Accurate HEp-2 cell classification based on sparse bag of words coding

Autoimmune diseases (AD) are the abnormal response of the immune system of the body to healthy tissues. ADs have generally been on the increase. Efficient computer aided diagnosis of ADs through classification of the human epithelial type 2 (HEp-2) cells become beneficial. These methods make lower diagnosis costs, faster response and better diagnosis repeatability. In this paper, we present an automated HEp-2 cell image classification technique that exploits the sparse coding of the visual features together with the Bag of Words model (SBoW). In particular, SURF (Speeded Up Robust Features) and SIFT (Scale-invariant feature transform) features are specially integrated to work in a complementary fashion. This method helps greatly improve the cell classification accuracy. Additionally, a hierarchical max-pooling method is proposed to aggregate the local sparse codes in different layers to provide final feature vector. Furthermore, various parameters of the dictionary learning including the dictionary size, the learning iteration number, and the pooling strategy is also investigated. Experiments conducted on publicly available datasets show that the proposed technique clearly outperforms state-of-the-art techniques in cell and specimen levels.