In many research laboratories, it is essential to determine the relative expression levels of
some proteins of interest in tissue samples. The semi-quantitative scoring of a set of images consists
of establishing a scale of scores ranging from zero or one to a maximum number set by the researcher
and assigning a score to each image that should represent some predefined characteristic of the IHC
staining, such as its intensity. However, manual scoring depends on the judgment of an observer and
therefore exposes the assessment to a certain level of bias. In this work, we present a fully automatic
and unsupervised method for comparative biomarker quantification in histopathological brightfield
images. The method relies on a color separation method that discriminates between two chromogens
expressed as brown and blue colors robustly, independent of color variation or biomarker expression
level. For this purpose, we have adopted a two-stage stain separation approach in the optical density
space. First, a preliminary separation is performed using a deconvolution method in which the color
vectors of the stains are determined after an eigendecomposition of the data. Then, we adjust the
separation using the non-negative matrix factorization method with beta divergences, initializing
the algorithm with the matrices resulting from the previous step. After that, a feature vector of
each image based on the intensity of the two chromogens is determined. Finally, the images are
annotated using a systematically initialized k-means clustering algorithm with beta divergences. The
method clearly defines the initial boundaries of the categories, although some flexibility is added.
Experiments for the semi-quantitative scoring of images in five categories have been carried out
by comparing the results with the scores of four expert researchers yielding accuracies that range
between 76.60% and 94.58%. These results show that the proposed automatic scoring system, which
is definable and reproducible, produces consistent results.FEDER / Junta de Andalucía-Consejería de Economía y Conocimiento US-1264994Fondo de Desarrollo (FEDER). Unión Europea PGC2018-096244-B-I00, SAF2016-75442-RMinisterio de Economía, Industria y Competitividad (MINECO). España TEC2017- 82807-