A Fast Optimization Method for Level Set Segmentation

Thord Andersson, Gunnar Läthén, Reiner Lenz, Magnus Borga
Proceedings of the 16th Scandinavian conference on Image analysis (SCIA), page 400--409 - 2009
Download the publication : FULLTEXT.pdf [629Ko]  
Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.

Images and movies

 

BibTex references

@InProceedings\{ALLB09,
  author       = "Andersson, Thord and L\äth\én, Gunnar and Lenz, Reiner and Borga, Magnus",
  title        = "A Fast Optimization Method for Level Set Segmentation",
  booktitle    = "Proceedings of the 16th Scandinavian conference on Image analysis (SCIA)",
  series       = "Lecture Notes in Computer Science",
  pages        = "400--409",
  year         = "2009",
  editor       = "A.-B. Salberg, J.Y. Hardeberg, and R. Jenssen",
  publisher    = "Springer Link"
}

Author publication list