In off-axis digital holographic microscopy, a camera records the spatial interference intensity pattern between light scattered from the specimen and the unperturbed reference light. Digital propagation using the numerical reconstruction algorithm allows both phase-contrast and amplitude-contrast images of the sample to be retrieved. This is possible when the exact distance between the image sensor (such as CCD) plane and image plane is provided. In this paper, we give an overview of our work on a deep-learning convolutional neural network with a regression layer as the top layer to estimate the best focus distance. The experimental results obtained using microsphere beads and red blood cells show that the proposed method can accurately estimate the propagation distance from a filtered hologram. This method can significantly accelerate the numerical reconstruction time since the correct focus is provided by the CNN model with no need for digital propagation at different distances.
This paper overviews the methods to quantitatively measure the cell membrane fluctuation (CMF) rate of red blood cells (RBCs) with different storage periods with millisecond temporal sensitivity at the single-cell level by using marker-free holographic imaging techniques. We quantitatively measured fluctuations in the discocyte shape of RBCs membrane, ring and dimple in the case of storage lesion with time-lapse phase images of RBCs. Our experimental results demonstrate that normal RBCs with a discocyte shape become stiffer with storage period and there is a significant negative correlation between CMFs and the sphericity coefficient, which describes the RBCs morphology. Correlation between CMF and projected surface area is also performed.
This paper overviews the time-lapse off-axis digital holographic microscopy (DHM) integrated with information processing algorithms for automatically measuring dynamic quantitative phase profiles of beating cardiomyocytes. The off-axis DHM provides time-lapse quantitative phase images (QPI) of cardiomyocytes at 10Hz for one minute. Experimental results show that multiple dynamic parameters of beating cardiomyocytes can be analyzed by the presented automated procedures specifically dedicated to process the time-lapse DHM phase images. The presented method can be useful in quantitative analysis between normal cardiomyocytes beating profile and all other abnormal activities and these multiple beating parameters of cardiomyocytes can be used to characterize the physiological state of cardiomyocytes.
We present unsupervised clustering methods for automatic grouping of human red blood cells (RBCs) extracted from RBC quantitative phase images obtained by digital holographic microscopy into three RBC clusters with regular shapes, including biconcave, stomatocyte, and sphero-echinocyte. We select some good features related to the RBC profile and morphology, such as RBC average thickness, sphericity coefficient, and mean corpuscular volume, and clustering methods, including density-based spatial clustering applications with noise, k-medoids, and k-means, are applied to the set of morphological features. The clustering results of RBCs using a set of three-dimensional features are compared against a set of two-dimensional features. Our experimental results indicate that by utilizing the introduced set of features, two groups of biconcave RBCs and old RBCs (suffering from the sphero-echinocyte process) can be perfectly clustered. In addition, by increasing the number of clusters, the three RBC types can be effectively clustered in an automated unsupervised manner with high accuracy. The performance evaluation of the clustering techniques reveals that they can assist hematologists in further diagnosis.
KEYWORDS: Digital holography, Holography, Microscopy, Blood, Phase measurement, 3D image processing, Stereoscopy, 3D displays, Internet, Principal component analysis, 3D metrology, Reconstruction algorithms, Tissues, Microscopes
Digital holographic microscopy can provide quantitative phase images (QPIs) of 3D profile of red blood cell (RBC) with nanometer accuracy. In this paper we propose applying k-means clustering method to cluster RBCs into two groups of young and old RBCs by using a four-dimensional feature vector. The features are RBC thickness average, surface area-volume ratio, sphericity coefficient and RBC perimeter that can be obtained from QPIs. The proposed features are related to the morphology of RBC. The experimental result shows that by utilizing the proposed method two groups of sphero-echinocytes (old RBCs) and non-spheroechinocytes RBCs can be perfectly clustered.
The classification of erythrocytes plays an important role in the field of hematological diagnosis, specifically blood disorders. Since the biconcave shape of red blood cell (RBC) is altered during the different stages of hematological disorders, we believe that the three-dimensional (3-D) morphological features of erythrocyte provide better classification results than conventional two-dimensional (2-D) features. Therefore, we introduce a set of 3-D features related to the morphological and chemical properties of RBC profile and try to evaluate the discrimination power of these features against 2-D features with a neural network classifier. The 3-D features include erythrocyte surface area, volume, average cell thickness, sphericity index, sphericity coefficient and functionality factor, MCH and MCHSD, and two newly introduced features extracted from the ring section of RBC at the single-cell level. In contrast, the 2-D features are RBC projected surface area, perimeter, radius, elongation, and projected surface area to perimeter ratio. All features are obtained from images visualized by off-axis digital holographic microscopy with a numerical reconstruction algorithm, and four categories of biconcave (doughnut shape), flat-disc, stomatocyte, and echinospherocyte RBCs are interested. Our experimental results demonstrate that the 3-D features can be more useful in RBC classification than the 2-D features. Finally, we choose the best feature set of the 2-D and 3-D features by sequential forward feature selection technique, which yields better discrimination results. We believe that the final feature set evaluated with a neural network classification strategy can improve the RBC classification accuracy.
In single exposure off-axis interferometry, multiple information can be recorded by spatial frequency multiplexing. We
investigate optimum conditions for designing 2D sampling schemes to record larger field of view in off-axis
interferometry multiplexing. The spatial resolution of the recorded image is related to the numerical aperture of the
system and sensor pixel size. The spatial resolution should preserve by avoiding crosstalk in the frequency domain.
Furthermore, the field of view depends on the sensor size and magnification of the imaging system. In order to preserve
resolution and have a larger field of view, the frequency domain should be designed correctly. The experimental results
demonstrate that selecting the wrong geometrical scheme in frequency domain decrease the recorded image area.
KEYWORDS: Blood, Digital holography, Chemical analysis, 3D image processing, Microscopy, Holography, Oxygen, 3D image reconstruction, Image segmentation, Image storage
Quantitative phase information obtained by digital holographic microscopy (DHM) can provide new insight into the functions and morphology of single red blood cells (RBCs). Since the functionality of a RBC is related to its three-dimensional (3-D) shape, quantitative 3-D geometric changes induced by storage time can help hematologists realize its optimal functionality period. We quantitatively investigate RBC 3-D geometric changes in the storage lesion using DHM. Our experimental results show that the substantial geometric transformation of the biconcave-shaped RBCs to the spherocyte occurs due to RBC storage lesion. This transformation leads to progressive loss of cell surface area, surface-to-volume ratio, and functionality of RBCs. Furthermore, our quantitative analysis shows that there are significant correlations between chemical and morphological properties of RBCs.
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