Efficient k-NN based HEp-2 cells classifier

Human Epithelial (HEp-2) cells are commonly used in the Indirect Immunofluorescence (IIF) tests to detect autoimmune diseases. The diagnosis consists of searching and classification to specific patterns created by Anti-Nuclear Antibodies (ANAs) in the patient serum. Evaluation of the IIF test is mostly done by humans, which means that it is highly dependent on the experience and expertise of the physician. Therefore, a significant amount of research has been focused on the development of computer aided diagnostic systems which could help with the analysis of images from microscopes. This work deals with the design and development of HEp-2 cells classifier. The classifier is able to categorize pre-segmented images of HEp-2 cells into 6 classes. The core of this engine consists of the following image descriptors: Haralick features, Local Binary Patterns, SIFT, surface description and a granulometry-based descriptor. These descriptors produce vectors that form metric spaces. k-NN classification is based on aggregated distance function which combines several features together. An extensive set of evaluations was performed on the publicly available MIVIA HEp-2 images dataset which allows a direct comparison of our approach with other solutions. The results show that our approach is one of the leading classifiers when comparing with other participants in the HEp-2 Cells Classification Contest [1]. (C) 2013 Elsevier Ltd. All rights reserved.