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Negative correlation between cell replication degree and stain level. We observed that Live cell imaging reveals cell replication degree by negative correlation with stain level: (a) high cell replication with low stain for the Scr samples, (b) medium cell replication activity with mediumn stain for BRCA1 samples, (c) low cell replication with high staining level for p63 samples.
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live cell imaging is a useful tool to monitor cellular activities in living systems. It is often necessary in cancer research or experimental research to quantify the dividing capabilities of cells or the cell proliferation level when investigating manipulations of the cells or their environment. Manual quantification of fluorescence microscopic im...
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... et al. [2] showed that live cell imaging reveals replication of individual replicons in eukaryotic replica- tion factories, using time-lapse microscopy. In our inves- tigation on BRCA1 [3], p63 [4] and Scr [5] in breast cancer, a negative correlation was discovered by manual observa- tion in live cell imaging between the cell replication activities and stain expression level using fluorescence microscopy ( Figure 1). That is the higher degree of blue stain appears, the less cell replication activity occurs. ...Context 2
... the first row vector is used to compute the blue stain channel information (Figure 10(b)), I B , and the second row is for the background color channel information (Figure 10(c)). Cell segmentation (section 4.3.2) is based on the extracted blue color information to identify the foreground cell (Figure 10(d)) for quantification. ...Context 3
... the first row vector is used to compute the blue stain channel information (Figure 10(b)), I B , and the second row is for the background color channel information (Figure 10(c)). Cell segmentation (section 4.3.2) is based on the extracted blue color information to identify the foreground cell (Figure 10(d)) for quantification. ...Context 4
... the first row vector is used to compute the blue stain channel information (Figure 10(b)), I B , and the second row is for the background color channel information (Figure 10(c)). Cell segmentation (section 4.3.2) is based on the extracted blue color information to identify the foreground cell (Figure 10(d)) for quantification. ...Context 5
... the image entropy E(P) is calculated using discrete histogram P as follows. Figure 10 Positive correlation between the quantitative results by the presented technique and the actual cell replication level. The distribution of the quantitative cell replication scores by the proposed technique is positively correlated to the actual cell replication level. ...Citations
... For biological images, although the dyes used are visualized as having different colors, the resulting stains actually have complex overlapping absorption spectra. In the previous studies, color deconvolution was used to achieve color separation in forensic image processing 31 and to achieve stain separation 32,33 in biological image processing. Our goal is to extract the eosinophilic structures, which are generally composed of intracellular or extracellular protein, as image features for image registration, and the color decomposition technique is utilized to extract independent haematoxylin and eosin stain contributions from individual histopathological images using orthonormal transformation of RGB. ...
Image registration of biological data is challenging as complex deformation problems are common. Possible deformation effects can be caused in individual data preparation processes, involving morphological deformations, stain variations, stain artifacts, rotation, translation, and missing tissues. The combining deformation effects tend to make existing automatic registration methods perform poor. In our experiments on serial histopathological images, the six state of the art image registration techniques, including TrakEM2, SURF + affine transformation, UnwarpJ, bUnwarpJ, CLAHE + bUnwarpJ and BrainAligner, achieve no greater than 70% averaged accuracies, while the proposed method achieves 91.49% averaged accuracy. The proposed method has also been demonstrated to be significantly better in alignment of laser scanning microscope brain images and serial ssTEM images than the benchmark automatic approaches (p < 0.001). The contribution of this study is to introduce a fully automatic, robust and fast image registration method for 2D image registration.
... To our knowledge, this macroscopic computer vision approach to rare cell fluorescence IVFC has never been studied previously. It is important note that the idea of computer vision "cell tracking" or "cell counting" is not novel (21)(22)(23)(24)(25)(26). However, previously reported methods typically identify clearly defined objects with strong background contrast, for example, of cells in culture on a microscope slide. ...
