A bag of cells approach for antinuclear antibodies HEp-2 image classification.

The antinuclear antibody (ANA) test via indirect immunofluorescence applied on Human Epithelial type 2 (HEp-2) cells is a pathology test commonly used to identify connective tissue diseases (CTDs). Despite its effectiveness, the test is still considered labor intensive and time consuming. Applying image-based computer aided diagnosis (CAD) systems is one of the possible ways to address these issues. Ideally, a CAD system should be able to classify ANA HEp-2 images taken by a camera fitted to a fluorescence microscope. Unfortunately, most prior works have primarily focused on the HEp-2 cell image classification problem which is one of the early essential steps in the system pipeline. In this work we directly tackle the specimen image classification problem. We aim to develop a system that can be easily scaled and has competitive accuracy. ANA HEp-2 images or ANA images are generally comprised of a number of cells. Patterns exhibiting in the cells are then used to make inference on the ANA image pattern. To that end, we adapted a popular approach for general image classification problems, namely a bag of visual words approach. Each specimen is considered as a visual document containing visual vocabularies represented by its cells. A specimen image is then represented by a histogram of visual vocabulary occurrences. We name this approach as the Bag of Cells approach. We studied the performance of the proposed approach on a set of images taken from 262 ANA positive patient sera. The results show the proposed approach has competitive performance compared to the recent state-of-the-art approaches. Our proposal can also be expanded to other tests involving examining patterns of human cells to make inferences.