scholarly journals Influence of digitization input device on calculation of print (non)uniformity value of prints using ISO 13660 method

2020 ◽  
Author(s):  
Ivana Jurič ◽  
◽  
Dragoljub Novaković ◽  
Nemanja Kašiković ◽  
Sandra Dedijer ◽  
...  
Keyword(s):  
Image Analysis ◽  
Light Source ◽  
Mobile Phones ◽  
Quality Attributes ◽  
Input Device ◽  
Strongly Correlated ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Input Devices ◽  
Matlab Code

This paper examines the influence of the digitization input device on the print nonuniformity value when using the ISO 13660 method. This method belongs to the group of techniques called Image Analysis Method (IAM), so the basis for calculating the quality attributes is a digitized print. We selected six different devices: three flatbed scanners and three mobile phones. All settings were constant, such as the scan resolution (600 spi) and light source (D50). To have controlled prints, they were simulated using the MATLAB code - Macro Uniformity Toolbox and printed using the Epson Stylus PRO 7800 InkJet machine. We simulated random print nonuniformity know as small-scaled (graininess) and large-scaled (mottle). The calculated values differ drastically by changing the digitization device, while the values within the same group of devices are strongly correlated. The obtained results indicate the need to expand the standard and define more precise settings for input devices.

scholarly journals Automated image analysis method for oil-release test of lipid-based materials

MethodsX ◽  
2021 ◽  
pp. 101447
Author(s):  
Fabio Valoppi ◽  
Petri Lassila ◽  
Ari Salmi ◽  
Edward Haeggström
Keyword(s):  
Image Analysis ◽  
Automated Image Analysis ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Release Test

A Computerized Image Analysis Method for Measuring Elastic Tissue.

1989 ◽  
Vol 93 (3) ◽  
pp. 358-362 ◽  
Author(s):  
Thomas J. Flotte ◽  
Johanna M. Seddon ◽  
Yuqing Zhang ◽  
Robert J. Glynn ◽  
Kathleen M. Egan ◽  
...  
Keyword(s):  
Image Analysis ◽  
Elastic Tissue ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Computerized Image Analysis

ASSESSMENT OF OSTEOARTHRITIS INITIATIVE–KELLGREN AND LAWRENCE SCORING PROJECTS QUALITY USING COMPUTER ANALYSIS

2010 ◽  
Vol 13 (04) ◽  
pp. 197-201 ◽  
Author(s):  
Lior Shamir ◽  
David T. Felson ◽  
Luigi Ferrucci ◽  
Ilya G. Goldberg
Keyword(s):  
Image Analysis ◽  
Gold Standard ◽  
Population Studies ◽  
Detection Accuracy ◽  
X Rays ◽  
Analysis Method ◽  
Image Analysis Method ◽  
X Ray ◽  
Osteoarthritis Initiative ◽  
Kellgren And Lawrence

The detection of knee osteoarthritis (OA) is a subjective task, and even two highly experienced and well-trained readers might not always agree on a specific case. This problem is noticeable in OA population studies, in which different scoring projects provide significantly different scores for the same knee X-rays. Here we propose a method for quantitative assessment and comparison of knee X-ray scoring projects in OA population studies. The method works by applying an image analysis method that automatically detects OA in knee X-ray images, and comparing the consistency of the scores when using each of the scoring projects as "gold standard." The method was applied to compare the osteoarthritis initiative (OAI) clinic reading derived Kellgren and Lawrence (K&L) scores to central reading, and showed that when using the derived K&L scores the automatic image analysis method was able to accurately differentiate between healthy joints and moderate OA joints in ~70% of the cases. When the OAI central reading scores were used as gold standard, the detection accuracy was elevated to ~77%. These results show that the OAI central readings scores are more consistent with the X-rays, indicating that the central reading better reflects the radiographic features associated with OA, compared to the OAI K&L scores derived from clinic readings.

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