The immune system in homo sapiens protects the body against diseases by identifying and attacking foreign pathogens. However, when the system misidentifies native cells as threats, it results in an auto-immune response. The auto-antibodies generated during this phenomenon may be identified through the indirect immunofluorescence test. An important constituent process of this test is the automated identification of antigen patterns in the cell images, which is the focus of this research. We perform a detailed literature review and present a framework to automate the identification of antigen patterns. The efficacy of the framework, demonstrated on the MIVIA ICPR 2012 HEp-2 Cell Contest and SNP HEp-2 Cell datasets, suggests that the algorithm is comparable with the state-of-the-art approaches. © 2013 IEEE.