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Sample records for automated single-cell image

  1. Single-cell bacteria growth monitoring by automated DEP-facilitated image analysis.

    PubMed

    Peitz, Ingmar; van Leeuwen, Rien

    2010-11-01

    Growth monitoring is the method of choice in many assays measuring the presence or properties of pathogens, e.g. in diagnostics and food quality. Established methods, relying on culturing large numbers of bacteria, are rather time-consuming, while in healthcare time often is crucial. Several new approaches have been published, mostly aiming at assaying growth or other properties of a small number of bacteria. However, no method so far readily achieves single-cell resolution with a convenient and easy to handle setup that offers the possibility for automation and high throughput. We demonstrate these benefits in this study by employing dielectrophoretic capturing of bacteria in microfluidic electrode structures, optical detection and automated bacteria identification and counting with image analysis algorithms. For a proof-of-principle experiment we chose an antibiotic susceptibility test with Escherichia coli and polymyxin B. Growth monitoring is demonstrated on single cells and the impact of the antibiotic on the growth rate is shown. The minimum inhibitory concentration as a standard diagnostic parameter is derived from a dose-response plot. This report is the basis for further integration of image analysis code into device control. Ultimately, an automated and parallelized setup may be created, using an optical microscanner and many of the electrode structures simultaneously. Sufficient data for a sound statistical evaluation and a confirmation of the initial findings can then be generated in a single experiment. PMID:20842296

  2. Automated micropipette aspiration of single cells.

    PubMed

    Shojaei-Baghini, Ehsan; Zheng, Yi; Sun, Yu

    2013-06-01

    This paper presents a system for mechanically characterizing single cells using automated micropipette aspiration. Using vision-based control and position control, the system controls a micromanipulator, a motorized translation stage, and a custom-built pressure system to position a micropipette (4 μm opening) to approach a cell, form a seal, and aspirate the cell into the micropipette for quantifying the cell's elastic and viscoelastic parameters as well as viscosity. Image processing algorithms were developed to provide controllers with real-time visual feedback and to accurately measure cell deformation behavior on line. Experiments on both solid-like and liquid-like cells demonstrated that the system is capable of efficiently performing single-cell micropipette aspiration and has low operator skill requirements. PMID:23508635

  3. Automated Single Cell Data Decontamination Pipeline

    SciTech Connect

    Tennessen, Kristin; Pati, Amrita

    2014-03-21

    Recent technological advancements in single-cell genomics have encouraged the classification and functional assessment of microorganisms from a wide span of the biospheres phylogeny.1,2 Environmental processes of interest to the DOE, such as bioremediation and carbon cycling, can be elucidated through the genomic lens of these unculturable microbes. However, contamination can occur at various stages of the single-cell sequencing process. Contaminated data can lead to wasted time and effort on meaningless analyses, inaccurate or erroneous conclusions, and pollution of public databases. A fully automated decontamination tool is necessary to prevent these instances and increase the throughput of the single-cell sequencing process

  4. Automated cell-by-cell tissue imaging and single-cell analysis for targeted morphologies by laser ablation electrospray ionization mass spectrometry.

    PubMed

    Li, Hang; Smith, Brian K; Shrestha, Bindesh; Márk, László; Vertes, Akos

    2015-01-01

    Mass spectrometry imaging (MSI) is an emerging technology for the mapping of molecular distributions in tissues. In most of the existing studies, imaging is performed by sampling on a predefined rectangular grid that does not reflect the natural cellular pattern of the tissue. Delivering laser pulses by a sharpened optical fiber in laser ablation electrospray ionization (LAESI) mass spectrometry (MS) has enabled the direct analysis of single cells and subcellular compartments. Cell-by-cell imaging had been demonstrated using LAESI-MS, where individual cells were manually selected to serve as natural pixels for tissue imaging. Here we describe a protocol for a novel cell-by-cell LAESI imaging approach that automates cell recognition and addressing for systematic ablation of individual cells. Cell types with particular morphologies can also be selected for analysis. First, the cells are recognized as objects in a microscope image. The coordinates of their centroids are used by a stage-control program to sequentially position the cells under the optical fiber tip for laser ablation. This approach increases the image acquisition efficiency and stability, and enables the investigation of extended or selected tissue areas. In the LAESI process, the ablation events result in mass spectra that represent the metabolite levels in the ablated cells. Peak intensities of selected ions are used to represent the metabolite distributions in the tissue with single-cell resolution. PMID:25361672

  5. Automated single cell sorting and deposition in submicroliter drops

    NASA Astrophysics Data System (ADS)

    Salánki, Rita; Gerecsei, Tamás; Orgovan, Norbert; Sándor, Noémi; Péter, Beatrix; Bajtay, Zsuzsa; Erdei, Anna; Horvath, Robert; Szabó, Bálint

    2014-08-01

    Automated manipulation and sorting of single cells are challenging, when intact cells are needed for further investigations, e.g., RNA or DNA sequencing. We applied a computer controlled micropipette on a microscope admitting 80 PCR (Polymerase Chain Reaction) tubes to be filled with single cells in a cycle. Due to the Laplace pressure, fluid starts to flow out from the micropipette only above a critical pressure preventing the precise control of drop volume in the submicroliter range. We found an anomalous pressure additive to the Laplace pressure that we attribute to the evaporation of the drop. We have overcome the problem of the critical dropping pressure with sequentially operated fast fluidic valves timed with a millisecond precision. Minimum drop volume was 0.4-0.7 μl with a sorting speed of 15-20 s per cell. After picking NE-4C neuroectodermal mouse stem cells and human primary monocytes from a standard plastic Petri dish we could gently deposit single cells inside tiny drops. 94 ± 3% and 54 ± 7% of the deposited drops contained single cells for NE-4C and monocytes, respectively. 7.5 ± 4% of the drops contained multiple cells in case of monocytes. Remaining drops were empty. Number of cells deposited in a drop could be documented by imaging the Petri dish before and after sorting. We tuned the adhesion force of cells to make the manipulation successful without the application of microstructures for trapping cells on the surface. We propose that our straightforward and flexible setup opens an avenue for single cell isolation, critically needed for the rapidly growing field of single cell biology.

  6. When Phase Contrast Fails: ChainTracer and NucTracer, Two ImageJ Methods for Semi-Automated Single Cell Analysis Using Membrane or DNA Staining

    PubMed Central

    Syvertsson, Simon; Vischer, Norbert O. E.; Gao, Yongqiang; Hamoen, Leendert W.

    2016-01-01

    Within bacterial populations, genetically identical cells often behave differently. Single-cell measurement methods are required to observe this heterogeneity. Flow cytometry and fluorescence light microscopy are the primary methods to do this. However, flow cytometry requires reasonably strong fluorescence signals and is impractical when bacteria grow in cell chains. Therefore fluorescence light microscopy is often used to measure population heterogeneity in bacteria. Automatic microscopy image analysis programs typically use phase contrast images to identify cells. However, many bacteria divide by forming a cross-wall that is not detectable by phase contrast. We have developed ‘ChainTracer’, a method based on the ImageJ plugin ObjectJ. It can automatically identify individual cells stained by fluorescent membrane dyes, and measure fluorescence intensity, chain length, cell length, and cell diameter. As a complementary analysis method we developed 'NucTracer', which uses DAPI stained nucleoids as a proxy for single cells. The latter method is especially useful when dealing with crowded images. The methods were tested with Bacillus subtilis and Lactococcus lactis cells expressing a GFP-reporter. In conclusion, ChainTracer and NucTracer are useful single cell measurement methods when bacterial cells are difficult to distinguish with phase contrast. PMID:27008090

  7. Localized, macromolecular transport for thin, adherent, single cells via an automated, single cell electroporation biomanipulator.

    PubMed

    Sakaki, Kelly; Esmaeilsabzali, Hadi; Massah, Shabnam; Prefontaine, Gratien G; Dechev, Nikolai; Burke, Robert D; Park, Edward J

    2013-11-01

    Single cell electroporation (SCE), via microcapillary, is an effective method for molecular, transmembrane transport used to gain insight on cell processes with minimal preparation. Although possessing great potential, SCE is difficult to execute and the technology spans broad fields within cell biology and engineering. The technical complexities, the focus and expertise demanded during manual operation, and the lack of an automated SCE platform limit the widespread use of this technique, thus the potential of SCE has not been realized. In this study, an automated biomanipulator for SCE is presented. Our system is capable of delivering molecules into the cytoplasm of extremely thin cellular features of adherent cells. The intent of the system is to abstract the technical challenges and exploit the accuracy and repeatability of automated instrumentation, leaving only the focus of the experimental design to the operator. Each sequence of SCE including cell and SCE site localization, tip-membrane contact detection, and SCE has been automated. Positions of low-contrast cells are localized and "SCE sites" for microcapillary tip placement are determined using machine vision. In addition, new milestones within automated cell manipulation have been achieved. The system described herein has the capability of automated SCE of "thin" cell features less than 10 μm in thickness. Finally, SCE events are anticipated using visual feedback, while monitoring fluorescing dye entering the cytoplasm of a cell. The execution is demonstrated by inserting a combination of a fluorescing dye and a reporter gene into NIH/3T3 fibroblast cells. PMID:23771309

  8. Automated single cell isolation from suspension with computer vision.

    PubMed

    Ungai-Salánki, Rita; Gerecsei, Tamás; Fürjes, Péter; Orgovan, Norbert; Sándor, Noémi; Holczer, Eszter; Horvath, Robert; Szabó, Bálint

    2016-01-01

    Current robots can manipulate only surface-attached cells seriously limiting the fields of their application for single cell handling. We developed a computer vision-based robot applying a motorized microscope and micropipette to recognize and gently isolate intact individual cells for subsequent analysis, e.g., DNA/RNA sequencing in 1-2 nanoliters from a thin (~100 μm) layer of cell suspension. It can retrieve rare cells, needs minimal sample preparation, and can be applied for virtually any tissue cell type. Combination of 1 μm positioning precision, adaptive cell targeting and below 1 nl liquid handling precision resulted in an unprecedented accuracy and efficiency in robotic single cell isolation. Single cells were injected either into the wells of a miniature plate with a sorting speed of 3 cells/min or into standard PCR tubes with 2 cells/min. We could isolate labeled cells also from dense cultures containing ~1,000 times more unlabeled cells by the successive application of the sorting process. We compared the efficiency of our method to that of single cell entrapment in microwells and subsequent sorting with the automated micropipette: the recovery rate of single cells was greatly improved. PMID:26856740

  9. Single-cell printer: automated, on demand, and label free.

    PubMed

    Gross, Andre; Schöndube, Jonas; Niekrawitz, Sonja; Streule, Wolfgang; Riegger, Lutz; Zengerle, Roland; Koltay, Peter

    2013-12-01

    Within the past years, single-cell analysis has developed into a key topic in cell biology to study cellular functions that are not accessible by investigation of larger cell populations. Engineering approaches aiming to access single cells to extract information about their physiology, phenotype, and genotype at the single-cell level are going manifold ways, meanwhile allowing separation, sorting, culturing, and analysis of individual cells. Based on our earlier research toward inkjet-like printing of single cells, this article presents further characterization results obtained with a fully automated prototype instrument for printing of single living cells in a noncontact inkjet-like manner. The presented technology is based on a transparent microfluidic drop-on-demand dispenser chip coupled with a camera-assisted automatic detection system. Cells inside the chip are detected and classified with this detection system before they are expelled from the nozzle confined in microdroplets, thus enabling a "one cell per droplet" printing mode. To demonstrate the prototype instrument's suitability for biological and biomedical applications, basic experiments such as printing of single-bead and cell arrays as well as deposition and culture of single cells in microwell plates are presented. Printing efficiencies greater than 80% and viability rates about 90% were achieved. PMID:24222537

  10. Automated single cell isolation from suspension with computer vision

    PubMed Central

    Ungai-Salánki, Rita; Gerecsei, Tamás; Fürjes, Péter; Orgovan, Norbert; Sándor, Noémi; Holczer, Eszter; Horvath, Robert; Szabó, Bálint

    2016-01-01

    Current robots can manipulate only surface-attached cells seriously limiting the fields of their application for single cell handling. We developed a computer vision-based robot applying a motorized microscope and micropipette to recognize and gently isolate intact individual cells for subsequent analysis, e.g., DNA/RNA sequencing in 1–2 nanoliters from a thin (~100 μm) layer of cell suspension. It can retrieve rare cells, needs minimal sample preparation, and can be applied for virtually any tissue cell type. Combination of 1 μm positioning precision, adaptive cell targeting and below 1 nl liquid handling precision resulted in an unprecedented accuracy and efficiency in robotic single cell isolation. Single cells were injected either into the wells of a miniature plate with a sorting speed of 3 cells/min or into standard PCR tubes with 2 cells/min. We could isolate labeled cells also from dense cultures containing ~1,000 times more unlabeled cells by the successive application of the sorting process. We compared the efficiency of our method to that of single cell entrapment in microwells and subsequent sorting with the automated micropipette: the recovery rate of single cells was greatly improved. PMID:26856740

  11. 3D Image-Guided Automatic Pipette Positioning for Single Cell Experiments in vivo.

    PubMed

    Long, Brian; Li, Lu; Knoblich, Ulf; Zeng, Hongkui; Peng, Hanchuan

    2015-01-01

    We report a method to facilitate single cell, image-guided experiments including in vivo electrophysiology and electroporation. Our method combines 3D image data acquisition, visualization and on-line image analysis with precise control of physical probes such as electrophysiology microelectrodes in brain tissue in vivo. Adaptive pipette positioning provides a platform for future advances in automated, single cell in vivo experiments. PMID:26689553

  12. 3D Image-Guided Automatic Pipette Positioning for Single Cell Experiments in vivo

    PubMed Central

    Long, Brian; Li, Lu; Knoblich, Ulf; Zeng, Hongkui; Peng, Hanchuan

    2015-01-01

    We report a method to facilitate single cell, image-guided experiments including in vivo electrophysiology and electroporation. Our method combines 3D image data acquisition, visualization and on-line image analysis with precise control of physical probes such as electrophysiology microelectrodes in brain tissue in vivo. Adaptive pipette positioning provides a platform for future advances in automated, single cell in vivo experiments. PMID:26689553

  13. Automated analysis of dynamic behavior of single cells in picoliter droplets.

    PubMed

    Khorshidi, Mohammad Ali; Rajeswari, Prem Kumar Periyannan; Wählby, Carolina; Joensson, Haakan N; Andersson Svahn, Helene

    2014-03-01

    We present a droplet-based microfluidic platform to automatically track and characterize the behavior of single cells over time. This high-throughput assay allows encapsulation of single cells in micro-droplets and traps intact droplets in arrays of miniature wells on a PDMS-glass chip. Automated time-lapse fluorescence imaging and image analysis of the incubated droplets on the chip allows the determination of the viability of individual cells over time. In order to automatically track the droplets containing cells, we developed a simple method based on circular Hough transform to identify droplets in images and quantify the number of live and dead cells in each droplet. Here, we studied the viability of several hundred single isolated HEK293T cells over time and demonstrated a high survival rate of the encapsulated cells for up to 11 hours. The presented platform has a wide range of potential applications for single cell analysis, e.g. monitoring heterogeneity of drug action over time and rapidly assessing the transient behavior of single cells under various conditions and treatments in vitro. PMID:24385254

  14. Single-cell PCR of genomic DNA enabled by automated single-cell printing for cell isolation.

    PubMed

    Stumpf, F; Schoendube, J; Gross, A; Rath, C; Niekrawietz, S; Koltay, P; Roth, G

    2015-07-15

    Single-cell analysis has developed into a key topic in cell biology with future applications in personalized medicine, tumor identification as well as tumor discovery (Editorial, 2013). Here we employ inkjet-like printing to isolate individual living single human B cells (Raji cell line) and load them directly into standard PCR tubes. Single cells are optically detected in the nozzle of the microfluidic piezoelectric dispenser chip to ensure printing of droplets with single cells only. The printing process has been characterized by using microbeads (10µm diameter) resulting in a single bead delivery in 27 out of 28 cases and relative positional precision of ±350µm at a printing distance of 6mm between nozzle and tube lid. Process-integrated optical imaging enabled to identify the printing failure as void droplet and to exclude it from downstream processing. PCR of truly single-cell DNA was performed without pre-amplification directly from single Raji cells with 33% success rate (N=197) and Cq values of 36.3±2.5. Additionally single cell whole genome amplification (WGA) was employed to pre-amplify the single-cell DNA by a factor of >1000. This facilitated subsequent PCR for the same gene yielding a success rate of 64% (N=33) which will allow more sophisticated downstream analysis like sequencing, electrophoresis or multiplexing. PMID:25771302

  15. Imaging Single Cells in the Living Retina

    PubMed Central

    Williams, David R.

    2011-01-01

    A quarter century ago, we were limited to a macroscopic view of the retina inside the living eye. Since then, new imaging technologies, including confocal scanning laser ophthalmoscopy, optical coherence tomography, and adaptive optics fundus imaging, transformed the eye into a microscope in which individual cells can now be resolved noninvasively. These technologies have enabled a wide range of studies of the retina that were previously impossible. PMID:21596053

  16. Automated single-cell motility analysis on a chip using lensfree microscopy

    NASA Astrophysics Data System (ADS)

    Pushkarsky, Ivan; Lyb, Yunbo; Weaver, Westbrook; Su, Ting-Wei; Mudanyali, Onur; Ozcan, Aydogan; di Carlo, Dino

    2014-04-01

    Quantitative cell motility studies are necessary for understanding biophysical processes, developing models for cell locomotion and for drug discovery. Such studies are typically performed by controlling environmental conditions around a lens-based microscope, requiring costly instruments while still remaining limited in field-of-view. Here we present a compact cell monitoring platform utilizing a wide-field (24 mm2) lensless holographic microscope that enables automated single-cell tracking of large populations that is compatible with a standard laboratory incubator. We used this platform to track NIH 3T3 cells on polyacrylamide gels over 20 hrs. We report that, over an order of magnitude of stiffness values, collagen IV surfaces lead to enhanced motility compared to fibronectin, in agreement with biological uses of these structural proteins. The increased throughput associated with lensfree on-chip imaging enables higher statistical significance in observed cell behavior and may facilitate rapid screening of drugs and genes that affect cell motility.

  17. Single-Cell Genetic Analysis Using Automated Microfluidics to Resolve Somatic Mosaicism.

    PubMed

    Szulwach, Keith E; Chen, Peilin; Wang, Xiaohui; Wang, Jing; Weaver, Lesley S; Gonzales, Michael L; Sun, Gang; Unger, Marc A; Ramakrishnan, Ramesh

    2015-01-01

    Somatic mosaicism occurs throughout normal development and contributes to numerous disease etiologies, including tumorigenesis and neurological disorders. Intratumor genetic heterogeneity is inherent to many cancers, creating challenges for effective treatments. Unfortunately, analysis of bulk DNA masks subclonal phylogenetic architectures created by the acquisition and distribution of somatic mutations amongst cells. As a result, single-cell genetic analysis is becoming recognized as vital for accurately characterizing cancers. Despite this, methods for single-cell genetics are lacking. Here we present an automated microfluidic workflow enabling efficient cell capture, lysis, and whole genome amplification (WGA). We find that ~90% of the genome is accessible in single cells with improved uniformity relative to current single-cell WGA methods. Allelic dropout (ADO) rates were limited to 13.75% and variant false discovery rates (SNV FDR) were 4.11x10(-6), on average. Application to ER-/PR-/HER2+ breast cancer cells and matched normal controls identified novel mutations that arose in a subpopulation of cells and effectively resolved the segregation of known cancer-related mutations with single-cell resolution. Finally, we demonstrate effective cell classification using mutation profiles with 10X average exome coverage depth per cell. Our data demonstrate an efficient automated microfluidic platform for single-cell WGA that enables the resolution of somatic mutation patterns in single cells. PMID:26302375

  18. Trait Variability of Cancer Cells Quantified by High-Content Automated Microscopy of Single Cells

    PubMed Central

    Quaranta, Vito; Tyson, Darren R.; Garbett, Shawn P.; Weidow, Brandy; Harris, Mark P.; Georgescu, Walter

    2010-01-01

    Mapping quantitative cell traits (QCT) to underlying molecular defects is a central challenge in cancer research because heterogeneity at all biological scales, from genes to cells to populations, is recognized as the main driver of cancer progression and treatment resistance. A major roadblock to a multiscale framework linking cell to signaling to genetic cancer heterogeneity is the dearth of large-scale, single-cell data on QCT-such as proliferation, death sensitivity, motility, metabolism, and other hallmarks of cancer. High-volume single-cell data can be used to represent cell-to-cell genetic and nongenetic QCT variability in cancer cell populations as averages, distributions, and statistical subpopulations. By matching the abundance of available data on cancer genetic and molecular variability, QCT data should enable quantitative mapping of phenotype to genotype in cancer. This challenge is being met by high-content automated microscopy (HCAM), based on the convergence of several technologies including computerized microscopy, image processing, computation, and heterogeneity science. In this chapter, we describe an HCAM workflow that can be set up in a medium size interdisciplinary laboratory, and its application to produce high-throughput QCT data for cancer cell motility and proliferation. This type of data is ideally suited to populate cell-scale computational and mathematical models of cancer progression for quantitatively and predictively evaluating cancer drug discovery and treatment. PMID:19897088

  19. Single cell magnetic imaging using a quantum diamond microscope

    PubMed Central

    Park, H.; Weissleder, R.; Yacoby, A.; Lukin, M. D.; Lee, H.; Walsworth, R. L.; Connolly, C. B.

    2015-01-01

    We apply a quantum diamond microscope to detection and imaging of immunomagnetically labeled cells. This instrument uses nitrogen-vacancy (NV) centers in diamond for correlated magnetic and fluorescence imaging. Our device provides single-cell resolution and two orders of magnitude larger field of view (~1 mm2) than previous NV imaging technologies, enabling practical applications. To illustrate, we quantify cancer biomarkers expressed by rare tumor cells in a large population of healthy cells. PMID:26098019

  20. Automated Chemotactic Sorting and Single-cell Cultivation of Microbes using Droplet Microfluidics

    PubMed Central

    Dong, Libing; Chen, Dong-Wei; Liu, Shuang-Jiang; Du, Wenbin

    2016-01-01

    We report a microfluidic device for automated sorting and cultivation of chemotactic microbes from pure cultures or mixtures. The device consists of two parts: in the first part, a concentration gradient of the chemoeffector was built across the channel for inducing chemotaxis of motile cells; in the second part, chemotactic cells from the sample were separated, and mixed with culture media to form nanoliter droplets for encapsulation, cultivation, enumeration, and recovery of single cells. Chemotactic responses were assessed by imaging and statistical analysis of droplets based on Poisson distribution. An automated procedure was developed for rapid enumeration of droplets with cell growth, following with scale-up cultivation on agar plates. The performance of the device was evaluated by the chemotaxis assays of Escherichia coli (E. coli) RP437 and E. coli RP1616. Moreover, enrichment and isolation of non-labelled Comamonas testosteroni CNB-1 from its 1:10 mixture with E. coli RP437 was demonstrated. The enrichment factor reached 36.7 for CNB-1, based on its distinctive chemotaxis toward 4-hydroxybenzoic acid. We believe that this device can be widely used in chemotaxis studies without necessarily relying on fluorescent labelling, and isolation of functional microbial species from various environments. PMID:27074762

  1. Automated Chemotactic Sorting and Single-cell Cultivation of Microbes using Droplet Microfluidics

    NASA Astrophysics Data System (ADS)

    Dong, Libing; Chen, Dong-Wei; Liu, Shuang-Jiang; Du, Wenbin

    2016-04-01

    We report a microfluidic device for automated sorting and cultivation of chemotactic microbes from pure cultures or mixtures. The device consists of two parts: in the first part, a concentration gradient of the chemoeffector was built across the channel for inducing chemotaxis of motile cells; in the second part, chemotactic cells from the sample were separated, and mixed with culture media to form nanoliter droplets for encapsulation, cultivation, enumeration, and recovery of single cells. Chemotactic responses were assessed by imaging and statistical analysis of droplets based on Poisson distribution. An automated procedure was developed for rapid enumeration of droplets with cell growth, following with scale-up cultivation on agar plates. The performance of the device was evaluated by the chemotaxis assays of Escherichia coli (E. coli) RP437 and E. coli RP1616. Moreover, enrichment and isolation of non-labelled Comamonas testosteroni CNB-1 from its 1:10 mixture with E. coli RP437 was demonstrated. The enrichment factor reached 36.7 for CNB-1, based on its distinctive chemotaxis toward 4-hydroxybenzoic acid. We believe that this device can be widely used in chemotaxis studies without necessarily relying on fluorescent labelling, and isolation of functional microbial species from various environments.

  2. Analysis of Intracellular Glucose at Single Cells Using Electrochemiluminescence Imaging.

    PubMed

    Xu, Jingjing; Huang, Peiyuan; Qin, Yu; Jiang, Dechen; Chen, Hong-Yuan

    2016-05-01

    Here, luminol electrochemiluminescence was first applied to analyze intracellular molecules, such as glucose, at single cells. The individual cells were retained in cell-sized microwells on a gold coated indium tin oxide (ITO) slide, which were treated with luminol, triton X-100, and glucose oxidase simultaneously. The broken cellular membrane in the presence of triton X-100 released intracellular glucose into the microwell and reacted with glucose oxidase to generate hydrogen peroxide, which induced luminol luminescence under positive potential. To achieve fast analysis, the luminescences from 64 individual cells on one ITO slide were imaged in 60 s using a charge-coupled device (CCD). More luminescence was observed at all the microwells after the introduction of triton X-100 and glucose oxidase suggested that intracellular glucose was detected at single cells. The starvation of cells to decrease intracellular glucose produced less luminescence, which confirmed that our luminescence intensity was correlated with the concentration of intracellular glucose. Large deviations in glucose concentration at observed single cells revealed high cellular heterogeneity in intracellular glucose for the first time. This developed electrochemiluminescence assay will be potentially applied for fast analysis of more intracellular molecules in single cells to elucidate cellular heterogeneity. PMID:27094779

  3. Automated platform for multiparameter stimulus response studies of metabolic activity at the single-cell level

    NASA Astrophysics Data System (ADS)

    Ashili, Shashanka P.; Kelbauskas, Laimonas; Houkal, Jeff; Smith, Dean; Tian, Yanqing; Youngbull, Cody; Zhu, Haixin; Anis, Yasser H.; Hupp, Michael; Lee, Kristen B.; Kumar, Ashok V.; Vela, Juan; Shabilla, Andrew; Johnson, Roger H.; Holl, Mark R.; Meldrum, Deirdre R.

    2011-02-01

    We have developed a fully automated platform for multiparameter characterization of physiological response of individual and small numbers of interacting cells. The platform allows for minimally invasive monitoring of cell phenotypes while administering a variety of physiological insults and stimuli by means of precisely controlled microfluidic subsystems. It features the capability to integrate a variety of sensitive intra- and extra-cellular fluorescent probes for monitoring minute intra- and extra-cellular physiological changes. The platform allows for performance of other, post- measurement analyses of individual cells such as transcriptomics. Our method is based on the measurement of extracellular metabolite concentrations in hermetically sealed ~200-pL microchambers, each containing a single cell or a small number of cells. The major components of the system are a) a confocal laser scan head to excite and detect with single photon sensitivity the emitted photons from sensors; b) a microfluidic cassette to confine and incubate individual cells, providing for dynamic application of external stimuli, and c) an integration module consisting of software and hardware for automated cassette manipulation, environmental control and data collection. The custom-built confocal scan head allows for fluorescence intensity detection with high sensitivity and spatial confinement of the excitation light to individual pixels of the sensor area, thus minimizing any phototoxic effects. The platform is designed to permit incorporation of multiple optical sensors for simultaneous detection of various metabolites of interest. The modular detector structure allows for several imaging modalities, including high resolution intracellular probe imaging and extracellular sensor readout. The integrated system allows for simulation of physiologically relevant microenvironmental stimuli and simultaneous measurement of the elicited phenotypes. We present details of system design, system

  4. Automated single cell microbioreactor for monitoring intracellular dynamics and cell growth in free solution†

    PubMed Central

    Johnson-Chavarria, Eric M.; Agrawal, Utsav; Tanyeri, Melikhan; Kuhlman, Thomas E.

    2014-01-01

    We report an automated microfluidic-based platform for single cell analysis that allows for cell culture in free solution with the ability to control the cell growth environment. Using this approach, cells are confined by the sole action of gentle fluid flow, thereby enabling non-perturbative analysis of cell growth away from solid boundaries. In addition, the single cell microbioreactor allows for precise and time-dependent control over cell culture media, with the combined ability to observe the dynamics of non-adherent cells over long time scales. As a proof-of-principle demonstration, we used the platform to observe dynamic cell growth, gene expression, and intracellular diffusion of repressor proteins while precisely tuning the cell growth environment. Overall, this microfluidic approach enables the direct observation of cellular dynamics with exquisite control over environmental conditions, which will be useful for quantifying the behaviour of single cells in well-defined media. PMID:24836754

  5. Automated single-cell motility analysis on a chip using lensfree microscopy

    PubMed Central

    Pushkarsky, Ivan; Lyb, Yunbo; Weaver, Westbrook; Su, Ting-Wei; Mudanyali, Onur; Ozcan, Aydogan; Di Carlo, Dino

    2014-01-01

    Quantitative cell motility studies are necessary for understanding biophysical processes, developing models for cell locomotion and for drug discovery. Such studies are typically performed by controlling environmental conditions around a lens-based microscope, requiring costly instruments while still remaining limited in field-of-view. Here we present a compact cell monitoring platform utilizing a wide-field (24 mm2) lensless holographic microscope that enables automated single-cell tracking of large populations that is compatible with a standard laboratory incubator. We used this platform to track NIH 3T3 cells on polyacrylamide gels over 20 hrs. We report that, over an order of magnitude of stiffness values, collagen IV surfaces lead to enhanced motility compared to fibronectin, in agreement with biological uses of these structural proteins. The increased throughput associated with lensfree on-chip imaging enables higher statistical significance in observed cell behavior and may facilitate rapid screening of drugs and genes that affect cell motility. PMID:24739819

  6. A stochastic transcriptional switch model for single cell imaging data

    PubMed Central

    Hey, Kirsty L.; Momiji, Hiroshi; Featherstone, Karen; Davis, Julian R.E.; White, Michael R.H.; Rand, David A.; Finkenstädt, Bärbel

    2015-01-01

    Gene expression is made up of inherently stochastic processes within single cells and can be modeled through stochastic reaction networks (SRNs). In particular, SRNs capture the features of intrinsic variability arising from intracellular biochemical processes. We extend current models for gene expression to allow the transcriptional process within an SRN to follow a random step or switch function which may be estimated using reversible jump Markov chain Monte Carlo (MCMC). This stochastic switch model provides a generic framework to capture many different dynamic features observed in single cell gene expression. Inference for such SRNs is challenging due to the intractability of the transition densities. We derive a model-specific birth–death approximation and study its use for inference in comparison with the linear noise approximation where both approximations are considered within the unifying framework of state-space models. The methodology is applied to synthetic as well as experimental single cell imaging data measuring expression of the human prolactin gene in pituitary cells. PMID:25819987

  7. A stochastic transcriptional switch model for single cell imaging data.

    PubMed

    Hey, Kirsty L; Momiji, Hiroshi; Featherstone, Karen; Davis, Julian R E; White, Michael R H; Rand, David A; Finkenstädt, Bärbel

    2015-10-01

    Gene expression is made up of inherently stochastic processes within single cells and can be modeled through stochastic reaction networks (SRNs). In particular, SRNs capture the features of intrinsic variability arising from intracellular biochemical processes. We extend current models for gene expression to allow the transcriptional process within an SRN to follow a random step or switch function which may be estimated using reversible jump Markov chain Monte Carlo (MCMC). This stochastic switch model provides a generic framework to capture many different dynamic features observed in single cell gene expression. Inference for such SRNs is challenging due to the intractability of the transition densities. We derive a model-specific birth-death approximation and study its use for inference in comparison with the linear noise approximation where both approximations are considered within the unifying framework of state-space models. The methodology is applied to synthetic as well as experimental single cell imaging data measuring expression of the human prolactin gene in pituitary cells. PMID:25819987

  8. High resolution ultrasound and photoacoustic imaging of single cells

    PubMed Central

    Strohm, Eric M.; Moore, Michael J.; Kolios, Michael C.

    2016-01-01

    High resolution ultrasound and photoacoustic images of stained neutrophils, lymphocytes and monocytes from a blood smear were acquired using a combined acoustic/photoacoustic microscope. Photoacoustic images were created using a pulsed 532 nm laser that was coupled to a single mode fiber to produce output wavelengths from 532 nm to 620 nm via stimulated Raman scattering. The excitation wavelength was selected using optical filters and focused onto the sample using a 20× objective. A 1000 MHz transducer was co-aligned with the laser spot and used for ultrasound and photoacoustic images, enabling micrometer resolution with both modalities. The different cell types could be easily identified due to variations in contrast within the acoustic and photoacoustic images. This technique provides a new way of probing leukocyte structure with potential applications towards detecting cellular abnormalities and diseased cells at the single cell level. PMID:27114911

  9. The Singapore high resolution single cell imaging facility

    NASA Astrophysics Data System (ADS)

    Watt, Frank; Chen, Xiao; Vera, Armin Baysic De; Udalagama, Chammika N. B.; Ren, M.; Kan, Jeroen A. van; Bettiol, Andrew A.

    2011-10-01

    The Centre for Ion Beam Applications, National University of Singapore has recently expanded from three state-of-the-art beam lines to five. Two new beam lines have been constructed: A second generation proton beam writing line, and a high resolution single cell imaging facility. Both systems feature high demagnification lens systems based on compact Oxford Microbeams OM52 lenses, coupled with reduced lens/image distances. The single cell imaging facility is designed around OM52 compact lenses capable of operating in a variety of high demagnification configurations including the spaced Oxford triplet and the double crossover Russian quadruplet. The new facility has design specifications aimed at spatial resolutions below 50 nm, with a variety of techniques including STIM, secondary electron and fluorescence imaging, and an in-built optical and fluorescence microscope for sample imaging, identification and positioning. Preliminary tests using the single space Oxford triplet configuration have indicated a beam spot size of 31 × 39 nm in the horizontal and vertical directions respectively, at beam currents of ∼10,000 protons per second. However, a weakness in the specifications of the electrostatic scanning system has been identified, and a more stable scanning system needs to be implemented before we can fully realize the optimum performance. A single whole fibroblast cell has been scanned using 1.5 MeV protons, and a median fit to the proton transmission energy loss data has shown that proton STIM gives excellent details of the cell structure despite the relatively poor contrast of proton STIM compared with alpha STIM.

  10. Quantum dot imaging platform for single-cell molecular profiling

    NASA Astrophysics Data System (ADS)

    Zrazhevskiy, Pavel; Gao, Xiaohu

    2013-03-01

    Study of normal cell physiology and disease pathogenesis heavily relies on untangling the complexity of intracellular molecular mechanisms and pathways. To achieve this goal, comprehensive molecular profiling of individual cells within the context of microenvironment is required. Here we report the development of a multicolour multicycle in situ imaging technology capable of creating detailed quantitative molecular profiles for individual cells at the resolution of optical imaging. A library of stoichiometric fluorescent probes is prepared by linking target-specific antibodies to a universal quantum dot-based platform via protein A in a quick and simple procedure. Surprisingly, despite the potential for multivalent binding between protein A and antibody and the intermediate affinity of this non-covalent bond, fully assembled probes do not aggregate or exchange antibodies, facilitating highly multiplexed parallel staining. This single-cell molecular profiling technology is expected to open new opportunities in systems biology, gene expression studies, signalling pathway analysis and molecular diagnostics.

  11. Trypanosoma cruzi: single cell live imaging inside infected tissues.

    PubMed

    Ferreira, Bianca Lima; Orikaza, Cristina Mary; Cordero, Esteban Mauricio; Mortara, Renato Arruda

    2016-06-01

    Although imaging the live Trypanosoma cruzi parasite is a routine technique in most laboratories, identification of the parasite in infected tissues and organs has been hindered by their intrinsic opaque nature. We describe a simple method for in vivo observation of live single-cell Trypanosoma cruzi parasites inside mammalian host tissues. BALB/c or C57BL/6 mice infected with DsRed-CL or GFP-G trypomastigotes had their organs removed and sectioned with surgical blades. Ex vivo organ sections were observed under confocal microscopy. For the first time, this procedure enabled imaging of individual amastigotes, intermediate forms and motile trypomastigotes within infected tissues of mammalian hosts. PMID:26639617

  12. Preparation of Single Cells for Imaging Mass Spectrometry

    SciTech Connect

    Berman, E S; Fortson, S L; Kulp, K S; Checchi, K D; Wu, L; Felton, J S; Wu, K J

    2007-10-24

    Characterizing chemical changes within single cells is important for determining fundamental mechanisms of biological processes that will lead to new biological insights and improved disease understanding. Imaging biological systems with mass spectrometry (MS) has gained popularity in recent years as a method for creating precise chemical maps of biological samples. In order to obtain high-quality mass spectral images that provide relevant molecular information about individual cells, samples must be prepared so that salts and other cell-culture components are removed from the cell surface and the cell contents are rendered accessible to the desorption beam. We have designed a cellular preparation protocol for imaging MS that preserves the cellular contents for investigation and removes the majority of the interfering species from the extracellular matrix. Using this method, we obtain excellent imaging results and reproducibility in three diverse cell types: MCF7 human breast cancer cells, Madin-Darby canine kidney (MDCK) cells, and NIH/3T3 mouse fibroblasts. This preparation technique allows routine imaging MS analysis of cultured cells, allowing for any number of experiments aimed at furthering scientific understanding of molecular processes within individual cells.

  13. Making colourful sense of Raman images of single cells.

    PubMed

    Ashton, Lorna; Hollywood, Katherine A; Goodacre, Royston

    2015-03-21

    In order to understand biological systems it is important to gain pertinent information on the spatial localisation of chemicals within cells. With the relatively recent advent of high-resolution chemical imaging this is being realised and one rapidly developing area of research is the Raman mapping of single cells, an approach whose success has vast potential for numerous areas of biomedical research. However, there is a danger of undermining the potential routine use of Raman mapping due to a lack of consistency and transparency in the way false-shaded Raman images are constructed. In this study we demonstrate, through the use of simulated data and real Raman maps of single human keratinocyte (HaCaT) cells, how changes in the application of colour shading can dramatically alter the final Raman images. In order to avoid ambiguity and potential subjectivity in image interpretation we suggest that data distribution plots are used to aid shading approaches and that extreme care is taken to use the most appropriate false-shading for the biomedical question under investigation. PMID:25666258

  14. Mass spectrometry imaging and profiling of single cells

    PubMed Central

    Lanni, Eric J.; Rubakhin, Stanislav S.; Sweedler, Jonathan V.

    2012-01-01

    Mass spectrometry imaging and profiling of individual cells and subcellular structures provide unique analytical capabilities for biological and biomedical research, including determination of the biochemical heterogeneity of cellular populations and intracellular localization of pharmaceuticals. Two mass spectrometry technologies—secondary ion mass spectrometry (SIMS) and matrix assisted laser desorption ionization mass spectrometry (MALDI MS)—are most often used in micro-bioanalytical investigations. Recent advances in ion probe technologies have increased the dynamic range and sensitivity of analyte detection by SIMS, allowing two- and three-dimensional localization of analytes in a variety of cells. SIMS operating in the mass spectrometry imaging (MSI) mode can routinely reach spatial resolutions at the submicron level; therefore, it is frequently used in studies of the chemical composition of subcellular structures. MALDI MS offers a large mass range and high sensitivity of analyte detection. It has been successfully applied in a variety of single-cell and organelle profiling studies. Innovative instrumentation such as scanning microprobe MALDI and mass microscope spectrometers enable new subcellular MSI measurements. Other approaches for MS-based chemical imaging and profiling include those based on near-field laser ablation and inductively-coupled plasma MS analysis, which offer complementary capabilities for subcellular chemical imaging and profiling. PMID:22498881

  15. Preparation of single cells for imaging mass spectrometry.

    PubMed

    Berman, Elena S F; Fortson, Susan L; Kulp, Kristen S

    2010-01-01

    Characterizing the molecular contents of individual cells is critical for understanding fundamental mechanisms of biological processes. Imaging mass spectrometry (IMS) of biological systems has been steadily gaining popularity for its ability to create precise chemical images of biological samples, thereby revealing new biological insights and improving understanding of disease. In order to acquire mass spectral images from single cells that contain relevant molecular information, samples must be prepared such that cell-culture components, especially salts, are eliminated from the cell surface and that the cell contents are accessible to the mass spectrometer. We have demonstrated a cellular preparation technique for IMS that preserves the basic morphology of cultured cells, allows mass spectrometric chemical profiling of cytosol, and removes the majority of the interfering species derived from the cellular growth medium. Using this protocol, we achieve high-quality, reproducible IMS images from three diverse cell types: MCF7 human breast cancer cells, Madin-Darby canine kidney (MDCK) cells, and NIH/3T3 mouse fibroblasts. This preparation method allows rapid and routine IMS analysis of cultured cells, making possible a wide variety of experiments to further scientific understanding of molecular processes within individual cells. PMID:20680596

  16. Fast and high resolution single-cell BRET imaging.

    PubMed

    Goyet, Elise; Bouquier, Nathalie; Ollendorff, Vincent; Perroy, Julie

    2016-01-01

    Resonance Energy Transfer (RET)-based technologies are used to report protein-protein interactions in living cells. Among them, Bioluminescence-initiated RET (BRET) provides excellent sensitivity but the low light intensity intrinsic to the bioluminescent process hampers its use for the localization of protein complexes at the sub-cellular level. Herein we have characterized the methodological conditions required to reliably perform single-cell BRET imaging using an extremely bright luciferase, Nanoluciferase (Nluc). With this, we achieved an unprecedented performance in the field of protein-protein interaction imaging in terms of temporal and spatial resolution, duration of signal stability, signal sensitivity and dynamic range. As proof-of-principle, an Nluc-containing BRET-based sensor of ERK activity enabled the detection of subtle, transient and localized variations in ERK activity in neuronal dendritic spines, induced by the activation of endogenous synaptic NMDA receptors. This development will improve our comprehension of both the spatio-temporal dynamics of protein-protein interactions and the activation patterns of specific signaling pathways. PMID:27302735

  17. Fast and high resolution single-cell BRET imaging

    PubMed Central

    Goyet, Elise; Bouquier, Nathalie; Ollendorff, Vincent; Perroy, Julie

    2016-01-01

    Resonance Energy Transfer (RET)-based technologies are used to report protein-protein interactions in living cells. Among them, Bioluminescence-initiated RET (BRET) provides excellent sensitivity but the low light intensity intrinsic to the bioluminescent process hampers its use for the localization of protein complexes at the sub-cellular level. Herein we have characterized the methodological conditions required to reliably perform single-cell BRET imaging using an extremely bright luciferase, Nanoluciferase (Nluc). With this, we achieved an unprecedented performance in the field of protein-protein interaction imaging in terms of temporal and spatial resolution, duration of signal stability, signal sensitivity and dynamic range. As proof-of-principle, an Nluc-containing BRET-based sensor of ERK activity enabled the detection of subtle, transient and localized variations in ERK activity in neuronal dendritic spines, induced by the activation of endogenous synaptic NMDA receptors. This development will improve our comprehension of both the spatio-temporal dynamics of protein-protein interactions and the activation patterns of specific signaling pathways. PMID:27302735

  18. Integrated microfluidic device for automated single cell analysis using electrophoretic separation and electrospray ionization mass spectrometry.

