Fully automated and fast image analysis of autoradiographs with a TAS-Leitz. Determination of size, Feulgen fluorescence and grain counts of individual nuclei and their evaluation by a simplified cluster analysis

1984 ◽  
Vol 81 (3) ◽  
pp. 227-236 ◽  
Author(s):  
E. D. Wachsmuth ◽  
M. Becker ◽  
A. P. Grieve ◽  
B. Maurer-Schultze
Keyword(s):  
Cluster Analysis ◽  
Image Analysis

Gender determination of caucasoid hair using image analysis

1993 ◽  
Vol 51 ◽  
pp. 216-217
Author(s):  
T.B. Ball ◽  
W.M. Hess
Keyword(s):  
Image Analysis ◽  
Cross Sections ◽  
Scalp Hair ◽  
The Body ◽  
Equivalent Diameter ◽  
Cross Sectional ◽  
Hair Samples ◽  
Length Width ◽  
Morphological Differences

It has been demonstrated that cross sections of bundles of hair can be effectively studied using image analysis. These studies can help to elucidate morphological differences of hair from one region of the body to another. The purpose of the present investigation was to use image analysis to determine whether morphological differences could be demonstrated between male and female human Caucasian terminal scalp hair.Hair samples were taken from the back of the head from 18 caucasoid males and 13 caucasoid females (Figs. 1-2). Bundles of 50 hairs were processed for cross-sectional examination and then analyzed using Prism Image Analysis software on a Macintosh llci computer. Twenty morphological parameters of size and shape were evaluated for each hair cross-section. The size parameters evaluated were area, convex area, perimeter, convex perimeter, length, breadth, fiber length, width, equivalent diameter, and inscribed radius. The shape parameters considered were formfactor, roundness, convexity, solidity, compactness, aspect ratio, elongation, curl, and fractal dimension.

A quantitative image analysis method for the determination of cocontinuity in polymer blends

1993 ◽  
Vol 51 ◽  
pp. 894-895
Author(s):  
William A. Heeschen
Keyword(s):  
Image Analysis ◽  
Polymer Blends ◽  
Quantitative Measurement ◽  
Morphological Evolution ◽  
Phase Ratio ◽  
Irregular Shapes ◽  
Quantitative Image ◽  
Discrete Domains ◽  
A Domains

Two new morphological measurements based on digital image analysis, CoContinuity and CoContinuity Balance, have been developed and implemented for quantitative measurement of morphology in polymer blends. The morphology of polymer blends varies with phase ratio, composition and processing. A typical morphological evolution for increasing phase ratio of polymer A to polymer B starts with discrete domains of A in a matrix of B (A/B < 1), moves through a cocontinuous distribution of A and B (A/B ≈ 1) and finishes with discrete domains of B in a matrix of A (A/B > 1). For low phase ratios, A is often seen as solid convex particles embedded in the continuous B phase. As the ratio increases, A domains begin to evolve into irregular shapes, though still recognizable as separate domains. Further increase in the phase ratio leads to A domains which extend into and surround the B phase while the B phase simultaneously extends into and surrounds the A phase.

scholarly journals Determination of the alpha-actinin-binding site on actin filaments by cryoelectron microscopy and image analysis.

1994 ◽  
Vol 126 (2) ◽  
pp. 433-443 ◽  
Author(s):  
A McGough ◽  
M Way ◽  
D DeRosier
Keyword(s):  
Image Analysis ◽  
Smooth Muscle ◽  
Binding Sites ◽  
Actin Filaments ◽  
Three Dimensional ◽  
Actin Binding ◽  
Dimensional Structure ◽  
Outer Face ◽  
Actin Binding Domain

The three-dimensional structure of actin filaments decorated with the actin-binding domain of chick smooth muscle alpha-actinin (alpha A1-2) has been determined to 21-A resolution. The shape and location of alpha A1-2 was determined by subtracting maps of F-actin from the reconstruction of decorated filaments. alpha A1-2 resembles a bell that measures approximately 38 A at its base and extends 42 A from its base to its tip. In decorated filaments, the base of alpha A1-2 is centered about the outer face of subdomain 2 of actin and contacts subdomain 1 of two neighboring monomers along the long-pitch (two-start) helical strands. Using the atomic model of F-actin (Lorenz, M., D. Popp, and K. C. Holmes. 1993. J. Mol. Biol. 234:826-836.), we have been able to test directly the likelihood that specific actin residues, which have been previously identified by others, interact with alpha A1-2. Our results indicate that residues 86-117 and 350-375 comprise distinct binding sites for alpha-actinin on adjacent actin monomers.

