant and non-lactating Pelibuey ewes using a digital camera and analyzed by DIA. The BM determined from both in vivo and by DIA presented a positive and moderate (P <0.05) correlation coefficients (r) of 0.43, 0.66, 0.73 and 0.75 for BL, BDL, WH and RD, respectively. Regression equations from BM by DIA had a determination coefficient (R2) of 0.19, 0.44, 0.54 and 0.56 for BL, BDL, WH and RD, respectively. The equations developed were from low to moderate precision (R2 = 0.18 to 55), moderate to high accuracy (Cb > 0.69) and low to moderate reproducibility index (> 0.30). Overall, the use of DIA was able to predict the BM in Pelibuey ewes with low to moderate precision and accuracy. Factors affecting the accuracy and precision of this relationship should be further investigated.

DIA of centrifuge model tests on geogrid reinforced soil walls with low-permeable backfills subjected to rainfall

Geogrid reinforced soil walls (GRSWs) constructed using low-permeable backfills often experience failures when subjected to rainfall. The objective of this paper is to employ centrifuge modelling to investigate the effect of geogrid types on the performance of GRSW models constructed with low-permeable backfill, when subjected to rainfall intensity of 10 mm/h. A 4.5 m radius large beam centrifuge facility was used, and rainfall was simulated using a custom-designed rainfall simulator at 40 gravities. Digital Image Analysis (DIA) was employed to understand the deformation behaviour of GRSWs with low stiffness geogrid layers with and without drainage provision subjected to rainfall. Additionally, the effect of varying stiffness of geogrid reinforcement layers across the height of GRSW was also investigated. The interpretation of DIA helped to quantify displacement vector fields, face movements, surface settlement profiles and geogrid strain distribution with depth. Irrespective of drainage provision, GRSWs reinforced with low stiffness geogrid layers experienced a catastrophic failure at the onset of rainfall. However, GRSW reinforced with geogrid layers of varying stiffness was observed to perform well. This study demonstrates the effective use of DIA of GRSWs subjected to rainfall along with centrifuge-based physical model testing.

Abstract P4-05-01: Results from the first UK NEQAS ICC & ISH external quality assessment for Ki-67 demonstration in breast cancer

