The nature of statistical learning theory
The nature of statistical learning theory
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Analysis & Applications
Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis
IEEE Transactions on Information Technology in Biomedicine
Mining knowledge for HEp-2 cell image classification
Artificial Intelligence in Medicine
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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.