scholarly journals Automatic Quantification of Immunohistochemically Stained Cell Nuclei Using Unsupervised Image Analysis

1998 ◽  
Vol 16 (1) ◽  
pp. 29-43 ◽  
Author(s):  
Petter Ranefall ◽  
Kenneth Wester ◽  
Ewert Bengtsson
Keyword(s):  
Principal Component ◽  
Cell Nuclei ◽  
Maximum Likelihood Classification ◽  
Automatic Quantification ◽  
Principal Component Transform ◽  
Independent Test ◽  
Principal Component Approach ◽  
Component Approach ◽  
Fully Automatic ◽  
Component Image

A method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions is presented. The image is transformed by a principal component transform. The resulting first component image is used to segment the objects from the background using dynamic thresholding of theP2/A‐histogram, whereP2/Ais a global roundness measure. Then the image is transformed into principal component hue, defined as the angle around the first principal component. This image is used to segment positive and negative objects. The method is fully automatic and the principal component approach makes it robust with respect to illumination and focus settings. An independent test set consisting of images grabbed with different focus and illumination for each field of view was used to test the method, and the proposed method showed less variation than the intraoperator variation using supervised Maximum Likelihood classification.

Polarization analysis of high-frequency, three-component seismic data

1991 ◽  
Vol 81 (2) ◽  
pp. 622-642
Author(s):  
K. Bataille ◽  
J. M. Chiu
Keyword(s):  
High Frequency ◽  
Time Windows ◽  
Random Noise ◽  
Polarization State ◽  
Principal Component ◽  
Small Time ◽  
Body Waves ◽  
Local Data ◽  
Principal Component Approach ◽  
Component Approach

Abstract We present a method to determine the polarization of body waves from three-component, high-frequency data and examples of its application. The method is based on the principal component approach. One advantage of this approach is that the polarization state can be determined for small time windows compared with the predominant period of the wave. This is particularly useful for identifying converted waves within the crust. The stability of the result is analyzed with synthetic cases by adding simultaneous arrivals from waves and random noise. The method works well with both synthetic and local data in the detection of the polarization of the wave by separating arrivals from different directions. From the local data, some seismic phases related to crustal conversions are observed that require strong lateral variations.

scholarly journals A principal component approach to measuring investor sentiment in China

2014 ◽  
Vol 14 (4) ◽  
pp. 573-579 ◽  
Author(s):  
Haiqiang Chen ◽  
Terence Tai Leung Chong ◽  
Yingni She
Keyword(s):  
Investor Sentiment ◽  
Principal Component ◽  
Principal Component Approach ◽  
Component Approach

scholarly journals Automatic Quantification of Immunohistochemically Stained Cell Nuclei Based on Standard Reference Cells

1998 ◽  
Vol 17 (2) ◽  
pp. 111-123 ◽  
Author(s):  
Petter Ranefall ◽  
Kenneth Wester ◽  
Ann-Catrin Andersson ◽  
Christer Busch ◽  
Ewert Bengtsson
Keyword(s):  
Cyclin A ◽  
Cell Nuclei ◽  
Cultured Fibroblasts ◽  
Automatic Quantification ◽  
Formalin Fixed Paraffin ◽  
Ki67 Antigen ◽  
Formalin Fixed Paraffin Embedded ◽  
Fully Automatic ◽  
Formalin Fixed ◽  
Nuclear Texture

A fully automatic method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions, is presented. Agarose embedded cultured fibroblasts were fixed, paraffin embedded and sectioned at 4 µm. They were then stained together with 4 µm sections of the test specimen obtained from bladder cancer material.A colour based classifier is automatically computed from the control cells. The method was tested on formalin fixed paraffin embedded tissue section material, stained with monoclonal antibodies against the Ki67 antigen and cyclin A protein. Ki67 staining results in a detailed nuclear texture with pronounced nucleoli and cyclin A staining is obtained in a more homogeneously distributed pattern.However, different staining patterns did not seem to influence labelling index quantification, and the sensitivity to variations in light conditions and choice of areas within the control population was low. Thus, the technique represents a robust and reproducible quantification method.In tests measuring proportions of stained area an average standard deviation of about 1.5% for the same field was achieved when classified with classifiers created from different control samples.

scholarly journals The Evolution of Financial Integration on Selected European Stock Markets: a Dynamic Principal Component Approach

2017 ◽  
Vol 20 (4) ◽  
pp. 45-63 ◽  
Author(s):  
Elżbieta Majewska ◽  
Joanna Olbryś
Keyword(s):  
Stock Markets ◽  
Financial Integration ◽  
Global Financial Crisis ◽  
Principal Component ◽  
The European Union ◽  
Principal Component Approach ◽  
Component Approach ◽  
The Global Financial Crisis ◽  
European Stock Markets ◽  
European Markets

The goal of this paper is to recognize the dynamics of financial integration across the European stock markets over the last two decades. We investigate two groups of markets: (1) three developed European markets in the U.K., France, and Germany; and (2) three emerging Central and Eastern European markets in Poland, the Czech Republic, and Hungary (CEE–3). The evolution of the integration process is analyzed using a dynamic principal component approach. The index of integration serves as a robust measure of integration. The empirical results reveal that the dynamics of integration across the whole group of markets increased significantly following the CEEC–3’s accession to the European Union. An inverted U‑shape in the index of integration has been found in this case. Moreover, the average index of integration was significantly different during the Global Financial Crisis compared to the pre‑crisis period. 

scholarly journals Different Patterns of the SARS-CoV-2 Epidemic across Italian Regions: a Hierarchical Clustering on Principal Components approach

2020 ◽  
Author(s):  
Andrea Maugeri ◽  
Martina Barchitta ◽  
Guido Basile ◽  
Antonella Agodi
Keyword(s):  
Hierarchical Clustering ◽  
Principal Component ◽  
Control Measures ◽  
Cluster Solution ◽  
Italian Region ◽  
Testing Strategies ◽  
Italian Regions ◽  
Principal Component Approach ◽  
Component Approach ◽  
Optimal Cluster

Abstract Italy has experienced the epidemic of severe acute respiratory syndrome coronavirus 2, which spread at different times and with different intensities throughout its territory. We aimed to identify clusters with similar epidemic patterns across Italian regions. To do that, we defined a set of regional indicators reflecting different domains and employed a hierarchical clustering on principal component approach to obtain an optimal cluster solution. As of 24 April 2020, Lombardy was the worst hit Italian region and entirely separated from all the others. Sensitivity analysis - by excluding data from Lombardy - partitioned the remaining regions into four clusters. Although cluster 1 (i.e. Veneto) and 2 (i.e. Piedmont and Emilia-Romagna) included the most hit regions beyond Lombardy, this partition reflected differences in the efficacy of restrictions and testing strategies. Cluster 3 was heterogeneous and comprised regions where the epidemic started later and/or where it spread with the lowest intensity. Regions within cluster 4 were those where the epidemic started slightly after Veneto, Emilia-Romagna and Piedmont, favoring timely adoption of control measures. Our findings provide policymakers with a snapshot of the epidemic in Italy, which might help guiding the adoption of countermeasures in accordance with the situation at regional level.

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