Abstract AIMS: We previously described use of digital image analysis (DIA) to improve objectivity and reproducibility in external quality assessment (EQA) for immunocytochemical (ICC) staining in pathology [1]. Here we extend the work and report on: (1) use in EQA of the proliferation marker, Ki-67 in breast cancer, (2) use of a commercial prototype cell-line microarray (CLMA). MATERIALS AND METHODS: 34 laboratories stained slides provided by UK NEQAS using their routine ICC methods and returned them for central review and analysis. Slides were provided in duplicate; 29 laboratories returned both and 5 one slide (N = 63). Centrally performed DIA used the Cognition Master Professional Suite (CogM, VMscope GmbH) to quantify Ki67 labelling index (LI) [2]. Qualitative assessment aimed to separate submissions that were not useful from those which could be reported. This followed UK NEQAS standard methods. Materials assessed comprised two different breast cancer samples and a Ki-67 CLMA (Karpas Prototype, Array Science). RESULTS: Descriptive statistics on Ki-67 scores for each sample are presented in Table 1. Analysis of individual sample types for difference in Ki-67 expression associated with primary antibody showed the following: mean Ki-67 LI on breast cancer A had a range of 13.6% (K2, Leica Biosystems) to 17.6% (30-9, Ventana Medical Systems), but no significant difference was found for any primary antibody clone. For breast cancer sample B, the range was 2.0% (MIB-1, Dako Agilent) to 3.2% (30-9), and the difference was significant at P<0.05. In contrast, the CLMA showed no significant difference in expression for either MIB-1 or 30-9 (K2 could not be reported).The Ki-67-positive Karpas cell line in the CLMA had a small nuclear size compared to that of ‘standard’ breast cancer, which led to undercalling by CogM and potentially other DIA software; it also demonstrated a restricted dynamic range of high staining intensity, which reduced its ability to demonstrate differential staining in response to methodological variations.When assessed qualitatively 11.1% of submissions were not reportable (fail), 12.7% were sub-optimal but were scoreable (borderline), 23.8% had minor defects (adequate) and 52.4% were optimally stained (good). For both breast cancer samples, but not for the CLMA these qualitative categories were associated with increasing Ki-67 LI scores. For breast cancer A the mean Ki-67 LI score for the non-reportable group was significantly different from all other categories, which were not significantly different from each other; for breast cancer B, significant differences were seen between the non-reportable group and the adequate and good groups only (all at P<0.05). CONCLUSIONS: Centrally performed DIA of Ki-67 LI on breast cancer tissue samples produces additional information that allows the identification of statistically significant differences between primary antibodies (MIB-1 and 30-9). It also validates qualitative assessment by demonstrating significant differences between qualitative categories and is likely to be useful to identify lab-specific differences in staining performance in future rounds of EQA.The prototype CLMA produced using the Karpas cell-line did not demonstrate statistically significant differences between primary antibody clones or qualitative categories and is not a useful tool for use in EQA.REFERENCES: 1. Warrick N, et al. UK IBCS 2020. Breast Cancer Res Treat 180, 527-596 (2020)2. http://vmscope.com/index BC_ABC_BCLMA_0%CLMA_5%CLMA_10%CLMA_20%CLMA_30%Number of values124125164165110165165Minimum0.30.00.00.01.93.30.025% Percentile11.31.00.01.74.07.613.5Median15.22.20.02.35.09.416.375% Percentile20.03.50.23.26.111.519.8Maximum32.212.112.09.111.721.335.995% CI of medianActual confidence level96.2%95.1%96.5%95.7%95.5%95.7%95.7%Lower confidence limit14.01.80.02.04.99.115.5Upper confidence limit16.62.60.02.65.59.817.1Mean15.72.50.42.65.510.017.1Std. Deviation6.52.11.71.42.23.35.7Std. Error of Mean0.60.20.10.10.20.30.4Lower 95% CI of mean14.52.20.22.45.09.516.2Upper 95% CI of mean16.82.90.72.85.910.518.0 Citation Format: Andrew Dodson, Dawn Wilkinson, Mitch Dowsett, Suzanne Parry. Results from the first UK NEQAS ICC & ISH external quality assessment for Ki-67 demonstration in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-05-01.

Ki67 and LSD1 Expression in Testicular Germ Cell Tumors Is Not Associated with Patient Outcome: Investigation Using a Digital Pathology Algorithm.

TGCTs represent a model of curable disease afflicting especially young men. Defining tumor biological characteristics is crucial to increase current knowledge and tailor the best clinical management. Ki67, a potential prognostic marker, still exhibits heterogenous associations with patient outcomes, thus bringing the need of corroboration with larger cohorts in clinical practice. LSD1, an epigenetic enzyme, represents a future target for epigenetic drugs that may lower treatment-associated morbidity. This study aimed to assess Ki67/LSD1 immunoexpression across all TGCT histological subtypes and correlate it with clinicopathological features. Results were compared with an in silico analysis of the TCGA database. Immunohistochemistry for Ki67 and LSD1 was carried out in a cohort of 157 TGCT tumor samples and assessed using a digital pathology algorithm. LSD1 protein expression was explored in TGCT cell lines, including ATRA-differentiated clones. There was a significant positive correlation between Ki67 and LSD1 H-scores (rs = 0.182, p = 0.037). Ki67 positivity percentage and H-score were significantly higher in non-seminomas (p = 0.0316 and 0.0113, respectively). Expression was not significantly different according to clinicopathological features, including stage, IGCCCG prognosis-based system, or relapse/progression-free survival, which was corroborated by in silico analysis. Our study, making use of digital image analysis, does not confirm the utility of these biomarkers in a daily practice cohort. Although not affecting patient outcome in our cohort, LSD1 is expressed overall in TGCTs, suggesting sensitivity to LSD1 inhibitors.

