Abstract

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.

© 2013 Optical Society of America

1. Introduction

Digital holography [18] comes to the forefront as promising tools for three-dimensional (3D) biological cells imaging, pattern recognition and study of their dynamics [914]. RBCs play an important role in delivering oxygen from lungs to body tissues and transporting carbon dioxide from the tissues to the lungs. It has been extensively studied in bio-medical fields due to its important functionality. Recent research has showed that there are numerous biochemical, structural, inflammatory, and physiologic changes in stored red cells, referred to as red cell storage lesion, which to some extent impacts the clinical outcome in transfused patients [15, 16]. Therefore, the analysis of some quantitative cell parameters provided by digital holographic microscopy (DMH), including 3D morphology, mean corpuscular volume (MCV), and hemoglobin content (MCH) at different storage periods will be helpful to characterize red cell storage lesions.

For semitransparent or transparent biological cells, the intensity based two-dimensional (2D) microscopic system suffers from losses of a large amount of quantitative detailed information about cell structure and content. Also, it has fundamental limitations on quantifying information about 3D morphology, dry mass production, and density of the biological cells. On the other hand, DHM has shown the superiority in non-invasive imaging and recognition of biological cells [1720]. In DHM [912, 2123], the quantitative analysis of the 3D information of the biological cell can be obtained by using numerical reconstruction algorithms [21, 22]. Moreover, quantitative phase image obtained by DHM enables one to measure characteristic properties such as volume, projected surface area, mean corpuscular hemoglobin (MCH) and MCH surface density (MCHSD) of the biological cell [9]. Therefore, the DHM is a promising tool for biological applications which cover wide areas such as medical diagnostics, medical therapeutics, environmental monitoring and food safety.

In this paper, we study modifications of the 3D morphology and MCH in RBCs induced by the length of storage time for the purpose of 3D classification of RBC’s having different storage periods by using an off-axis DHM [24]. To analyze the morphological changes in RBC’s induced by the length of storage time, we use data sets from blood samples stored for 8, 13, 16, 23, 27, 30, 34, 37, 40, 47, and 57 days, respectively. The data sets are divided into eleven classes of RBCs stored in eleven different periods. The eleven classes have more than 3,300 blood cells, 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, off-axis digital holographic microscopy reconstructs several RBC quantitative phase images from each class of blood sample. In order to automatically calculate the characteristic properties such as projected surface area, averaged phase value, corpuscular volume, hemoglobin content and hemoglobin surface density of RBCs, the image segmentation method [25, 26] based on marker-controlled watershed algorithm [27] is applied to remove the unnecessary background in the RBC quantitative phase image. All the RBCs that exist in the quantitative phase image are extracted to measure the characteristic properties of RBCs. More than 300 RBCs are extracted from the segmented quantitative phase images for each class of blood sample. The sample size is large enough to allow us to obtain statistical distributions of the characteristic properties of RBCs at a specific storage time. Our main focus is to quantitatively analyze the relationship between the RBC characteristic properties and their aging. This analysis will be beneficial to the understanding of the features of RBCs with different storage periods and evaluation of any modifications in the 3D cell morphology and hemoglobin content induced by the length of storage time.

2. Automatic analysis of RBCs with different storage periods

RBC quantitative phase images are reconstructed by using an off-axis interferometry [28] and the numerical algorithm described in [21, 22]. The schematic of the off-axis digital holographic microscopy (DHM) is illustrated in Fig. 1.

 

Fig. 1 Schematic of the off-axis digital holographic microscopy.

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As shown in Fig. 1, the off-axis hologram recorded on the CCD camera is the interference pattern between the reference beam originating from a laser and the object beam diffracted by the biological sample through a microscope objective. In our configuration, the wavelength of the laser diode source is 682nm and the magnification of microscopy is 40 and the field of view is around 150μm. To analyze modifications in 3D morphology and hemoglobin content in RBCs as the storage time increases, off-axis DHM reconstructs quantitative phase images of the eleven classes of blood samples stored for 8, 13, 16, 23, 27, 30, 34, 37, 40, 47, and 57 days, respectively. Most of the contained RBCs are discocyte RBCs which have a discoid-shape. The characteristic properties used to analyze the 3D morphological changes in RBC’s induced by the length of storage time are the projected surface area, the mean phase value allowing to calculate mean corpuscular volume (MCV), hemoglobin (MCH) - the average volume and mass of hemoglobin per red blood cell in a sample of blood - and MCH surface density (MCHSD), defined as the ratio of MCH to the projected surface, three highly relevant parameters, altered in various pathological states. To obtain statistical distributions of the characteristic properties, several quantitative phase images are reconstructed from each class of blood sample.

For automated investigation of the characteristic properties of RBCs, individual RBC must be extracted from the quantitative phase image. In this paper, the segmentation method based on marker-controlled watershed algorithm [27] is applied to remove the background from the quantitative phase images. For the purpose of comparing the RBC quantitative phase images with different storage times, we set the phase value of the background of the RBC quantitative phase image to 0°. After segmentation, the characteristic properties of RBCs are computed for all RBCs extracted from the quantitative phase images.

The average phase value Φinduced by the whole RBC which is related to the dry mass [9] and the projected surface area S which may influence the functionality of RBCs are defined as follows:

Φ=1Ni=1Nφi,S=Np2,
where N is the total number of pixels within a RBC, p denotes the pixel size in the quantitative phase image, and φi is the phase value of each pixel within the RBC. RBC volume or the size of RBC is a good indicator of the functionality of RBCs. A RBC with a larger volume means a larger surface area and thus can transform more oxygen [29]. It is also beneficial to the diagnosis of polycythemia vera [30]. According to Ref [9], the volume of a single RBC, or the corpuscular volume is denoted as:
Vp2λiNφi2π(nrbcnm),
where p is the pixel size in quantitative phase image,φiis the phase value of each pixel within the RBC, and λis the wavelength of the light source. Coherently, when a population of RBCs is considered, Eq. (2) allows us to derive the mean corpuscular volume (MCV) [28]. The refractive index of RBCs, nrbc, has been measured with a dual-wavelength digital holographic microscope as described in [31]. Here, nrbc, is 1.396 with no significant difference between groups of different ages. The index of refraction of the HEPA medium, nm, is 1.3334. Another important characteristic property is the dry mass which measures the weight of the cell after dehydration. The dry mass is a reliable biomass, which is widely used to compare cells since it is free from the disturbance of water existing in living beings [9]. According to Ref [9, 32], the dry mass of a cell is related to the phase value and can be defined as follows:
DryMass(DM)=10λ2παSφds=10λ2παΦS,
whereλis the wavelength of the light source, Φis the average phase value induced by the whole cell, α is a constant known as the specific refraction increment (in m3/kg or dl/g) related mainly to the protein concentration [29]. As far as RBCs are concerned, α=αHb= 0.00196dl/g is the hemoglobin refraction increment between 663nm and 682nm [28]. When a RBC population is considered Eq. (3) provides the mean corpuscular hemoglobin (MCH) according to Ref [28, 32]. The MCH is used as an important parameter for the investigation of change in the hemoglobin content in the RBCs.

The last property used to evaluate the morphological changes in RBC induced by the length of storage time is the MCH surface density (MCHSD), which can show the hemoglobin concentration. It is defined as the ratio between MCH and projected surface area S as follows:

MCHSD=MCHS.
When all RBC’s are successfully extracted from the quantitative phase images, all the properties defined as in the above equations can be appropriately calculated and compared.

3. Experimental results and discussion

The original RBC’s were donated by healthy people and stored in a transfusion bag which were obtained from the Service Régional Vaudois de Transfusion Sanguine in Switzerland and stored at 4°C during the storage period. The erythrocyte concentrate was extracted from the blood transfusion bag and diluted in HEPA buffer (15 mM HEPES pH 7.4, 130 mM NaCl, 5.4 mM KCl, 10 mM glucose, 1 mM CaCl2, 0.5 mM MgCl2 and 1 mg/ml bovine serum albumin) at a concentration of ~0.15% Vol. 0.2 ml of the erythrocyte suspension was then introduced into the experimental chamber, consisting of two coverslips separated by spacers 1.2 mm thick. In order to allow for sedimentation of the cells on the bottom coverslip, cells were incubated for 30 min at a temperature of 37°C before mounting the chamber on the DHM stage. All experiments were conducted at room temperature.

To analyze morphological changes in RBCs induced by the length of storage time, eleven classes of blood samples stored for 8, 13, 16, 23, 27, 30, 34, 37, 40, 47, and 57 days were prepared. The off-axis DHM reconstructed several RBC quantitative phase images for each class of blood samples, where holograms of RBC preparations were acquired in an out-of-focus plane. In-focus phase images of RBCs were obtained through a numerical reconstruction of the object wavefront and subsequent digital propagation of the reconstructed field to the focus plane [21].

After obtaining the RBC quantitative phase images, they were segmented. In order to remove the unnecessary background in the quantitative phase images; the phase value of the background was set to 0°. Figures 2(a)2(f) shows six of the original RBC quantitative phase images, and Figs. 2(g)2(l) shows their corresponding segmentation results obtained by the modified marker-controlled watershed algorithm.

 

Fig. 2 Original RBC quantitative phase image and corresponding segmentation results. (a), (b), (c), (d), (e) and (f) are RBC’s with 8, 16, 30, 34, 47 and 57 days of storage, respectively, while (g), (h), (i), (j), (k) and (l) are the corresponding segmented images of (a), (b), (c), (d), (e) and (f).

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The most important part for this extraction algorithm is to appropriately identify the internal and external markers which are the key factors to reduce over and under segmentation problem. In [27], we briefly get the RBC objects with Otsu’s method [33] at first. Then, morphological dilation and erosion operations [33] are applied to image obtained in the previous step so as to separate some connected targets. Since the morphological erosion would make small targets disappeared, morphological reconstruction processes are used repeatedly to preserve RBC samples having different size. With these operations (more details can be found in [27]), the internal makers which are inside each of the RBC samples are identified. Consequently, the external markers that are contained in the background are achieved based on internal image with distance transform algorithm [33]. Finally, RBC samples can be segmented by using internal and external markers with watershed algorithm [33]. Once the image is successfully segmented, each cell in the image is extracted individually and the corresponding properties are computed automatically. Averagely, 2.5 seconds are needed to segment each image with size of 954 × 928. Then, the time spent on the property calculation is around 0.7 seconds. That is, about 3.2 seconds are necessary to process each image after the RBC phase image is reconstructed. This experiment is conducted on Matlab R2010a on a computer with a 32-bit Window 7 operating system, including a 3.30 GHz Intel® Core(TM) i5-2500 processor with 3 GB RAM and 4 cores.

After segmentation and extraction of RBCs from the quantitative phase images, the characteristic properties including the mean projected surface areaS¯, the mean average phase value Φ¯, MCV, MCH and MCHSD as well as their standard deviation were calculated for each class of blood sample with method of moments [34]. In the same way, the mean and standard deviation of the projected surface area for RBCs with different storage periods can be calculated. The other properties of RBCs including corpuscular volume (CV) hemoglobin (CH) and hemoglobin surface density (CHSD) can also be obtained by the method of moments. Table 1 shows the calculated mean and standard deviation for all of the characteristic properties of RBCs with different storage times.

Tables Icon

Table 1. Characteristic properties of RBCs with different storage days.

All of the property values are measured automatically once the RBC phase images are successfully segmented. It can be seen from Table 1 that the mean phase value and CH surface density have similar variation tendency that tend to increase as the increase of storage time. For CV and CH, they don’t change quickly as the extension of storage time and seem to fluctuate around their respective mean value. In contrast, the RBC projected area is obviously decreased as the increase of storage days. On the other hand, the standard deviations for all of these properties achieve significant value when the RBCs storage time is around 30 days (27-34 days). Around 30 days’ storage, the standard deviations of projected area, CV and CH achieve big value. As RBCs mean phase value and CH surface density, the standard deviations are inclined to increase at the time with 30 storage days. These phenomena may be explained that the RBCs are suffering from drastic variation when the stored days are around 30 days.

In order to view the trend of the modification of the 3D morphology of RBCs as a function of storage time, the mean and standard deviation are plotted against storage time.

Figure 3 presents the relationship between the mean projected surface area, mean average phase value and the varied storage time of RBC samples. It was discovered that the variation trends of RBC projected surface area and the average phase value are almost opposite as the increase of storage days.

 

Fig. 3 Relationship between the mean projected surface areas, mean average phase value and the different storage times. (a) Relationship between the mean projected surface areaS¯of RBCs and storage time. Square is S¯and bar is the standard deviation ofS.(b) Relationship between Φ¯ of RBCs [see Eq. (1)] and storage time. Square is the mean and bar is the standard deviation of Φ¯.

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Figure 3(a) shows the relationship between the mean projected surface area S¯and storage time. Roughly, the trend of S¯of RBCs decreases with increasing storage time. As shown in Fig. 3(a), it is noted that when the storage time of RBCs is less than 27 days, S¯remains quite constant. Furthermore, the decrease rate of S¯for RBCs with storage time less than 34 days is much smaller than that for those with storage time longer than 34 days. When the storage time is longer than 34 days, S¯experiences a substantial decrease. On the other hand, Fig. 3(b) shows the relationship between the mean average phase value Φ¯ [see Eq. (1)] and storage time. Φ¯and its standard deviation are obtained from 300 individual RBCs in each class of blood sample. From Fig. 3(b), it can be observed that Φ¯does not experience a significant change when the storage time is less than around 34 days. In contrast, when the storage time is longer than 34 days, Φ¯apparently increases. As can be seen from Figs. 3(a) and 3(b), both S¯and Φ¯remain constant when the storage time is less than around 34 days. However, S¯and Φ¯change in the opposite directions when the storage time of RBCs is longer than 34 days. Decrease in S¯ results in the increasing occurrence of echinocytes in the RBC preparations. RBCs that have aged significantly begin decomposition and have a serrated margin and burr appearance. The RBCs with this morphologic abnormality are called echinocytes.

Figure 4 illustrates the relationship between RBCs MCV, MCH and the different storage time of RBC samples. The results reveal that the trends for RBCs MCV and MCH with increase storage time are almost the same. In addition, both MCV and MCH values seem to swing around their respective mean value.

 

Fig. 4 Relationship between MCV, MCH of RBCs and varied storage time. (a) Relationship between MCV and storage time. Square is the mean and bar is the standard deviation of corpuscular volume. (b) Relationship between MCH of RBCs and storage time. Square represents the mean and bar represents the standard deviation of the corpuscular hemoglobin content.

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Figure 4(a) shows the relationship between the mean corpuscular volume MCV and the storage time. It is noted that the MCV fluctuates around a value of 94μm3 even if the storage time of RBCs is increased. Therefore, one conclusion that may be derived is that the MCV is not significantly affected by the storage time. In contrast, Fig. 4(b) shows the relationship between the mean corpuscular hemoglobin MCH and RBC storage time. As shown in Fig. 4(b), although the MCH fluctuates around a value of 32pg, the MCH is almost stable even if the storage time is increased. Therefore, we may conclude that the hemoglobin content within RBCs does not change as a function of storage time. Since MCV and nrbc do not vary over storage time, this is an expected result.

Figure 5 shows the relationship between the mean corpuscular hemoglobin surface density MCHSD and storage time. Even though the MCH in Fig. 4(b) shows little fluctuations over the storage period, the MCHSD tends to increase as shown in Fig. 5. This can be explained by the fact that the MCH remains constant while the mean projected surface area of RBCs tends to-decrease with increased storage time. It can also be noted that the MCHSD is almost constant when the storage time of RBCs is less than 34 days. When the storage time is longer than 34 days, the increase in MCHSD is noticeable and the 3D morphological modification of RBC is drastic.

 

Fig. 5 Relationship between MCHSD of RBCs and storage time. Square represents the mean and bar represents the standard deviation of the dry mass surface density.

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From the relationship between 3D morphological modifications of RBCs and the storage time, we find that the MCH of RBCs with different storage times remains constant while the other properties such asS¯,Φ¯and MCHSD change more substantially. When the storage time is less than 34 days, there is no change in the 3D morphology of RBCs. However, when the storage time is longer than 34 days, the morphological change of RBCs is drastic such that the functionality of RBCs can be altered. From these experimental results, it can be seen that the modifications of the 3D morphology and the MCHSD of RBCs are induced by the length of storage time and that the shelf-life of RBC’s may be about 35 days when the blood is stored at refrigerator temperatures [35, 36].

4. Conclusions

We have investigated the 3D morphology and MCH of RBCs with different storage periods. First, RBC quantitative phase images have been obtained by off-axis digital holographic microscopy and numerical reconstruction method. Then, many single RBCs have been extracted from the RBC quantitative phase images by using the segmentation method based on marker-controlled watershed algorithm. Finally, the characteristic properties of RBCs including projected surface area, average phase value, volume, hemoglobin content and surface density measured at a single cell level have been evaluated over samples of several hundred RBCs using an automated analysis of the quantitative phase images. A statistical analysis indicates that 3D morphological changes in RBCs are induced by the length of storage time, while the hemoglobin content within RBCs is not changed substantially. In addition, we conclude that 34 days of storage may be a threshold across which the morphology of RBCs starts to change substantially and hence possibly alter their functionality. Automated analysis of the relationship between the characteristic properties of RBCs and storage time may be helpful for drug tests.

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2013R1A2A2A05005687). We thank the Service Régional Vaudois de Transfusion Sanguine and the R&D unit (Lausanne, Switzerland) that provided the erythrocyte concentrates.

References and links

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2. L. Onural and P. Scott, “Digital decoding of in-line holograms,” Opt. Eng. 26(11), 1124–1132 (1987). [CrossRef]  

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5. C. Wagner, W. Osten, and S. Seebacher, “Direct shape measurement by digital wavefront reconstruction and multi-wavelength contouring,” Opt. Eng. 39(1), 79–85 (2000). [CrossRef]  

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8. M. Paturzo, F. Merola, S. Grilli, S. De Nicola, A. Finizio, and P. Ferraro, “Super-resolution in digital holography by a two-dimensional dynamic phase grating,” Opt. Express 16(21), 17107–17118 (2008). [CrossRef]   [PubMed]  

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References

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  1. J. Goodman and R. Lawrence, “Digital image formation from electronically detected holograms,” Appl. Phys. Lett. 11(3), 77–79 (1967).
    [Crossref]
  2. L. Onural and P. Scott, “Digital decoding of in-line holograms,” Opt. Eng. 26(11), 1124–1132 (1987).
    [Crossref]
  3. U. Schnars, “Direct phase determination in hologram interferometry with use of digitally recorded holograms,” J. Opt. Soc. Am. A 11(7), 2011–2015 (1994).
    [Crossref]
  4. D. Carl, B. Kemper, G. Wernicke, and G. von Bally, “Parameter-optimized digital holographic microscope for high-resolution living-cell analysis,” Appl. Opt. 43(36), 6536–6544 (2004).
    [Crossref] [PubMed]
  5. C. Wagner, W. Osten, and S. Seebacher, “Direct shape measurement by digital wavefront reconstruction and multi-wavelength contouring,” Opt. Eng. 39(1), 79–85 (2000).
    [Crossref]
  6. Y. Zhang, G. Pedrini, W. Osten, and H. J. Tiziani, “Reconstruction of in-line digital holograms from two intensity measurements,” Opt. Lett. 29(15), 1787–1789 (2004).
    [Crossref] [PubMed]
  7. Y. Frauel, T. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94(3), 636–653 (2006).
    [Crossref]
  8. M. Paturzo, F. Merola, S. Grilli, S. De Nicola, A. Finizio, and P. Ferraro, “Super-resolution in digital holography by a two-dimensional dynamic phase grating,” Opt. Express 16(21), 17107–17118 (2008).
    [Crossref] [PubMed]
  9. B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
    [Crossref] [PubMed]
  10. F. Dubois, L. Joannes, and J. C. Legros, “Improved three-dimensional imaging with a digital holography microscope with a source of partial spatial coherence,” Appl. Opt. 38(34), 7085–7094 (1999).
    [Crossref] [PubMed]
  11. W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U. S. A. 98(20), 11301–11305 (2001).
    [Crossref] [PubMed]
  12. E. Cuche, F. Bevilacqua, and C. Depeursinge, “Digital holography for quantitative phase-contrast imaging,” Opt. Lett. 24(5), 291–293 (1999).
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  13. A. Mahalanobis and F. Goudail, “Methods for automatic target recognition by use of electro-optic sensors: introduction to the feature issue,” Appl. Opt. 43(2), 207–209 (2004).
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  14. F. Sadjadi and A. Mahalanobis, “Automatic target recognition XXIII,” Proc. SPIE 8744, 358 (2013).
  15. D. J. Triulzi and M. H. Yazer, “Clinical studies of the effect of blood storage on patient outcomes,” Transfus. Apheresis Sci. 43(1), 95–106 (2010).
    [Crossref] [PubMed]
  16. J. Laurie, D. Wyncoll, and C. Harrison, “New versus old blood - the debate continues,” Crit. Care 14(2), 130 (2010).
    [Crossref] [PubMed]
  17. B. Javidi, I. Moon, S. Yeom, and E. Carapezza, “Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography,” Opt. Express 13(12), 4492–4506 (2005).
    [Crossref] [PubMed]
  18. A. Stern and B. Javidi, “Theoretical analysis of three-dimensional imaging and recognition of micro-organisms with a single-exposure on-line holographic microscope,” J. Opt. Soc. Am. A 24, 163–168 (2007).
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  19. I. Moon and B. Javidi, “Three-dimensional identification of stem cells by computational holographic imaging,” J. R. Soc. Interface 4, 305–313 (2007).
    [Crossref] [PubMed]
  20. I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
    [Crossref]
  21. E. Cuche, P. Marquet, and C. Depeursinge, “Simultaneous amplitude and quantitative phase contrast microscopy by numerical reconstruction of Fresnel off-axis holograms,” Appl. Opt. 38(34), 6994–7001 (1999).
    [Crossref] [PubMed]
  22. T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, F. Montfort, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation,” Appl. Opt. 45(5), 851–863 (2006).
    [Crossref] [PubMed]
  23. P. Ferraro, S. Grilli, D. Alfieri, S. De Nicola, A. Finizio, G. Pierattini, B. Javidi, G. Coppola, and V. Striano, “Extended focused image in microscopy by digital holography,” Opt. Express 13(18), 6738–6749 (2005).
    [Crossref] [PubMed]
  24. P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
    [Crossref] [PubMed]
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  28. B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital Holographic microscopy, confocal microscopy, and impedance volume analyzer,” Cytometry 73A(10), 895–903 (2008).
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  31. D. Boss, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Measurement of absolute cell volume, osmotic membrane water permeability, and refractive index of transmembrane water and solute flux by digital holographic microscopy,” J. Biomed. Opt. 18(3), 036007 (2013).
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2013 (3)

