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



Adele P. Peskin, Joe Chalfoun, Alden A. Dima, John T. Elliott, James J. Filliben


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.
Proceedings Title
ISVC 2010
Conference Dates
November 28-December 1, 2010
Conference Location
Las Vegas, NV


edge detection, segmentation


Peskin, A. , Chalfoun, J. , Dima, A. , Elliott, J. and Filliben, J. (2010), A Human Inspired Local Ratio-Based Algorithm for Edge Detection in Fluorescent Cell Images, ISVC 2010, Las Vegas, NV (Accessed May 23, 2024)


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Created November 25, 2010, Updated February 19, 2017