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

ABSTRACT: 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

SUBMITTER: Ensink E 

PROVIDER: S-EPMC4854282 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature