HEp-2 Cell Images Classification Based on Textural and Statistic Features Using Self-Organizing Map

摘要

Indirect immunofluorescence (IIF) with HEp-2 cells has been used to detect antinuclear auto-antibodies (ANA) for diagnosing systemic autoimmune diseases. The aim of this study is to develop an automatic scheme to identify the fluorescence patterns of HEp-2 cell in IIF images. The self-organizing map (SOM) neural network with 14 textural and statistic features were utilized to classify the fluorescence patterns. This study evaluated 1020 autoantibody fluorescence patterns that were divided into six pattern categories, i.e. diffuse, peripheral, coarse speckled, fine speckled, discrete speckled and nucleolar patterns. Experimental results show that the proposed approach can identify autoantibody fluorescence patterns with a high accuracy and is therefore clinically useful to provide a second opinion for diagnosing systemic autoimmune diseases.