    PubMed

    Mellors, J Scott; Jorabchi, Kaveh; Smith, Lloyd M; Ramsey, J Michael

    2010-02-01

    A microfabricated fluidic device was developed for the automated real-time analysis of individual cells using capillary electrophoresis (CE) and electrospray ionization-mass spectrometry (ESI-MS). The microfluidic structure incorporates a means for rapid lysis of single cells within a free solution electrophoresis channel, where cellular constituents were separated, and an integrated electrospray emitter for ionization of separated components. The eluent was characterized using mass spectrometry. Human erythrocytes were used as a model system for this study. In this monolithically integrated device, cell lysis occurs at a channel intersection using a combination of rapid buffer exchange and an increase in electric field strength. An electroosmotic pump is incorporated at the end of the electrophoretic separation channel to direct eluent to the integrated electrospray emitter. The dissociated heme group and the alpha and beta subunits of hemoglobin from individual erythrocytes were detected as cells continuously flowed through the device. The average analysis throughput was approximately 12 cells per minute, demonstrating the potential of this method for high-throughput single cell analysis. PMID:20058879

  19. Integrated Microfluidic Device for Automated Single Cell Analysis using Electrophoretic Separation and Electrospray Ionization Mass Spectrometry

    PubMed Central

    Mellors, J. Scott; Jorabchi, Kaveh; Smith, Lloyd M.; Ramsey, J. Michael

    2010-01-01

    A microfabricated fluidic device was developed for the automated real-time analysis of individual cells using capillary electrophoresis (CE) and electrospray ionization-mass spectrometry (ESI-MS). The microfluidic structure incorporates a means for rapid lysis of single cells within a free solution electrophoresis channel where cellular constituents were separated and an integrated electrospray emitter for ionization of separated components. The eluent was characterized using mass spectrometry. Human erythrocytes were used as a model system for this study. In this monolithically integrated device, cell lysis occurs at a channel intersection using a combination of rapid buffer exchange and an increase in electric field strength. An electroosmotic pump is incorporated at the end of the electrophoretic separation channel to direct eluent to the integrated electrospray emitter. The dissociated heme group and the α and β subunits of hemoglobin from individual erythrocytes were detected as cells continuously flowed through the device. The average analysis throughput was approximately 12 cells per minute demonstrating the potential of this method for high-throughput single cell analysis. PMID:20058879

  20. Nystatin-induced changes in yeast monitored by time-resolved automated single cell electrorotation.

    PubMed

    Hölzel, R

    1998-10-23

    A widespread use of electrorotation for the determination of cellular and subcellular properties has been hindered so far by the need for manual recording of cell movements. Therefore a system has been developed that allows the automatic collection of electrorotation spectra of single cells in real time. It employs a hardware based registration of image moments from which object orientation is calculated. Since the camera's video signal is processed without intermediate image storage a high data throughput of about two recordings per second could be achieved independently of image resolution. This made it possible to monitor changes in cell membrane and cytoplasm of the yeast Saccharomyces cerevisiae under the influence of the antibiotic nystatin with a temporal resolution of 3 min. Up to 20 electrorotation spectra of an individual cell could be collected in the frequency range between 1 kHz and 1 GHz. Two distinct events 7 and 75 min after addition of nystatin were observed with a fast increase in membrane permeability accompanied by a nearly simultaneous drop in cytoplasmic conductivity. PMID:9795246

  1. A microchip electrophoresis-mass spectrometric platform with double cell lysis nano-electrodes for automated single cell analysis.

    PubMed

    Li, Xiangtang; Zhao, Shulin; Hu, Hankun; Liu, Yi-Ming

    2016-06-17

    Capillary electrophoresis-based single cell analysis has become an essential approach in researches at the cellular level. However, automation of single cell analysis has been a challenge due to the difficulty to control the number of cells injected and the irreproducibility associated with cell aggregation. Herein we report the development of a new microfluidic platform deploying the double nano-electrode cell lysis technique for automated analysis of single cells with mass spectrometric detection. The proposed microfluidic chip features integration of a cell-sized high voltage zone for quick single cell lysis, a microfluidic channel for electrophoretic separation, and a nanoelectrospray emitter for ionization in MS detection. Built upon this platform, a microchip electrophoresis-mass spectrometric method (MCE-MS) has been developed for automated single cell analysis. In the method, cell introduction, cell lysis, and MCE-MS separation are computer controlled and integrated as a cycle into consecutive assays. Analysis of large numbers of individual PC-12 neuronal cells (both intact and exposed to 25mM KCl) was carried out to determine intracellular levels of dopamine (DA) and glutamic acid (Glu). It was found that DA content in PC-12 cells was higher than Glu content, and both varied from cell to cell. The ratio of intracellular DA to Glu was 4.20±0.8 (n=150). Interestingly, the ratio drastically decreased to 0.38±0.20 (n=150) after the cells are exposed to 25mM KCl for 8min, suggesting the cells released DA promptly and heavily while they released Glu at a much slower pace in response to KCl-induced depolarization. These results indicate that the proposed MCE-MS analytical platform may have a great potential in researches at the cellular level. PMID:27207575

  2. CalQuo: automated, simultaneous single-cell and population-level quantification of global intracellular Ca2+ responses

    PubMed Central

    Fritzsche, Marco; Fernandes, Ricardo A.; Colin-York, Huw; Santos, Ana M.; Lee, Steven F.; Lagerholm, B. Christoffer; Davis, Simon J.; Eggeling, Christian

    2015-01-01

    Detecting intracellular calcium signaling with fluorescent calcium indicator dyes is often coupled with microscopy techniques to follow the activation state of non-excitable cells, including lymphocytes. However, the analysis of global intracellular calcium responses both at the single-cell level and in large ensembles simultaneously has yet to be automated. Here, we present a new software package, CalQuo (Calcium Quantification), which allows the automated analysis and simultaneous monitoring of global fluorescent calcium reporter-based signaling responses in up to 1000 single cells per experiment, at temporal resolutions of sub-seconds to seconds. CalQuo quantifies the number and fraction of responding cells, the temporal dependence of calcium signaling and provides global and individual calcium-reporter fluorescence intensity profiles. We demonstrate the utility of the new method by comparing the calcium-based signaling responses of genetically manipulated human lymphocytic cell lines. PMID:26563585

  3. Quantitative semi-automated analysis of morphogenesis with single-cell resolution in complex embryos

    PubMed Central

    Giurumescu, Claudiu A.; Kang, Sukryool; Planchon, Thomas A.; Betzig, Eric; Bloomekatz, Joshua; Yelon, Deborah; Cosman, Pamela; Chisholm, Andrew D.

    2012-01-01

    A quantitative understanding of tissue morphogenesis requires description of the movements of individual cells in space and over time. In transparent embryos, such as C. elegans, fluorescently labeled nuclei can be imaged in three-dimensional time-lapse (4D) movies and automatically tracked through early cleavage divisions up to ~350 nuclei. A similar analysis of later stages of C. elegans development has been challenging owing to the increased error rates of automated tracking of large numbers of densely packed nuclei. We present Nucleitracker4D, a freely available software solution for tracking nuclei in complex embryos that integrates automated tracking of nuclei in local searches with manual curation. Using these methods, we have been able to track >99% of all nuclei generated in the C. elegans embryo. Our analysis reveals that ventral enclosure of the epidermis is accompanied by complex coordinated migration of the neuronal substrate. We can efficiently track large numbers of migrating nuclei in 4D movies of zebrafish cardiac morphogenesis, suggesting that this approach is generally useful in situations in which the number, packing or dynamics of nuclei present challenges for automated tracking. PMID:23052905

  4. Automated satellite image navigation

    NASA Astrophysics Data System (ADS)

    Bassett, Robert M.

    1992-12-01

    The automated satellite image navigation method (Auto-Avian) developed and tested by Spaulding (1990) at the Naval Postgraduate School is investigated. The Auto-Avian method replaced the manual procedure of selecting Ground Control Points (GCP's) with an autocorrelation process that utilizes the World Vector Shoreline (WVS) provided by the Defense Mapping Agency (DMA) as a string of GCP's to rectify satellite images. The automatic cross-correlation of binary reference (WVS) and search (image) windows eliminated the subjective error associated with the manual selection of GCP's and produced accuracies comparable to the manual method. The scope of Spaulding's (1990) research was expanded. The worldwide application of the Auto-Avian method was demonstrated in three world regions (eastern North Pacific Ocean, eastern North Atlantic Ocean, and Persian Gulf). Using five case studies, the performance of the Auto-Avian method on 'less than optimum' images (i.e., islands, coastlines affected by lateral distortion and/or cloud cover) was investigated.

  5. Imaging of magnetic microfield distortions allows sensitive single-cell detection.

    PubMed

    Lindquist, Randall L; Papazoglou, Sebastian; Scharlach, Constantin; Waiczies, Helmar; Schnorr, Jörg; Taupitz, Matthias; Hamm, Bernd; Schellenberger, Eyk

    2013-01-01

    Cell tracking with magnetic resonance imaging (MRI) is mostly performed using superparamagnetic iron oxide (SPIO) nanoparticle-labeled cells. However, negative contrast in T2*-weighted imaging is inherently problematic as a homogeneous background signal is required to visualize the negative signal. In a magnetic field, SPIO-labeled cells develop their own magnetization, distorting the main field. We show here a method to visualize these distortions and use them to identify single cells with increased sensitivity and certainty compared to T2* images. We labeled HeLa cells with SPIOs, suspended labeled cells in agarose to make phantoms, and performed high-resolution gradient-echo MRI. Phase images were processed to enhance the visibility of single cells. To quantify SPIO content, we generated a map of frequency differences. MRI of cell phantoms showed that single cells could be detected at concentrations ranging from 200 to 10,000 cells mL(-1). Postprocessing of the magnetic resonance phase images reveals characteristic microfield distortions, increasing dramatically the sensitivity of cell recognition, compared to unprocessed T2* images. Calculating frequency shifts and comparing microfield distortions to simulations permit estimation of the nanoparticle load of single cells. We expect the ability to detect and quantify the iron load of single cells to prove useful in studies of cell trafficking, especially in rare cell populations. PMID:23415396

  6. AUTOMATING SHALLOW SEISMIC IMAGING

    SciTech Connect

    Steeples, Don W.

    2003-09-14

    The current project is a continuation of an effort to develop ultrashallow seismic imaging as a cost-effective method potentially applicable to DOE facilities. The objective of the present research is to develop and demonstrate the use of a cost-effective, automated method of conducting shallow seismic surveys, an approach that represents a significant departure from conventional seismic-survey field procedures. Initial testing of a mechanical geophone-planting device suggests that large numbers of geophones can be placed both quickly and automatically. The development of such a device could make the application of SSR considerably more efficient and less expensive. The imaging results obtained using automated seismic methods will be compared with results obtained using classical seismic techniques. Although this research falls primarily into the field of seismology, for comparison and quality-control purposes, some GPR data will be collected as well. In the final year of th e research, demonstration surveys at one or more DOE facilities will be performed. An automated geophone-planting device of the type under development would not necessarily be limited to the use of shallow seismic reflection methods; it also would be capable of collecting data for seismic-refraction and possibly for surface-wave studies. Another element of our research plan involves monitoring the cone of depression of a pumping well that is being used as a proxy site for fluid-flow at a contaminated site. Our next data set will be collected at a well site where drawdown equilibrium has been reached. Noninvasive, in-situ methods such as placing geophones automatically and using near-surface seismic methods to identify and characterize the hydrologic flow regimes at contaminated sites support the prospect of developing effective, cost-conscious cleanup strategies for DOE and others.

  7. Automated ship image acquisition

    NASA Astrophysics Data System (ADS)

    Hammond, T. R.

    2008-04-01

    The experimental Automated Ship Image Acquisition System (ASIA) collects high-resolution ship photographs at a shore-based laboratory, with minimal human intervention. The system uses Automatic Identification System (AIS) data to direct a high-resolution SLR digital camera to ship targets and to identify the ships in the resulting photographs. The photo database is then searchable using the rich data fields from AIS, which include the name, type, call sign and various vessel identification numbers. The high-resolution images from ASIA are intended to provide information that can corroborate AIS reports (e.g., extract identification from the name on the hull) or provide information that has been omitted from the AIS reports (e.g., missing or incorrect hull dimensions, cargo, etc). Once assembled into a searchable image database, the images can be used for a wide variety of marine safety and security applications. This paper documents the author's experience with the practicality of composing photographs based on AIS reports alone, describing a number of ways in which this can go wrong, from errors in the AIS reports, to fixed and mobile obstructions and multiple ships in the shot. The frequency with which various errors occurred in automatically-composed photographs collected in Halifax harbour in winter time were determined by manual examination of the images. 45% of the images examined were considered of a quality sufficient to read identification markings, numbers and text off the entire ship. One of the main technical challenges for ASIA lies in automatically differentiating good and bad photographs, so that few bad ones would be shown to human users. Initial attempts at automatic photo rating showed 75% agreement with manual assessments.

  8. Noninvasive Pigment Identification in Single Cells from Living Phototrophic Biofilms by Confocal Imaging Spectrofluorometry

    PubMed Central

    Roldán, M.; Thomas, F.; Castel, S.; Quesada, A.; Hernández-Mariné, M.

