Unsupervised clustering method for pattern recognition in IIF images

Abstract

Autoimmune diseases are a family of more than 80 chronic, and often disabling, illnesses that develop when underlying defects in the immune system lead the body to attack its own organs, tissues, and cells. Diagnosis of autoimmune pathologies is based on research and identification of antinuclear antibodies (ANA) through indirect immunofluorescence (IIF) method and is performed by analyzing patterns and fluorescence intensity. We propose here a method to automatically classify the centromere pattern based on the grouping of centromeres on the cells through a clustering K-means algorithm. The described method was tested on a public database (MIVIA). The results of the test showed an Accuracy equal to (92.0 ± 1.0)%. Comparing our results with the results obtained on the MIVIA database it is possible to note that our method has a performance comparable with the three best values obtained. Indeed, the method here proposed allows an automatic segmentation and counting of the cells in the images, while the participants to the contest received the training set with the original images of the cells already segmented by specialists.