F. Sadjadi and A. Mahalanobis, “Automatic target recognition XXIII,” Proc. SPIE 8744, 358 (2013).

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
[Crossref] [PubMed]

D. Boss, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Measurement of absolute cell volume, osmotic membrane water permeability, and refractive index of transmembrane water and solute flux by digital holographic microscopy,” J. Biomed. Opt. 18(3), 036007 (2013).
[Crossref] [PubMed]

2010 (2)

D. J. Triulzi and M. H. Yazer, “Clinical studies of the effect of blood storage on patient outcomes,” Transfus. Apheresis Sci. 43(1), 95–106 (2010).
[Crossref] [PubMed]

J. Laurie, D. Wyncoll, and C. Harrison, “New versus old blood - the debate continues,” Crit. Care 14(2), 130 (2010).
[Crossref] [PubMed]

2009 (2)

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
[Crossref]

2008 (2)

B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital Holographic microscopy, confocal microscopy, and impedance volume analyzer,” Cytometry 73A(10), 895–903 (2008).
[Crossref] [PubMed]

M. Paturzo, F. Merola, S. Grilli, S. De Nicola, A. Finizio, and P. Ferraro, “Super-resolution in digital holography by a two-dimensional dynamic phase grating,” Opt. Express 16(21), 17107–17118 (2008).
[Crossref] [PubMed]

2007 (2)

2006 (2)

2005 (4)

2004 (3)

2003 (1)

W. B. Lockwood, R. W. Hudgens, I. O. Szymanski, R. A. Teno, and A. D. Gray, “Effects of rejuvenation and frozen storage on 42-day-old AS-3 RBCs,” Transfusion 43(11), 1527–1532 (2003).
[Crossref] [PubMed]

2001 (1)

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U. S. A. 98(20), 11301–11305 (2001).
[Crossref] [PubMed]

2000 (1)

C. Wagner, W. Osten, and S. Seebacher, “Direct shape measurement by digital wavefront reconstruction and multi-wavelength contouring,” Opt. Eng. 39(1), 79–85 (2000).
[Crossref]

1999 (4)

1996 (1)

V. Fairbanks, G. Klee, G. Wiseman, J. Hoyer, A. Tefferi, R. Petitt, and M. Silverstein, “Measurement of blood volume and red cell mass: Re-examination of 51Cr and 125I methods,” Blood Cells Foundation 22(2), 169–186 (1996).
[Crossref]

1994 (1)

1987 (1)

L. Onural and P. Scott, “Digital decoding of in-line holograms,” Opt. Eng. 26(11), 1124–1132 (1987).
[Crossref]

1967 (1)

J. Goodman and R. Lawrence, “Digital image formation from electronically detected holograms,” Appl. Phys. Lett. 11(3), 77–79 (1967).
[Crossref]

1952 (1)

R. Barer, “Interference microscopy and mass determination,” Nature 169(4296), 366–367 (1952).
[Crossref] [PubMed]

Alfieri, D.

Aspert, N.

Barbul, A.

B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital Holographic microscopy, confocal microscopy, and impedance volume analyzer,” Cytometry 73A(10), 895–903 (2008).
[Crossref] [PubMed]

Barer, R.

R. Barer, “Interference microscopy and mass determination,” Nature 169(4296), 366–367 (1952).
[Crossref] [PubMed]

Bevilacqua, F.

Boss, D.

D. Boss, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Measurement of absolute cell volume, osmotic membrane water permeability, and refractive index of transmembrane water and solute flux by digital holographic microscopy,” J. Biomed. Opt. 18(3), 036007 (2013).
[Crossref] [PubMed]

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
[Crossref] [PubMed]

Boulet, V.