    2004-01-01

    A new imaging technique for the analysis of fluorescent pigments from a single cell is reported. It is based on confocal scanning laser microscopy coupled with spectrofluorometric methods. The setup allows simultaneous establishment of the relationships among pigment analysis in vivo, morphology, and three-dimensional localization inside thick intact microbial assemblages. PMID:15184183

  9. High-throughput de novo screening of receptor agonists with an automated single-cell analysis and isolation system.

    PubMed

    Yoshimoto, Nobuo; Tatematsu, Kenji; Iijima, Masumi; Niimi, Tomoaki; Maturana, Andrés D; Fujii, Ikuo; Kondo, Akihiko; Tanizawa, Katsuyuki; Kuroda, Shun'ichi

    2014-01-01

    Reconstitution of signaling pathways involving single mammalian transmembrane receptors has not been accomplished in yeast cells. In this study, intact EGF receptor (EGFR) and a cell wall-anchored form of EGF were co-expressed on the yeast cell surface, which led to autophosphorylation of the EGFR in an EGF-dependent autocrine manner. After changing from EGF to a conformationally constrained peptide library, cells were fluorescently labeled with an anti-phospho-EGFR antibody. Each cell was subjected to an automated single-cell analysis and isolation system that analyzed the fluorescent intensity of each cell and automatically retrieved each cell with the highest fluorescence. In ~3.2 × 10(6) peptide library, we isolated six novel peptides with agonistic activity of the EGFR in human squamous carcinoma A431 cells. The combination of yeast cells expressing mammalian receptors, a cell wall-anchored peptide library, and an automated single-cell analysis and isolation system might facilitate a rational approach for de novo drug screening. PMID:24577528

  10. High-throughput de novo screening of receptor agonists with an automated single-cell analysis and isolation system

    PubMed Central

    Yoshimoto, Nobuo; Tatematsu, Kenji; Iijima, Masumi; Niimi, Tomoaki; Maturana, Andrés D.; Fujii, Ikuo; Kondo, Akihiko; Tanizawa, Katsuyuki; Kuroda, Shun'ichi

    2014-01-01

    Reconstitution of signaling pathways involving single mammalian transmembrane receptors has not been accomplished in yeast cells. In this study, intact EGF receptor (EGFR) and a cell wall-anchored form of EGF were co-expressed on the yeast cell surface, which led to autophosphorylation of the EGFR in an EGF-dependent autocrine manner. After changing from EGF to a conformationally constrained peptide library, cells were fluorescently labeled with an anti-phospho-EGFR antibody. Each cell was subjected to an automated single-cell analysis and isolation system that analyzed the fluorescent intensity of each cell and automatically retrieved each cell with the highest fluorescence. In ~3.2 × 106 peptide library, we isolated six novel peptides with agonistic activity of the EGFR in human squamous carcinoma A431 cells. The combination of yeast cells expressing mammalian receptors, a cell wall-anchored peptide library, and an automated single-cell analysis and isolation system might facilitate a rational approach for de novo drug screening. PMID:24577528

  11. Cryo-imaging of fluorescently labeled single cells in a mouse

    NASA Astrophysics Data System (ADS)

    Steyer, Grant J.; Roy, Debashish; Salvado, Olivier; Stone, Meredith E.; Wilson, David L.

    2009-02-01

    We developed a cryo-imaging system to provide single-cell detection of fluorescently labeled cells in mouse, with particular applicability to stem cells and metastatic cancer. The Case cryoimaging system consists of a fluorescence microscope, robotic imaging positioner, customized cryostat, PC-based control system, and visualization/analysis software. The system alternates between sectioning (10-40 μm) and imaging, collecting color brightfield and fluorescent blockface image volumes >60GB. In mouse experiments, we imaged quantum-dot labeled stem cells, GFP-labeled cancer and stem cells, and cell-size fluorescent microspheres. To remove subsurface fluorescence, we used a simplified model of light-tissue interaction whereby the next image was scaled, blurred, and subtracted from the current image. We estimated scaling and blurring parameters by minimizing entropy of subtracted images. Tissue specific attenuation parameters were found [uT : heart (267 +/- 47.6 μm), liver (218 +/- 27.1 μm), brain (161 +/- 27.4 μm)] to be within the range of estimates in the literature. "Next image" processing removed subsurface fluorescence equally well across multiple tissues (brain, kidney, liver, adipose tissue, etc.), and analysis of 200 microsphere images in the brain gave 97+/-2% reduction of subsurface fluorescence. Fluorescent signals were determined to arise from single cells based upon geometric and integrated intensity measurements. Next image processing greatly improved axial resolution, enabled high quality 3D volume renderings, and improved enumeration of single cells with connected component analysis by up to 24%. Analysis of image volumes identified metastatic cancer sites, found homing of stem cells to injury sites, and showed microsphere distribution correlated with blood flow patterns. We developed and evaluated cryo-imaging to provide single-cell detection of fluorescently labeled cells in mouse. Our cryo-imaging system provides extreme (>60GB), micron

  12. Automating Shallow Seismic Imaging

    SciTech Connect

    Steeples, Don W.

    2004-12-09

    This seven-year, shallow-seismic reflection research project had the aim of improving geophysical imaging of possible contaminant flow paths. Thousands of chemically contaminated sites exist in the United States, including at least 3,700 at Department of Energy (DOE) facilities. Imaging technologies such as shallow seismic reflection (SSR) and ground-penetrating radar (GPR) sometimes are capable of identifying geologic conditions that might indicate preferential contaminant-flow paths. Historically, SSR has been used very little at depths shallower than 30 m, and even more rarely at depths of 10 m or less. Conversely, GPR is rarely useful at depths greater than 10 m, especially in areas where clay or other electrically conductive materials are present near the surface. Efforts to image the cone of depression around a pumping well using seismic methods were only partially successful (for complete references of all research results, see the full Final Technical Report, DOE/ER/14826-F), but peripheral results included development of SSR methods for depths shallower than one meter, a depth range that had not been achieved before. Imaging at such shallow depths, however, requires geophone intervals of the order of 10 cm or less, which makes such surveys very expensive in terms of human time and effort. We also showed that SSR and GPR could be used in a complementary fashion to image the same volume of earth at very shallow depths. The primary research focus of the second three-year period of funding was to develop and demonstrate an automated method of conducting two-dimensional (2D) shallow-seismic surveys with the goal of saving time, effort, and money. Tests involving the second generation of the hydraulic geophone-planting device dubbed the ''Autojuggie'' showed that large numbers of geophones can be placed quickly and automatically and can acquire high-quality data, although not under rough topographic conditions. In some easy-access environments, this device could

  13. An integrated image analysis platform to quantify signal transduction in single cells.

    PubMed

    Pelet, Serge; Dechant, Reinhard; Lee, Sung Sik; van Drogen, Frank; Peter, Matthias

    2012-10-01

    Microscopy can provide invaluable information about biological processes at the single cell level. It remains a challenge, however, to extract quantitative information from these types of datasets. We have developed an image analysis platform named YeastQuant to simplify data extraction by offering an integrated method to turn time-lapse movies into single cell measurements. This platform is based on a database with a graphical user interface where the users can describe their experiments. The database is connected to the engineering software Matlab, which allows extracting the desired information by automatically segmenting and quantifying the microscopy images. We implemented three different segmentation methods that recognize individual cells under different conditions, and integrated image analysis protocols that allow measuring and analyzing distinct cellular readouts. To illustrate the power and versatility of YeastQuant, we investigated dynamic signal transduction processes in yeast. First, we quantified the expression of fluorescent reporters induced by osmotic stress to study noise in gene expression. Second, we analyzed the dynamic relocation of endogenous proteins from the cytoplasm to the cell nucleus, which provides a fast measure of pathway activity. These examples demonstrate that YeastQuant provides a versatile and expandable database and an experimental framework that improves image analysis and quantification of diverse microscopy-based readouts. Such dynamic single cell measurements are highly needed to establish mathematical models of signal transduction pathways. PMID:22976484

  14. Studying the organization of DNA repair by single-cell and single-molecule imaging

    PubMed Central

    Uphoff, Stephan; Kapanidis, Achillefs N.

    2014-01-01

    DNA repair safeguards the genome against a diversity of DNA damaging agents. Although the mechanisms of many repair proteins have been examined separately in vitro, far less is known about the coordinated function of the whole repair machinery in vivo. Furthermore, single-cell studies indicate that DNA damage responses generate substantial variation in repair activities across cells. This review focuses on fluorescence imaging methods that offer a quantitative description of DNA repair in single cells by measuring protein concentrations, diffusion characteristics, localizations, interactions, and enzymatic rates. Emerging single-molecule and super-resolution microscopy methods now permit direct visualization of individual proteins and DNA repair events in vivo. We expect much can be learned about the organization of DNA repair by linking cell heterogeneity to mechanistic observations at the molecular level. PMID:24629485

  15. High-content analysis of single cells directly assembled on CMOS sensor based on color imaging.

    PubMed

    Tanaka, Tsuyoshi; Saeki, Tatsuya; Sunaga, Yoshihiko; Matsunaga, Tadashi

    2010-12-15

    A complementary metal oxide semiconductor (CMOS) image sensor was applied to high-content analysis of single cells which were assembled closely or directly onto the CMOS sensor surface. The direct assembling of cell groups on CMOS sensor surface allows large-field (6.66 mm×5.32 mm in entire active area of CMOS sensor) imaging within a second. Trypan blue-stained and non-stained cells in the same field area on the CMOS sensor were successfully distinguished as white- and blue-colored images under white LED light irradiation. Furthermore, the chemiluminescent signals of each cell were successfully visualized as blue-colored images on CMOS sensor only when HeLa cells were placed directly on the micro-lens array of the CMOS sensor. Our proposed approach will be a promising technique for real-time and high-content analysis of single cells in a large-field area based on color imaging. PMID:20728336

  16. Single-Cell Quantification of Cytosine Modifications by Hyperspectral Dark-Field Imaging.

    PubMed

    Wang, Xiaolei; Cui, Yi; Irudayaraj, Joseph

    2015-12-22

    Epigenetic modifications on DNA, especially on cytosine, play a critical role in regulating gene expression and genome stability. It is known that the levels of different cytosine derivatives are highly dynamic and are regulated by a variety of factors that act on the chromatin. Here we report an optical methodology based on hyperspectral dark-field imaging (HSDFI) using plasmonic nanoprobes to quantify the recently identified cytosine modifications on DNA in single cells. Gold (Au) and silver (Ag) nanoparticles (NPs) functionalized with specific antibodies were used as contrast-generating agents due to their strong local surface plasmon resonance (LSPR) properties. With this powerful platform we have revealed the spatial distribution and quantity of 5-carboxylcytosine (5caC) at the different stages in cell cycle and demonstrated that 5caC was a stably inherited epigenetic mark. We have also shown that the regional density of 5caC on a single chromosome can be mapped due to the spectral sensitivity of the nanoprobes in relation to the interparticle distance. Notably, HSDFI enables an efficient removal of the scattering noises from nonspecifically aggregated nanoprobes, to improve accuracy in the quantification of different cytosine modifications in single cells. Further, by separating the LSPR fingerprints of AuNPs and AgNPs, multiplex detection of two cytosine modifications was also performed. Our results demonstrate HSDFI as a versatile platform for spatial and spectroscopic characterization of plasmonic nanoprobe-labeled nuclear targets at the single-cell level for quantitative epigenetic screening. PMID:26505210

  17. Automated medical image segmentation techniques

    PubMed Central

    Sharma, Neeraj; Aggarwal, Lalit M.

    2010-01-01

    Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images. PMID:20177565

  18. Resonant waveguide grating imagers for single cell analysis and high throughput screening

    NASA Astrophysics Data System (ADS)

    Fang, Ye

    2015-08-01

    Resonant waveguide grating (RWG) systems illuminate an array of diffractive nanograting waveguide structures in microtiter plate to establish evanescent wave for measuring tiny changes in local refractive index arising from the dynamic mass redistribution of living cells upon stimulation. Whole-plate RWG imager enables high-throughput profiling and screening of drugs. Microfluidics RWG imager not only manifests distinct receptor signaling waves, but also differentiates long-acting agonism and antagonism. Spatially resolved RWG imager allows for single cell analysis including receptor signaling heterogeneity and the invasion of cancer cells in a spheroidal structure through 3-dimensional extracellular matrix. High frequency RWG imager permits real-time detection of drug-induced cardiotoxicity. The wide coverage in target, pathway, assay, and cell phenotype has made RWG systems powerful tool in both basic research and early drug discovery process.

  19. Single-cell imaging detection of nanobarcoded nanoparticle biodistributions in tissues for nanomedicine

    NASA Astrophysics Data System (ADS)

    Eustaquio, Trisha; Cooper, Christy L.; Leary, James F.

    2011-03-01

    In nanomedicine, biodistribution studies are critical to evaluate the safety and efficacy of nanoparticles. Currently, extensive biodistribution studies are hampered by the limitations of bulk tissue and single-cell imaging techniques. To ameliorate these limitations, we have developed a novel method for single nanoparticle detection that incorporates a conjugated oligonucleotide as a "nanobarcode" for detection via in situ PCR. This strategy magnifies the detection signal from single nanoparticles, facilitating rapid evaluation of nanoparticle uptake by cell type over larger areas. The nanobarcoding method can enable precise analysis of nanoparticle biodistributions and expedite translation of these nanoparticles to the clinic.

  20. Optical Clearing Delivers Ultrasensitive Hyperspectral Dark-Field Imaging for Single-Cell Evaluation.

    PubMed

    Cui, Yi; Wang, Xiaolei; Ren, Wen; Liu, Jing; Irudayaraj, Joseph

    2016-03-22

    A single-cell optical clearing methodology is developed and demonstrated in hyperspectral dark-field microscopy (HSDFM) and imaging of plasmonic nanoprobes. Our strategy relies on a combination of delipidation and refractive index (RI) matching with highly biocompatible and affordable agents. Before applying the RI-matching solution, the delipidation step by using a mild solvent effectively eliminates those high-density, lipid-enriched granular structures which emit strong scattering. Upon treatment, the background scattering from cellular organelles could be repressed to a negligible level while the scattering signals from plasmonic nanomaterials increase, leading to a significant improvement of the signal-to-noise ratio (SNR). With this method established, the versatility and applicability of HSDFM are greatly enhanced. In our demonstration, quantitative mapping of the dimerization-activated receptor kinase HER2 is achieved in a single cancer cell by a nonfluorescent approach. High-resolution imaging for oncogenic mRNAs, namely ER, PR, and HER2, is performed with single labeling. More importantly, in situ multiplex detection of mRNA and protein is made possible by HSDFM since it overcomes the difficulties of complex staining and signal imbalance suffered by the conventional optical imaging. Last, we show that with optical clearing, characterization of intracellularly grown gold particulates is accomplished at an unprecedented spatiotemporal resolution. Taken together, the uniqueness of optical clearing and HSDFM is expected to open ample avenues for single-cell studies and biomedical engineering. PMID:26895095

  1. Intravital imaging of cardiac function at the single-cell level

    PubMed Central

    Aguirre, Aaron D.; Vinegoni, Claudio; Sebas, Matt; Weissleder, Ralph

    2014-01-01

    Knowledge of cardiomyocyte biology is limited by the lack of methods to interrogate single-cell physiology in vivo. Here we show that contracting myocytes can indeed be imaged with optical microscopy at high temporal and spatial resolution in the beating murine heart, allowing visualization of individual sarcomeres and measurement of the single cardiomyocyte contractile cycle. Collectively, this has been enabled by efficient tissue stabilization, a prospective real-time cardiac gating approach, an image processing algorithm for motion-artifact-free imaging throughout the cardiac cycle, and a fluorescent membrane staining protocol. Quantification of cardiomyocyte contractile function in vivo opens many possibilities for investigating myocardial disease and therapeutic intervention at the cellular level. PMID:25053815

  2. Simultaneous photoacoustic and optical attenuation imaging of single cells using photoacoustic microscopy

    NASA Astrophysics Data System (ADS)

    Moore, Michael J.; Strohm, Eric M.; Kolios, Michael C.

