Segment and Fit Thresholding: A New Method for Image Analysis Applied to Microarray and Immunofluorescence Data

Experiments involving the high-throughput quantification of image data require algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we pres...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2015-10, Vol.87 (19), p.9715-9721
Main Authors: Ensink, Elliot, Sinha, Jessica, Sinha, Arkadeep, Tang, Huiyuan, Calderone, Heather M., Hostetter, Galen, Winter, Jordan, Cherba, David, Brand, Randall E., Allen, Peter J., Sempere, Lorenzo F., Haab, Brian B.
Format: Article
Language:English
Subjects:
Algorithms
Analytical chemistry
Antibodies - analysis
Automation
Fluorescence
Fluorescent Antibody Technique - methods
Humans
Image analysis
Image Interpretation, Computer-Assisted - methods
Image Processing, Computer-Assisted - methods
Immunofluorescence
Immunoglobulins
Manuals
Microscopy
Pattern Recognition, Automated - methods
Protein Array Analysis - methods
Segments
Signal transduction
Software
Thresholds
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Summary:Experiments involving the high-throughput quantification of image data require algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multicolor, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu’s method for selected images. SFT promises to advance the goal of full automation in image analysis.
ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.5b03159