ArticleBiomedical optics express2016
Cell morphology-based classification of red blood cells using holographic imaging informatics.
Article in Biomedical optics express, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 84 citations in OpenAlex.
- AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms.Biomedical optics express · 2026Review
- A Hybrid Framework for Red Blood Cell Labeling Using Elliptical Fitting, Autoencoding, and Data Augmentation.Journal of imaging · 2025Article
- Diffraction-informed deep learning for molecular-specific holograms of breast cancer cells.APL bioengineering · 2025Article
- Enhanced detection of malaria infected red blood cells through phase driven classification.Scientific reports · 2025Article
- All-optical dual module platform for motility-based functional scrutiny of microencapsulated probiotic bacteria.Biomedical optics express · 2024Article
- A dataset of digital holograms of normal and thalassemic cells.Scientific data · 2024Article
- Blood cell image segmentation and classification: a systematic review.PeerJ. Computer science · 2024Article
- On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning.Scientific reports · 2023Article
- Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images.Cells · 2021Article
- Red Blood Cell Classification Based on Attention Residual Feature Pyramid Network.Frontiers in medicine · 2021Article
- Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network.Journal of biomedical optics · 2020Article
- Deep learning-based cell identification and disease diagnosis using spatio-temporal cellular dynamics in compact digital holographic microscopy.Biomedical optics express · 2020Article
- Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition.Journal of biomedical optics · 2020Article
- Deep learning-based hologram generation using a white light source.Scientific reports · 2020Article
- Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images.Computational and mathematical methods in medicine · 2020Article
- Immune cell type, cell activation, and single cell heterogeneity revealed by label-free optical methods.Scientific reports · 2019Article
- Noninvasive detection of macrophage activation with single-cell resolution through machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present methods that automatically select a linear or nonlinear classifier for red blood cell (RBC) classification by analyzing the equality of the covariance matrices in Gabor-filtered holographic images. First, the phase images of the RBCs are numerically reconstructed from their holograms, which are recorded using off-axis digital holographic microscopy (DHM). Second, each RBC is segmented using a marker-controlled watershed transform algorithm and the inner part of the RBC is identified and analyzed. Third, the Gabor wavelet transform is applied to the segmented cells to extract a series of features, which then undergo a multivariate statistical test to evaluate the equality of the covariance matrices of the different shapes of the RBCs using selected features. When these covariance matrices are not equal, a nonlinear classification scheme based on quadratic functions is applied; otherwise, a linear classification is applied. We used the stomatocyte, discocyte, and echinocyte RBC for classifier training and testing. Simulation results demonstrated that 10 of the 14 RBC features are useful in RBC classification. Experimental results also revealed that the covariance matrices of the three main RBC groups are not equal and that a nonlinear classification method has a much lower misclassification rate. The proposed automated RBC classification method has the potential for use in drug testing and the diagnosis of RBC-related diseases.
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