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 38 Clinical validation of an image analysis assay for determining programmed death-ligand 1 (22C3) in non-small cell lung cancer

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A39-A39
Author(s):  
Roberto Gianani ◽  
Will Paces ◽  
Elliott Ergon ◽  
Kristin Shotts ◽  
Vitria Adisetiyo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Analysis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Clinical Validation ◽  
Small Cell Lung ◽  
Programmed Death Ligand 1 ◽  
Programmed Death

BackgroundDetermination of programmed death-ligand 1 (PD-L1) level in tumor by immunohistochemistry (IHC) is widely used to predict response to check point inhibitor therapy. In particular, the Dako PD-L1 (22C3) antibody is a common companion diagnostic to the monoclonal antibody drug Keytruda® (pembrolizumab) in non-small cell lung cancer (NSCLC).1 However, for the practicing pathologist, interpretation of the PD-L1 (22C3) assay is cumbersome and time consuming. Manual pathologist scoring also suffers from poor intra- and inter-pathologist precision, particularly around the cut-off point.2 In this clinical validation study, we developed an image analysis (IA) based solution to accurately and precisely score digital images obtained from PD-L1 stained NSCLC tissues for making clinical enrollment decisions.Methods10 NSCLC tissue samples were purchased from a qualified vendor and IHC stained for PD-L1; 4 of these samples had serial sections stained on two separate days. Stained slides were scanned at 20X magnification and analyzed using Flagship Biosciences’ IA solutions that quantify PD-L1 expression and separate tumor and stromal compartments. Resulting image markups of cell detection and PD-L1 expression were reviewed by an MD pathologist for acceptance. PD-L1 staining was evaluated by digital IA in the sample’s tumor compartment for Total Proportion Score (TPS,%). Assay specificity was defined by ≥ 90% of the tissue cohort exhibiting appropriate cell recognition (≥ 90% cells correctly recognized as determined by the pathologist), with ≤ 10% false positive rate for staining classification. Sensitivity was defined by ≥ 90% of the cohort exhibiting appropriate cell identification (≥ 90% cells correctly identified), with ≤ 10% false negative rate for staining classification. Accuracy was defined by the combination of sensitivity and specificity and precision was defined by concordance of the binned TPS (<1%, ≥ 1%, ≥ 50%) in ≥ 80% of the samples stained on multiple days.ResultsThe preliminary results show that IA can yield high analytical sensitivity, specificity, accuracy, and precision in the determination of the PD-L1 score. 100% of the tissue cohort met criteria for analytical specificity, sensitivity, and accuracy and 100% of the samples stained on multiple days met the precision criteria.ConclusionsThis data demonstrates the feasibility of an IA approach as applied to PD-L1 (22C3) scoring. Ongoing experiments include application of the developed 22C3 algorithm on a separate cohort of 20 NSCLC samples to determine the correlation of digital scoring and scoring obtained by three pathologists. Additionally, we will evaluate the precision obtained by digital scoring in relation to the intra- and inter-pathologist concordance.ReferencesIncorvaia L, Fanale D, Badalamenti G, et al. Programmed death ligand 1 (PD-L1) as a predictive biomarker for pembrolizumab therapy in patients with advanced non-small-cell lung cancer (NSCLC). Adv Ther 2019;36:2600–2617.Rimm DL, Han G, Taube JM, et al. A prospective, multi-institutional, pathologist-based assessment of 4 immunohistochemistry assays for PD-L1 expression in non–small cell lung cancer. JAMA Oncol 2017;3:1051–1058.

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