A single-pixel and non-redundant branching-based algorithm for nailfold capillary skeleton line extraction.

Background: Static nailfold capillary parameters are important parameters that reflect the health of the human body. Disease onset or progression is often accompanied by changes in the physiological parameters of the nailfold. Hence, the physiological parameters of the nailfold are closely related to the study of disease, with their automated and high-precision measurements playing a crucial role in these studies. Currently, manually measured values of the nailfold's parameters are the gold standard; however, they are time consuming and labor intensive, making the development of automated measurement methods essential. Most automated measurement methods use skeleton lines; however, current skeleton-thinning algorithms have non-single pixels and redundant branches that lead to reduced measurement accuracy. This study proposes a single-pixel and non-redundant branching-based skeleton line extraction algorithm for nailfold capillaries, which is then applied to nailfold static parameter calculations to improve accuracy. Methods: The algorithm includes deletion and restoration templates combined with the depth-first search method to obtain single-pixel skeleton lines without redundant branches. These lines are applied to the static nailfold capillary parameter measurement method based on digital image processing to calculate the blood vessel diameter. Results: The results show that the proposed method can obtain the single-pixel skeleton line without the redundant branches that are required for the parameter calculations and improve the accuracy of the nailfold capillary diameter measurement. Experiments showed that the root mean square errors (RMSEs) of the labeled apical diameter, arterial limb diameter, and venous limb diameter were 0.794, 0.756, and 0.830 µm, respectively, when the calculated results were compared with those of the manual calculations. According to the accuracy formula, the accuracy of the method in this study is 90%. We calculated the P values of the algorithmic and manual measurements to P<0.001 and found that the difference in the measurements of the proposed algorithm is statistically significant. Therefore, the method in this study has high sensitivity and specificity for the measurement of normal nailfold capillaries. Conclusions: The proposed algorithm could obtain single-pixel skeleton lines without redundant branches, thereby improving the nailfold static parameter measurement accuracy.