Noninvasive enumeration of rare circulating cell populations in small animals is of great importance in many areas of biomedical research. In this work, we describe a macroscopic fluorescence imaging system and automated computer vision algorithm that allows in vivo detection, enumeration and tracking of circulating fluorescently-labeled cells from multiple large blood vessels in the ear of a mouse. This imaging system uses a 660 nm laser and a high sensitivity electron-multiplied charge coupled device camera (EMCCD) to acquire fluorescence image sequences from relatively large (∼5 × 5 mm(2) ) imaging areas. The primary technical challenge was developing an automated method for identifying and tracking rare cell events in image sequences with substantial autofluorescence and noise content. To achieve this, we developed a two-step image analysis algorithm that first identified cell candidates in individual frames, and then merged cell candidates into tracks by dynamic analysis of image sequences. The second step was critical since it allowed rejection of >97% of false positive cell counts. Overall, our computer vision IVFC (CV-IVFC) approach allows single-cell detection sensitivity at estimated concentrations of 20 cells/mL of peripheral blood. In addition to simple enumeration, the technique recovers the cell's trajectory, which in the future could be used to automatically identify, for example, in vivo homing and docking events. © 2013 International Society for Advancement of Cytometry.
... For biological images, although the dyes used are visualized as having different colors, the resulting stains actually have complex overlapping absorption spectra. In the previous studies, color deconvolution was used to achieve color separation in forensic image processing (Berger et al., 2006) and to achieve stain separation (Ruifrok and Johnston, 2001;Wang, 2012) in biological image processing. Our goal is to extract the eosinophilic structures, which are generally composed of intracellular or extracellular protein, as image features for image registration, and the color decomposition technique is used to extract independent hematoxylin and eosin stain contributions from individual histopathological images using orthonormal transformation of RGB. ...
Motivation: Alignment of medical images is a vital component of a large number of applications throughout the clinical track of events; not only within clinical diagnostic settings, but prominently so in the area of planning, consummation and evaluation of surgical and radiotherapeutical procedures. However, image registration of medical images is challenging because of variations on data appearance, imaging artifacts and complex data deformation problems. Hence, the aim of this study is to develop a robust image alignment method for medical images.Results: An improved image registration method is proposed, and the method is evaluated with two types of medical data, including biological microscopic tissue images and dental X-ray images and compared with five state-of-the-art image registration techniques. The experimental results show that the presented method consistently performs well on both types of medical images, achieving 88.44 and 88.93% averaged registration accuracies for biological tissue images and X-ray images, respectively, and outperforms the benchmark methods. Based on the Tukey's honestly significant difference test and Fisher's least square difference test tests, the presented method performs significantly better than all existing methods (P ≤ 0.001) for tissue image alignment, and for the X-ray image registration, the proposed method performs significantly better than the two benchmark b-spline approaches (P < 0.001).Availability: The software implementation of the presented method and the data used in this study are made publicly available for scientific communities to use (http://www-o.ntust.edu.tw/∼cweiwang/ImprovedImageRegistration/). Contact: © 2013 The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected]
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... Another technique that uses contour detection and contour optimization combined with local gradient information and color deconvolution has been used to detect the optimal threshold values for nuclei segmentation [5]. Entropic-based thresholding methods for cell nuclei segmentation are proposed by Wang and Gudla et al. [6, 7]. A popular technique in the realm of image processing known as region growing is combined with a graph-cutsbased algorithm that incorporates Laplacian of Gaussian (LoG) filtering to detect cell nuclei [8]. ...
The segmentation and quantification of cell nuclei are two very significant tasks in the analysis of histological images. Accurate results of cell nuclei segmentation are often adapted to a variety of applications such as the detection of cancerous cell nuclei and the observation of overlapping cellular events occurring during wound healing process in the human body. In this paper, an automated entropy-based thresholding system for segmentation and quantification of cell nuclei from histologically stained images has been presented. The proposed translational computation system aims to integrate clinical insight and computational analysis by identifying and segmenting objects of interest within histological images. Objects of interest and background regions are automatically distinguished by dynamically determining 3 optimal threshold values for the 3 color components of an input image. The threshold values are determined by means of entropy computations that are based on probability distributions of the color intensities of pixels and the spatial similarity of pixel intensities within neighborhoods. The effectiveness of the proposed system was tested over 21 histologically stained images containing approximately 1800 cell nuclei, and the overall performance of the algorithm was found to be promising, with high accuracy and precision values.
