Quantitative investigation of red blood cell three-dimensional geometric and chemical changes in the storage lesion using digital holographic microscopy

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.

Automated multi-parameter measurement of cardiomyocytes dynamics with digital holographic microscopy

Compounds tested during drug development may have adverse effects on the heart; therefore all new chemical entities have to undergo extensive preclinical assessment for cardiac liability. Conventional intensity-based imaging techniques are not robust enough to provide detailed information for cell structure and the captured images result in low-contrast, especially to cell with semi-transparent or transparent feature, which would affect the cell analysis. In this paper we show, for the first time, that digital holographic microscopy (DHM) integrated with information processing algorithms automatically provide dynamic quantitative phase profiles of beating cardiomyocytes. We experimentally demonstrate that relevant parameters of cardiomyocytes can be obtained by our automated algorithm based on DHM phase signal analysis and used to characterize the physiological state of resting cardiomyocytes. Our study opens the possibility of automated quantitative analysis of cardiomyocyte dynamics suitable for further drug safety testing and compounds selection as a new paradigm in drug toxicity screens.

Three-dimensional counting of morphologically normal human red blood cells via digital holographic microscopy

Counting morphologically normal cells in human red blood cells (RBCs) is extremely beneficial in the health care field. We propose a three-dimensional (3-D) classification method of automatically determining the morphologically normal RBCs in the phase image of multiple human RBCs that are obtained by off-axis digital holographic microscopy (DHM). The RBC holograms are first recorded by DHM, and then the phase images of multiple RBCs are reconstructed by a computational numerical algorithm. To design the classifier, the three typical RBC shapes, which are stomatocyte, discocyte, and echinocyte, are used for training and testing. Nonmain or abnormal RBC shapes different from the three normal shapes are defined as the fourth category. Ten features, including projected surface area, average phase value, mean corpuscular hemoglobin, perimeter, mean corpuscular hemoglobin surface density, circularity, mean phase of center part, sphericity coefficient, elongation, and pallor, are extracted from each RBC after segmenting the reconstructed phase images by using a watershed transform algorithm. Moreover, four additional properties, such as projected surface area, perimeter, average phase value, and elongation, are measured from the inner part of each cell, which can give significant information beyond the previous 10 features for the separation of the RBC groups; these are verified in the experiment by the statistical method of Hotelling's T-quare test. We also apply the principal component analysis algorithm to reduce the dimension number of variables and establish the Gaussian mixture densities using the projected data with the first eight principal components. Consequently, the Gaussian mixtures are used to design the discriminant functions based on Bayesian decision theory. To improve the performance of the Bayes classifier and the accuracy of estimation of its error rate, the leaving-one-out technique is applied. Experimental results show that the proposed method can yield good results for calculating the percentage of each typical normal RBC shape in a reconstructed phase image of multiple RBCs that will be favorable to the analysis of RBC-related diseases. In addition, we show that the discrimination performance for the counting of normal shapes of RBCs can be improved by using 3-D features of an RBC.

Simultaneous reconstruction of multiple depth images without off-focus points in integral imaging using a graphics processing unit

The reconstruction of multiple depth images with a ray back-propagation algorithm in three-dimensional (3D) computational integral imaging is computationally burdensome. Further, a reconstructed depth image consists of a focus and an off-focus area. Focus areas are 3D points on the surface of an object that are located at the reconstructed depth, while off-focus areas include 3D points in free-space that do not belong to any object surface in 3D space. Generally, without being removed, the presence of an off-focus area would adversely affect the high-level analysis of a 3D object, including its classification, recognition, and tracking. Here, we use a graphics processing unit (GPU) that supports parallel processing with multiple processors to simultaneously reconstruct multiple depth images using a lookup table containing the shifted values along the x and y directions for each elemental image in a given depth range. Moreover, each 3D point on a depth image can be measured by analyzing its statistical variance with its corresponding samples, which are captured by the two-dimensional (2D) elemental images. These statistical variances can be used to classify depth image pixels as either focus or off-focus points. At this stage, the measurement of focus and off-focus points in multiple depth images is also implemented in parallel on a GPU. Our proposed method is conducted based on the assumption that there is no occlusion of the 3D object during the capture stage of the integral imaging process. Experimental results have demonstrated that this method is capable of removing off-focus points in the reconstructed depth image. The results also showed that using a GPU to remove the off-focus points could greatly improve the overall computational speed compared with using a CPU.

