Classification of human epithelial type 2 cell images using independent component analysis

Identifying the presence of Anti-Nuclear Antibody in Human Epithelial type 2 (HEp-2) cells via Indirect Immunofluorescence (IIF) is commonly used to diagnose various connective tissue diseases in clinical pathology tests. This pathology test can be automated by computer vision algorithms. However, the existing automated systems, namely Computer Aided Diagnostic (CAD) systems, suffer from numerous shortcomings such as using pre-selected features. To overcome such shortcomings, we propose a novel approach by learning filters from image statistics. Specifically, we train a filter bank from unlabelled cell images by using Independent Component Analysis (ICA). The filter bank is then applied to images in order to extract a set of filter responses. We extract regions from this set of responses and stack them into “cubic regions”. Average filter responses in 1 × 1, 2 × 2, 4 × 4 grids from the cubic-region are used as “ICA feature”. ICA features in multiple regions are stored in a feature collection matrix to represent each image. Finally, we use Support Vector Machine (SVM) in conjunction with histogram correlation kernel to classify the cell images. We show that our approach outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.