Modified Gradient Search for Level Set Based Image Segmentation

Thord Andersson, Gunnar Läthén, Reiner Lenz, Magnus Borga
IEEE Transactions on Image Processing, Volume 22, Number 2, page 621--630 - 2013
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Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.

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BibTex references

@Article\{ALLB13,
  author       = "Andersson, Thord and L\äth\én, Gunnar and Lenz, Reiner and Borga, Magnus",
  title        = "Modified Gradient Search for Level Set Based Image Segmentation",
  journal      = "IEEE Transactions on Image Processing",
  number       = "2",
  volume       = "22",
  pages        = "621--630",
  year         = "2013"
}

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