Level set with embedded conditional random fields and shape priors for segmentation of overlapping objects

Authors:
Xuqing Wu;Shishir K. Shah
Affiliations:
Quantitative Imaging Laboratory, Department of Computer Science, University of Houston, Houston, TX;Quantitative Imaging Laboratory, Department of Computer Science, University of Houston, Houston, TX
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Year:
2010

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Abstract

Traditional methods for segmenting touching or overlapping objects may lead to the loss of accurate shape information which is a key descriptor in many image analysis applications. While experimental results have shown the effectiveness of using statistical shape priors to overcome such difficulties in a level set based variational framework, problems in estimation of parameters that balance evolution forces from image information and shape priors remain unsolved. In this paper, we extend the work of embedded Conditional Random Fields (CRF) by incorporating shape priors so that accurate estimation of those parameters can be obtained by the supervised training of the discrete CRF. In addition, non-parametric kernel density estimation with adaptive window size is applied as a statistical measure that locally approximates the variation of intensities to address intensity inhomogeneities. The model is tested for the problem of segmenting overlapping nuclei in cytological images.