C. Chesnaud, P. Réfrégier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Mach. Intell. 21(11), 1145–1157 (1999).
[Crossref]

Cano, E.

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

Carapezza, E.

Carl, D.

Charrière, F.

Chesnaud, C.

C. Chesnaud, P. Réfrégier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Mach. Intell. 21(11), 1145–1157 (1999).
[Crossref]

Colomb, T.

Coppola, G.

Cuche, E.

Daneshpanah, M.

I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
[Crossref]

De Nicola, S.

Depeursinge, C.

D. Boss, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Measurement of absolute cell volume, osmotic membrane water permeability, and refractive index of transmembrane water and solute flux by digital holographic microscopy,” J. Biomed. Opt. 18(3), 036007 (2013).
[Crossref] [PubMed]

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital Holographic microscopy, confocal microscopy, and impedance volume analyzer,” Cytometry 73A(10), 895–903 (2008).
[Crossref] [PubMed]

T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, F. Montfort, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation,” Appl. Opt. 45(5), 851–863 (2006).
[Crossref] [PubMed]

P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
[Crossref] [PubMed]

E. Cuche, P. Marquet, and C. Depeursinge, “Simultaneous amplitude and quantitative phase contrast microscopy by numerical reconstruction of Fresnel off-axis holograms,” Appl. Opt. 38(34), 6994–7001 (1999).
[Crossref] [PubMed]

E. Cuche, F. Bevilacqua, and C. Depeursinge, “Digital holography for quantitative phase-contrast imaging,” Opt. Lett. 24(5), 291–293 (1999).
[Crossref] [PubMed]

Dubois, F.

Emery, Y.

B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital Holographic microscopy, confocal microscopy, and impedance volume analyzer,” Cytometry 73A(10), 895–903 (2008).
[Crossref] [PubMed]

P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
[Crossref] [PubMed]

Fairbanks, V.

V. Fairbanks, G. Klee, G. Wiseman, J. Hoyer, A. Tefferi, R. Petitt, and M. Silverstein, “Measurement of blood volume and red cell mass: Re-examination of 51Cr and 125I methods,” Blood Cells Foundation 22(2), 169–186 (1996).
[Crossref]

Ferraro, P.

Finizio, A.

Frauel, Y.

Y. Frauel, T. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94(3), 636–653 (2006).
[Crossref]

Galland, F.

Goodman, J.

J. Goodman and R. Lawrence, “Digital image formation from electronically detected holograms,” Appl. Phys. Lett. 11(3), 77–79 (1967).
[Crossref]

Goudail, F.

Gray, A. D.

W. B. Lockwood, R. W. Hudgens, I. O. Szymanski, R. A. Teno, and A. D. Gray, “Effects of rejuvenation and frozen storage on 42-day-old AS-3 RBCs,” Transfusion 43(11), 1527–1532 (2003).
[Crossref] [PubMed]

Grilli, S.

Harrison, C.

J. Laurie, D. Wyncoll, and C. Harrison, “New versus old blood - the debate continues,” Crit. Care 14(2), 130 (2010).
[Crossref] [PubMed]

Hoyer, J.

V. Fairbanks, G. Klee, G. Wiseman, J. Hoyer, A. Tefferi, R. Petitt, and M. Silverstein, “Measurement of blood volume and red cell mass: Re-examination of 51Cr and 125I methods,” Blood Cells Foundation 22(2), 169–186 (1996).
[Crossref]

Hudgens, R. W.

W. B. Lockwood, R. W. Hudgens, I. O. Szymanski, R. A. Teno, and A. D. Gray, “Effects of rejuvenation and frozen storage on 42-day-old AS-3 RBCs,” Transfusion 43(11), 1527–1532 (2003).
[Crossref] [PubMed]

Javidi, B.

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
[Crossref] [PubMed]

I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
[Crossref]

I. Moon and B. Javidi, “Three-dimensional identification of stem cells by computational holographic imaging,” J. R. Soc. Interface 4, 305–313 (2007).
[Crossref] [PubMed]

A. Stern and B. Javidi, “Theoretical analysis of three-dimensional imaging and recognition of micro-organisms with a single-exposure on-line holographic microscope,” J. Opt. Soc. Am. A 24, 163–168 (2007).
[Crossref]

Y. Frauel, T. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94(3), 636–653 (2006).
[Crossref]

B. Javidi, I. Moon, S. Yeom, and E. Carapezza, “Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography,” Opt. Express 13(12), 4492–4506 (2005).
[Crossref] [PubMed]

P. Ferraro, S. Grilli, D. Alfieri, S. De Nicola, A. Finizio, G. Pierattini, B. Javidi, G. Coppola, and V. Striano, “Extended focused image in microscopy by digital holography,” Opt. Express 13(18), 6738–6749 (2005).
[Crossref] [PubMed]

Jericho, M. H.

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U. S. A. 98(20), 11301–11305 (2001).
[Crossref] [PubMed]

Joannes, L.

Jourdain, P.

D. Boss, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Measurement of absolute cell volume, osmotic membrane water permeability, and refractive index of transmembrane water and solute flux by digital holographic microscopy,” J. Biomed. Opt. 18(3), 036007 (2013).
[Crossref] [PubMed]

Kemper, B.

Klee, G.

