Accurate HEp-2 cell classification based on Sparse Coding of Superpixels

Abstract

Autoimmune Diseases (AD) are among the top 10 leading causes of death in female children and women in all age groups up to 64 years. They are widely diagnosed by various antibody tests that typically apply the Indirect Immunofluorescence (IIF) to the Human Epithelial Type-2 (HEp-2) cells. Automated classification of Hep-2 cells has attracted much research interest in recent years, and many of these approaches employ patch-based models and the Bag of Words (BoW) scheme, but often face several typical constraints such as the need to process a huge number of overlapped image patches, tuning of various parameters and etc. We propose a superpixel based Hep-2 cell classification technique by calculating the sparse codes of image patches which are prepared in a more intelligent way. In particular, the superpixel approach guides the determination of the right image patches while aggregating the neighboring pixels of similar patterns. In addition, we introduce “extended superpixels” which is able to capture the most discriminative gradient information across the boundary of the HEp-2 cell images. The proposed technique has been evaluated over two public datasets (ICPR2012 and ICIP2013) and experiments show superior performance in both classification accuracy and speed of model training and cell classification.