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Anal Chem. 2015 Oct 6;87(19):9715-21. doi: 10.1021/acs.analchem.5b03159. Epub 2015 Sep 11.

Segment and fit thresholding: a new method for image analysis applied to microarray and immunofluorescence data.

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Van Andel Research Institute, 333 Bostwick Avenue NE, Grand Rapids, Michigan 49503, United States.
Thomas Jefferson University, 1025 Walnut Street, Philadelphia, Pennsylvania 19107, United States.
University of Pittsburgh Medical Center , 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, United States.
Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, New York 10065, United States.


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.

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