scholarly journals An investigation of using grey scale image analysis for predicting the amount of deposited electrospun nanofibres

2019 ◽  
Vol 13 (1) ◽  
pp. 4679-4692
Author(s):  
A. H. Nurfaizey ◽  
F. C. Long ◽  
M. A. M. Daud ◽  
N. Muhammad ◽  
M. R. Mansor ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deposition Time ◽  
Control Method ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Grey Scale ◽  
Significant Area ◽  
Electrospun Nanofibres ◽  
Weighing Method ◽  
Non Destructive

When electrospinning, the amount of electrospun fibres deposited is difficult to determine due to the extremely small size and light weight of the fibres.  Several methods have been used to predict the amount of deposited fibres including weighing, imaging and direct measurement. Yet, these methods have drawbacks that make them unsuitable for commercial scale process control. In this study, an image analysis method is used to predict the amount of deposited fibres over a significant area. When images of electrospun fibres are converted into grey scale images, it is suggested that the amount of fibres deposited can be predicted by measuring the grey scale intensity. Weighing method was used to validate the image analysis results. Weighing method was found wanting when the deposition time was short (p>0.05) due to the insignificant fibre masses compared to the variation of substrates. The results suggest that image analysis method could be used to predict the amount of deposited electrospun nanofibres. Test on different polymers and substrates showed that the method was still capable to distinguish the samples. The developed method has the potential to be applied as an in-line non-destructive quality control method for electrospun fibre manufacture.

scholarly journals The ultrasonic and image analysis method for non-destructive quantification of the thermal shock damage in refractory specimens

2009 ◽  
Vol 30 (8) ◽  
pp. 3338-3343 ◽  
Author(s):  
M. Posarac ◽  
M. Dimitrijevic ◽  
T. Volkov-Husovic ◽  
J. Majstorovic ◽  
B. Matovic
Keyword(s):  
Image Analysis ◽  
Thermal Shock ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Non Destructive

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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