V. Fairbanks, G. Klee, G. Wiseman, J. Hoyer, A. Tefferi, R. Petitt, and M. Silverstein, “Measurement of blood volume and red cell mass: Re-examination of 51Cr and 125I methods,” Blood Cells Foundation 22(2), 169–186 (1996).
[Crossref]

Korenstein, R.

B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital Holographic microscopy, confocal microscopy, and impedance volume analyzer,” Cytometry 73A(10), 895–903 (2008).
[Crossref] [PubMed]

Kreuzer, H. J.

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U. S. A. 98(20), 11301–11305 (2001).
[Crossref] [PubMed]

Kühn, J.

D. Boss, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Measurement of absolute cell volume, osmotic membrane water permeability, and refractive index of transmembrane water and solute flux by digital holographic microscopy,” J. Biomed. Opt. 18(3), 036007 (2013).
[Crossref] [PubMed]

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, F. Montfort, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation,” Appl. Opt. 45(5), 851–863 (2006).
[Crossref] [PubMed]

Laurie, J.

J. Laurie, D. Wyncoll, and C. Harrison, “New versus old blood - the debate continues,” Crit. Care 14(2), 130 (2010).
[Crossref] [PubMed]

Lawrence, R.

J. Goodman and R. Lawrence, “Digital image formation from electronically detected holograms,” Appl. Phys. Lett. 11(3), 77–79 (1967).
[Crossref]

Legros, J. C.

Lockwood, W. B.

W. B. Lockwood, R. W. Hudgens, I. O. Szymanski, R. A. Teno, and A. D. Gray, “Effects of rejuvenation and frozen storage on 42-day-old AS-3 RBCs,” Transfusion 43(11), 1527–1532 (2003).
[Crossref] [PubMed]

Magistretti, P. J.

D. Boss, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Measurement of absolute cell volume, osmotic membrane water permeability, and refractive index of transmembrane water and solute flux by digital holographic microscopy,” J. Biomed. Opt. 18(3), 036007 (2013).
[Crossref] [PubMed]

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital Holographic microscopy, confocal microscopy, and impedance volume analyzer,” Cytometry 73A(10), 895–903 (2008).
[Crossref] [PubMed]

P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
[Crossref] [PubMed]

Mahalanobis, A.

Marquet, P.

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
[Crossref] [PubMed]

D. Boss, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Measurement of absolute cell volume, osmotic membrane water permeability, and refractive index of transmembrane water and solute flux by digital holographic microscopy,” J. Biomed. Opt. 18(3), 036007 (2013).
[Crossref] [PubMed]

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital Holographic microscopy, confocal microscopy, and impedance volume analyzer,” Cytometry 73A(10), 895–903 (2008).
[Crossref] [PubMed]

T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, F. Montfort, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation,” Appl. Opt. 45(5), 851–863 (2006).
[Crossref] [PubMed]

P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
[Crossref] [PubMed]

E. Cuche, P. Marquet, and C. Depeursinge, “Simultaneous amplitude and quantitative phase contrast microscopy by numerical reconstruction of Fresnel off-axis holograms,” Appl. Opt. 38(34), 6994–7001 (1999).
[Crossref] [PubMed]

Matoba, O.

Y. Frauel, T. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94(3), 636–653 (2006).
[Crossref]

Meinertzhagen, I. A.

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U. S. A. 98(20), 11301–11305 (2001).
[Crossref] [PubMed]

Merola, F.

Montfort, F.

Moon, I.

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
[Crossref] [PubMed]

I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
[Crossref]

I. Moon and B. Javidi, “Three-dimensional identification of stem cells by computational holographic imaging,” J. R. Soc. Interface 4, 305–313 (2007).
[Crossref] [PubMed]

B. Javidi, I. Moon, S. Yeom, and E. Carapezza, “Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography,” Opt. Express 13(12), 4492–4506 (2005).
[Crossref] [PubMed]

Naughton, T.

Y. Frauel, T. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94(3), 636–653 (2006).
[Crossref]

Onural, L.

L. Onural and P. Scott, “Digital decoding of in-line holograms,” Opt. Eng. 26(11), 1124–1132 (1987).
[Crossref]

Osten, W.

Y. Zhang, G. Pedrini, W. Osten, and H. J. Tiziani, “Reconstruction of in-line digital holograms from two intensity measurements,” Opt. Lett. 29(15), 1787–1789 (2004).
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B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital Holographic microscopy, confocal microscopy, and impedance volume analyzer,” Cytometry 73A(10), 895–903 (2008).
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L. Onural and P. Scott, “Digital decoding of in-line holograms,” Opt. Eng. 26(11), 1124–1132 (1987).
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C. Wagner, W. Osten, and S. Seebacher, “Direct shape measurement by digital wavefront reconstruction and multi-wavelength contouring,” Opt. Eng. 39(1), 79–85 (2000).
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Silverstein, M.

V. Fairbanks, G. Klee, G. Wiseman, J. Hoyer, A. Tefferi, R. Petitt, and M. Silverstein, “Measurement of blood volume and red cell mass: Re-examination of 51Cr and 125I methods,” Blood Cells Foundation 22(2), 169–186 (1996).
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Simanis, V.

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
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Y. Frauel, T. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94(3), 636–653 (2006).
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Wiseman, G.

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Yeom, S.

Yi, F.