Assessment of Sensory and Texture Profiles of Grape Seeds at Real Maturity Stages Using Image Analysis.

The usefulness of digital image analysis in estimating sensory attributes of grape seeds in relation to maturation level was evaluated for the first time. Seeds from Syrah grapes harvested throughout the ripening period were grouped according to maturity using the DigiEye® system. The discriminant ability, homogeneity, repeatability, and uniformity of a sensory panel were assessed after training on grape seeds. The aim was to evaluate the use of digital image techniques in order to accurately establish the maturity level of grape seeds, based on sensory and textural features. All sensory attributes (color, hardness, cracking, vegetal, bitterness and astringency) showed significant (p < 0.05) correlations with the chemical maturity stage. Color and vegetal (sensory attributes), together with deformation energy (instrumental texture parameter) (De), allowed for the classification of the seeds into four real maturity stages, hence their usefulness as grape seed ripening indicators. Significant (p < 0.05) and high-correlation factors were also found between instrumental and sensory attributes. Therefore, digital analysis can be a useful tool to better define the maturity stage in the vineyard, and to dispose of grape seeds with well-defined sensory profiles for specific oenological applications. This could help to determine the optimal harvest date to manage winemaking, in order to produce high quality wines in warm climates.

The Potential of Digital Image Analysis to Determine Tumor Cell Content in Biobanked Formalin-Fixed, Paraffin-Embedded Tissue Samples.

Introduction: Best practices dictate that biobanks ensure accurate determination of tumor content before supplying formalin-fixed, paraffin-embedded (FFPE) tissue samples to researchers for nucleic acid extraction and downstream molecular testing. It is advisable that trained and competent individuals, who understand the requirements of the downstream molecular tests, perform the microscopic morphological examination. However, the special skills, time, and costs associated with these assessments can be prohibitive, especially in large case cohorts requiring extensive pathological review. Determination of tumor content reliably by digital image analysis (DIA) could represent a significant advantage if validated, utilized, and deployed by biobanks. Materials and Methods: Whole slide digital scanned images of colorectal, lung, and breast cancer specimens were created. The scanned images were imported into the DIA software QuPath and digital annotations were completed by biobank technicians, under the direction of trained histopathology senior scientists. Automated cell detection was conducted and tumor epithelial cells were classified and quantified. Results: DIA scores were highly concordant with the manual assessment for 376 of 435 samples (86%). A detailed review of discordant cases indicated digital scores had a higher accuracy than the manual estimation. Conclusion: Automated digital quantification has the potential to replace visual estimations with reduced subjectivity and increased reliability compared with manual tumor estimations. We recommend the use of DIA by biobanks involved in provision of FFPE tissue samples, especially in large research studies requiring high volumes of cases to be analyzed.

Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm

In preclinical studies that involve animal models for hepatic fibrosis, accurate quantification of the fibrosis is of utmost importance. The use of digital image analysis based on deep learning artificial intelligence (AI) algorithms can facilitate accurate evaluation of liver fibrosis in these models. In the present study, we compared the quantitative evaluation of collagen proportionate area in the carbon tetrachloride model of liver fibrosis in the mouse by a newly developed AI algorithm to the semiquantitative assessment of liver fibrosis performed by a board-certified toxicologic pathologist. We found an excellent correlation between the 2 methods of assessment, most evident in the higher magnification (×40) as compared to the lower magnification (×10). These findings strengthen the confidence of using digital tools in the toxicologic pathology field as an adjunct to an expert toxicologic pathologist.

Use of digital image analysis to determine fracture properties of alkali-activated mortars

Results of three-point bending tests on notched beams cast with three alkali-activated mortars that differ by the type and size of the aggregates are presented. Digital image correlation is used to estimate the size of the fracture process zone. The fracture energy is computed by means of the work-of-fracture method and found to be strongly dependent on the type of aggregate used and does not differ whether the LDVT or DIC load-deflection curves are used. Finally, low values of the fracture energy in alkali-activated mortars might be associated with the presence of unreacted fly ash.

