Leukemia identification from bone marrow cells images using a machine vision and data mining strategy

  • Authors:

  • Jesus A. Gonzalez;Ivan Olmos;Leopoldo Altamirano;Blanca A. Morales;Carolina Reta;Martha C. Galindo;Jose E. Alonso;Ruben Lobato

  • Affiliations:

  • (Correspd. Tel.: +52 222 266 3100, ext, 8303/ Fax: +52 222 266 3152/ E-mail: jagonzalez@inaoep.mx) Department of Computer Science, National Institute of Astrophysics, Optics and Electronics, Puebl ...;Department of Computer Science, Autonomous University of Puebla, Puebla, Mexico;Department of Computer Science, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico;Department of Computer Science, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico;Department of Computer Science, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico;Department of Computer Science, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico;Department of Hematology, Mexican Social Security Institute, Puebla, Mexico;Department of Hematology, Mexican Social Security Institute, Puebla, Mexico

  • Venue:
  • Intelligent Data Analysis
  • Year:
  • 2011

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Abstract

The morphological analysis of medical images to support medical diagnosis is an important research area. This is the case of leukemia identification from bone marrow smears in which cells morphology is studied in order to classify the disease into its main family and subtype, so that a proper treatment can be indicated to the patient. In this paper we present a method to identify leukemia from bone marrow cells images using a combined machine vision and data mining strategy. Our process starts with a segmentation method to obtain leukemia cells and extract from them descriptive characteristics (geometrical, texture, statistical) and eigenvalues. We use these attributes to feed machine learning algorithms that learn to classify acute leukemia families and subtypes according to the FAB system. We show how the combination of descriptive features and eigenvalues helps to improve classification accuracy. Our method achieved accuracy above 95.5% to distinguish between the acute myeloblastic and lymphoblastic leukemia families and accuracy of 90% (and above) among five leukemia subtypes (after the acute leukemia families classification).