Three-dimensional identification of stem cells by computational holographic imaging

Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessment

Manufacturing regenerative medicine requires continuous monitoring of pluripotent cell culture and quality assessment while eliminating cell destruction and contaminants. In this study, we employed a novel method to monitor the pluripotency of stem cells through image analysis, avoiding the traditionally used invasive procedures. This approach employs machine learning algorithms to analyze stem cell images to predict the expression of pluripotency markers, such as OCT4 and NANOG, without physically interacting with or harming cells. We cultured induced pluripotent stem cells under various conditions to induce different pluripotent states and imaged the cells using bright-field microscopy. Pluripotency states of induced pluripotent stem cells were assessed using invasive methods, including qPCR, immunostaining, flow cytometry, and RNA sequencing. Unsupervised and semi-supervised learning models were applied to evaluate the results and accurately predict the pluripotency of the cells using only image analysis. Our approach directly links images to invasive assessment results, making the analysis of cell labeling and annotation of cells in images by experts dispensable. This core achievement not only contributes for safer and more reliable stem cell research but also opens new avenues for real-time monitoring and quality control in regenerative medicine manufacturing. Our research fills an important gap in the field by providing a viable, noninvasive alternative to traditional invasive methods for assessing pluripotency. This innovation is expected to make a significant contribution to improving regenerative medicine manufacturing because it will enable a more detailed and feasible understanding of cellular status during the manufacturing process.

Digital in-line holographic microscopy for label-free identification and tracking of biological cells

Digital in-line holographic microscopy (DIHM) is a non-invasive, real-time, label-free technique that captures three-dimensional (3D) positional, orientational, and morphological information from digital holographic images of living biological cells. Unlike conventional microscopies, the DIHM technique enables precise measurements of dynamic behaviors exhibited by living cells within a 3D volume. This review outlines the fundamental principles and comprehensive digital image processing procedures employed in DIHM-based cell tracking methods. In addition, recent applications of DIHM technique for label-free identification and digital tracking of various motile biological cells, including human blood cells, spermatozoa, diseased cells, and unicellular microorganisms, are thoroughly examined. Leveraging artificial intelligence has significantly enhanced both the speed and accuracy of digital image processing for cell tracking and identification. The quantitative data on cell morphology and dynamics captured by DIHM can effectively elucidate the underlying mechanisms governing various microbial behaviors and contribute to the accumulation of diagnostic databases and the development of clinical treatments.

Roadmap on digital holography [Invited]

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

3D monitoring of the surface slippage effect on micro-particle sedimentation by digital holographic microscopy

In several phenomena in biology and industry, it is required to understand the comprehensive behavior of sedimenting micro-particles in fluids. Here, we use the numerical refocusing feature of digital holographic microscopy (DHM) to investigate the slippage effect on micro-particle sedimentation near a flat wall. DHM provides quantitative phase contrast and three-dimensional (3D) imaging in arbitrary time scales, which suggests it as an elegant approach to investigate various phenomena, including dynamic behavior of colloids. 3D information is obtained by post-processing of the recorded digital holograms. Through analysis of 3D trajectories and velocities of multiple sedimenting micro-particles, we show that proximity to flat walls of higher slip lengths causes faster sedimentation. The effect depends on the ratio of the particle size to (1) the slip length and (2) its distance to the wall. We corroborate our experimental findings by a theoretical model which considers both the proximity and the particle interaction to a wall of different hydrophobicity in the hydrodynamic forces.

Holography applications toward medical field: An overview

Purpose:

3D Holography is a commercially available, disruptive innovation, which can be customised as per the requirements and is supporting Industry 4.0. The purpose of this paper is to study the potential applications of 3D holography in the medical field. This paper explores the concept of holography and its significant benefits in the medical field.

Methods:

The paper is derived through the study of various research papers on Holography and its applications in the medical field. The study tries to identify the direction of research &development and see how this innovative technology can be used effectively for better treatment of patients.

Results:

Holography uses digital imaging inputs and provides an extensive visualisation of the data for training doctors, surgeons and students. Holography converts information about the body into a digital format and has the potential to inform, promote and entertain the medical students and doctors. However, it needs a large amount of space for data storage and extensive software support for analysis and skills for customising. This technology seems good to solve a variety of medical issues by storing and using patient data in developing 3D holograms, which are useful to assist successful treatment and surgery. It seems useful in providing flexible solutions in the area of medical research. Finally, the paper identifies 13 significant applications of this technology in the medical field and discusses them appropriately.

