Title:
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Image processing and machine learning in the morphological analysis of blood cells
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Author:
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Rodellar Benedé, José; Alférez Baquero, Edwin Santiago; Acevedo, Andrea; Molina Borrás, Ángel; Merino, Anna
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Matemàtiques; Universitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions |
Abstract:
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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. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Enginyeria biomèdica -Blood cells -Image analysis -Machine learning -Sang -- Cèl·lules -Aprenentatge automàtic |
Rights:
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Document type:
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Article - Published version Article |
Published by:
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Wiley
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