Avalanche and bit independence characteristics of double random phase encoding in the Fourier and Fresnel domains

In this work, we evaluate the avalanche effect and bit independence properties of the double random phase encoding (DRPE) algorithm in the Fourier and Fresnel domains. Experimental results show that DRPE has excellent bit independence characteristics in both the Fourier and Fresnel domains. However, DRPE achieves better avalanche effect results in the Fresnel domain than in the Fourier domain. DRPE gives especially poor avalanche effect results in the Fourier domain when only one bit is changed in the plaintext or in the encryption key. Despite this, DRPE shows satisfactory avalanche effect results in the Fresnel domain when any other number of bits changes in the plaintext or in the encryption key. To the best of our knowledge, this is the first report on the avalanche effect and bit independence behaviors of optical encryption approaches for bit units.

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.

Extraction of target specimens from bioholographic images using interactive graph cuts

It is necessary to extract target specimens from bioholographic images for high-level analysis such as object identification, recognition, and tracking with the advent of application of digital holographic microscopy to transparent or semi-transparent biological specimens. We present an interactive graph cuts approach to segment the needed target specimens in the reconstructed bioholographic images. This method combines both regional and boundary information and is robust to extract targets with weak boundaries. Moreover, this technique can achieve globally optimal results while minimizing an energy function. We provide a convenient user interface, which can easily differentiate the foreground/background for various types of holographic images, as well as a dynamically modified coefficient, which specifies the importance of the regional and boundary information. The extracted results from our scheme have been compared with those from an advanced level-set-based segmentation method using an unbiased comparison algorithm. Experimental results show that this interactive graph cut technique can not only extract different kinds of target specimens in bioholographic images, but also yield good results when there are multiple similar objects in the holographic image or when the object boundaries are very weak.

Automated segmentation of multiple red blood cells with digital holographic microscopy

We present a method to automatically segment red blood cells (RBCs) visualized by digital holographic microscopy (DHM), which is based on the marker-controlled watershed algorithm. Quantitative phase images of RBCs can be obtained by using off-axis DHM along to provide some important information about each RBC, including size, shape, volume, hemoglobin content, etc. The most important process of segmentation based on marker-controlled watershed is to perform an accurate localization of internal and external markers. Here, we first obtain the binary image via Otsu algorithm. Then, we apply morphological operations to the binary image to get the internal markers. We then apply the distance transform algorithm combined with the watershed algorithm to generate external markers based on internal markers. Finally, combining the internal and external markers, we modify the original gradient image and apply the watershed algorithm. By appropriately identifying the internal and external markers, the problems of oversegmentation and undersegmentation are avoided. Furthermore, the internal and external parts of the RBCs phase image can also be segmented by using the marker-controlled watershed combined with our method, which can identify the internal and external markers appropriately. Our experimental results show that the proposed method achieves good performance in terms of segmenting RBCs and could thus be helpful when combined with an automated classification of RBCs.

Automatic calculation of tree diameter from stereoscopic image pairs using digital image processing

Automatic operations play an important role in societies by saving time and improving efficiency. In this paper, we apply the digital image processing method to the field of lumbering to automatically calculate tree diameters in order to reduce culler work and enable a third party to verify tree diameters. To calculate the cross-sectional diameter of a tree, the image was first segmented by the marker-controlled watershed transform algorithm based on the hue saturation intensity (HSI) color model. Then, the tree diameter was obtained by measuring the area of every isolated region in the segmented image. Finally, the true diameter was calculated by multiplying the diameter computed in the image and the scale, which was derived from the baseline and disparity of correspondence points from stereoscopic image pairs captured by rectified configuration cameras.

Photon-counting three-dimensional integral imaging with compression of elemental images

In this paper, we present lossless compression of elemental images in photon-counting integral imaging. In order to verify the performance of the compression method applied to low light level three-dimensional (3D) integral imaging, we compute the correlation coefficient and peak to mean square error (PSNR) as metrics for 3D scene reconstruction integrity. We show quantitatively via experiments that a considerable compression of the elemental images in photon-counting integral imaging may be achievable without significant loss in the performance in terms of correlation and PSNR metrics. To the best of our knowledge, this is the first report on applying lossless compression algorithms in photon-counting 3D computational integral imaging.

