  The Anti-Nuclear Antibody (ANA) clinical pathology test is commonly used to
identify the existence of various diseases. A hallmark method for identifying
the presence of ANAs is the Indirect Immunofluorescence method on Human
Epithelial (HEp-2) cells, due to its high sensitivity and the large range of
antigens that can be detected. However, the method suffers from numerous
shortcomings, such as being subjective as well as time and labour intensive.
Computer Aided Diagnostic (CAD) systems have been developed to address these
problems, which automatically classify a HEp-2 cell image into one of its known
patterns (eg., speckled, homogeneous). Most of the existing CAD systems use
handpicked features to represent a HEp-2 cell image, which may only work in
limited scenarios. In this paper, we propose a cell classification system
comprised of a dual-region codebook-based descriptor, combined with the Nearest
Convex Hull Classifier. We evaluate the performance of several variants of the
descriptor on two publicly available datasets: ICPR HEp-2 cell classification
contest dataset and the new SNPHEp-2 dataset. To our knowledge, this is the
first time codebook-based descriptors are applied and studied in this domain.
Experiments show that the proposed system has consistent high performance and
is more robust than two recent CAD systems.
