Feature analysis for automatic classification of HEp-2 florescence patterns : Computer-Aided Diagnosis of Auto-immune diseases

Indirect ImmunoFluorescence (IIF) is currently the recommended method for the detection of antinuclear autoantibodies(ANA). It is an effective technique to reveal the presence of auto immune diseases; however, it is a subjective method and hence dependent on the experience and expertise of the physician. Moreover, inter-observer variability limits the reproducibility of IIF reading. To this end, we propose feature extraction methods for automatic recognition of staining patterns of HEp-2 images (provided as a part of the ICPR 2012 HEp-2 Cells Classification Contest) to develop a Computer-Aided Diagnosis system and support the specialists' decision. We compare the performances of various individual and combined features and show that a combination of HOG(Histogram of Oriented Gradients), Texture and ROI(Region of Interest) features are best suited for our task achieving an overall accuracy of 91.13% using a Support Vector Machine as classifier.