A Shape Prior-Based Active Contour Model for Automatic Images Segmentation
Xiaoliang Jiang, Jinyun Jiang · IEEE Access · 2020
Due to the variable shapes of objects, high noise intensity and complex environments, the field of image segmentation still has great challenges. To address these issues, we present a new image segmentation strategy based on active contour model and shape priori information, which can accurately and efficiently segment various images. The data fitting term, inspired by Chan-Vese (C-V) model, is used to guide the curve evolving to desired target boundary. Meanwhile, the contour is utilized to reconstruct a prior shape so that can help to deal with images in the presence of complex target. After that, the length regularity term of energy functional is incorporated to ensure the stable calculation of the evolution curve. The quantitative and qualitative experiments on various real and medical images indicate that our method is more efficient and accurate than the existing unified models.