Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

1977 ◽  
Vol 35 ◽  
pp. 220-221
Author(s):  
S.F. Stinson ◽  
J.C. Lilga ◽  
M.B. Sporn
Keyword(s):  
Image Analysis ◽  
Pattern Analysis ◽  
Relative Proportion ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Cross Sectional ◽  
Exfoliated Cells ◽  
Neoplastic Lesions ◽  
Analysis System ◽  
Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

Application of Image Analysis System for Study of Activated Sludge Flocs

1986 ◽  
Vol 21 (1) ◽  
pp. 130-140 ◽  
Author(s):  
Da-hong Li ◽  
J. J. Ganczarczyk
Keyword(s):  
Image Analysis ◽  
Activated Sludge ◽  
Statistical Processing ◽  
Equivalent Diameter ◽  
Image Analysis System ◽  
Sectional Area ◽  
Cross Sectional ◽  
Computerized Image Analysis ◽  
Sludge Flocs ◽  
Analysis System

Abstract The computerized image analysis system has been successfully used for determination and statistical processing of the following geometric characteristics of activated sludge flocs: longest dimension, breadth, equivalent diameter, cross-sectional area, perimeter, elongation, and circularity. These parameters could be effectively and precisely determined by the system applied. In addition, the studied method, as compared to direct microscope observation and photography floc-sizing methods, was found to be more accurate, less time-consuming, and less dependent on the investigators.

scholarly journals Yeast Morphology Assessment through Automated Image Analysis during Fermentation

Fermentation ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 44
Author(s):  
Mario Guadalupe-Daqui ◽  
Mandi Chen ◽  
Katherine A. Thompson-Witrick ◽  
Andrew J. MacIntosh
Keyword(s):  
Image Analysis ◽  
High Stress ◽  
Cross Sectional Area ◽  
Automated Image Analysis ◽  
Yeast Cells ◽  
Morphological Properties ◽  
Cell Area ◽  
Sectional Area ◽  
Cross Sectional ◽  
Industrial Fermentation

The kinetics and success of an industrial fermentation are dependent upon the health of the microorganism(s) responsible. Saccharomyces sp. are the most commonly used organisms in food and beverage production; consequently, many metrics of yeast health and stress have been previously correlated with morphological changes to fermentations kinetics. Many researchers and industries use machine vision to count yeast and assess health through dyes and image analysis. This study assessed known physical differences through automated image analysis taken throughout ongoing high stress fermentations at various temperatures (30 °C and 35 °C). Measured parameters included sugar consumption rate, number of yeast cells in suspension, yeast cross-sectional area, and vacuole cross-sectional area. The cell morphological properties were analyzed automatically using ImageJ software and validated using manual assessment. It was found that there were significant changes in cell area and ratio of vacuole to cell area over the fermentation. These changes were temperature dependent. The changes in morphology have implications for rates of cellular reactions and efficiency within industrial fermentation processes. The use of automated image analysis to quantify these parameters is possible using currently available systems and will provide additional tools to enhance our understanding of the fermentation process.

Automated image analysis of Euglena gracilis Klebs (Euglenophyta) for measuring sublethal effects of three model contaminants

2012 ◽  
Vol 66 (8) ◽  
pp. 1708-1715 ◽  
Author(s):  
I. Netto ◽  
V. Bostan ◽  
L. McCarthy ◽  
A. Laursen ◽  
K. Gilbride ◽  
...  
Keyword(s):  
Image Analysis ◽  
Euglena Gracilis ◽  
Cell Shape ◽  
Early Warning Systems ◽  
Visual Observation ◽  
Automated Image Analysis ◽  
Swimming Velocity ◽  
Image Analysis System ◽  
Behavioural Changes ◽  
Analysis System

The short-term impacts of atrazine (herbicide), tributyltin (organometal) and copper on the behaviour of Euglena gracilis Klebs (Euglenophyta) were assessed. First, the ECOTOX automated image analysis system was used, which measured swimming velocity, cell shape, percentage of cells swimming upwards, and randomness of swimming. Next, visual observation by microscopy was used to measure percentage of cell motility and cell shape. Behavioural changes can be used as an indicator of stress in less than 24 h, potentially making them suitable for inclusion in early-warning systems for water quality. Findings indicate that E. gracilis is a very sensitive organism to copper, showing inhibition of motility with visual observation at 0.8 μmol/L within 1 h. The image analysis system was in general less sensitive than visual observation for detecting behavioural changes after incubation in copper. In contrast, after exposure to organic contaminants atrazine and tributyltin, the ECOTOX system detected small changes in the number of cells swimming upwards (antigravitactic behaviour) at higher concentrations.

An automated image analysis system for major maceral group analysis in coals

Fuel ◽  
1994 ◽  
Vol 73 (11) ◽  
pp. 1729-1734 ◽  
Author(s):  
Edward Lester ◽  
Martin Allen ◽  
Michael Cloke ◽  
Nick J. Miles
Keyword(s):  
Image Analysis ◽  
Group Analysis ◽  
Automated Image Analysis ◽  
Image Analysis System ◽  
Analysis System
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