HEp-2 Cell Classification Using Multi-dimensional Local Binary Patterns and Ensemble Classification

Abstract

Indirect immunofluorescence imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells. This is particularly useful for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and Centro mere cells, which give indications on different autoimmune diseases. This categorisation is typically performed by manual evaluation which is time consuming and subjective. In this paper, we present a method for automatic classification of HEp-2 cells using local binary pattern (LBP) based texture descriptors and ensemble classification. In our approach, we utilise multi-dimensional LBP (MD-LBP) histograms, which record multi-scale texture information while maintaining the relationships between the scales. Our dedicated ensemble classification approach is based on a set of heterogeneous base classifiers obtained through application of different feature selection algorithms, a diversity based pruning stage and a neural network classifier fuser. We test our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to outperform all algorithms that were entered in the competition as well as to exceed the performance of a human expert.