The quantificational comparison of robustness of ILEE and other algorithms by segmentation accuracy. The dataset of Fig. 4 is added with a series of gaussian noise (μ = 0, σ as variable). Since Gaussian noise is random and potential unstable, each sample-noise combination is technically repeated by 12 times and the averaged result is used. The transparent area with light color indicates 95% confidence interval of each algorithm. The binary images rendered by different algorithms are compared with manually portraited ground truth to count the pixels that are matched (ideally 1.0), false-positive (ideally 0), and false-negative (ideally 0). Generally speaking, ILEE is the best-performing algorithm with high match, low false-positive, and low false-negative rate at both low and high noise. Other algorithms have one or more flaws.