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.
Citation: NIST Interagency/Internal Report (NISTIR) - 7871
NIST Pub Series: NIST Interagency/Internal Report (NISTIR)
Pub Type: NIST Pubs
Cell image segmentation, Measure, Total error rate, Total probability, Misclassification error rate, False negative rate, False positive rate.