Image processing and machine learning in the morphological analysis of blood cells

J. Rodellar, S. Alférez, A. Acevedo, A. Molina, A. Merino

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Introduction: This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. Methods: The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. Results: There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. Conclusion: Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies.

Original languageEnglish (US)
Pages (from-to)46-53
Number of pages8
JournalInternational Journal of Laboratory Hematology
Volume40
DOIs
StatePublished - May 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Hematology
  • Clinical Biochemistry
  • Biochemistry, medical

Cite this