    2016-03-01

    A new technique for simultaneously acquiring photoacoustic images as well as images based on the optical attenuation of single cells in a human blood smear was developed. An ultra-high frequency photoacoustic microscope equipped with a 1 GHz transducer and a pulsed 532 nm laser was used to generate the images. The transducer and 20X optical objective used for laser focusing were aligned coaxially on opposing sides of the sample. Absorption of laser photons by the sample yielded conventional photoacoustic (PA) signals, while incident photons which were not attenuated by the sample were absorbed by the transducer, resulting in the formation of a photoacoustic signal (tPA) within the transducer itself. Both PA and tPA signals, which are separated in time, were recorded by the system in a single RF-line. Areas of strong signal in the PA images corresponded to dark regions in the tPA images. Additional details, including the clear delineation of the cell cytoplasm and features in red blood cells, were visible in the tPA image but not the corresponding PA image. This imaging method has applications in probing the optical absorption and attenuation characteristics of biological cells with sub-cellular resolution.

  3. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method.

    PubMed

    Lin, Jia-Ren; Fallahi-Sichani, Mohammad; Sorger, Peter K

    2015-01-01

    Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivated development of imaging methods that require specialized instrumentation, exotic reagents or proprietary protocols that are difficult to reproduce in most laboratories. Here we report a public-domain method for achieving high multiplicity single-cell IF using cyclic immunofluorescence (CycIF), a simple and versatile procedure in which four-colour staining alternates with chemical inactivation of fluorophores to progressively build a multichannel image. Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications. PMID:26399630

  4. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method

    PubMed Central

    Lin, Jia-Ren; Fallahi-Sichani, Mohammad; Sorger, Peter K.

    2015-01-01

    Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivated development of imaging methods that require specialized instrumentation, exotic reagents or proprietary protocols that are difficult to reproduce in most laboratories. Here we report a public-domain method for achieving high multiplicity single-cell IF using cyclic immunofluorescence (CycIF), a simple and versatile procedure in which four-colour staining alternates with chemical inactivation of fluorophores to progressively build a multichannel image. Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications. PMID:26399630

  5. Fluorescence time-lapse imaging of single cells targeted with a focused scanning charged-particle microbeam

    NASA Astrophysics Data System (ADS)

    Bourret, Stéphane; Vianna, François; Devès, Guillaume; Atallah, Vincent; Moretto, Philippe; Seznec, Hervé; Barberet, Philippe

    2014-04-01

    Charged particle microbeams provide unique features to study targeted and non-targeted radiation response and have recently emerged as a powerful tool to investigate radiation-induced DNA damage and repair. We have developed a charged particle microbeam delivering protons and alpha particles in the MeV energy range equipped with online time-lapse imaging capabilities. The beam is focused to a sub-micrometer beam spot under vacuum by means of a triplet of magnetic quadrupoles and extracted in air through a 200 nm Si3N4 window. The end-station is equipped with an automated fluorescence microscope used for single cell targeting and online time-lapse imaging. Cells are kept in their medium during the irradiation procedure and the sample temperature is regulated to 37 °C. An overall targeting accuracy of 2.0 ± 0.7 μm has been measured by tracking the re-localization of the XRCC1 protein. First measurements of this re-localization shows the ability of our system to follow online the radiation-induced re-localization of proteins in the first minutes after irradiation.

  6. Single-Cell Quantification of Cytosine Modifications by Hyperspectral Dark-Field Imaging

    PubMed Central

    Wang, Xiaolei; Cui, Yi; Irudayaraj, Joseph

    2016-01-01

    Epigenetic modifications on DNA, especially on cytosine, play a critical role in regulating gene expression and genome stability. It is known that the levels of different cytosine derivatives are highly dynamic and are regulated by a variety of factors that act on the chromatin. Here we report an optical methodology based on hyperspectral dark-field imaging (HSDFI) using plasmonic nanoprobes to quantify the recently identified cytosine modifications on DNA in single cells. Gold (Au) and silver (Ag) nanoparticles (NPs) functionalized with specific antibodies were used as contrast-generating agents due to their strong Local Surface Plasmon Resonance (LSPR) properties. With this powerful platform we have revealed the spatial distribution and quantity of 5-carboxylcytosine (5caC) at the different stages in cell cycle, and demonstrated that 5caC was a stably inherited epigenetic mark. We have also shown that the regional density of 5caC on a single chromosome can be mapped due to the spectral sensitivity of the nanoprobes in relation to the inter-particle distance. Notably, HSDFI enables an efficient removal of the scattering noises from non-specifically aggregated nanoprobes, to improve accuracy in the quantification of different cytosine modifications in single cells. Further, by separating the LSPR fingerprints of AuNPs and AgNPs, multiplex detection of two cytosine modifications was also performed. Our results demonstrate HSDFI as a versatile platform for spatial and spectroscopic characterization of plasmonic nanoprobe-labeled nuclear targets at the single-cell level for quantitative epigenetic screening. PMID:26505210

  7. In Vivo X-Ray Fluorescence Microtomographic Imaging of Elements in Single-Celled Fern Spores

    SciTech Connect

    Hirai, Yasuharu; Yoneyama, Akio; Hisada, Akiko; Uchida, Kenko

    2007-01-19

    We have observed in vivo three-dimensional distributions of constituent elements of single-celled spores of the fern Adiantum capillus-veneris using an X-ray fluorescence computed microtomography method. The images of these distributions are generated from a series of slice data, each of which is acquired by a sample translation-rotation method. An incident X-ray microbeam irradiates the sample with a spot size of 1 {mu}m. The high Ca concentration in the testa and the localized and overlapping Fe and Zn concentrations inside the spore are shown in three-dimensional images. The K concentration is high throughout the cell, and there are localized regions of higher density. The atomic number densities of these elements in the testa and inside the cell in a tomographic slice are estimated with a resolution of about 1 {mu}m.

  8. Vibrio cholerae biofilm growth program and architecture revealed by single-cell live imaging.

    PubMed

    Yan, Jing; Sharo, Andrew G; Stone, Howard A; Wingreen, Ned S; Bassler, Bonnie L

    2016-09-01

    Biofilms are surface-associated bacterial communities that are crucial in nature and during infection. Despite extensive work to identify biofilm components and to discover how they are regulated, little is known about biofilm structure at the level of individual cells. Here, we use state-of-the-art microscopy techniques to enable live single-cell resolution imaging of a Vibrio cholerae biofilm as it develops from one single founder cell to a mature biofilm of 10,000 cells, and to discover the forces underpinning the architectural evolution. Mutagenesis, matrix labeling, and simulations demonstrate that surface adhesion-mediated compression causes V. cholerae biofilms to transition from a 2D branched morphology to a dense, ordered 3D cluster. We discover that directional proliferation of rod-shaped bacteria plays a dominant role in shaping the biofilm architecture in V. cholerae biofilms, and this growth pattern is controlled by a single gene, rbmA Competition analyses reveal that the dense growth mode has the advantage of providing the biofilm with superior mechanical properties. Our single-cell technology can broadly link genes to biofilm fine structure and provides a route to assessing cell-to-cell heterogeneity in response to external stimuli. PMID:27555592

  9. Ex vivo Live Imaging of Single Cell Divisions in Mouse Neuroepithelium

    PubMed Central

    Piotrowska-Nitsche, Karolina; Caspary, Tamara

    2013-01-01

    We developed a system that integrates live imaging of fluorescent markers and culturing slices of embryonic mouse neuroepithelium. We took advantage of existing mouse lines for genetic cell lineage tracing: a tamoxifen-inducible Cre line and a Cre reporter line expressing dsRed upon Cre-mediated recombination. By using a relatively low level of tamoxifen, we were able to induce recombination in a small number of cells, permitting us to follow individual cell divisions. Additionally, we observed the transcriptional response to Sonic Hedgehog (Shh) signaling using an Olig2-eGFP transgenic line 1-3 and we monitored formation of cilia by infecting the cultured slice with virus expressing the cilia marker, Sstr3-GFP 4. In order to image the neuroepithelium, we harvested embryos at E8.5, isolated the neural tube, mounted the neural slice in proper culturing conditions into the imaging chamber and performed time-lapse confocal imaging. Our ex vivo live imaging method enables us to trace single cell divisions to assess the relative timing of primary cilia formation and Shh response in a physiologically relevant manner. This method can be easily adapted using distinct fluorescent markers and provides the field the tools with which to monitor cell behavior in situ and in real time. PMID:23666396

  10. Fluorescent metal nanoshell and CK19 detection on single cell image.

    PubMed

    Zhang, Jian; Fu, Yi; Li, Ge; Lakowicz, Joseph R; Zhao, Richard Y

    2011-09-16

    In this article, we report the synthesis strategy and optical properties of a novel type of fluorescence metal nanoshell when it was used as imaging agent for fluorescence cell imaging. The metal nanoshells were made with 40 nm silica cores and 10nm silver shells. Unlike typical fluorescence metal nanoshells which contain the organic dyes in the cores, novel metal nanoshells were composed of Cy5-labelled monoclonal anti-CK19 antibodies (mAbs) on the external surfaces of shells. Optical measurements to the single nanoparticles showed that in comparison with the metal free labelled mAbs, the mAb-Ag complexes displayed significantly enhanced emission intensity and dramatically shortened lifetime due to near-field interactions of fluorophores with metal. These metal nanoshells were found to be able to immunoreact with target cytokeratin 19 (CK19) molecules on the surfaces of LNCAP and HeLa cells. Fluorescence cell images were recorded on a time-resolved confocal microscope. The emissions from the metal nanoprobes could be clearly isolated from the cellular autofluorescence backgrounds on the cell images as either individuals or small clusters due to their stronger emission intensities and shorter lifetimes. These emission signals could also be precisely counted on single cell images. The count number may provide an approach for quantifying the target molecules in the cells. PMID:21867692

  11. Applications of the Single-probe: Mass Spectrometry Imaging and Single Cell Analysis under Ambient Conditions

    PubMed Central

    Rao, Wei; Pan, Ning; Yang, Zhibo

    2016-01-01

    Mass spectrometry imaging (MSI) and in-situ single cell mass spectrometry (SCMS) analysis under ambient conditions are two emerging fields with great potential for the detailed mass spectrometry (MS) analysis of biomolecules from biological samples. The single-probe, a miniaturized device with integrated sampling and ionization capabilities, is capable of performing both ambient MSI and in-situ SCMS analysis. For ambient MSI, the single-probe uses surface micro-extraction to continually conduct MS analysis of the sample, and this technique allows the creation of MS images with high spatial resolution (8.5 µm) from biological samples such as mouse brain and kidney sections. Ambient MSI has the advantage that little to no sample preparation is needed before the analysis, which reduces the amount of potential artifacts present in data acquisition and allows a more representative analysis of the sample to be acquired. For in-situ SCMS, the single-probe tip can be directly inserted into live eukaryotic cells such as HeLa cells, due to the small sampling tip size (< 10 µm), and this technique is capable of detecting a wide range of metabolites inside individual cells at near real-time. SCMS enables a greater sensitivity and accuracy of chemical information to be acquired at the single cell level, which could improve our understanding of biological processes at a more fundamental level than previously possible. The single-probe device can be potentially coupled with a variety of mass spectrometers for broad ranges of MSI and SCMS studies. PMID:27341402

  12. Fluorescent metal nanoshell and CK19 detection on single cell image

    SciTech Connect

    Zhang, Jian; Fu, Yi; Li, Ge; Lakowicz, Joseph R.; Zhao, Richard Y.

    2011-09-16

    Highlights: {yields} Novel metal nanoshell as fluorescence imaging agent. {yields} Fluorescent mAb-metal complex with enhanced intensity and shortened lifetime. {yields} Immuno-interactions of mAb-metal complexes with CK19 molecules on CNCAP and HeLa cell surfaces. {yields} Isolation of conjugated mAb-metal complexes from cellular autofluorescence on cell image. -- Abstract: In this article, we report the synthesis strategy and optical properties of a novel type of fluorescence metal nanoshell when it was used as imaging agent for fluorescence cell imaging. The metal nanoshells were made with 40 nm silica cores and 10 nm silver shells. Unlike typical fluorescence metal nanoshells which contain the organic dyes in the cores, novel metal nanoshells were composed of Cy5-labelled monoclonal anti-CK19 antibodies (mAbs) on the external surfaces of shells. Optical measurements to the single nanoparticles showed that in comparison with the metal free labelled mAbs, the mAb-Ag complexes displayed significantly enhanced emission intensity and dramatically shortened lifetime due to near-field interactions of fluorophores with metal. These metal nanoshells were found to be able to immunoreact with target cytokeratin 19 (CK19) molecules on the surfaces of LNCAP and HeLa cells. Fluorescence cell images were recorded on a time-resolved confocal microscope. The emissions from the metal nanoprobes could be clearly isolated from the cellular autofluorescence backgrounds on the cell images as either individuals or small clusters due to their stronger emission intensities and shorter lifetimes. These emission signals could also be precisely counted on single cell images. The count number may provide an approach for quantifying the target molecules in the cells.

  13. Automated labeling in document images

    NASA Astrophysics Data System (ADS)

    Kim, Jongwoo; Le, Daniel X.; Thoma, George R.

