Blind Color Deconvolution of Histopathological Images Using a Variational Bayesian Approach

Whole-slide histological images are routinely used by medical doctors in diagnosis. Most of these images are stained with the very common and inexpensive hematoxylin and eosin dyes. Slide stain separation and color normalization are crucial steps within the digital pathology workflow which require a previous color deconvolution step. This image processing task is not easy, especially when working with images taken from different microscopes and slides stained in different laboratories. In this paper, based on Variational Bayes inference, an efficient new blind color deconvolution method is proposed. The new model takes into account both spatial relations among image pixels and similarity to a given reference color-vector matrix. A comparison with classical and current state-of-the-art color deconvolution algorithms, using real images with known ground truth hematoxylin and eosin values, has been carried out. This comparison has demonstrated the superiority of the proposed approach.