scholarly journals In vitro validation of Digital Image Analysis Sequence (DIAS) for the assessment of the marginal fit of cement-retained implant-supported experimental crowns

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Aristeidis A. Villias ◽  
Stefanos G. Kourtis ◽  
Hercules C. Karkazis ◽  
Gregory L. Polyzois
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Confidence Level ◽  
Digital Image Analysis ◽  
Statistical Tests ◽  
Interobserver Reliability ◽  
Intraclass Correlation ◽  
Marginal Fit ◽  
Smallest Detectable Change ◽  
The Mean

Abstract Background The replica technique with its modifications (negative replica) has been used for the assessment of marginal fit (MF). However, identification of the boundaries between prosthesis, cement, and abutment is challenging. The recently developed Digital Image Analysis Sequence (DIAS) addresses this limitation. Although DIAS is applicable, its reliability has not yet been proven. The purpose of this study was to verify the DIAS as an acceptable method for the quantitative assessment of MF at cemented crowns, by conducting statistical tests of agreement between different examiners. Methods One hundred fifty-one implant-supported experimental crowns were cemented. Equal negative replicas were produced from the assemblies. Each replica was sectioned in six parts, which were photographed under an optical microscope. From the 906 standardized digital photomicrographs (0.65 μm/pixel), 130 were randomly selected for analysis. DIAS included tracing the profile of the crown and the abutment and marking the margin definition points before cementation. Next, the traced and marked outlines were superimposed on each digital image, highlighting the components’ boundaries and enabling MF measurements. One researcher ran the analysis twice and three others once, independently. Five groups of 130 measurements were formed. Intra- and interobserver reliability was evaluated with intraclass correlation coefficient (ICC). Agreement was estimated with the standard error of measurement (SEM), the smallest detectable change at the 95% confidence level (SDC95%), and the Bland and Altman method of limits of agreement (LoA). Results Measured MF ranged between 22.83 and 286.58 pixels. Both the intra- and interobserver reliability were excellent, ICC = 1 at 95% confidence level. The intra- and interobserver SEM and SDC95% were less than 1 and 3 pixels, respectively. The Bland–Altman analysis presented graphically high level of agreement between the mean measurement of the first observer and each of the three other observers’ measurements. Differences between observers were normally distributed. In all three cases, the mean difference was less than 1 pixel and within ± 3 pixels LoA laid at least 95% of differences. T tests of the differences did not reveal any fixed bias (P > .05, not significant). Conclusion The DIAS is an objective and reliable method able to detect and quantify MF at ranges observed in clinical practice.

scholarly journals COMPARISON OF EYELID ANGLE MEASURED USING MANUAL AND DIGITAL IMAGE ANALYSIS METHOD

2021 ◽  
Author(s):  
Shravya N ◽  
Swetha Ravichandran ◽  
Rinu Thomas
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Palpebral Fissure ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Interpupillary Distance ◽  
Significant Difference ◽  
The Mean ◽  
Imagej Software

Aim: To compare the eyelid angle measured by using a manual method (Using protractor) and digital image analysis method (Using ImageJ software) at different distances of eye gaze. Methodology: This prospective cross-sectional study was conducted in the preclinical lab at Manipal College of Health Professions. Subjects with no eyelid abnormalities were included in the study and they were asked to fixate at different distances a) at 3 metre (Distance gaze) b) at 70 cm (Intermediategaze) and c) at 40 cm(Near gaze). Using a protractor, the eyelid angle measurements were repeated at various distances which comprised the manual measurement. In the image analysis method, images were captured during distance, intermediate and near gaze using smartphone placed on theside of the face. These images were then analysed using ImageJ software for determining eyelid angle using image analysis method. Palpebral fissure height, Palpebral fissure width, Interpupillary distance, Intercanthal width, Binocular width, Height of open upper lid were some additional anthropometry measurements that were done using meter scale and PD ruler. Results: The mean age of the participants was 20±0.5 years. Anthropometry measurements of the eyelid and Palpebral fissure were done using meter scaleand PD ruler. The mean and standard deviation of the measured parametersare as follows Interpupillary distance: 60.95±2.37 mm, Endo Inter canthal distance: 32.20±2.39 mm, Exo Inter cantal distance: 95.50±3.80 mm, Palpebralfissure height_OD: 12.11±1.32 mm, Palpebral fissure height_OS:12.16±1.46mm, PFW_OD: 32.00±1.10 mm, PFW_OS: 32.11±1.24 mm, Height of upper eyelids_OD: 10.26±1.66 mm and Height of upper eyelids_OS:10.42±1.83 mm. In the right eye, there was no statistically significant difference (p>0.05) between manual protractor method and digital image analysismethod at distance but there was a statistically significant difference (p<0.05)between manual protractor method and digital image analysis method atIntermediate and near. In left eye, there was statistically significant difference(p<0.05) between manual protractor method and digital image analysis method at all three distances. Conclusion: There is a significant difference in eyelid angle measured using manual protractor method and digital image analysis method. The measurement of eyelid angle serves as a critical reference point during cosmetic and reconstructive surgical interventions of the eyelid and accurate measurements are essential for preoperative assessment, surgical planning and postoperative evaluation. Hence more studies on the validation of the anthropometry measurements and eyelid angle using digital image analysis areessential to use digital image analysis in routine eye care practice.