Conclusion:

The paper explores holographic applications in medical research due to its extensive capability of image processing. Holographic images are non-contact 3D images having a large field of depth. A physician can now zoom the holographic image for a better view of the medical part. This innovative technology can create advancements in the diagnosis and treatment process, which can improve medical practice. It helps in quick detection of problems in various organs like brain, heart, liver, kidney etc. By using this technology, medical practitioners can see colourful organs at multiple angles with better accuracy. It opens up an innovative way of planning, testing of procedures and diagnosis. With technological developments, compact hardware and software are now available to help medical research and related applications.

Design and development of volume phase holographic grating based digital holographic interferometer for label-free quantitative cell imaging

In this paper, a volume phase holographic optical element based digital holographic interferometer is designed and used for quantitative phase imaging of biological cells [white blood cells, red blood cells, platelets, and Staphylococcus aureus (S. aureus) bacteria cells]. The experimental results reveal that sharp images of the S. aureus bacteria cells of the order of ${\sim}{1}\;{\unicode{x00B5}{\rm m}}$∼1µm can be clearly seen. The volume phase holographic grating will remove the stray light from the system reaching toward the grating and will minimize the coherent noise (speckle noise). This will improve the sharpness in the image reconstructed from the recorded digital hologram.

Flat Wall Proximity Effect on Micro-Particle Sedimentation in Non-Newtonian Fluids

We investigate the sedimentation of colloidal micro-spheres and red blood cells (RBCs) in non-Newtonian fluid - silicone oil with different viscosities. We use digital holographic microscopy (DHM) to obtain volumetric information of the sedimenting micro-objects. Especially, the numerical refocusing feature of DHM is used to extract the depth information of multiple particles moving inside the fluid. The effects of proximity to a flat wall and the non-Newtonian behavior on the sedimenting micro-spheres and RBCs are studied by trajectory analyzing and velocimetry. We observe that for lower viscosity values the proximity effect is more pronounced. The variation rate of the particle falling velocities versus their distance to the flat wall decreases by increasing the viscosity of the fluid. For RBCs, however, the decreasing of the velocity variations have a smoother trend. The experimental results verify the theoretical prediction that, similar to Newtonian case, a correction factor in Stokes’ law suffices for describing the wall effect.

Quantification of stored red blood cell fluctuations by time-lapse holographic cell imaging

We propose methods to quantitatively calculate the fluctuation rate of red blood cells with nanometric axial and millisecond temporal sensitivity at the single-cell level by using time-lapse holographic cell imaging. For this quantitative analysis, cell membrane fluctuations (CMFs) were measured for RBCs stored at different storage times. Measurements were taken over the whole membrane for both the ring and dimple sections separately. The measurements show that healthy RBCs that maintain their discocyte shape become stiffer with storage time. The correlation analysis demonstrates a significant negative correlation between CMFs and the sphericity coefficient, which characterizes the morphological type of erythrocyte. In addition, we show the correlation results between CMFs and other morphological properties such as projected surface area, surface area, mean corpuscular volume, and mean corpuscular hemoglobin.

Noninvasive detection of macrophage activation with single-cell resolution through machine learning

We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.

Sample and substrate preparation for exploring living neurons in culture with quantitative-phase imaging

Quantitative-phase imaging (QPI) has recently emerged as a powerful new quantitative microscopy technique suitable for the noninvasive exploration of the structure and dynamics of transparent specimens, including living cells in culture. Indeed, the quantitative-phase signal (QPS), induced by transparent living cells, can be detected with a nanometric axial sensitivity, and contains a wealth of information about both cell morphology and content. However, QPS is also sensitive to various sources of experimental noise. In this chapter, we emphasize how to properly and specifically measure the cell-mediated QPS in a wet-lab environment, when measuring with a digital holographic microscope (DHM). First, we present the substrate-requisite characteristics for properly achieving such cell-mediated QPS measurements at single-cell level. Then, we describe how quantitative-phase digital holographic microscopy (QP-DHM) can be used to numerically process holograms and subsequently reshape wavefronts in association with post-processing algorithms, thereby allowing for highly stable QPS obtainable over extended periods of time. Such stable QPS is a prerequisite for exploring the dynamics of specific cellular processes. We also describe experimental procedures that make it possible to extract important biophysical cellular parameters from QPS including absolute cell volume, transmembrane water permeability, and the movements of water in and out of the cell. To illustrate how QP-DHM can reveal the dynamics of specific cellular processes, we show how the monitoring of transmembrane water movements can be used to resolve the neuronal network dynamics at single-cell level. This is possible because QPS can measure the activity of electroneutral cotransports, including NKCC1 and KCC2, during a neuronal firing mediated by glutamate, the main excitatory neurotransmitter in the brain. Finally, we added a supplemental section, with more technical details, for readers who are interested in troubleshooting live-cell QP-DHM.

