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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Ensink, E; Sinha, J; Sinha, A; Tang, H; Calderone, HM; Hostetter, G; Winter, J; Cherba, D; Brand, RE; Allen, PJ; Sempere, LF; Haab, BB

Published Date

  • October 6, 2015

Published In

Volume / Issue

  • 87 / 19

Start / End Page

  • 9715 - 9721

PubMed ID

  • 26339978

Pubmed Central ID

  • PMC4854282

Electronic International Standard Serial Number (EISSN)

  • 1520-6882

Digital Object Identifier (DOI)

  • 10.1021/acs.analchem.5b03159


  • eng

Conference Location

  • United States