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.

Pharmacogenetics of the mycophenolic acid targets inosine monophosphate dehydrogenases IMPDH1 and IMPDH2: gene sequence variation and functional genomics

BACKGROUND AND PURPOSE

Inosine monophosphate dehydrogenases, encoded by IMPDH1 and IMPDH2, are targets for the important immunosuppressive drug, mycophenolic acid (MPA). Variation in MPA response may result, in part, from genetic variation in IMPDH1 and IMPDH2.

EXPERIMENTAL APPROACH

We resequenced IMPDH1 and IMPDH2 using DNA from 288 individuals from three ethnic groups and performed functional genomic studies of the sequence variants observed.

KEY RESULTS

We identified 73 single nucleotide polymorphisms (SNPs) in IMPDH1, 59 novel, and 25 SNPs, 24 novel, in IMPDH2. One novel IMPDH1 allozyme (Leu275) had 10.2% of the wild-type activity as a result of accelerated protein degradation. Decreased activity of the previously reported IMPDH2 Phe263 allozyme was primarily due to decreased protein quantity, also with accelerated degradation. These observations with regard to the functional implications of variant allozymes were supported by the IMPDH1 and IMPDH2 X-ray crystal structures. A novel IMPDH2 intron 1 SNP, G > C IVS1(93), was associated with decreased mRNA quantity, possibly because of altered transcription.

CONCLUSIONS AND IMPLICATIONS

These results provide insight into the nature and extent of sequence variation in the IMPDH1 and IMPDH2 genes. They also describe the influence of gene sequence variation that alters the encoded amino acids on IMPDH function and provide a foundation for future translational studies designed to correlate sequence variation in these genes with outcomes in patients treated with MPA.

Automated three-dimensional microbial sensing and recognition using digital holography and statistical sampling

We overview an approach to providing automated three-dimensional (3D) sensing and recognition of biological micro/nanoorganisms integrating Gabor digital holographic microscopy and statistical sampling methods. For 3D data acquisition of biological specimens, a coherent beam propagates through the specimen and its transversely and longitudinally magnified diffraction pattern observed by the microscope objective is optically recorded with an image sensor array interfaced with a computer. 3D visualization of the biological specimen from the magnified diffraction pattern is accomplished by using the computational Fresnel propagation algorithm. For 3D recognition of the biological specimen, a watershed image segmentation algorithm is applied to automatically remove the unnecessary background parts in the reconstructed holographic image. Statistical estimation and inference algorithms are developed to the automatically segmented holographic image. Overviews of preliminary experimental results illustrate how the holographic image reconstructed from the Gabor digital hologram of biological specimen contains important information for microbial recognition.

Three dimensional imaging and recognition using truncated photon counting model and parametric maximum likelihood estimator

In this paper, a statistical approach is presented for three-dimensional (3D) visualization and recognition of objects having very small number of photons based on a parametric estimator. A truncated Poisson probability density function is assumed for modeling the distribution of small number of photons count observation. For 3D visualization and recognition of photon-limited objects, an integral imaging system is employed. We utilize virtual geometrical ray propagation for 3D reconstruction of objects. A maximum likelihood estimator (MLE) and statistical inference algorithms are applied to small number of photons counted elemental images captured with integral imaging. We have demonstrated that the MLE using a truncated Poisson model for estimating the average number of photon for each voxel of a photon starved 3D object has a small estimation error compared with the MLE using a Poisson model. Also, we present experiments to investigate the effect of 3D sensing parallax on the recognition performance under a fixed mean number of photons.

