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

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

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

71 Citas (Scopus)


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.

Idioma originalInglés estadounidense
Páginas (desde-hasta)46-53
Número de páginas8
PublicaciónInternational Journal of Laboratory Hematology
EstadoPublicada - may. 2018
Publicado de forma externa

Áreas temáticas de ASJC Scopus

  • Hematología
  • Bioquímica clínica
  • Bioquímica médica


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