    2000-12-01

    The National Library of Medicine (NLM) is developing an automated system to produce bibliographic records for its MEDLINER database. This system, named Medical Article Record System (MARS), employs document image analysis and understanding techniques and optical character recognition (OCR). This paper describes a key module in MARS called the Automated Labeling (AL) module, which labels all zones of interest (title, author, affiliation, and abstract) automatically. The AL algorithm is based on 120 rules that are derived from an analysis of journal page layouts and features extracted from OCR output. Experiments carried out on more than 11,000 articles in over 1,000 biomedical journals show the accuracy of this rule-based algorithm to exceed 96%.

  14. High-content screening of drug-induced cardiotoxicity using quantitative single cell imaging cytometry on microfluidic device.

    PubMed

    Kim, Min Jung; Lee, Su Chul; Pal, Sukdeb; Han, Eunyoung; Song, Joon Myong

    2011-01-01

    Drug-induced cardiotoxicity or cytotoxicity followed by cell death in cardiac muscle is one of the major concerns in drug development. Herein, we report a high-content quantitative multicolor single cell imaging tool for automatic screening of drug-induced cardiotoxicity in an intact cell. A tunable multicolor imaging system coupled with a miniaturized sample platform was destined to elucidate drug-induced cardiotoxicity via simultaneous quantitative monitoring of intracellular sodium ion concentration, potassium ion channel permeability and apoptosis/necrosis in H9c2(2-1) cell line. Cells were treated with cisapride (a human ether-à-go-go-related gene (hERG) channel blocker), digoxin (Na(+)/K(+)-pump blocker), camptothecin (anticancer agent) and a newly synthesized anti-cancer drug candidate (SH-03). Decrease in potassium channel permeability in cisapride-treated cells indicated that it can also inhibit the trafficking of the hERG channel. Digoxin treatment resulted in an increase of intracellular [Na(+)]. However, it did not affect potassium channel permeability. Camptothecin and SH-03 did not show any cytotoxic effect at normal use (≤300 nM and 10 μM, respectively). This result clearly indicates the potential of SH-03 as a new anticancer drug candidate. The developed method was also used to correlate the cell death pathway with alterations in intracellular [Na(+)]. The developed protocol can directly depict and quantitate targeted cellular responses, subsequently enabling an automated, easy to operate tool that is applicable to drug-induced cytotoxicity monitoring with special reference to next generation drug discovery screening. This multicolor imaging based system has great potential as a complementary system to the conventional patch clamp technique and flow cytometric measurement for the screening of drug cardiotoxicity. PMID:21060932

  15. Design of a single-cell positioning controller using electroosmotic flow and image processing.

    PubMed

    Ay, Chyung; Young, Chao-Wang; Chen, Jhong-Yin

    2013-01-01

    The objective of the current research was not only to provide a fast and automatic positioning platform for single cells, but also improved biomolecular manipulation techniques. In this study, an automatic platform for cell positioning using electroosmotic flow and image processing technology was designed. The platform was developed using a PCI image acquisition interface card for capturing images from a microscope and then transferring them to a computer using human-machine interface software. This software was designed by the Laboratory Virtual Instrument Engineering Workbench, a graphical language for finding cell positions and viewing the driving trace, and the fuzzy logic method for controlling the voltage or time of an electric field. After experiments on real human leukemic cells (U-937), the success of the cell positioning rate achieved by controlling the voltage factor reaches 100% within 5 s. A greater precision is obtained when controlling the time factor, whereby the success rate reaches 100% within 28 s. Advantages in both high speed and high precision are attained if these two voltage and time control methods are combined. The control speed with the combined method is about 5.18 times greater than that achieved by the time method, and the control precision with the combined method is more than five times greater than that achieved by the voltage method. PMID:23698272

  16. Single-cell in vivo imaging of adult neural stem cells in the zebrafish telencephalon.

    PubMed

    Barbosa, Joana S; Di Giaimo, Rossella; Götz, Magdalena; Ninkovic, Jovica

    2016-08-01

    Adult neural stem cells (aNSCs) in zebrafish produce mature neurons throughout their entire life span in both the intact and regenerating brain. An understanding of the behavior of aNSCs in their intact niche and during regeneration in vivo should facilitate the identification of the molecular mechanisms controlling regeneration-specific cellular events. A greater understanding of the process in regeneration-competent species may enable regeneration to be achieved in regeneration-incompetent species, including humans. Here we describe a protocol for labeling and repetitive imaging of aNSCs in vivo. We label single aNSCs, allowing nonambiguous re-identification of single cells in repetitive imaging sessions using electroporation of a red-reporter plasmid in Tg(gfap:GFP)mi2001 transgenic fish expressing GFP in aNSCs. We image using two-photon microscopy through the thinned skull of anesthetized and immobilized fish. Our protocol allows imaging every 2 d for a period of up to 1 month. This methodology allowed the visualization of aNSC behavior in vivo in their natural niche, in contrast to previously available technologies, which rely on the imaging of either dissociated cells or tissue slices. We used this protocol to follow the mode of aNSC division, fate changes and cell death in both the intact and injured zebrafish telencephalon. This experimental setup can be widely used, with minimal prior experience, to assess key factors for processes that modulate aNSC behavior. A typical experiment with data analysis takes up to 1.5 months. PMID:27362338

  17. Protein-Specific Imaging of O-GlcNAcylation in Single Cells.

    PubMed

    Lin, Wei; Gao, Ling; Chen, Xing

    2015-12-01

    Thousands of intracellular proteins are post-translationally modified with O-GlcNAc, and O-GlcNAcylation impacts the function of modified proteins and mediates diverse biological processes. However, the ubiquity of this important glycosylation makes it highly challenging to probe the O-GlcNAcylation state of a specific protein at the cellular level. Herein, we report the development of a FLIM-FRET-based strategy, which exploits the spatial proximity of the O-GlcNAc moiety and the attaching protein, for protein-specific imaging of O-GlcNAcylation in single cells. We demonstrated this strategy by imaging the O-GlcNAcylation state of tau and β-catenin inside the cells. Furthermore, the changes in tau O-GlcNAcylation were monitored when the overall cellular O-GlcNAc was pharmacologically altered by using the OGT and OGA inhibitors. We envision that the FLIM-FRET strategy will be broadly applicable to probe the O-GlcNAcylation state of various proteins in the cells. PMID:26488919

  18. A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

    PubMed Central

    Raghu, Deepa; Christodoulides, Joseph A.; Delehanty, James B.; Byers, Jeff M.; Raphael, Marc P.

    2015-01-01

    Inter-cellular communication is an integral part of a complex system that helps in maintaining basic cellular activities. As a result, the malfunctioning of such signaling can lead to many disorders. To understand cell-to-cell signaling, it is essential to study the spatial and temporal nature of the secreted molecules from the cell without disturbing the local environment. Various assays have been developed to study protein secretion, however, these methods are typically based on fluorescent probes which disrupt the relevant signaling pathways. To overcome this limitation, a label-free technique is required. In this paper, we describe the fabrication and application of a label-free localized surface plasmon resonance imaging (LSPRi) technology capable of detecting protein secretions from a single cell. The plasmonic nanostructures are lithographically patterned onto a standard glass coverslip and can be excited using visible light on commercially available light microscopes. Only a small fraction of the coverslip is covered by the nanostructures and hence this technique is well suited for combining common techniques such as fluorescence and bright-field imaging. A multidisciplinary approach is used in this protocol which incorporates sensor nanofabrication and subsequent biofunctionalization, binding kinetics characterization of ligand and analyte, the integration of the chip and live cells, and the analysis of the measured signal. As a whole, this technology enables a general label-free approach towards mapping cellular secretions and correlating them with the responses of nearby cells. PMID:26650542

  19. Single-cell real-time imaging of transgene expression upon lipofection.

    PubMed

    Fiume, Giuseppe; Di Rienzo, Carmine; Marchetti, Laura; Pozzi, Daniela; Caracciolo, Giulio; Cardarelli, Francesco

    2016-05-20

    Here we address the process of lipofection by quantifying the expression of a genetically-encoded fluorescent reporter at the single-cell level, and in real-time, by confocal imaging in live cells. The Lipofectamine gold-standard formulation is compared to the alternative promising DC-Chol/DOPE formulation. In both cases, we report that only dividing cells are able to produce a detectable amount of the fluorescent reporter protein. Notably, by measuring fluorescence over time in each pair of daughter cells, we find that Lipofectamine-based transfection statistically yields a remarkably higher degree of "symmetry" in protein expression between daughter cells as compared to DC-Chol/DOPE. A model is envisioned in which the degree of symmetry of protein expression is linked to the number of bioavailable DNA copies within the cell before nuclear breakdown. Reported results open new perspectives for the understanding of the lipofection mechanism and define a new experimental platform for the quantitative comparison of transfection reagents. PMID:27012199

  20. Automated analysis of clonal cancer cells by intravital imaging.

    PubMed

    Coffey, Sarah Earley; Giedt, Randy J; Weissleder, Ralph

    2013-07-01

    Longitudinal analyses of single cell lineages over prolonged periods have been challenging particularly in processes characterized by high cell turn-over such as inflammation, proliferation, or cancer. RGB marking has emerged as an elegant approach for enabling such investigations. However, methods for automated image analysis continue to be lacking. Here, to address this, we created a number of different multicolored poly- and monoclonal cancer cell lines for in vitro and in vivo use. To classify these cells in large scale data sets, we subsequently developed and tested an automated algorithm based on hue selection. Our results showed that this method allows accurate analyses at a fraction of the computational time required by more complex color classification methods. Moreover, the methodology should be broadly applicable to both in vitro and in vivo analyses. PMID:24349895

  1. Cell-specific localization of alkaloids in Catharanthus roseus stem tissue measured with Imaging MS and Single-cell MS.

    PubMed

    Yamamoto, Kotaro; Takahashi, Katsutoshi; Mizuno, Hajime; Anegawa, Aya; Ishizaki, Kimitsune; Fukaki, Hidehiro; Ohnishi, Miwa; Yamazaki, Mami; Masujima, Tsutomu; Mimura, Tetsuro

    2016-04-01

    Catharanthus roseus (L.) G. Don is a medicinal plant well known for producing antitumor drugs such as vinblastine and vincristine, which are classified as terpenoid indole alkaloids (TIAs). The TIA metabolic pathway in C. roseus has been extensively studied. However, the localization of TIA intermediates at the cellular level has not been demonstrated directly. In the present study, the metabolic pathway of TIA in C. roseus was studied with two forefront metabolomic techniques, that is, Imaging mass spectrometry (MS) and live Single-cell MS, to elucidate cell-specific TIA localization in the stem tissue. Imaging MS indicated that most TIAs localize in the idioblast and laticifer cells, which emit blue fluorescence under UV excitation. Single-cell MS was applied to four different kinds of cells [idioblast (specialized parenchyma cell), laticifer, parenchyma, and epidermal cells] in the stem longitudinal section. Principal component analysis of Imaging MS and Single-cell MS spectra of these cells showed that similar alkaloids accumulate in both idioblast cell and laticifer cell. From MS/MS analysis of Single-cell MS spectra, catharanthine, ajmalicine, and strictosidine were found in both cell types in C. roseus stem tissue, where serpentine was also accumulated. Based on these data, we discuss the significance of TIA synthesis and accumulation in the idioblast and laticifer cells of C. roseus stem tissue. PMID:27001858

  2. GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images.

    PubMed

    Trinh, Anne; Rye, Inga H; Almendro, Vanessa; Helland, Aslaug; Russnes, Hege G; Markowetz, Florian

    2014-01-01

    Molecular analysis has revealed extensive intra-tumor heterogeneity in human cancer samples, but cannot identify cell-to-cell variations within the tissue microenvironment. In contrast, in situ analysis can identify genetic aberrations in phenotypically defined cell subpopulations while preserving tissue-context specificity. GoIFISHGoIFISH is a widely applicable, user-friendly system tailored for the objective and semi-automated visualization, detection and quantification of genomic alterations and protein expression obtained from fluorescence in situ analysis. In a sample set of HER2-positive breast cancers GoIFISHGoIFISH is highly robust in visual analysis and its accuracy compares favorably to other leading image analysis methods. GoIFISHGoIFISH is freely available at www.sourceforge.net/projects/goifish/. PMID:25168174

  3. Single-cell imaging of inflammatory caspase dimerization reveals differential recruitment to inflammasomes.

    PubMed

    Sanders, M G; Parsons, M J; Howard, A G A; Liu, J; Fassio, S R; Martinez, J A; Bouchier-Hayes, L

    2015-01-01

    The human inflammatory caspases, including caspase-1, -4, -5 and -12, are considered as key regulators of innate immunity protecting from sepsis and numerous inflammatory diseases. Caspase-1 is activated by proximity-induced dimerization following recruitment to inflammasomes but the roles of the remaining inflammatory caspases in inflammasome assembly are unclear. Here, we use caspase bimolecular fluorescence complementation to visualize the assembly of inflammasomes and dimerization of inflammatory caspases in single cells. We observed caspase-1 dimerization induced by the coexpression of a range of inflammasome proteins and by lipospolysaccharide (LPS) treatment in primary macrophages. Caspase-4 and -5 were only dimerized by select inflammasome proteins, whereas caspase-12 dimerization was not detected by any investigated treatment. Strikingly, we determined that certain inflammasome proteins could induce heterodimerization of caspase-1 with caspase-4 or -5. Caspase-5 homodimerization and caspase-1/-5 heterodimerization was also detected in LPS-primed primary macrophages in response to cholera toxin subunit B. The subcellular localization and organization of the inflammasome complexes varied markedly depending on the upstream trigger and on which caspase or combination of caspases were recruited. Three-dimensional imaging of the ASC (apoptosis-associated speck-like protein containing a caspase recruitment domain)/caspase-1 complexes revealed a large spherical complex of ASC with caspase-1 dimerized on the outer surface. In contrast, NALP1 (NACHT leucine-rich repeat protein 1)/caspase-1 complexes formed large filamentous structures. These results argue that caspase-1, -4 or -5 can be recruited to inflammasomes under specific circumstances, often leading to distinctly organized and localized complexes that may impact the functions of these proteases. PMID:26158519

  4. Single-cell imaging of inflammatory caspase dimerization reveals differential recruitment to inflammasomes

    PubMed Central

    Sanders, M G; Parsons, M J; Howard, A G A; Liu, J; Fassio, S R; Martinez, J A; Bouchier-Hayes, L

