Literature DB >> 29741256

Optimizing morphology through blood cell image analysis.

A Merino1, L Puigví2, L Boldú1, S Alférez2, J Rodellar2.   

Abstract

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.
© 2018 John Wiley & Sons Ltd.

Entities:  

Keywords:  blood; leukemia; lymphoma; morphology

Mesh:

Year:  2018        PMID: 29741256     DOI: 10.1111/ijlh.12832

Source DB:  PubMed          Journal:  Int J Lab Hematol        ISSN: 1751-5521            Impact factor:   2.877


  5 in total

1.  Color clustering segmentation framework for image analysis of malignant lymphoid cells in peripheral blood.

Authors:  Santiago Alférez; Anna Merino; Andrea Acevedo; Laura Puigví; José Rodellar
Journal:  Med Biol Eng Comput       Date:  2019-02-07       Impact factor: 2.602

2.  A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection.

Authors:  José Rodellar; Kevin Barrera; Santiago Alférez; Laura Boldú; Javier Laguna; Angel Molina; Anna Merino
Journal:  Bioengineering (Basel)       Date:  2022-05-23

3.  Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia.

Authors:  Siba El Hussein; Pingjun Chen; L Jeffrey Medeiros; Ignacio I Wistuba; David Jaffray; Jia Wu; Joseph D Khoury
Journal:  J Pathol       Date:  2021-10-25       Impact factor: 9.883

Review 4.  Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology.

Authors:  Hanadi El Achi; Joseph D Khoury
Journal:  Cancers (Basel)       Date:  2020-03-26       Impact factor: 6.639

5.  Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study.

Authors:  Hong Jin; Xinyan Fu; Xinyi Cao; Mingxia Sun; Xiaofen Wang; Yuhong Zhong; Suwen Yang; Chao Qi; Bo Peng; Xin He; Fei He; Yongfang Jiang; Haiyan Gao; Shun Li; Zhen Huang; Qiang Li; Fengqi Fang; Jun Zhang
Journal:  J Med Syst       Date:  2020-09-07       Impact factor: 4.460

  5 in total

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