Diagnostic color estimation of tissue components in pathology images via von Mises mixture model

In this paper, we present a novel approach to achieve diagnostic color estimation for histological objects in pathology images. The method is based on a von Mises mixture model for hue histogram, followed by implicit pixel clustering via maximum likelihood estimation and representative color computation. Unlike conventional approaches adopting linear processing algorithms to analyze hue histogram which is characterized by a nature of periodicity, we build a circular cluster model composed of multiple von Mises distributions to address the directional nature of hue. Experimental results on synthetic circular data suggest that the proposed circular model outperforms both classical linear thresholding methods and the state-of-art circular thresholding approach in terms of cluster parameter estimation. The color estimation experiment on publicly-accessible cytopathology images demonstrates that our method is capable to accurately estimate object´s diagnostic color, which can be used for subsequent image analysis.