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Leukocytes Segmentation Using Markov Random Fields

  • C. Reta
  • J. A. Gonzalez
  • R. Diaz
  • J. S. Guichard
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 696)

Abstract

The segmentation of leukocytes and their components plays an important role in the extraction of geometric, texture, and morphological characteristics used to diagnose different diseases. This paper presents a novel method to segment leukocytes and their respective nucleus and cytoplasm from microscopic bone marrow leukemia cell images. Our method uses color and texture contextual information of image pixels to extract cellular elements from images, which show heterogeneous color and texture staining and high-cell population. The CIEL  ∗  a  ∗  b  ∗  color space is used to extract color features, whereas a 2D Wold Decomposition model is applied to extract structural and stochastic texture features. The color and texture contextual information is incorporated into an unsupervised binary Markov Random Field segmentation model. Experimental results show the performance of the proposed method on both synthetic and real leukemia cell images. An average accuracy of 95% was achieved in the segmentation of real cell images by comparing those results with manually segmented cell images.

Keywords

Discrete Fourier Transform Synthetic Image Markov Random Cell Segmentation Harmonic Field 
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 Science+Business Media, LLC 2011

Authors and Affiliations

  • C. Reta
    • 1
  • J. A. Gonzalez
  • R. Diaz
  • J. S. Guichard
  1. 1.National Institute for Astrophysics, Optics, and ElectronicsPueblaMexico

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