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Segmentation of cells/nuclei is a challenging problem in 2-D histological and cytological images. Although a large number of algorithms have been proposed, newer efforts continue to be devoted to investigate robust models that could have high level of adaptability with regard to considerable amount of image variability. In this paper, we propose a multiclassification conditional random fields (CRFs)...
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...
Cell segmentation is a challenging problem in histology and cytology that can benefit from additional information obtained in using multispectral imaging. Unique transmission spectra of biological tissues are potentially useful for better classification and segmentation of sub-cellular structures. In this paper, we propose a conditional random field (CRF) model that interprets high-dimensional spectral...
Multispectral microscopy for applications in histology and cytology has shown that the unique transmission spectra of biological tissue provides additional information that is potentially useful for better classification and segmentation of sub-cellular structures. In this paper, we propose a conditional random field (CRF) model that incorporates spectral data during inference for the problem of segmenting...
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