Exploring illumination robust descriptors for human epithelial type 2 cell classification

Abstract

Strong illumination variation is a key challenge in the Human Epithelial Type 2 (HEp-2) cell classification task. Aiming to improve the robustness of the HEp-2 classification system to the illumination variation, this paper deeply explores discriminative and illumination robust descriptors. Specifically, we propose a novel Spatial Shape Index Descriptor (SSID) to capture spatial layout information of the second-order structures, and utilize a Local Orientation Adaptive Descriptor (LOAD), which was originally designed for texture classification, to the HEp-2 cell classification task. Both SSID and LOAD show strong discrimination and great complementarity to each other.Four different sets of experiments were carried out to evaluate SSID, LOAD and their combination. Our two submissions achieved superior performance on the new Executable Thematic of Pattern Recognition Techniques for Indirect Immunofluorescence images analysis. Compared to the rank 1st method in the ICPR 2014 HEp-2 cell classification contest, both of our submissions achieved a better performance when only using the provided training data. Our approaches also demonstrated superior performance on a newly compiled large-scale HEp-2 data set with 63,445 cell images.