Label-free 3D computational imaging of spermatozoon locomotion, head spin and flagellum beating over a large volume

We report a high-throughput and label-free computational imaging technique that simultaneously measures in three-dimensional (3D) space the locomotion and angular spin of the freely moving heads of microswimmers and the beating patterns of their flagella over a sample volume more than two orders-of-magnitude larger compared to existing optical modalities. Using this platform, we quantified the 3D locomotion of 2133 bovine sperms and determined the spin axis and the angular velocity of the sperm head, providing the perspective of an observer seated at the moving and spinning sperm head. In this constantly transforming perspective, flagellum-beating patterns are decoupled from both the 3D translation and spin of the head, which provides the opportunity to truly investigate the 3D spatio-temporal kinematics of the flagellum. In addition to providing unprecedented information on the 3D locomotion of microswimmers, this computational imaging technique could also be instrumental for micro-robotics and sensing research, enabling the high-throughput quantification of the impact of various stimuli and chemicals on the 3D swimming patterns of sperms, motile bacteria and other micro-organisms, generating new insights into taxis behaviors and the underlying biophysics.

Automated red blood cells extraction from holographic images using fully convolutional neural networks

In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm.

Holographic deep learning for rapid optical screening of anthrax spores

Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be used in realistic settings of biological warfare. We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and subgenus specificity. The unique "representation learning" capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate point-of-care diagnosis of pathogens.

Holographic deep learning for rapid optical screening of anthrax spores

Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be used in realistic settings of biological warfare. We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and subgenus specificity. The unique "representation learning" capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate point-of-care diagnosis of pathogens.

Simple and flexible phase compensation for digital holographic microscopy with electrically tunable lens

In a digital holographic microscopy (DHM) system, different microscope objectives (MOs) will introduce different phase distortions and thus lead to measurement errors. To address this problem, we present a simple and flexible method to compensate all phase distortions by introducing an electrically tunable lens (ETL) in the reference arm for a DHM system with multiple MOs. By exactly controlling the external currents of the ETL, we can change the reference wave front to match the wave front introduced by different MOs without complex alignment or additional numerical postprocessing manipulations. This method is suitable for quantitative real-time phase imaging especially when it refers to multiple MOs. To demonstrate the validity and effectiveness of our scheme, we did a series of simulations and carried out some real experiments with two different MOs (4× and 10×).

Cell morphology-based classification of red blood cells using holographic imaging informatics

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.

Label-free identification of individual bacteria using Fourier transform light scattering

Rapid identification of bacterial species is crucial in medicine and food hygiene. In order to achieve rapid and label-free identification of bacterial species at the single bacterium level, we propose and experimentally demonstrate an optical method based on Fourier transform light scattering (FTLS) measurements and statistical classification. For individual rod-shaped bacteria belonging to four bacterial species (Listeria monocytogenes, Escherichia coli, Lactobacillus casei, and Bacillus subtilis), two-dimensional angle-resolved light scattering maps are precisely measured using FTLS technique. The scattering maps are then systematically analyzed, employing statistical classification in order to extract the unique fingerprint patterns for each species, so that a new unidentified bacterium can be identified by a single light scattering measurement. The single-bacterial and label-free nature of our method suggests wide applicability for rapid point-of-care bacterial diagnosis.

Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders

Quantitative phase microscopy (QPM) has recently emerged as a new powerful quantitative imaging technique well suited to noninvasively explore a transparent specimen with a nanometric axial sensitivity. In this review, we expose the recent developments of quantitative phase-digital holographic microscopy (QP-DHM). Quantitative phase-digital holographic microscopy (QP-DHM) represents an important and efficient quantitative phase method to explore cell structure and dynamics. In a second part, the most relevant QPM applications in the field of cell biology are summarized. A particular emphasis is placed on the original biological information, which can be derived from the quantitative phase signal. In a third part, recent applications obtained, with QP-DHM in the field of cellular neuroscience, namely the possibility to optically resolve neuronal network activity and spine dynamics, are presented. Furthermore, potential applications of QPM related to psychiatry through the identification of new and original cell biomarkers that, when combined with a range of other biomarkers, could significantly contribute to the determination of high risk developmental trajectories for psychiatric disorders, are discussed.

