HEp-2 staining pattern recognition using stacked fisher network for encoding weber local descriptor

This study addresses the recognition problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Generally, the method used for IIF analysis remains subjective, and depends too heavily on the experience and expertise of the physician. This study aims to explore an automatic HEp-2 cell recognition system, in which how to extract highly discriminate visual features plays a key role in this recognition application. In order to realize this purpose, our main efforts include: (1) a simple but robust local descriptor without any quantization for local patch representation; (2)A transformation of the difference values between the surrounding pixels and the center one to the perception degree, which is based on the fact that human perception for disguising a pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus; called as Weber local descriptor (WLD); (3) a data-driven coding strategy with a parametric probability process, and the extraction of not only low- but also high-order statistics for image representation called as Fisher vector; (4) the stacking of the Fisher network into multi-layer framework for more discriminate feature. Experiments using the open HEp-2 cell dataset released in the ICIP2013 contest validate that the proposed strategy can achieve a much better performance than the state-of-the-art approaches, and that the achieved recognition error rate is even very significantly below the observed intra-laboratory variability.