Development and evaluation of an automated digital image analysis software for obtaining seagrass leaf metrics

2012 ◽  
Vol 55 (6) ◽  
Author(s):  
Alexis Ramfos ◽  
Andreas Gazis ◽  
George Katselis
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Analysis Software ◽  
Image Analysis Software

Estimation of bodyweight from body measurements and determination of body measurements on Limousin cattle using digital image analysis

2016 ◽  
Vol 56 (12) ◽  
pp. 2060 ◽  
Author(s):  
Serkan Ozkaya ◽  
Wojciech Neja ◽  
Sylwia Krezel-Czopek ◽  
Adam Oler
Keyword(s):  
Image Analysis ◽  
Body Length ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Digital Camera ◽  
Body Measurements ◽  
Analysis Software ◽  
Body Area ◽  
Image Analysis Software

The objective of this study was to predict bodyweight and estimate body measurements of Limousin cattle using digital image analysis (DIA). Body measurements including body length, wither height, chest depth, and hip height of cattle were determined both manually (by measurements stick) and by using DIA. Body area was determined by using DIA. The images of Limousin cattle were taken while cattle were standing in a squeeze chute by a digital camera and analysed by image analysis software to obtain body measurements of each animal. While comparing the actual and predicted body measurements, the accuracy was determined as 98% for wither height, 97% for hip height, 94% for chest depth and 90.6% for body length. Regression analysis between body area and bodyweight yielded an equation with R2 of 61.5%. The regression equation, which included all body traits, resulted in an R2 value of 88.7%. The results indicated that DIA can be used for accurate prediction of body measurements and bodyweight of Limousin cattle.

scholarly journals Anatomical Characteristics of Garcinia lucida (Vesque) and Scorodophloeus zenkeri (Harms) Wood and Debarking Response in the South Region Cameroon

2017 ◽  
Vol 6 (4) ◽  
pp. 132
Author(s):  
Marie Caroline Momo Solefack ◽  
Hans Beeckman ◽  
Lucie Felicite Temgoua ◽  
Ghislain Kenguem Kinjouo
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Analysis Software ◽  
Anatomical Changes ◽  
Image Analysis Software ◽  
Conductive Zone ◽  
Before And After

The aim of this work was to investigate the possible anatomical changes of Garcinia lucida and Scorodophloeus zenkeri after the removal of their bark. Debarking was done on individuals of each species at 1.30 m from the soil. The wound was rectangular in shape with 30 cm side. There was a follow-up every three months for nine months during which the survival and rate of regeneration of the bark were recorded. A block of cube was cut from the regenerated and intact wood of species for microtomy and microscopy activities. On the cross-section of each wood, vessel features like density and diameter were measured before and after wounding. Semi-automatic measurements were made using the SpectrumSee digital image analysis software. In the wood of the two species, it appeared that the density of the vessels before debarking was significantly comparable to the density after debarking, while the diameter of vessels in the regenerated wood was smaller. The cambial area increased slightly in the rainy season for all species. After nine months all the species started the restoration of their conductive zone. G. lucida heals its wound more rapidly than S. zenkeri.

scholarly journals Free digital image analysis software helps to resolve equivocal scores in HER2 immunohistochemistry

2015 ◽  
Vol 468 (2) ◽  
pp. 191-198 ◽  
Author(s):  
Henrik O. Helin ◽  
Vilppu J. Tuominen ◽  
Onni Ylinen ◽  
Heikki J. Helin ◽  
Jorma Isola
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Analysis Software ◽  
Image Analysis Software

Tumor Volume in Radical Prostatectomy Specimens Assessed by Digital Image Analysis Software Correlates With Other Prognostic Factors

2010 ◽  
Vol 183 (5) ◽  
pp. 1808-1815 ◽  
Author(s):  
Justin C. Sherwin ◽  
George Mirmilstein ◽  
John Pedersen ◽  
Nathan Lawrentschuk ◽  
Damien Bolton ◽  
...  
Keyword(s):  
Image Analysis ◽  
Prognostic Factors ◽  
Radical Prostatectomy ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Tumor Volume ◽  
Analysis Software ◽  
Image Analysis Software

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

2020 ◽  
Vol 58 (4) ◽  
Author(s):  
Matthew L. Faron ◽  
Blake W. Buchan ◽  
Ryan F. Relich ◽  
James Clark ◽  
Nathan A. Ledeboer
Keyword(s):  
Image Analysis ◽  
Digital Image Analysis ◽  
Clinical Microbiology Laboratory ◽  
Normal Flora ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Bacterial Cultures ◽  
Highly Sensitive ◽  
Recent Advancement ◽  
Laboratory Information Systems

ABSTRACT 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.

scholarly journals Digital image analysis of immunohistochemistry KI-67 using QuPath software in breast cancer

2021 ◽  
Vol 12 (1) ◽  
pp. 34-43
Author(s):  
Hermin Aminah Usman ◽  
Fauzan Ali Zainal Abidin
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Diagnostic Evaluation ◽  
Observer Variability ◽  
Analysis Software ◽  
Visual Evaluation ◽  
Inter Observer Variability ◽  
Significant Difference ◽  
Bioimage Analysis

Background: Today, pathology services are more developed for quantitative diagnostic evaluation. The quantitative diagnostic evaluation requires detailed accuracy and can be done using digital image analysis (DIA). Assessment of the Ki67 labelling index (LI) in breast carcinoma needs to be done quantitatively. A visual evaluation of Ki67 LI using light microscopy has high inter-observer variability. The evaluation of Ki67 LI could be done digitally with the DIA technique to overcome the inter-observer variability. The DIA technique is carried out by counting the Ki67 LI manually or automatically with bioimage analysis software. QuPath is one of the bioimage analysis software, has characteristics of cross-platform, intended for bioimage analysis and digital pathology. Objective: This study aims to compare the manual and automatic calculation of Ki67 LI digitally. Methods: This study was a cross-sectional study; a total of 240 digital Ki67 images from 30 slides were analyzed by counting manually and automatically using QuPath. Results: Statistical analysis using the T-test showed no significant difference between the manual and automatic counting of Ki67 LI (p = 0,801, α = 0,05). Conclusion: Digital image analysis using QuPath can be used to calculate the Ki67 LI automatically.

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