  This paper presents a proof of concept for the usefulness of second-order
texture features for the qualitative analysis and classification of chromogenic
in-situ hybridization whole slide images in high-throughput imaging
experiments. The challenge is that currently, the gold standard for gene
expression grading in such images is expert assessment. The idea of the
research team is to use different approaches in the analysis of these images
that will be used for structural segmentation and functional analysis in gene
expression. The article presents such perspective idea to select a number of
textural features that are going to be used for classification. In our
experiment, natural grouping of image samples (tiles) depending on their local
texture properties was explored in an unsupervised classification procedure.
The features are reduced to two dimensions with fuzzy c-means clustering. The
overall conclusion of this experiment is that Haralick features are a viable
choice for classification and analysis of chromogenic in-situ hybridization
image data. The principal component analysis approach produced slightly more
"understandable" from an annotator's point of view classes.
