Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
International Journal of Computer Vision
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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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 cells in images of cytological smears. Relationship between neighboring bands is weighted by the gradient of spectral profile of the sample. Experimental results show that the proposed approach effectively suppresses the non-uniform appearance of the cell chromatin by integrating spatial and spectral constraints within the segmentation process and robustly labels fine textured cells. Comparative analysis of the proposed model against the commonly used 2-D model in color space is also performed. Results of this evaluation show the benefits of the proposed model.