HEp-2 cell images classification based on textural and statistic features using self-organizing map

Authors:
Yi-Chu Huang;Tsu-Yi Hsieh;Chin-Yuan Chang;Wei-Ta Cheng;Yu-Chih Lin;Yu-Len Huang
Affiliations:
Department of Computer Science, Tunghai University, Taichung, Taiwan;Division of Allergy, Immunology and Rheumatology, Taichung Veterans General Hospital, Taichung, Taiwan;Department of Computer Science, Tunghai University, Taichung, Taiwan;Department of Computer Science, Tunghai University, Taichung, Taiwan;Department of Computer Science, Tunghai University, Taichung, Taiwan;Department of Computer Science, Tunghai University, Taichung, Taiwan
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2012

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Abstract

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.