scholarly journals Immunocytochemical detection of regional protein changes in rat brain sections using computer-assisted image analysis.

1996 ◽  
Vol 44 (9) ◽  
pp. 981-987 ◽  
Author(s):  
X Huang ◽  
S Chen ◽  
E I Tietz
Keyword(s):  
Image Analysis ◽  
Rat Brain ◽  
Immunohistochemical Analysis ◽  
Brain Homogenate ◽  
Model System ◽  
Computer Assisted ◽  
Antigen Concentration ◽  
Immunocytochemical Detection ◽  
Low Degree ◽  
Computer Assisted Image

We used several approaches to assess the reliability and sensitivity of computer-assisted densitometry to detect regional changes in tissue antigen content as a function of immunohistochemical staining density. We designed a model system to mimic variations in antigen concentration in postfixed, slide-mounted rat brain sections by varying the ratios of conjugated (biotinylated) to unconjugated secondary antibody. Antigen concentration was also varied in tissue discs made from mixing rat brain homogenate with increasing amounts of tissue embedding compound. The monoclonal antibody bd-17 to the beta2/3 subunit of the GABAA receptor was used as the primary antibody. Immunostaining density was visualized with diaminobenzidine (DAB). There was a significant, positive linear relationship (r = 0.97-0.99) between immunostaining intensity and antigen concentration. With this approach, changes in antigen content of less than 10%, as reflected in immunostaining intensity, were detectable in brain sections. The low degree of variability in measures of regional variation in immunostaining in sections from naive rats (n = 7) suggested that the method was suitable for quantitative analysis and indicated the reliability of the method. This systematic study of the utility of computer-assisted image analysis for semiquantitative immunohistochemical analysis found the method to be both reliable and sensitive.

Validation of endobronchial biopsy specimens for nerve quantitation by computer-assisted image analysis

2000 ◽  
Vol 192 (4) ◽  
pp. 545-548 ◽  
Author(s):  
Friedrich Jesenik ◽  
David R. Springall ◽  
Anthony E. Redington ◽  
Caroline J. Dor� ◽  
Don-Carlos Abrams ◽  
...  
Keyword(s):  
Image Analysis ◽  
Computer Assisted ◽  
Biopsy Specimens ◽  
Computer Assisted Image

Allo-activated CD4+ and CD8+ lymphocyte subsets: New ultrastructural findings based on computer-assisted image analysis

2009 ◽  
Vol 44 (3) ◽  
pp. 179-185 ◽  
Author(s):  
G. Balercia ◽  
A. Sbarbati ◽  
F. Franceschini ◽  
A. Bravo-Cuellar ◽  
A. Osculati ◽  
...  
Keyword(s):  
Image Analysis ◽  
Lymphocyte Subsets ◽  
Computer Assisted ◽  
Computer Assisted Image ◽  
Ultrastructural Findings

scholarly journals Computer-Assisted Image Analysis of Plant Growth, Thigmomorphogenesis, and Gravitropism

1985 ◽  
Vol 77 (3) ◽  
pp. 722-730 ◽  
Author(s):  
Mordecai J. Jaffe ◽  
Andrew H. Wakefield ◽  
Frank Telewski ◽  
Edward Gulley ◽  
Ronald Biro
Keyword(s):  
Image Analysis ◽  
Plant Growth ◽  
Computer Assisted ◽  
Computer Assisted Image

Evaluation of the quality of cold meats by computer-assisted image analysis

LWT ◽  
2016 ◽  
Vol 67 ◽  
pp. 37-49 ◽  
Author(s):  
Piotr Zapotoczny ◽  
Piotr M. Szczypiński ◽  
Tomasz Daszkiewicz
Keyword(s):  
Image Analysis ◽  
Computer Assisted ◽  
Computer Assisted Image

scholarly journals Color Subtractive–Computer-assisted Image Analysis for Quantification of Cutaneous Nerves in a Diabetic Mouse Model

2001 ◽  
Vol 49 (10) ◽  
pp. 1285-1291 ◽  
Author(s):  
Robert A. Underwood ◽  
Nicole S. Gibran ◽  
Lara A. Muffley ◽  
Marcia L. Usui ◽  
John E. Olerud
Keyword(s):  
Image Analysis ◽  
Unit Area ◽  
Mouse Skin ◽  
Area Fraction ◽  
Computer Assisted ◽  
Area Density ◽  
Pgp 9.5 ◽  
Tissue Sections ◽  
Background Removal ◽  
Computer Assisted Image

Immunohistochemistry (IHC) is a valuable tool for labeling structures in tissue samples. Quantification of immunolabeled structures using traditional approaches has proved to be difficult. Manual counts of IHC-stained structures are inherently biased, require multiple observers, and generate qualitative data. Stereological methods provide accurate quantification but are complex and labor-intensive when staining must be compared among large numbers of samples. In an effort to quickly, objectively, and reproducibly quantify cutaneous innervation in a large number of counterstained tissue sections, we developed a color subtractive–computer-assisted image analysis (CS–CAIA) system. To develop and test the CS–CAIA method, tissue sections of diabetic (db/db) mouse skin and their wild-type (db/–) littermates were stained by IHC for the neural marker PGP 9.5. The brown-red PGP 9.5 peroxidase stain was colorimetrically isolated through a scripted process of color background removal. The remaining stain was thresholded and binarized for computer determination of nerve profile counts (number of stained regions), area fraction (total area of nerve profiles per unit area of tissue), and area density (total number of nerve profiles per unit area of tissue). Using CS–CAIA, epidermal nerve profile counts, area fraction, and area density were significantly lower in db/db compared to db/– mice.

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