Random field model for cell segmentation in transmission mode multispectral microscopy images

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.