TR2010-019

Exploiting User Labels with Generalized Distance Transforms Random Field Level Sets

    •  Zhu, Y., Tieu, K.H., "Exploiting User Labels with Generalized Distance Transforms Random Field Level Sets", IEEE International Symposium on Biomedical Imaging: From Nano to Macro, April 2010.
      BibTeX TR2010-019 PDF
      • @inproceedings{Zhu2010apr,
      • author = {Zhu, Y. and Tieu, K.H.},
      • title = {Exploiting User Labels with Generalized Distance Transforms Random Field Level Sets},
      • booktitle = {IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
      • year = 2010,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2010-019}
      • }
  • Research Area:

    Computer Vision

Abstract:

We present an approach for exploiting user labels with random field level sets in image segmentation. A sparse set of user labels is propagated to the rest of the image by computing a generalized distance transform which takes into account image intensity information. The region-based level set formulation is modified to use random field level sets whose range is restricted to the probability values. These two ideas are combined in a single level set functional. Improved results are shown on a liver segmentation task.

 

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