Automatic Analysis of Leishmania Infected Microscopy Images via Gaussian Mixture Models

  • Pedro A. Nogueira
  • Luís Filipe Teófilo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7589)


This work addresses the issue of automatic organic component detection and segmentation in confocal microscopy images. The proposed method performs cellular/parasitic identification through adaptive segmentation using a two-level Otsu’s Method. Segmented regions are divided using a rule-based classifier modeled on a decreasing harmonic function and a Support Vector Machine trained with features extracted from several Gaussian mixture models of the segmented regions. Results indicate the proposed method is able to count cells and parasites with accuracies above 90%, as well as perform individual cell/parasite detection in multiple nucleic regions with approximately 85% accuracy. Runtime measures indicate the proposed method is also adequate for real-time usage.


Gaussian Mixture Model Support Vector Machine Classifier Error Margin Segmented Region Fluorescence Microscopy Imaging 
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 2012

Authors and Affiliations

  • Pedro A. Nogueira
    • 1
  • Luís Filipe Teófilo
    • 1
  1. 1.Laboratória de Inteligência Artificial e Ciência de ComputadoresFaculdade de Engenharia da Universidade do Porto, FEUPPortoPortugal

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