Investigation of osteoblast cells behavior in polymeric 3D micropatterned scaffolds using digital holographic microscopy

The effect of micropatterned polymeric scaffolds on the features of the cultured cells at different time intervals after seeding was investigated by digital holographic microscopy. Both parallel and perpendicular walls, with different heights, were fabricated using two-photon lithography on photopolymers. The walls were subsequently coated with polypyrrole-based thin films using the matrix assisted pulsed laser evaporation technique. Osteoblast-like cells, MG-63 line, were cultured on these polymeric 3D micropatterned scaffolds. To analyze these scaffolds with/without cultured cells, an inverted digital holographic microscope, which provides 3D images, was used. Information about the samples' refractive indices and heights was obtained from the phase shift introduced in the optical path. Characteristics of cell adhesion, alignment, orientation, and morphology as a function of the wall heights and time from seeding were highlighted.

Entropy-based clustering of embryonic stem cells using digital holographic microscopy

Embryonic stem (ES) cells are an important factor in the development of cell-based therapeutic strategies. In this work, the use of digital holographic interferometric microscopy and statistical identification for automatic discrimination of ES cells and fibroblast (FB) cells is discussed in detail. The proposed algorithm first reduces the complex data structure to lower dimensions. Then, based on asymptotic normality, model-based clustering and linear discriminant analysis are applied to the transformed data to obtain the classification between ES and FB cells. The proposed algorithm is robust because it does not depend on parametric assumptions and can be extended to the classification of other cell image data. Experimental results are presented to demonstrate the performance of the system.

Accurate single-shot quantitative phase imaging of biological specimens with telecentric digital holographic microscopy

The advantages of using a telecentric imaging system in digital holographic microscopy (DHM) to study biological specimens are highlighted. To this end, the performances of nontelecentric DHM and telecentric DHM are evaluated from the quantitative phase imaging (QPI) point of view. The evaluated stability of the microscope allows single-shot QPI in DHM by using telecentric imaging systems. Quantitative phase maps of a section of the head of the drosophila melanogaster fly and of red blood cells are obtained via single-shot DHM with no numerical postprocessing. With these maps we show that the use of telecentric DHM provides larger field of view for a given magnification and permits more accurate QPI measurements with less number of computational operations.

Automated quantitative analysis of 3D morphology and mean corpuscular hemoglobin in human red blood cells stored in different periods

Quantitative phase (QP) images of red blood cells (RBCs), which are obtained by off-axis digital holographic microscopy, can provide quantitative information about three-dimensional (3D) morphology of human RBCs and the characteristic properties such as mean corpuscular hemoglobin (MCH) and MCH surface density (MCHSD). In this paper, we investigate modifications of the 3D morphology and MCH in RBCs induced by the period of storage time for the purpose of classification of RBCs with different periods of storage by using off-axis digital holographic microscopy. The classification of RBCs based on the duration of storage is highly relevant because a long storage of blood before transfusion may alter the functionality of RBCs and, therefore, cause complications in patients. To analyze any changes in the 3D morphology and MCH of RBCs due to storage, we use data sets from RBC samples stored for 8, 13, 16, 23, 27, 30, 34, 37, 40, 47, and 57 days, respectively. The data sets consist of more than 3,300 blood cells in eleven classes, with more than 300 blood cells per class. The classes indicate the storage period of RBCs and are listed in chronological order. Using the RBCs donated by healthy persons, the off-axis digital holographic microscopy reconstructs several quantitative phase images of RBC samples stored for eleven different periods. We employ marker-controlled watershed transform to remove the background in the RBC quantitative phase images obtained by the off-axis digital holographic microscopy. More than 300 single RBCs are extracted from the segmented quantitative phase images for each class. Such a large number of RBC samples enable us to obtain statistical distributions of the characteristic properties of RBCs after a specific period of storage. Experimental results show that the 3D morphology of the RBCs, in contrast to MCH, is essentially related to the aging of the RBCs.

Testing Equality of Cell Populations Based on Shape and Geodesic Distance

Image cytometry has emerged as a valuable in vitro screening tool and advances in automated microscopy have made it possible to readily analyze large cellular populations of image data. The purpose of this paper is to illustrate the viability of using cell shape to test equality of cell populations based on image data. Shape space theory is reviewed, from which differences between shapes can be quantified in terms of geodesic distance. Several multivariate nonparametric statistical hypothesis tests are adapted to test equality of cell populations. It is illustrated that geodesic distance can be a better feature than cell spread area and roundness in distinguishing between cell populations. Tests based on geodesic distance are able to detect natural perturbations of cells, whereas Kolmogorov-Smirnov tests based on area and roundness are not.

