HEp-2 image classification using intensity order pooling based features and bag of words

Human Epithelial type 2 (HEp-2) cells play an important role in the diagnosis of autoimmune disorder. Traditional approach relies on specialists to observe HEp-2 slides via the fluorescence microscope, which suffers from a number of shortcomings like being subjective and labor intensive. Pattern recognition techniques have been recently introduced to this research issue to make the process automatic. However, performances of current systems available in literature are not satisfying. We propose in this paper a framework using intensity order pooling based gradient feature and bag of words for HEp-2 classification. By pooling the gradient features based on the intensity orders of local grid points, the pooled feature is rotationally invariant without requirement of orientation estimation. The proposed approach was fully tested using publicly available ICPR dataset and our own SZU dataset. Experimental results show that the propose method significantly outperformed widely used SIFT feature and the winner of ICPR contest 2012. Encouraging 100% image level accuracy was achieved on the SZU dataset.