Applying Textural Features to the Classification of HEp-2 Cell Patterns in IIF images

The analysis of anti-nuclear antibodies in HEp-2
cells by indirect immunofluorescence (IIF) is fundamental
for the diagnosis of important immune pathologies;
in particular, classifying the staining pattern of the cell
is critical for the differential diagnosis of several types
of diseases. Current tests based on human evaluation
are time-consuming and suffer from very high variability,
which impacts on the reliability of the results. As
a solution to this problem, in this work we propose a
technique that performs automated classification of the
staining pattern. Our method combines textural feature
extraction and a two-step feature selection scheme to
select a limited number of image attributes that are best
suited to the classification purpose and then recognizes
the staining pattern by means of a Support Vector Machine
module. Experiments on IIF images showed that
our method is able to identify staining patterns with average
accuracy of about 87%.