TY - JOUR
T1 - Optimizing morphology through blood cell image analysis
AU - Merino, A.
AU - Puigví, L.
AU - Boldú, L.
AU - Alférez, S.
AU - Rodellar, J.
N1 - Publisher Copyright:
© 2018 John Wiley & Sons Ltd
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/5
Y1 - 2018/5
N2 - Introduction: Morphological review of the peripheral blood smear is still a crucial diagnostic aid as it provides relevant information related to the diagnosis and is important for selection of additional techniques. Nevertheless, the distinctive cytological characteristics of the blood cells are subjective and influenced by the reviewer's interpretation and, because of that, translating subjective morphological examination into objective parameters is a challenge. Methods: The use of digital microscopy systems has been extended in the clinical laboratories. As automatic analyzers have some limitations for abnormal or neoplastic cell detection, it is interesting to identify quantitative features through digital image analysis for morphological characteristics of different cells. Result: Three main classes of features are used as follows: geometric, color, and texture. Geometric parameters (nucleus/cytoplasmic ratio, cellular area, nucleus perimeter, cytoplasmic profile, RBC proximity, and others) are familiar to pathologists, as they are related to the visual cell patterns. Different color spaces can be used to investigate the rich amount of information that color may offer to describe abnormal lymphoid or blast cells. Texture is related to spatial patterns of color or intensities, which can be visually detected and quantitatively represented using statistical tools. Conclusion: This study reviews current and new quantitative features, which can contribute to optimize morphology through blood cell digital image processing techniques.
AB - Introduction: Morphological review of the peripheral blood smear is still a crucial diagnostic aid as it provides relevant information related to the diagnosis and is important for selection of additional techniques. Nevertheless, the distinctive cytological characteristics of the blood cells are subjective and influenced by the reviewer's interpretation and, because of that, translating subjective morphological examination into objective parameters is a challenge. Methods: The use of digital microscopy systems has been extended in the clinical laboratories. As automatic analyzers have some limitations for abnormal or neoplastic cell detection, it is interesting to identify quantitative features through digital image analysis for morphological characteristics of different cells. Result: Three main classes of features are used as follows: geometric, color, and texture. Geometric parameters (nucleus/cytoplasmic ratio, cellular area, nucleus perimeter, cytoplasmic profile, RBC proximity, and others) are familiar to pathologists, as they are related to the visual cell patterns. Different color spaces can be used to investigate the rich amount of information that color may offer to describe abnormal lymphoid or blast cells. Texture is related to spatial patterns of color or intensities, which can be visually detected and quantitatively represented using statistical tools. Conclusion: This study reviews current and new quantitative features, which can contribute to optimize morphology through blood cell digital image processing techniques.
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U2 - 10.1111/ijlh.12832
DO - 10.1111/ijlh.12832
M3 - Review article
C2 - 29741256
AN - SCOPUS:85046714905
SN - 1751-5521
VL - 40
SP - 54
EP - 61
JO - International Journal of Laboratory Hematology
JF - International Journal of Laboratory Hematology
ER -