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A Human Inspired Local Ratio-Based Algorithm for Edge Detection in Fluorescent Cell Images

  • Joe Chalfoun
  • Alden A. Dima
  • Adele P. Peskin
  • John T. Elliott
  • James J. Filliben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6453)

Abstract

We have developed a new semi-automated method for segmenting images of biological cells seeded at low density on tissue culture substrates, which we use to improve the generation of reference data for the evaluation of automated segmentation algorithms. The method was designed to mimic manual cell segmentation and is based on a model of human visual perception. We demonstrate a need for automated methods to assist with the generation of reference data by comparing several sets of masks from manually segmented cell images created by multiple independent hand-selections of pixels that belong to cell edges. We quantify the differences in these manually segmented masks and then compare them with masks generated from our new segmentation method which we use on cell images acquired to ensure very sharp, clear edges. The resulting masks from 16 images contain 71 cells and show that our semi-automated method for reference data generation locates cell edges more consistently than manual segmentation alone and produces better edge detection than other techniques like 5-means clustering and active contour segmentation for our images.

Keywords

NIH3T3 Cell Manual Segmentation Cell Edge Background Pixel Edge Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Joe Chalfoun
    • 1
  • Alden A. Dima
    • 1
  • Adele P. Peskin
    • 2
  • John T. Elliott
    • 1
  • James J. Filliben
    • 1
  1. 1.NISTGaithersburg
  2. 2.NISTBoulder

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