Infected Cell Identification in Thin Blood Images Based on Color Pixel Classification: Comparison and Analysis

  • Gloria Díaz
  • Fabio Gonzalez
  • Eduardo Romero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

Malaria is an infectious disease which is mainly diagnosed by visual microscopical evaluation of Giemsa-stained thin blood films using a differential analysis of color features. This paper presents the evaluation of a color segmentation technique, based on standard supervised classification algorithms. The whole approach uses a general purpose classifier, which is parameterized and adapted to the problem of separating image pixels into three different classes: parasite, blood red cells and background. Assessment included not only four different supervised classification techniques - KNN, Naive Bayes, SVM and MLP - but different color spaces -RGB, normalized RGB, HSV and YCbCr-. Results show better performance for the KNN classifiers along with an improving feature characterization in the normalized RGB color space.

Keywords

Cell detection Supervised classification Color spaces Performance comparison 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gloria Díaz
    • 1
  • Fabio Gonzalez
    • 1
  • Eduardo Romero
    • 1
  1. 1.Bioingenium Research Group, National University of Colombia, BogotáColombia

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