Exploiting user labels with generalized distance transforms random field level sets

Authors:
Yingxuan Zhu;Kinh Tieu
Affiliations:
Syracuse University, Department of Electrical Engineering and Computer Science, Syracuse, NY;Mitsubishi Electric Research Laboratories, Cambridge, MA
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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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.