Abstract
In order to completely separate objects with large sections of occluded boundaries in an image, we devise a new variational level set model for image segmentation combining the Chan-Vese model with elastica and landmark constraints. For computational efficiency, we design its Augmented Lagrangian Method (ALM) or Alternating Direction Method of Multiplier (ADMM) method by introducing some auxiliary variables, Lagrange multipliers, and penalty parameters. In each loop of alternating iterative optimization, the sub-problems of minimization can be easily solved via the Gauss-Seidel iterative method and generalized soft thresholding formulas with projection, respectively. Numerical experiments show that the proposed model can not only recover larger broken boundaries but can also improve segmentation efficiency, as well as decrease the dependence of segmentation on parameter tuning and initialization.
Highlights
I N recent years, deep learning methods are widely used in areas of image processing such as image segmentation
THE CVE MODEL WITH LANDMARK CONSTRAINTS AND ITS Alternating Direction Method of Multiplier (ADMM) ALGORITHM Combining (14) and (16), we propose the Chan-Vese model with elastica and landmark as minE(c1, c2, φ) =
We first considered whether the CVE model with landmark (CVEL) could speed up the segmentation process compared to the CVE by constructing the experiment in Fig 8, where we marked the entire contour of the palm with landmarks for the CVEL and compare performance with the CVE
Summary
I N recent years, deep learning methods are widely used in areas of image processing such as image segmentation These methods require considerable training data and are generally limited by the properties of the data. Variational level set methods [1] as classical model-based methods have been widely applied to image segmentation problems based on image features such as edge, region, texture and motion, etc. Motivated by image registration with landmarks [11]– [13], Pan et al [14] proposed a Chan-Vese model [15] with landmark constraints (CVL) under the variational level set framework.
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