Cell segmentation in multispectral images using level sets with priors for accurate shape recovery

In this paper, we demonstrate the effectiveness of using statistical shape priors to recover shape descriptors from occluded objects in a level set based variational framework. Parameters that balance curve evolution forces are estimated systematically through embedded discrete Conditional Random Field (CRF). In addition, our approach exploits the benefit of using spectral data to construct a local appearance model for images with intensity inhomogeneity. The proposed segmentation approach is evaluated on cytological smears imaged using spectral microscopy and compared against traditional cell segmentation algorithms.