Automatic recognition of five types of white blood cells in peripheral blood

Seyed Hamid Rezatofighi, Hamid Soltanian-Zadeh

Research output: Contribution to journalArticleResearchpeer-review

152 Citations (Scopus)


This paper proposes image processing algorithms to recognize five types of white blood cells in peripheral blood automatically. First, a method based on Gram-Schmidt orthogonalization is proposed along with a snake algorithm to segment nucleus and cytoplasm of the cells. Then, a variety of features are extracted from the segmented regions. Next, most discriminative features are selected using a Sequential Forward Selection (SFS) algorithm and performances of two classifiers, Artificial Neural Network (ANN) and Support Vector Machine (SVM), are compared. The results demonstrate that the proposed methods are accurate and sufficiently fast to be used in hematological laboratories.

Original languageEnglish
Pages (from-to)333-343
Number of pages11
JournalComputerized Medical Imaging and Graphics
Issue number4
Publication statusPublished - Jun 2011
Externally publishedYes


  • Classification
  • Feature selection
  • Gram-Schmidt orthogonalization
  • Peripheral blood
  • Segmentation
  • Texture feature
  • White blood cell

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