A Shape Prior-Based Active Contour Model for Automatic Images Segmentation

Abstract

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.

Segmentation of Optic Disc and Optic Cup Based on Two-Layer Level Set with Sparse Shape Prior Constraint in Fundus Images

“…It is an effective solution to constrain object contour evolution by introducing a shape prior model of the OD and OC region of glaucoma images, which can effectively reduce the occurrence of false segmentation and excessive shrinkage. Common shape prior constraint methods include introducing single or multiple predefined geometric shape equations [7], establishing a statistical probability model of image shape [8], establishing a sparse shape prior model [9], and others. The structure of fundus images is complex, and there are great differences among individuals.…”