Three-dimensional speckle-noise reduction by using coherent integral imaging

We present a 3D imaging method to reduce speckle noise that exists in coherent imaging systems. This approach is based on integral imaging (II). The elemental images set having speckle-noise patterns of a 3D object is obtained by II technique under coherent illumination. The computational geometrical ray-propagation algorithm is applied to the elemental images in order to reconstruct the original 3D object. A uniform probability-density function is assumed for modeling the phase distribution of the speckle patterns. The statistical point estimator is used for 3D speckle removal. Speckle index is calculated to compare the computational reconstruction using the proposed method with that of conventional coherent image degraded by speckle patterns for 3D object reconstruction and by object recognition. Experimental results are presented. The speckle index, mean square error, and signal-to-noise ratio are used as performance metrics and are shown to have been significantly improved by the proposed method to reduce speckle noise in the 3D object reconstruction. 3D reconstruction experiments of objects with reduced speckle noise are presented. To the best of our knowledge, this is the first report on 3D speckle removal using II and statistical estimation algorithms.

Three-dimensional recognition of photon-starved events using computational integral imaging and statistical sampling

We present a statistical approach to recognize three-dimensional (3D) objects with a small number of photons captured by using integral imaging (II). For 3D recognition of the events, the photon-limited elemental image set of a 3D object is obtained using the II technique. A computational geometrical ray propagation algorithm and the parametric maximum likelihood estimator are applied to the photon-limited elemental image set to reconstruct the irradiance of the original 3D scene voxels. The sampling distributions for the statistical parameters of the reconstructed image are determined. Finally, hypothesis testing for the equality of the statistical parameters between reference and input data sets is performed for statistical classification of populations on the basis of sampling distribution information. It is shown that large data sets of photon-limited 3D images can be converted into sampling distributions with their own statistical parameters, resulting in a substantial data dimensionality reduction for processing.

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.

Three-dimensional visualization of objects in scattering medium by use of computational integral imaging

In this paper, we propose a method to three-dimensionally visualize objects in a scattering medium using integral imaging. Our approach is based on a particular use of the interference phenomenon between the ballistic photons getting through the scattering medium and the scattered photons being scattered by the medium. For three-dimensional (3D) sensing of the scattered objects, the synthetic aperture integral imaging system under coherent illumination records the scattered elemental images of the objects. Then, the computational geometrical ray propagation algorithm is applied to the scattered elemental images in order to eliminate the interference patterns between scattered and object beams. The original 3D information of the scattered objects is recovered by multiple imaging channels, each with a unique perspective of the object. We present both simulation and experimental results with virtual and real objects to demonstrate the proposed concepts.

Three-dimensional identification of stem cells by computational holographic imaging

We present an optical imaging system and mathematical algorithms for three-dimensional sensing and identification of stem cells. Data acquisition of stem cells is based on holographic microscopy in the Fresnel domain by illuminating the cells with a laser. In this technique, the holograms of stem cells are optically recorded with an image sensor array interfaced with a computer and three-dimensional images of the stem cells are reconstructed from the Gabor-filtered digital holograms. The Gabor wavelet transformation for feature extraction of the digital hologram is performed to improve the process of identification. The inverse Fresnel transformation of the Gabor-filtered digital hologram is performed to reconstruct the multi-scale three-dimensional images of the stem cells at different depths along the longitudinal direction. For recognition and classification of stem cells, a statistical approach using an empirical cumulative density function is introduced. The experiments indicate that the proposed system can be potentially useful for recognizing and classifying stem cells. To the best of our knowledge, this is the first report on using three-dimensional holographic microscopy for automated identification of stem cells.

Three-dimensional identification of biological microorganism using integral imaging

In this paper, we address the identification of biological microorganisms using microscopic integral imaging (II). II senses multi-view directional information of 3D objects illuminated by incoherent light. A micro-lenslet array generates a set of elemental images by projecting a 3D scene onto a detector array. In computational reconstruction of II, 3D volumetric scenes are numerically reconstructed by means of a geometrical ray projection method. The identification of the biological samples is performed using the 3D volume of the reconstructed object. In one approach, the multivariate statistical distribution of the reference sample is measured in 3D space and compared with an unknown input sample by means of statistical discriminant functions. The multivariate empirical cumulative density of the 3D volume image is determined for classification. On the other approach, the graph matching technique is applied to 3D volumetric images with Gabor feature extraction. The reference morphology is identified in unknown input samples using 3D grids. Experimental results are presented for the identification of sphacelaria alga and tribonema aequale alga. We present experimental results for both 3D and 2D imaging. To the best of our knowledge, this is the first report on 3D identification of microorganisms using II.