Digital Image Analysis of CD8+ and CD3+ Tumor-Infiltrating Lymphocytes in Tongue Squamous Cell Carcinoma.

PurposeThe presence of CD8+ tumor-infiltrating lymphocytes (TILs) has been reported to be associated with treatment outcomes in many types of solid tumors. However, the results vary due to the various methods of visual estimation and subjective interpretation. The current study is the first to use digital image analyses to evaluate the density of CD8+/CD3+ TILs in tongue squamous cell carcinoma (TSCC).Patients and MethodsFrom 2005 to 2015, a cohort of 258 TSCC patients were consecutively enrolled in this study. After immunohistochemistry on representative sections, the density of CD8+/CD3+ TILs at tumor invasive margins was evaluated by digital image analysis. The subjects were stratified by the median values of CD3+ cell density, CD8+ cell density, CD8/CD3, and scores (0, 1, and 2) to demonstrate the association with various clinicopathological manifestations.ResultsLow CD8+ TIL counts were associated with advanced tumor stages (p=0.034), and low CD8/CD3 ratios were associated with perineural invasion (p=0.043). Both parameters were also associated with increased tumor depths (p=0.034 and 0.004, respectively). Univariate analyses revealed that advanced tumor stages, perineural invasion, extranodal extension, tumor depth, lower CD8 counts, and lower scores (score 0 vs 2) were associated with poorer overall survival, and multivariate analysis further indicated that extranodal extension and low scores (score 0 vs 2) were both independent factors for overall survival (p < 0.0001 and p = 0.0369, respectively).ConclusionThe use of digital image analysis to assess CD8+ TILs at the invasive margins provides an objective indicator of prognoses including overall survival.

Evaluation of digital image analysis as a supportive tool for the stratification of head and neck vascular anomalies

BackgroundThe histological differentiation of individual types of vascular anomalies (VA), such as lymphatic malformations (LM), hemangioma (Hem), paraganglioma (PG), venous malformations (VeM), arteriovenous malformations (AVM), pyogenic granulomas (GP), and (not otherwise classified) vascular malformations (VM n.o.c.) is frequently difficult due to the heterogeneity of these anomalies. The aim of the study was to evaluate digital image analysis as a method for VA stratificationMethodsA total of 40 VA tissues were examined immunohistologically using a selection of five vascular endothelial-associated markers (CD31, CD34, CLDN5, PDPN, VIM). The staining results were documented microscopically followed by digital image analyses based quantification of the candidate-marker-proteins using the open source program ImageJ/Fiji.ResultsDifferences in the expression patterns of the candidate proteins could be detected particularly when deploying the quotient of the quantified immunohistochemical signal values. Deploying signal marker quotients, LM could be fully distinguished from all other tested tissue types. GP achieved stratification from LM, Hem, VM, PG and AVM tissues, whereas Hem, PG, VM and AVM exhibited significantly different signal marker quotients compared with LM and GP tissues.ConclusionAlthough stratification of different VA from each other was only achieved in part with the markers used, the results of this study strongly support the usefulness of digital image analysis for the stratification of VA. Against the background of upcoming new diagnostic techniques involving artificial intelligence and deep (machine) learning, our data serve as a paradigm of how digital evaluation methods can be deployed to support diagnostic decision making in the field of VAs.

Evaluation of the WASPLab Segregation Software To Automatically Analyze Urine Cultures Using Routine Blood and MacConkey Agars.

