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A New Protocol for Semi-Automatic Generation of Reference Data for High Resolution Biological Images

Published

Author(s)

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

Abstract

We compare manually segmented masks of cell images that are independently hand-selected by several different people, with a new semi-automated method for estimating reference data for images of cells with very sharp clear edges. By quantifying the large differences in the hand selected masks, we validate the need for better precision. Mimicking the human eye, we select cell edges by a similar intensity differential used by the eye, but eliminating the human error that appears to arise. Resulting masks in our test of 16 images containing 71 cells, show consistently better results with the semi-automated method, and produce better results than using a kmeans-5 segmentation to estimate reference data.
Proceedings Title
18th International Conference on Intelligent Systems for Molecular Biology
Conference Dates
July 11-13, 2010
Conference Location
Boston, MA

Keywords

reference data, segmentation

Citation

Peskin, A. , Chalfoun, J. , Dima, A. and Filliben, J. (2008), A New Protocol for Semi-Automatic Generation of Reference Data for High Resolution Biological Images, 18th International Conference on Intelligent Systems for Molecular Biology, Boston, MA (Accessed April 14, 2024)
Created November 30, 2008, Updated February 19, 2017