Interactions between dendritic cells (DCs) and circulating tumor cells (CTCs) have attracted wide attention in tumor immunity research, especially on specifically targeted tumors. However, feature extraction and noninvasive tracking of DCs and CTCs are challenges that have long existed in biomedicine. In this study, we developed an automatic algorithm for identifying, counting, tracking, and segmenting fluorescently-labeled CTCs and DCs from the blood vessels of mouse ears. For fluorescence imaging, we constructed an in vivo image flow cytometry system to capture dual-channel (green and red) fluorescence image sequence simultaneously. To achieve real-time functions for the CTCs and DCs, we developed a motion detection method based on codebook which first performs background modeling and then cone-shaped area search procedures for postprocessing. We validated this novel algorithm through in vivo image sequencing, through which we observed the interaction of CTCs and DCs. Moreover, we used quantitative colocalization to determine the relationship between CTCs and DCs. The quantitative results illustrated the interactions between CTCs and DCs, as did image sequences which are promising for driving research on cancer immunotherapy in the future.
Phase change materials (PCMs) with high thermal conductivity and efficient solar energy conversion have recently attracted much attention. However, a facile strategy to enhance thermal conductivity and realize energy conversion and storage is still eagerly desired. In this work, a very low content of graphene nanoplatelets (GNP) is introduced into poly (ethylene glycol) (PEG)/boron nitride (BN) composite PCMs, resulting in great improvement in thermal conductivity and photoabsorption ability via a facile solution blending process. The presence of GNP contributes to enhancing thermal conductivity and realizing efficient solar energy conversion (including solar-to-thermal and solar-to-electric energy conversion) for the PEG/BN/GNP composite PCMs owing to the formation of improved BN/GNP thermally conductive network and the improvement of sunlight harvesting ability, respectively. Thermophysical properties demonstrate that compared with PEG/BN composite PCMs at the same BN content, PEG/BN/GNP composite PCMs containing a low GNP content maintain comparable energy storage density. The study sheds light on the realization of solar energy utilization and storage of the organic PCMs requiring high thermal conductivity.
In this study, granular phase change material (PCM) composites were developed by absorbing paraffin into the pores of expanded perlite particles with two grades of particle size. Because of the different particle
sizes and pore structures, the absorption mechanisms of the expanded perlite particles were found to differ. A significant amount of paraffin leakage was found when the PCM composites were applied
directly into the cement mixture. To prevent such leakage, a new method by using hydrophobic silane was investigated as surface modification for the PCM composites. The method was then compared with
nanosilica deposition method. Although both methods prevented leakage effectively, cement composites incorporating silane-modified PCM composites had lower compressive strength than those incorporating
nanosilica modified PCM composites. Thermal performances of expanded perlite/paraffin composites were compared with that of microencapsulated PCM and the results showed expanded perlite/paraffin
composites were superior under certain conditions.
Hybrid graphene aerogels (HGA) consisting of graphene oxide (GO) and graphene nanoplatelets (GNP) were prepared and introduced into polyethylene glycol (PEG) via vacuum impregnation, aiming at obtaining composite phase change materials (PCMs) with high thermal conductivity, outstanding shape-stabilization, high energy storage density, commendable thermal repeatability and the ability to light-to-heat energy storage. GO nanosheets formed a three-dimensional supporting network to keep the shape of PEG stable during phase change and GNP dispersed uniformity along the network structure of GO and thus a thermal conductive pathway was constructed. The incorporation of HGA remarkably enhanced the thermal conductivity and shape-stabilization of the composite PCMs. The PEG/HGA composite PCM with only ca. 0.45 wt% GO and ca. 1.8 wt% GNP, showed an enhanced thermal conductivity of 1.43 W/mK from 0.31 W/mK of pure PEG and an improvement of 361%, much higher than the improvement that can be achieved by solution or melt blending. Moreover, an energy conversion from light to heat was realized with the composite PCMs. Thus, this work provides a simple, green and environmentally friendly way to achieve simultaneous enhancement of the thermal conductivity, energy storage density and shape-stabilization of PCMs and realize light-to-thermal energy conversion.
Polyethylene glycol (PEG) is one of organic phase change materials with low thermal conductivity, which hindered the application of the PCM. In this paper, PEG/expanded graphite (EG) with the different mass fraction was prepared and the thermal properties were investigated first. And then, experiments of latent heat thermal energy storage (LHTES) with pure PEG and PEG/EG PCMs were conducted to investigate the temperature involutions at certain position in LHTES during the charging and discharging processes. Compared with the pure PEG, LHTES with PEG/EG can dramatically enhance the heat transfer rate with little latent heat loss.
