Analysis and Classification of Crithidia Luciliae fluorescent images

Autoantibody tests based on Crithidia Luciliae (CL) substrate are the recommended method to detect Systemic Lupus Erythematosus (SLE), a very serious sickness further to be classified as an invalidating chronic disease. CL is an unicellular organism containing a strongly tangled mass of circular dsDNA, named as kinetoplast, whose fluorescence determines the positiveness to the test. Conversely, the staining of other parts of cell body is not a disease marker, thus representing false positive fluorescence. Such readings are subjected to several issues limiting the reproducibility and reliability of the method, as the photo-bleaching effect and the inter-observer variability. Hence, Computer-Aided Diagnosis (CAD) tools can support physicians decision. In this paper we propose a system to classify CL wells based on a three stages recognition approach, which classify single cell, images and, finally, the well. The fusion of such different information permits to reduce the misclassifications effect. The approach has been successfully tested on an annotated dataset, proving its feasibility.