Diffraction imaging flow cytometry is a new biological cell research method developed recently, which can get abundant information of 3D morphology inside the cell without staining. However, the pattern of diffraction image is non-intuitive, and cannot be directly classified by the observer. On the contrary, the bright field images obtained by microscope are clear enough to be directly perceived through the senses by researchers. This paper will introduce a new flow cell imaging design incorporating the merits of these two kinds of methods that can obtain diffraction image and bright field image of the same cell simultaneously, which is based on the infinite microscopic architecture with two optical paths. The first path gets the diffraction image by defocusing the shared object lens, meanwhile, the second path gets the bright-field microscopic image by adjustable lens compensating for the defocus. In the new system diffraction and microscopic images of yeasts were captured with illumination of 532nm laser and 450nm LED respectively. Two classification models were set up for recognition of yeast-budding-state with diffraction images and microscopic images independently by using GLCM (Grayscale Co-occurrence Matrix) feature extraction method, which got the highest 97% accuracy of classification with diffraction images compared to the 94% with microscopic images.
A new method to remove metal artifacts utilizing virtual dual-energy CT image sets generated from monoenergetic CT images and dual-energy CT subtraction is presented in this work. CT images were derived from Optima CT580 (General Electric Company, Fairfield, Connecticut, USA). Optimized conversion model from CT numbers to linear attenuation coefficients (LAC) was applied to calculate an accurate LAC map at specific energy. According to mass attenuation coefficients (MAC) of base materials from the National Institute of Standards and Technology (NIST), a LAC map at another higher energy was obtained, and then a set of CT images was derived from the LAC map, which is at different but a known energy. Then, dual-energy subtraction was applied to remove metal artifacts. Results: Between the CT image sets of virtual high energy and the original, there is no significant difference in STD (standard deviation) (no more than 1.91%), while Merror (a parameter for quantification of the CT value differences between two images at the same position) varies from 58.83 to 101.6442. CNRs (Contrast-noise-ratio) in dual-energy subtracted CT images are 1.9% higher than those in the CT images processed by polar coordinate transformation. Conclusions: The Dual-energy subtraction is proved to be a better method for reduction of metal artifacts than the polar coordinate transformation scheme. Moreover, the dual-energy subtraction method is based on the reconstructed CT images obtained with a single energy CT scanner, which is more convenient for users not having access to the projection data.
Distribution of scattered image patterns hinges on morphological and optical characteristics of cells. This paper applied a numerical method to simulate scattered images of real cell morphologies, which were reconstructed from confocal image stacks dyed by fluorescent stains. Two approaches, contourlet transform (CT) and gray level co-occurrence matrix (GLCM), were then used to analyze the simulated scattered images. The results showed that features extracted using GLCM contained more information than those extracted using CT. Higher classification accuracy could be achieved with a single GLCM parameter than CT and GLCM could achieve higher accuracy with fewer parameters than CT when using multiple parameters. Meanwhile, GLCM requires less computational cost. Thus, GLCM is more suitable and efficient than CT for the analysis of cell-scattered images.
Motion blur (MB) presents a significant challenge for obtaining high-contrast image data from biological cells with a polarization diffraction imaging flow cytometry (p-DIFC) method. A new p-DIFC experimental system has been developed to evaluate the MB and its effect on image analysis using a time-delay-integration (TDI) CCD camera. Diffraction images of MCF-7 and K562 cells have been acquired with different speed-mismatch ratios and compared to characterize MB quantitatively. Frequency analysis of the diffraction images shows that the degree of MB can be quantified by bandwidth variations of the diffraction images along the motion direction. The analytical results were confirmed by the p-DIFC image data acquired at different speed-mismatch ratios and used to validate a method of numerical simulation of MB on blur-free diffraction images, which provides a useful tool to examine the blurring effect on diffraction images acquired from the same cell. These results provide insights on the dependence of diffraction image on MB and allow significant improvement on rapid biological cell assay with the p-DIFC method.
KEYWORDS: Diffraction, Polarization, Prostate, Data modeling, Confocal microscopy, 3D image processing, Image classification, 3D acquisition, Data acquisition, Light scattering
Accurate classification of malignant cells from benign ones can significantly enhance cancer diagnosis and prognosis by detection of circulating tumor cells (CTCs). We have investigated two approaches of quantitative morphology and polarization diffraction imaging on two prostate cell types to evaluate their feasibility as single-cell assay methods toward CTC detection after cell enrichment. The two cell types have been measured by a confocal imaging method to obtain their three-dimensional morphology parameters and by a polarization diffraction imaging flow cytometry (p-DIFC) method to obtain image texture parameters. The support vector machine algorithm was applied to examine the accuracy of cell classification with the morphology and diffraction image parameters. Despite larger mean values of cell and nuclear sizes of the cancerous prostate cells than the normal ones, it has been shown that the morphologic parameters cannot serve as effective classifiers. In contrast, accurate classification of the two prostate cell types can be achieved with high classification accuracies on measured data acquired separately in three measurements. These results provide strong evidence that the p-DIFC method has the potential to yield morphology-related “fingerprints” for accurate and label-free classification of the two prostate cell types.
With a diffraction imaging flow cytometer, we have acquired and analyzed the diffraction imaging data from 5 types of
cultured cells. A gray level co-occurrence matrix (GLCM) algorithm was applied to extract the interference fringe
related textures from the diffraction image data. Six GLCM parameters were chosen and imported into a support vector
machine algorithm for automated classification of about 20 cells for each of the 5 cell types. We found that the GLCM
based algorithm has the capacity for rapid processing of diffraction images and yield feature parameters for subsequent
cell classification except the T- and B-lymphocytes.
Laser marking is non-contact, non-pollution and permanence so it will be used widely. Laser marking is related
essentially with the CNC machining. It is proposed that the control method and algorithm in the CNC system can be
applied to the laser marking. The comparability of them is summarized on the base of analyzing the composition
characters of the open-architectured CNC system and the laser marking. The mode of Laser marking includes scan mode
and path one which are researched in detail in this paper. For path mode, the steps and code translation of CNC
machining are applied. This method is not only useful to settle the edge effect, but also improve the marking efficiency.
It has been very common that a pulse laser is used in derma surgery based on the theory of "Selective Photothermolysis". This method has also been accepted as the best way to treat the pigments by the medical textbook. A kind of double-pulsed laser which gets the name by two pulse output at one pumping process is developed for derma surgery lately, and this kind of laser has been proved more effective and safe than single-pulse laser. We also develop a multiple work mode YAG laser including two double-pulsed modes at 1064nm and 532nm, two single-pulsed modes at 1064nm and 532nm, and one free-running mode at 1064nm. Considering availability, security and reliability of the laser as a surgery machine, some important subsystems of the laser are optimized carefully, such as Q-switch driver, wavelength-switching system, power supply, and control system etc. At last we get a prototype laser which can run for longer than 30 minutes continuously, and output Max10 pulse per second (pps) with Max800mJ energy at 1064nm double Q-Switch mode, or Max400mJ at 532nm. Using double pulse mode of the laser we do some removal experiments of tattoos and other pigments, and obtain good effect.
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