NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.
Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.
An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
A New Measure in Cell Image Segmentation Data Analysis
Published
Author(s)
Jin Chu Wu, Michael W. Halter, Raghu N. Kacker, John T. Elliott
Abstract
Cell image segmentation (CIS) is critical for quantitative imaging in cytometric analyses. The data derived after segmentation can be used to infer cellular function. To evaluate CIS algorithms, first for dealing with comparisons of single cells treated as two-dimensional objects, a misclassification error rate (MER) is defined as a weighted sum of the false negative rate and the false positive rate. Then, all cells MERs are aggregated to constitute a new measure called the total error rate, which statistically takes account of the sizes of the cells in such a way that an algorithm pays larger penalty if larger sizes of cells are not segmented correctly. This total error rate is used to measure the performance level of CIS algorithms. It was tested by applying ten CIS algorithms taken from the ImageJ to our 106 cells with different sizes, which were also manually segmented to be treated as the ground-truth cells. The test results were supported by the primitive pairwise comparison between two algorithms MERs on all cells.
, J.
, Halter, M.
, Kacker, R.
and Elliott, J.
(2012),
A New Measure in Cell Image Segmentation Data Analysis, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.7871
(Accessed October 9, 2025)