Automation of the clinical microbiology laboratory has become more prominent as laboratories face higher specimen volumes and understaffing and are becoming more consolidated. One recent advancement is the use of digital image analysis to rapidly distinguish between chromogenic growth for screening bacterial cultures. In this study, colony segregation software developed by Copan (Brescia, Italy) was evaluated to distinguish between significant growth and no growth of urine cultures plated onto standard blood and MacConkey agars. Specimens from 3 sites were processed on a WASP instrument (Copan) and incubated on the WASPLab platform (Copan), and plates were imaged at 0 and 24 hours postinoculation. Images were read by technologists following validated laboratory protocols (VLPs), and results were recorded in the laboratory information systems (LIS). Image analysis performed colony counts on the 24-hour images, and results were compared with the VLP. A total of 12,931 urine cultures were tested and analyzed with an overall sensitivity and specificity of 99.8% and 72.0%, respectively. After secondary review, 91.1% of manual-positive/automation-negative specimens were due to expert rules that reported the plate as contaminated or growing only normal flora and not due to threshold counts. Nine specimens were found to be manual-positive/automation-negative; a secondary review demonstrated that the results of 8 of these specimens were due to growth of microcolonies that were programmed to be ignored by the software and 1 were due to a colony count near the limit of significance. Overall, the image analysis software proved to be highly sensitive and can be utilized by laboratories to batch-review negative cultures to improve laboratory workflow.

Morphometrical structural analysis of inter-thread pores in woven fabrics with the use of computer image analysis

The main aim of the study is to present a new innovative method designed by the author of non-destructive, accurate and morphometrical identification of fabric structural parameters, individual inter-thread pores (ITPs) in particular. The description parameters and fabric structure assessment methodology with the use of digital image analysis with author's copyrighted MagFABRIC software were created specifically for this analysis. The ITP parameters were described in three aspects: size, shape and location in the fabric structure. The study was conducted on two groups of fabrics: plain and twill weave. The need for a bimodal analysis of the fabric structure parameters was indicated, as it allowed for the structure differences identification in the test fabrics. The analysis results were compared to the air permeability test. The developed methodology is relevant to specialized fabrics (e.g. barrier fabrics, industrial filters and any other fabric where the ITP area is an important parameter).

Intelligent Precision Nitrogen Fertilizer Application Based on Speaking Plant Approach for Environmental Sustainability

In vegetable farming, one of important issues is determining the right dose application of nitrogen fertilizer. The advantages and disadvantages of nitrogen fertilizer in spinach (Amaranthus sp.) will have an adverse effect on its productivity. The total nitrogen measurement by chemical analysis is too expensive, inefficient, and cannot be applied directly. The use of digital image analysis and intelligent modelling such as artificial neural network (ANN) can provide a real-time and accurate solution to predict the content of nitrogen in spinach leaf. This study aims to model the relationship between texture parameter based on colour co-occurrence matrix (CCM) and nitrogen content in spinach leaves. The texture analysis consists of 40 CCM textural features derived from RGB and grey colour. From the 40 CCM textural features, some of the best CCM textural features are selected to be used as ANN inputs in predicting nitrogen content. The best parameter selection method applying two approaches are the filter method including: (1) correlation-based feature selection; (2) correlation attribute evaluation; (3) linear regression; and (4) relief attribute evaluation and the wrapper method which is neural-genetic algorithm (N-GA). The best parameter selection result in the filter method based on the validation result is correlation-based feature selection (using 10 CCM textural features, training MSE = 0.0028; validation MSE = 0.00016; testing accuracy R2 = 0.96). However, when compared to all filter methods, the wrapper method using N-GA still demonstrates better results (using 8 CCM textural features, training MSE = 0.00039; validation MSE = 0.000038; testing accuracy R2 = 0.993). From CCM textural features which have been selected, it can be used as input ANN to predict the nitrogen content of spinach leaves accurately. The best ANN structure has been built by using 1 input layer (8 inputs), 1 hidden layer (20 nodes), and 1 output layer (1 node).

An Evaluation of the Variation in the Morphometric Parameters of Grain of Six Triticum Species with the Use of Digital Image Analysis

Kernel images of six wheat species were subjected to shape and color analyses to determine variations in the morphometric parameters of grain. The values of kernel shape descriptors (area, perimeter, Feret diameter, minimal Feret diameter, circularity, aspect ratio, roundness, solidity) and color descriptors (H, S, I and L*a*b*) were investigated. The influence of grain colonization by endophytic fungi on the color of the seed coat was also evaluated. Polish wheat grain was characterized by the highest intraspecific variation in shape and color. Bread wheat was most homogeneous in terms of the studied shape and color descriptors. An analysis of variations in wheat lines revealed greater differences in phenotypic traits of relict wheats, which have a larger gene pool. The grain of ancient wheat species was characterized by low roundness values and relatively low solidity. Shape and color descriptors were strongly discriminating components in the studied wheat species. Their discriminatory power was determined mainly by genotype. A method that supports rapid discrimination of cereal species and admixtures of other cereals in grain batches is required to guarantee the quality and safety of grain. The results of this study indicate that digital image analysis can be effectively used for this purpose.

