scholarly journals Fully Automatic Quantification of Microarray Image Data

2002 ◽  
Vol 12 (2) ◽  
pp. 325-332 ◽  
Author(s):  
A. N. Jain
Keyword(s):  
Image Data ◽  
Automatic Quantification ◽  
Microarray Image ◽  
Fully Automatic

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.

Fully automatic quantification of individual ganglion cells from AO-OCT volumes via weakly supervised learning

2020 ◽  
Author(s):  
Somayyeh Soltanian-Zadeh ◽  
Kazuhiro Kurokawa ◽  
Zhuolin Liu ◽  
Daniel X. Hammer ◽  
Donald T. Miller ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Ganglion Cells ◽  
Weakly Supervised Learning ◽  
Automatic Quantification ◽  
Fully Automatic ◽  
Weakly Supervised

Algorithms to Preprocess Microarray Image Data

2021 ◽  
pp. 69-78
Author(s):  
Paolo Zaffino ◽  
Maria Francesca Spadea
Keyword(s):  
Image Data ◽  
Microarray Image

Web-based tissue microarray image data analysis: Initial validation testing through prostate cancer Gleason grading

2001 ◽  
Vol 32 (4) ◽  
pp. 417-427 ◽  
Author(s):  
G.Steven Bova ◽  
Giovanni Parmigiani ◽  
Jonathan I. Epstein ◽  
Thomas Wheeler ◽  
Neil R. Mucci ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Data Analysis ◽  
Tissue Microarray ◽  
Image Data ◽  
Web Based ◽  
Gleason Grading ◽  
Initial Validation ◽  
Microarray Image ◽  
Validation Testing

scholarly journals Automatic Quantification of Microvessels Using Unsupervised Image Analysis

1998 ◽  
Vol 17 (2) ◽  
pp. 83-92 ◽  
Author(s):  
Petter Ranefall ◽  
Kenneth Wester ◽  
Christer Busch ◽  
Per-Uno Malmström ◽  
Ewert Bengtsson
Keyword(s):  
Image Analysis ◽  
Segmentation Algorithm ◽  
Field Of View ◽  
Automatic Method ◽  
Watershed Segmentation ◽  
Test Set ◽  
Automatic Quantification ◽  
Component Size ◽  
Fully Automatic ◽  
Object Counting

An automatic method for quantification of images of microvessels by computing area proportions and number of objects is presented. The objects are segmented from the background using dynamic thresholding of the average component size histogram.To be able to count the objects, fragmented objects are connected, all objects are filled, and touching objects are separated using a watershed segmentation algorithm.The method is fully automatic and robust with respect to illumination and focus settings.A 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 manual threshold.Further, the method showed good correlation to manual object counting (r= 0.80) on an other test set.

AUTOMATED MICROARRAY IMAGE GRIDDING USING IMAGE PROJECTION VECTORS COUPLED WITH POWER SPECTRUM MODEL

2010 ◽  
Vol 24 (04) ◽  
pp. 581-600 ◽  
Author(s):  
NING DENG ◽  
HUILONG DUAN
Keyword(s):  
Power Spectrum ◽  
Image Data ◽  
Microarray Experiment ◽  
Microarray Image ◽  
Image Projection ◽  
Microarray Datasets ◽  
Microarray Analyses ◽  
Microarray Image Processing ◽  
Spectrum Model ◽  
Experimental Comparisons

Microarray technology has been increasingly recognized as a powerful means for monitoring the expression levels of thousands of genes simultaneously. Microarray image processing is an essential aspect of microarray experiment, of which gridding is thought to be the most important step of spot recognition. Many times, microarray image gridding requires assisted intervention to achieve the acceptable accuracy. In this paper, an automatic microarray image gridding algorithm was presented by using image projection vectors together with power spectrum model. For obtaining grid position, the image projection vectors were utilized by adequately considering the grid parameters. On the other hand, as a preprocessing procedure of microarray gridding, detection of the grid rotation was involved in our study by using power spectrum analyses of the image projection vectors. Our approach has been evaluated by three different microarray datasets. Experimental comparisons with up-to-date approaches by using both synthetic and real image data are demonstrated. The gridding result was shown to be very accurate, and able to provide correct gridding dataset for the downstream microarray analyses. In summary, our study demonstrated the combination of image projection vectors with power spectrum model as a powerful strategy for microarray image gridding.

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