Automatic analysis of leishmania infected microscopy images via gaussian mixture models

  • Authors:

  • Pedro A. Nogueira;Luís Filipe Teófilo

  • Affiliations:

  • Laboratória de Inteligência Artificial e Ciência de Computadores, Faculdade de Engenharia da Universidade do Porto, FEUP, Porto, Portugal;Laboratória de Inteligência Artificial e Ciência de Computadores, Faculdade de Engenharia da Universidade do Porto, FEUP, Porto, Portugal

  • Venue:
  • SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
  • Year:
  • 2012

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Abstract

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.