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

Abstract

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%.