Numerical simulation of digital holographic microscopy through transparent samples based on pupil imaging and finite-difference time-domain methods

Digital holographic microscopy (DHM) has been used to determine the morphology and shape of transparent objects. However, the obtained shape is often inaccurate depending on the object properties and the setup of the optical imaging system. To understand these effects, we developed a new DHM model on the basis of a hybrid pupil imaging and finite-difference time-domain method. To demonstrate this model, we compared the results of an experiment with those of a simulation using borosilicate glass microspheres and a mold with a linear step structure. The simulation and experimental results showed good agreement. We also showed how the curvature and refractive index of objects affect the accuracy of thickness measurements.

Automated statistical quantification of three-dimensional morphology and mean corpuscular hemoglobin of multiple red blood cells

In this paper, we present an automated approach to quantify information about three-dimensional (3D) morphology, hemoglobin content and density of mature red blood cells (RBCs) using off-axis digital holographic microscopy (DHM) and statistical algorithms. The digital hologram of RBCs is recorded by a CCD camera using an off-axis interferometry setup and quantitative phase images of RBCs are obtained by a numerical reconstruction algorithm. In order to remove unnecessary parts and obtain clear targets in the reconstructed phase image with many RBCs, the marker-controlled watershed segmentation algorithm is applied to the phase image. Each RBC in the segmented phase image is three-dimensionally investigated. Characteristic properties such as projected cell surface, average phase, sphericity coefficient, mean corpuscular hemoglobin (MCH) and MCH surface density of each RBC is quantitatively measured. We experimentally demonstrate that joint statistical distributions of the characteristic parameters of RBCs can be obtained by our algorithm and efficiently used as a feature pattern to discriminate between RBC populations that differ in shape and hemoglobin content. Our study opens the possibility of automated RBC quantitative analysis suitable for the rapid classification of a large number of RBCs from an individual blood specimen, which is a fundamental step to develop a diagnostic approach based on DHM.

Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis

We propose to apply statistical clustering algorithms on a three-dimensional profile of red blood cells (RBCs) obtained through digital holographic microscopy (DHM). We show that two classes of RBCs stored for 14 and 38 days can be effectively classified. Two-dimensional intensity images of these cells are virtually the same. DHM allows for measurement of the RBCs' biconcave profile, resulting in a discriminative dataset. Two statistical clustering algorithms are compared. A model-based clustering approach classifies the pixels of an RBC and recognizes the RBC as either new or old based. The K-means algorithm is applied to the four-dimensional feature vector extracted from the RBC profile.

Optofluidic system for three-dimensional sensing and identification of micro-organisms with digital holographic microscopy

Optofluidic devices offer flexibility for a variety of tasks involving biological specimen. We propose a system for three-dimensional (3D) sensing and identification of biological micro-organisms. This system consists of a microfluidic device along with a digital holographic microscope and relevant statistical recognition algorithms. The microfluidic channel is used to house the micro-organisms, while the holographic microscope and a CCD camera record their digital holograms. The holograms can be computationally reconstructed in 3D using a variety of algorithms, such as the Fresnel transform. Statistical recognition algorithms are used to analyze and identify the micro-organisms from the reconstructed wavefront. Experimental results are presented. Because of computational reconstruction of wavefronts in holographic imaging, this technique offers unique advantages that allow one to image micro-organisms within a deep channel while removing the inherent microfluidic-induced aberration through interferometery.

3-D visualization and identification of biological microorganisms using partially temporal incoherent light in-line computational holographic imaging

We present a new method for three-dimensional (3-D) visualization and identification of biological microorganisms using partially temporal incoherent light in-line (PTILI) computational holographic imaging and multivariate statistical methods. For 3-D data acquisition of biological microorganisms, the band-pass filtered white light is used to illuminate a biological sample. The transversely and longitudinally diffracted pattern of the biological sample is magnified by microscope objective (MO) and is optically recorded with an image sensor array interfaced with a computer. Three-dimensional reconstruction of the biological sample from the diffraction pattern is accomplished by using computational Fresnel propagation method. Principal components analysis and nonparametric inference algorithms are applied to the 3-D complex amplitude biological sample for identification purposes. Experiments indicate that the proposed system can be useful for identifying biological microorganisms. To the best of our knowledge, this is the first report on using PTILI computational holographic microscopy for identification of biological microorganisms.