Localised active contour method via local similarity measure for image segmentation
Xiaoliang Jiang, Jinyun Jiang · International Journal of Data Science · 2022
The accuracy of active contour methods is not always exact since there are many uncertainty factors, e.g., abundant noise, lack of clear boundaries, intensity inhomogeneity. To tackle these issues, a localised region-based segmentation framework is presented in this paper. In our method, a new adaptive local similarity measure is built in local regions as the spatial constraint to guarantee noise suppression and outlier resistance. Second, we construct an objective equation by integrating the local similarity measure into an active contour algorithm based on the local region. Furthermore, we design the local mean difference energy as a control constraint to enhance the efficiency and smoothness of the profile curve. Experimental data demonstrate that our algorithm, when compared with other classical region-based models, can achieve higher accuracy and has stronger robustness for images with higher noise levels.