scholarly journals Numerical modelling of agricultural products on the example of bean and yellow lupine seeds

2015 ◽  
Vol 29 (4) ◽  
pp. 397-403 ◽  
Author(s):  
Andrzej Anders ◽  
Zdzisław Kaliniewicz ◽  
Piotr Markowski
Keyword(s):  
Image Analysis ◽  
Surface Area ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Numerical Models ◽  
3D Scanner ◽  
Mathematical Formulas ◽  
Length Width ◽  
The Mean ◽  
Yellow Lupine

Abstract Numerical models of bean seeds cv. Złota Saxa and yellow lupine seeds cv. Juno were generated with the use of a 3D scanner, the geometric parameters of seeds were determined based on the models developed, and compared with the results of digital image analysis and micrometer measurements. Measurements of seed length, width and thickness performed with the use of a micrometer, 3D scanner and digital image analysis produced similar results that did not differ significantly at α = 0.05. The micrometer delivered the simplest and fastest measurements. The mean surface area of bean seeds cv. Złota Saxa and yellow lupine seeds cv. Juno, calculated with the use of mathematical formulas based on the results of micrometer measurements and digital image analysis, differed significantly from the mean surface area determined with a 3D scanner. No significant differences in seed volume were observed when this parameter was measured with a 3D scanner and determined with the use of mathematical formulas based on the results of digital image analysis and micrometer measurements. The only differences were noted when the volume of yellow lupine seeds cv. Juno was measured in a 25 ml liquid pycnometer.

scholarly journals MEASURING COLOR HUE IN ‘SUNRISE SOLO’ PAPAYA USING A FLATBED SCANNER

2017 ◽  
Vol 39 (2) ◽  
Author(s):  
THIAGO DE PAULA OLIVEIRA ◽  
SILVIO SANDOVAL ZOCCHI ◽  
ANGELO PEDRO JACOMINO
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Measurement Errors ◽  
Digital Image Analysis ◽  
Equatorial Region ◽  
Color Patterns ◽  
Flatbed Scanner ◽  
Fruit Peel ◽  
Peel Color ◽  
The Mean

ABSTRACT Color hue is a variable frequently used postharvest to assess the physiological maturity of various fruits. In general, mean color hue is quantified by visual techniques, but this method, based on human grading, is tedious and may be erroneous. The color of fruit peel is frequently determined at four points randomly distributed on the equatorial region of a fruit, but this practice can lead to biased results because these points represent the equatorial region only and not the total area of the fruit peel. Consequently, this bias is not due to equipment measurement errors, but to the bias in the sampling process of the points on the fruit epidermis. An alternative to such methods is the digital image which provides information about all regions of the fruit peel, and results in a more accurate mean hue. However, this technique requires calibration to correct the values of luminance, hue and intensity obtained through the scanner. This calibration can be performed from color patterns such that each pattern is assessed by means of a scanner and a colorimeter. Thus, an experiment was conducted using 297 color patterns based on the Munssel color chart for plant tissue. The results showed that the scanner could be used as a device for color assessment where the determination coefficients were above 0.9 for all color components. Next, a second experiment was conducted in order to compare the scanner and colorimeter methodologies. For this, we used a papaya fruit, cv. Sunrise Solo, which was assessed by these two devices for a period of 19 days. The results showed that the image analysis measures have a different mean hue when compared with the mean hue obtained by the method using a colorimeter. Therefore, it is recommended that digital image analysis be used for the evaluation of the hue of fruit peel color when fruit presents non-uniform coloration.

Digital Image Analysis of the Larynx

2000 ◽  
Vol 10 (2) ◽  
pp. 7-9
Author(s):  
Yaser Natour ◽  
Christine Sapienza ◽  
Mark Schmalz ◽  
Savita Collins
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis

scholarly journals Digital Image Analysis of BAP-1 Accurately Predicts Uveal Melanoma Metastasis

2019 ◽  
Vol 8 (3) ◽  
pp. 11 ◽  
Author(s):  
Gustav Stålhammar ◽  
Thonnie Rose O. See ◽  
Stephen Phillips ◽  
Stefan Seregard ◽  
Hans E. Grossniklaus
Keyword(s):  
Image Analysis ◽  
Uveal Melanoma ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Melanoma Metastasis

Quantitative evaluation of elderly skin based on digital image analysis

2008 ◽  
Vol 14 (2) ◽  
pp. 192-200 ◽  
Author(s):  
Hiromasa Tanaka ◽  
Gojiro Nakagami ◽  
Hiromi Sanada ◽  
Yunita Sari ◽  
Hiroshi Kobayashi ◽  
...  
Keyword(s):  
Image Analysis ◽  
Quantitative Evaluation ◽  
Digital Image ◽  
Digital Image Analysis
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