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
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Appl. Opt. (5)

Appl. Phys. Lett. (1)

J. Goodman and R. Lawrence, “Digital image formation from electronically detected holograms,” Appl. Phys. Lett. 11(3), 77–79 (1967).
[Crossref]

Blood Cells Foundation (1)

V. Fairbanks, G. Klee, G. Wiseman, J. Hoyer, A. Tefferi, R. Petitt, and M. Silverstein, “Measurement of blood volume and red cell mass: Re-examination of 51Cr and 125I methods,” Blood Cells Foundation 22(2), 169–186 (1996).
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Crit. Care (1)

J. Laurie, D. Wyncoll, and C. Harrison, “New versus old blood - the debate continues,” Crit. Care 14(2), 130 (2010).
[Crossref] [PubMed]

Cytometry (1)

B. Rappaz, A. Barbul, Y. Emery, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Comparative study of human erythrocytes by digital Holographic microscopy, confocal microscopy, and impedance volume analyzer,” Cytometry 73A(10), 895–903 (2008).
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IEEE Trans. Pattern Anal. Mach. Intell. (1)

C. Chesnaud, P. Réfrégier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Mach. Intell. 21(11), 1145–1157 (1999).
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J. Biomed. Opt. (3)

D. Boss, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Measurement of absolute cell volume, osmotic membrane water permeability, and refractive index of transmembrane water and solute flux by digital holographic microscopy,” J. Biomed. Opt. 18(3), 036007 (2013).
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B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
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J. Opt. Soc. Am. A (2)

J. R. Soc. Interface (1)

I. Moon and B. Javidi, “Three-dimensional identification of stem cells by computational holographic imaging,” J. R. Soc. Interface 4, 305–313 (2007).
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Nature (1)

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Opt. Eng. (2)

L. Onural and P. Scott, “Digital decoding of in-line holograms,” Opt. Eng. 26(11), 1124–1132 (1987).
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C. Wagner, W. Osten, and S. Seebacher, “Direct shape measurement by digital wavefront reconstruction and multi-wavelength contouring,” Opt. Eng. 39(1), 79–85 (2000).
[Crossref]

Opt. Express (3)

Opt. Lett. (4)

Proc. IEEE (2)

Y. Frauel, T. Naughton, O. Matoba, E. Tajahuerce, and B. Javidi, “Three dimensional imaging and processing using computational holographic imaging,” Proc. IEEE 94(3), 636–653 (2006).
[Crossref]

I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
[Crossref]

Proc. Natl. Acad. Sci. U. S. A. (1)

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U. S. A. 98(20), 11301–11305 (2001).
[Crossref] [PubMed]

Proc. SPIE (1)

F. Sadjadi and A. Mahalanobis, “Automatic target recognition XXIII,” Proc. SPIE 8744, 358 (2013).

Transfus. Apheresis Sci. (1)

D. J. Triulzi and M. H. Yazer, “Clinical studies of the effect of blood storage on patient outcomes,” Transfus. Apheresis Sci. 43(1), 95–106 (2010).
[Crossref] [PubMed]

Transfusion (1)

W. B. Lockwood, R. W. Hudgens, I. O. Szymanski, R. A. Teno, and A. D. Gray, “Effects of rejuvenation and frozen storage on 42-day-old AS-3 RBCs,” Transfusion 43(11), 1527–1532 (2003).
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Other (4)

“Transfusion handbook, summary information for red blood cells,” National Blood Transfusion Committee.

T. Tishko, T. Dmitry, and T. Vladimir, Holographic Microscopy of Phase Microscopic Objects Theory and Practice (World Scientific, 2011).

R. Gonzalez and R. Woods, Digital Imaging Processing (Prentice Hall, 2002).

E. Gose, R. Johnsonbaugh, and S. Jost, Pattern Recognition and Image Analysis (Prentice Hall, 1996).

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Figures (5)

Fig. 1
Fig. 1 Schematic of the off-axis digital holographic microscopy.
Fig. 2
Fig. 2 Original RBC quantitative phase image and corresponding segmentation results. (a), (b), (c), (d), (e) and (f) are RBC’s with 8, 16, 30, 34, 47 and 57 days of storage, respectively, while (g), (h), (i), (j), (k) and (l) are the corresponding segmented images of (a), (b), (c), (d), (e) and (f).
Fig. 3
Fig. 3 Relationship between the mean projected surface areas, mean average phase value and the different storage times. (a) Relationship between the mean projected surface area S ¯ of RBCs and storage time. Square is S ¯ and bar is the standard deviation of S . (b) Relationship between Φ ¯ of RBCs [see Eq. (1)] and storage time. Square is the mean and bar is the standard deviation of Φ ¯ .
Fig. 4
Fig. 4 Relationship between MCV, MCH of RBCs and varied storage time. (a) Relationship between MCV and storage time. Square is the mean and bar is the standard deviation of corpuscular volume. (b) Relationship between MCH of RBCs and storage time. Square represents the mean and bar represents the standard deviation of the corpuscular hemoglobin content.
Fig. 5
Fig. 5 Relationship between MCHSD of RBCs and storage time. Square represents the mean and bar represents the standard deviation of the dry mass surface density.

Tables (1)

Tables Icon

Table 1 Characteristic properties of RBCs with different storage days.

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

Φ= 1 N i=1 N φ i ,S=N p 2 ,
V p 2 λ i N φ i 2π( n rbc n m ) ,
DryMass(DM)= 10λ 2πα S φds = 10λ 2πα ΦS,
MCHSD= MCH S .

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