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

Seyed Hamid Rezatofighi, Kosar Khaksari, Hamid Soltanian-Zadeh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

22 Citations (Scopus)

Abstract

An automatic system which is capable of recognizing white blood cells can assist hematologists in the diagnosis of many diseases. In this paper, we propose a new system based on image processing techniques in order to recognize five types of white blood cells in the peripheral blood. To segment nucleus and cytoplasm, a Gram-Schmidt orthogonalization method and a snake algorithm are applied, respectively. Moreover, three kinds of features are extracted from the segmented areas and two groups of textural features extracted by Local Binary Pattern (LBP) and co-occurrence matrix are evaluated. Best features are selected using a Sequential Forward Selection (SFS) algorithm and performances of two classifiers, ANN and SVM, are compared. In this application, the best result is obtained using LBP as the textural feature and SVM as the classifier. In sum, the results demonstrate that the methods are accurate and fast enough to execute in hematological laboratories.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 7th International Conference, ICIAR 2010, Proceedings
PublisherSpringer
Pages161-172
Number of pages12
EditionPART 2
ISBN (Print)3642137741, 9783642137747
DOIs
Publication statusPublished - 2010
Externally publishedYes
EventInternational Conference on Image Analysis and Recognition 2010 - Povoa de Varzim, Portugal
Duration: 21 Jun 201023 Jun 2010
Conference number: 7th

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
NumberPART 2
Volume6112
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Image Analysis and Recognition 2010
Abbreviated titleICIAR 2010
CountryPortugal
CityPovoa de Varzim
Period21/06/1023/06/10

Keywords

  • classification
  • feature selection
  • peripheral blood
  • segmentation
  • textural feature
  • White blood cell

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