Abstract
We use variational level set method and transition region extraction techniques to achieve image segmentation task. The proposed scheme is done by two steps. We first develop a novel algorithm to extract transition region based on the morphological gradient. After this, we integrate the transition region into a variational level set framework and develop a novel geometric active contour model, which include an external energy based on transition region and fractional order edge indicator function. The external energy is used to drive the zero level set toward the desired image features, such as object boundaries. Due to this external energy, the proposed model allows for more flexible initialization. The fractional order edge indicator function is incorporated into the length regularization term to diminish the influence of noise. Moreover, internal energy is added into the proposed model to penalize the deviation of the level set function from a signed distance function. The results evolution of the level set function is the gradient flow that minimizes the overall energy functional. The proposed model has been applied to both synthetic and real images with promising results.
Highlights
Image segmentation is a functional problem and complex task in image processing and computer vision
In the view of mathematics, implicit active contour models can be categorized into two categories: one is pure PDE model [1, 3,4,5,6] whose evolution equation is directly constructed; another is the variational level set model [7,8,9,10] whose evolution equation is derived from the minimization problem for the energy functional defined on the level set function
We present the morphological gradients based transition region extraction method and use the mean of transition region to compute the weight of the external energy in the variational level set model
Summary
Image segmentation is a functional problem and complex task in image processing and computer vision. To perform the image segmentation task, many successful techniques including geometric active contour models using the level set method [1] have been presented. Active contour model, proposed by Kass et al [2], has been proved to be an efficient framework for image segmentation. These models suffer from the sensitivity to initial conditions and the difficulties associated with topological changes like the merging and splitting of the evolving curve. In order to overcome these problems, implicit active contour models, that is, active contour models in a level set formulation, have been proposed for image segmentation. We focus on the variational level set methods
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