Holographic Microscopy of Phase Microscopic Objects

Tishko, T. V.; Dmitry, Tishko; Vladimir, Titar

doi:10.1142/9789814289559

Cited by 2 publications

(6 citation statements)

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“…Recent studies showed that in RBCs stored for long periods, structural and functional changes occur that affect their viability, which, in turn, increases the latent risk to patients when the blood is transfused [1][2][3][4][5]. Moreover, the types of RBCs in human blood vary with respect to RBC-related diseases [2,6]. Therefore, a robust classification method is needed to analyze RBCs for medical diagnosis and therapeutics.…”

Section: Introductionmentioning

confidence: 99%

“…Digital holographic microscopy (DHM) has been widely investigated for use in observing, measuring, and tracking biological micro-and nano-organisms because it allows for fast, noninvasive imaging of the observed specimen with a nanometric axial sensitivity. DHM overcomes the depth-of-field limitation of classical optical microscopy [5][6][7][8][9][10][11][12][13][14][15] and is a quantitative phase-imaging technique that directly and noninvasively provides the optical thickness of a cell under dynamic conditions [9][10][11]. This phase information, which is related to cell morphology and biophysical cell parameters such as dry mass, volume, and density, can be used in the classification of RBCs.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present an automated RBC classification algorithm in which the holograms of the RBCs are recorded using off-axis DHM and the corresponding phase images are reconstructed from these holograms using numerical reconstruction techniques [9-11, 21, 22]. We used the three main types of RBCs based on shape, i.e., stomatocyte, discocyte, and echinocyte, for classification training and testing [2,6]. The RBC samples were extracted from multiple human RBC phase images and a biologist determined the type of each RBC.…”

Section: Introductionmentioning

confidence: 99%

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

Moon

Javidi

2016

Biomed. Opt. Express

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We present methods that automatically select a linear or nonlinear classifier for red blood cell (RBC) classification by analyzing the equality of the covariance matrices in Gabor-filtered holographic images. First, the phase images of the RBCs are numerically reconstructed from their holograms, which are recorded using off-axis digital holographic microscopy (DHM). Second, each RBC is segmented using a marker-controlled watershed transform algorithm and the inner part of the RBC is identified and analyzed. Third, the Gabor wavelet transform is applied to the segmented cells to extract a series of features, which then undergo a multivariate statistical test to evaluate the equality of the covariance matrices of the different shapes of the RBCs using selected features. When these covariance matrices are not equal, a nonlinear classification scheme based on quadratic functions is applied; otherwise, a linear classification is applied. We used the stomatocyte, discocyte, and echinocyte RBC for classifier training and testing. Simulation results demonstrated that 10 of the 14 RBC features are useful in RBC classification. Experimental results also revealed that the covariance matrices of the three main RBC groups are not equal and that a nonlinear classification method has a much lower misclassification rate. The proposed automated RBC classification method has the potential for use in drug testing and the diagnosis of RBC-related diseases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Moon

Javidi

2016

Biomed. Opt. Express

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“…In addition, counting cell types in the blood is an important task for evaluating clinical status. In this paper, three main types of RBC shapes, stomatocyte, discocyte, and echinocyte, [20][21][22][23] are defined and used for the quantitative determination of the percentage of morphologically normal cell shapes in multiple human RBCs. Without a doubt, the RBC with the discocyte shape accounts for the majority of RBCs in a normal human, and the percentage of other RBC types varies between healthy and nonhealthy individuals.…”

Section: Introductionmentioning

confidence: 99%

“…Scientists can also predict that, for a variety of RBC-related diseases, the percentages of different types of RBCs will be distinct. 23 Furthermore, under the effects of specific drugs or agents, the structure and function of RBCs can be modified. Thus, it will be helpful to measure the percentage of typical normal shapes of RBCs in a reconstructed RBC phase image that consists of multiple RBCs for disease diagnosis and drug testing.…”

Section: Introductionmentioning

confidence: 99%

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

Moon

Lee

2015

J. Biomed. Opt

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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.

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Holographic Microscopy of Phase Microscopic Objects

Cited by 2 publications

References 0 publications

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

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

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

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