HEp-2 cell images classification based on statistical texture analysis and fuzzy logic

Autoimmune diseases occur when an inappropriate immune response takes place and produces autoantibodies to fight against human antigens. In order to detect autoimmune disease, a test, called indirect immunofluorescence (IIF) is carried out to identify antinuclear autoantibodies (ANA) in the HEp-2 cell. Current method of analyzing the results is inconsistent as it is limited to subjective factors such as experience and skill of the medical experts. Thus, there is a need for an automated recognition system to reduce the variability and increase the reliability of the test results. This paper proposes a pattern recognition algorithm consisting of statistical methods to extract seven textural features from the HEp-2 cell images followed by classification of staining patterns by using fuzzy logic. This method is applied to the data set of the ICPR 2012 contest. The textural features extracted are based on the first-order statistics and second-order statistics computed from grey level co-occurrence matrices (GLCM). The extracted features are then used as an input parameter to classify five staining patterns by using fuzzy logic. A working classification algorithm is developed and gives a mean accuracy of 84% out of 125 test images.