Analysis of high-speed photography of cavitating flow in convergent-divergent nozzle

Cavitation is a phenomenon that is difficult to observe, as well as to analyse. Many methods are being applied in order to study cavitation – high-speed photography is one of them. Analysis of images that were obtained in this way gives the possibility of superficial assessment of the phenomenon, but the use of digital image analysis allows obtaining more information on the studied series of images. In the paper, we propose two methods intended for such analysis - they are destined specifically for obtaining variability maps of cavitating flow based on series of high-speed photography. The work is based on the analysis of three different cavitating flows induced in convergent-divergent nozzles. Obtained results show that described methods can be helpful in analysis of cavitation - variability maps can be used for example as reference images for validation of results of numerical simulations of cavitating flow, which is not possible in case of a direct use of high-speed photographs.

Determination of flakiness index (FI) and shape index (SI) using open source digital image analysis software

The aim of the article was to verify the possibility of using open source digital image analysis software to determine the particle shape. Devices using laser diffraction or dynamic image analysis could be used to determine size and shape of particles. However, the laboratory devices for digital image analysis are relatively expensive. For this reason, it was decided to use open source digital image analysis software Fiji Is just ImageJ and mobile phone with a 12 Mpx camera. Coarse aggregates of basalt and granite were used in the study. Flakiness index (FI) and shape index (SI) were determined using two methods: in accordance with the European Standard and with the use of digital image analysis (reference method). Based on the obtained results it was found that there is an appropriate relationship between the European Standard method and the reference method.

Comparison of Visual Assessment and Digital Image Analysis for Canopy Cover Estimation

Core Ideas Cover crops provide ecosystem services linked to canopy cover development. Visual assessment of canopy cover is compared with two digital image analysis methods. Visual estimation is well correlated with image estimation but showed lower values. The choice of the canopy cover estimation method depends on the objective. Currently, in the context of agriculture, cover crops are crops cultivated with the sole aim of providing important ecosystem services such as erosion prevention. Many services offered by these crops are directly linked to the development of their vegetation, and especially of canopy cover. A proper estimation of this cover is thus necessary to evaluate cover crop performance. Many methods to estimate canopy cover exist, but differ in terms of effort and time needed to implement them. In this study, we compared visual assessment of canopy cover in the field with two methods of digital image analysis (Assess and Canopeo), for different cover crop species and vegetation types. Visual estimation was positively correlated with both type of image analysis estimations. However, it showed systematically lower values of canopy cover, especially at intermediate canopy cover values. The type of vegetation influenced the visual and digital image estimations, narrow leaf species being the most difficult to evaluate visually. This study showed that depending on its utilization, visual canopy cover assessment could be useful, especially when only relative estimation of canopy cover is needed. When absolute canopy cover estimation is needed, the use of digital image analysis should be preferred.

Possibility use of digital image analysis for the estimation of the rapeseed maturity stage

ABSTRACTGreen seeds are considered undesirable impurities in rapeseed mass for edible oil production. The rapeseed maturity degree is mostly determined by visual evaluation of the embryo colour. In this study, digital image analysis (DIA) was used for characterisation of rapeseed samples harvested at different maturity stages. The seed size, surface and cross-section colour in RGB, HSI and L*a*b* colour spaces as well as share of green seeds and content of pigments were determined. The statistically significant correlation coefficients calculated between the chlorophyll pigment content and cross-section colour attributes indicate the potential use of DAI to determine the rapeseed maturity stage.