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Analysis and Classification of Crithidia Luciliae Fluorescent Images

  • Paolo Soda
  • Leonardo Onofri
  • Amelia Rigon
  • Giulio Iannello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

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.

Keywords

Systemic Lupus Erythematosus Support Vector Machine Local Binary Pattern Confusion Matrix Zernike Moment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Paolo Soda
    • 1
  • Leonardo Onofri
    • 1
  • Amelia Rigon
    • 2
  • Giulio Iannello
    • 1
  1. 1.Integrated Research Centre, Medical Informatics & Computer Science LaboratoryUniversity Campus Bio-Medico of RomeRomeItaly
  2. 2.Integrated Research Centre, ImmunologyUniversity Campus Bio-Medico of RomeRomeItaly

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