A slightly supervised approach for positive/negative classification of fluorescence intensity in HEp-2 images

Indirect Immunofluorescence on HEp-2 slides is the recommended technique to detect antinuclear autoantibodies in patient serum. Such slides are read at the fluorescence microscope by experts of IIF, who classify the fluorescence intensity, recognize mitotic cells and classify the staining patterns for each well. The crucial need of accurately performed and correctly reported laboratory determinations has motivated recent research on computer-aided diagnosis tools in IIF to support the HEp-2 image classification. Such systems adopt a fully supervised classification approach and, hence, their chance of success depends on the quality of ground truth used to train the classification algorithms. Besides being expensive and time consuming, collecting a large and reliable ground truth in IIF is intrinsically hard due to the inter- and intra-observer variability. In order to overcome such limitations, this paper presents a slightly supervised approach for positive/negative fluorescence intensity classification. The classification phase consists in matching parts of interest automatically detected in the test image with a Gaussian mixture model built over few control images. The approach, whose operating configuration can be adapted to the cost of misclassifications, has been tested over a database with 914 images acquired from 304 different wells, achieving remarkable results on positive/negative screening task.