Take a sneak peek at the new NIST.gov and let us know what you think!
(Please note: some content may not be complete on the beta site.).
NIST Authors in Bold
|Author(s):||Adele P. Peskin; Joe Chalfoun; Alden A. Dima; John T. Elliott; James J. Filliben;|
|Title:||A Human Inspired Local Ratio-Based Algorithm for Edge Detection in Fluorescent Cell Images|
|Published:||November 25, 2010|
|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.|
|Pages:||pp. 23 - 34|
|Location:||Las Vegas, NV|
|Dates:||November 28-December 1, 2010|
|Keywords:||edge detection, segmentation|
|Research Areas:||Life Sciences Research, Bioscience & Health|