    2015-01-01

    The human inflammatory caspases, including caspase-1, -4, -5 and -12, are considered as key regulators of innate immunity protecting from sepsis and numerous inflammatory diseases. Caspase-1 is activated by proximity-induced dimerization following recruitment to inflammasomes but the roles of the remaining inflammatory caspases in inflammasome assembly are unclear. Here, we use caspase bimolecular fluorescence complementation to visualize the assembly of inflammasomes and dimerization of inflammatory caspases in single cells. We observed caspase-1 dimerization induced by the coexpression of a range of inflammasome proteins and by lipospolysaccharide (LPS) treatment in primary macrophages. Caspase-4 and -5 were only dimerized by select inflammasome proteins, whereas caspase-12 dimerization was not detected by any investigated treatment. Strikingly, we determined that certain inflammasome proteins could induce heterodimerization of caspase-1 with caspase-4 or -5. Caspase-5 homodimerization and caspase-1/-5 heterodimerization was also detected in LPS-primed primary macrophages in response to cholera toxin subunit B. The subcellular localization and organization of the inflammasome complexes varied markedly depending on the upstream trigger and on which caspase or combination of caspases were recruited. Three-dimensional imaging of the ASC (apoptosis-associated speck-like protein containing a caspase recruitment domain)/caspase-1 complexes revealed a large spherical complex of ASC with caspase-1 dimerized on the outer surface. In contrast, NALP1 (NACHT leucine-rich repeat protein 1)/caspase-1 complexes formed large filamentous structures. These results argue that caspase-1, -4 or -5 can be recruited to inflammasomes under specific circumstances, often leading to distinctly organized and localized complexes that may impact the functions of these proteases. PMID:26158519

  5. Binary System for MicroRNA-Targeted Imaging in Single Cells and Photothermal Cancer Therapy.

    PubMed

    Qian, Ruo-Can; Cao, Yue; Long, Yi-Tao

    2016-09-01

    Abnormal expression of microRNAs (miRNAs) is often associated with tumorigenesis, metastasis, and progression. Among them, miRNA-21 is found to be overexpressed in most of the cancer cells. Here, a binary system is designed for miRNA-21 targeted imaging and photothermal treatment in single cells. The binary system is composed by a pair of probes (probe-1 and probe-2), which are encapsulated in liposomes for cell delivery. Both of the two probes adopt gold nanoparticles (AuNPs) as the core material, and the AuNPs are functionalized with Cy5-marked molecular beacon (MB-1/MB-2 for probe-1/probe-2, respectively). The loop part of MBs are designed to be complementary with miRNA-21. Therefore, after the binary system enters into the cytoplasm, MBs can be opened upon miRNA-21 triggered hybridization, which turns "on" the fluorescence of Cy5 for the localization of miRNA-21. At the same time, a cross-linking between the probes occurs since the far ends of MB-1 and MB-2 are designed to be complementary with each other. The miRNA-induced aggregation shifts the absorption of AuNPs to near-infrared, which can be observed under dark-field microscopy (DFM) and used for the following photothermal therapy. Under near-infrared (NIR) irradiation, MCF-7 breast cancer cells are successfully killed. The proposed system can be further applied in tumor-bearing mice and shows significant therapeutic effect. This work provides a new tool for intracellular miRNA analysis and targeted treatment against cancer. PMID:27482754

  6. Fast vibrational imaging of single cells and tissues by stimulated Raman scattering microscopy.

    PubMed

    Zhang, Delong; Wang, Ping; Slipchenko, Mikhail N; Cheng, Ji-Xin

    2014-08-19

    Traditionally, molecules are analyzed in a test tube. Taking biochemistry as an example, the majority of our knowledge about cellular content comes from analysis of fixed cells or tissue homogenates using tools such as immunoblotting and liquid chromatography-mass spectrometry. These tools can indicate the presence of molecules but do not provide information on their location or interaction with each other in real time, restricting our understanding of the functions of the molecule under study. For real-time imaging of labeled molecules in live cells, fluorescence microscopy is the tool of choice. Fluorescent labels, however, are too bulky for small molecules such as fatty acids, amino acids, and cholesterol. These challenges highlight a critical need for development of chemical imaging platforms that allow in situ or in vivo analysis of molecules. Vibrational spectroscopy based on spontaneous Raman scattering is widely used for label-free analysis of chemical content in cells and tissues. However, the Raman process is a weak effect, limiting its application for fast chemical imaging of a living system. With high imaging speed and 3D spatial resolution, coherent Raman scattering microscopy is enabling a new approach for real-time vibrational imaging of single cells in a living system. In most experiments, coherent Raman processes involve two excitation fields denoted as pump at ωp and Stokes at ωs. When the beating frequency between the pump and Stokes fields (ωp - ωs) is resonant with a Raman-active molecular vibration, four major coherent Raman scattering processes occur simultaneously, namely, coherent anti-Stokes Raman scattering (CARS) at (ωp - ωs) + ωp, coherent Stokes Raman scattering (CSRS) at ωs - (ωp - ωs), stimulated Raman gain (SRG) at ωs, and stimulated Raman loss (SRL) at ωp. In SRG, the Stokes beam experiences a gain in intensity, whereas in SRL, the pump beam experiences a loss. Both SRG and SRL belong to stimulated Raman scattering (SRS

  7. Fast Vibrational Imaging of Single Cells and Tissues by Stimulated Raman Scattering Microscopy

    PubMed Central

    2015-01-01

    Conspectus Traditionally, molecules are analyzed in a test tube. Taking biochemistry as an example, the majority of our knowledge about cellular content comes from analysis of fixed cells or tissue homogenates using tools such as immunoblotting and liquid chromatography–mass spectrometry. These tools can indicate the presence of molecules but do not provide information on their location or interaction with each other in real time, restricting our understanding of the functions of the molecule under study. For real-time imaging of labeled molecules in live cells, fluorescence microscopy is the tool of choice. Fluorescent labels, however, are too bulky for small molecules such as fatty acids, amino acids, and cholesterol. These challenges highlight a critical need for development of chemical imaging platforms that allow in situ or in vivo analysis of molecules. Vibrational spectroscopy based on spontaneous Raman scattering is widely used for label-free analysis of chemical content in cells and tissues. However, the Raman process is a weak effect, limiting its application for fast chemical imaging of a living system. With high imaging speed and 3D spatial resolution, coherent Raman scattering microscopy is enabling a new approach for real-time vibrational imaging of single cells in a living system. In most experiments, coherent Raman processes involve two excitation fields denoted as pump at ωp and Stokes at ωs. When the beating frequency between the pump and Stokes fields (ωp – ωs) is resonant with a Raman-active molecular vibration, four major coherent Raman scattering processes occur simultaneously, namely, coherent anti-Stokes Raman scattering (CARS) at (ωp – ωs) + ωp, coherent Stokes Raman scattering (CSRS) at ωs – (ωp – ωs), stimulated Raman gain (SRG) at ωs, and stimulated Raman loss (SRL) at ωp. In SRG, the Stokes beam experiences a gain in intensity, whereas in SRL, the pump beam experiences a loss. Both SRG and SRL belong to

  8. An automated imaging system for radiation biodosimetry.

    PubMed

    Garty, Guy; Bigelow, Alan W; Repin, Mikhail; Turner, Helen C; Bian, Dakai; Balajee, Adayabalam S; Lyulko, Oleksandra V; Taveras, Maria; Yao, Y Lawrence; Brenner, David J

    2015-07-01

    We describe here an automated imaging system developed at the Center for High Throughput Minimally Invasive Radiation Biodosimetry. The imaging system is built around a fast, sensitive sCMOS camera and rapid switchable LED light source. It features complete automation of all the steps of the imaging process and contains built-in feedback loops to ensure proper operation. The imaging system is intended as a back end to the RABiT-a robotic platform for radiation biodosimetry. It is intended to automate image acquisition and analysis for four biodosimetry assays for which we have developed automated protocols: The Cytokinesis Blocked Micronucleus assay, the γ-H2AX assay, the Dicentric assay (using PNA or FISH probes) and the RABiT-BAND assay. PMID:25939519

  9. An Automated Imaging System for Radiation Biodosimetry

    PubMed Central

    Garty, Guy; Bigelow, Alan W.; Repin, Mikhail; Turner, Helen C.; Bian, Dakai; Balajee, Adayabalam S.; Lyulko, Oleksandra V.; Taveras, Maria; Yao, Y. Lawrence; Brenner, David J.

    2015-01-01

    We describe here an automated imaging system developed at the Center for High Throughput Minimally Invasive Radiation Biodosimetry. The imaging system is built around a fast, sensitive sCMOS camera and rapid switchable LED light source. It features complete automation of all the steps of the imaging process and contains built-in feedback loops to ensure proper operation. The imaging system is intended as a back end to the RABiT – a robotic platform for radiation biodosimetry. It is intended to automate image acquisition and analysis for four biodosimetry assays for which we have developed automated protocols: The Cytokinesis Blocked Micronucleus assay, the γ-H2AX assay, the Dicentric assay (using PNA or FISH probes) and the RABiT-BAND assay. PMID:25939519

  10. Dual-modal three-dimensional imaging of single cells with isometric high resolution using an optical projection tomography microscope

    NASA Astrophysics Data System (ADS)

    Miao, Qin; Rahn, J. Richard; Tourovskaia, Anna; Meyer, Michael G.; Neumann, Thomas; Nelson, Alan C.; Seibel, Eric J.

    2009-11-01

    The practice of clinical cytology relies on bright-field microscopy using absorption dyes like hematoxylin and eosin in the transmission mode, while the practice of research microscopy relies on fluorescence microscopy in the epi-illumination mode. The optical projection tomography microscope is an optical microscope that can generate 3-D images of single cells with isometric high resolution both in absorption and fluorescence mode. Although the depth of field of the microscope objective is in the submicron range, it can be extended by scanning the objective's focal plane. The extended depth of field image is similar to a projection in a conventional x-ray computed tomography. Cells suspended in optical gel flow through a custom-designed microcapillary. Multiple pseudoprojection images are taken by rotating the microcapillary. After these pseudoprojection images are further aligned, computed tomography methods are applied to create 3-D reconstruction. 3-D reconstructed images of single cells are shown in both absorption and fluorescence mode. Fluorescence spatial resolution is measured at 0.35 μm in both axial and lateral dimensions. Since fluorescence and absorption images are taken in two different rotations, mechanical error may cause misalignment of 3-D images. This mechanical error is estimated to be within the resolution of the system.

  11. Automated imaging system for single molecules

    DOEpatents

    Schwartz, David Charles; Runnheim, Rodney; Forrest, Daniel

    2012-09-18

    There is provided a high throughput automated single molecule image collection and processing system that requires minimal initial user input. The unique features embodied in the present disclosure allow automated collection and initial processing of optical images of single molecules and their assemblies. Correct focus may be automatically maintained while images are collected. Uneven illumination in fluorescence microscopy is accounted for, and an overall robust imaging operation is provided yielding individual images prepared for further processing in external systems. Embodiments described herein are useful in studies of any macromolecules such as DNA, RNA, peptides and proteins. The automated image collection and processing system and method of same may be implemented and deployed over a computer network, and may be ergonomically optimized to facilitate user interaction.

  12. A robotics platform for automated batch fabrication of high density, microfluidics-based DNA microarrays, with applications to single cell, multiplex assays of secreted proteins

    PubMed Central

    Ahmad, Habib; Sutherland, Alex; Shin, Young Shik; Hwang, Kiwook; Qin, Lidong; Krom, Russell-John; Heath, James R.

    2011-01-01

    Microfluidics flow-patterning has been utilized for the construction of chip-scale miniaturized DNA and protein barcode arrays. Such arrays have been used for specific clinical and fundamental investigations in which many proteins are assayed from single cells or other small sample sizes. However, flow-patterned arrays are hand-prepared, and so are impractical for broad applications. We describe an integrated robotics/microfluidics platform for the automated preparation of such arrays, and we apply it to the batch fabrication of up to eighteen chips of flow-patterned DNA barcodes. The resulting substrates are comparable in quality with hand-made arrays and exhibit excellent substrate-to-substrate consistency. We demonstrate the utility and reproducibility of robotics-patterned barcodes by utilizing two flow-patterned chips for highly parallel assays of a panel of secreted proteins from single macrophage cells. PMID:21974603

  13. Automated reduction of instantaneous flow field images

    NASA Technical Reports Server (NTRS)

    Reynolds, G. A.; Short, M.; Whiffen, M. C.

    1987-01-01

    An automated data reduction system for the analysis of interference fringe patterns obtained using the particle image velocimetry technique is described. This system is based on digital image processing techniques that have provided the flexibility and speed needed to obtain more complete automation of the data reduction process. As approached here, this process includes scanning/searching for data on the photographic record, recognition of fringe patterns of sufficient quality, and, finally, analysis of these fringes to determine a local measure of the velocity magnitude and direction. The fringe analysis as well as the fringe image recognition are based on full frame autocorrelation techniques using parallel processing capabilities.

  14. In vivo X-ray elemental imaging of single cell model organisms manipulated by laser-based optical tweezers

    PubMed Central

    Vergucht, Eva; Brans, Toon; Beunis, Filip; Garrevoet, Jan; De Rijcke, Maarten; Bauters, Stephen; Deruytter, David; Vandegehuchte, Michiel; Van Nieuwenhove, Ine; Janssen, Colin; Burghammer, Manfred; Vincze, Laszlo

    2015-01-01

    We report on a radically new elemental imaging approach for the analysis of biological model organisms and single cells in their natural, in vivo state. The methodology combines optical tweezers (OT) technology for non-contact, laser-based sample manipulation with synchrotron radiation confocal X-ray fluorescence (XRF) microimaging for the first time. The main objective of this work is to establish a new method for in vivo elemental imaging in a two-dimensional (2D) projection mode in free-standing biological microorganisms or single cells, present in their aqueous environment. Using the model organism Scrippsiella trochoidea, a first proof of principle experiment at beamline ID13 of the European Synchrotron Radiation Facility (ESRF) demonstrates the feasibility of the OT XRF methodology, which is applied to study mixture toxicity of Cu-Ni and Cu-Zn as a result of elevated exposure. We expect that the new OT XRF methodology will significantly contribute to the new trend of investigating microorganisms at the cellular level with added in vivo capability. PMID:25762511

  15. Monitoring metabolic responses to chemotherapy in single cells and tumors using nanostructure-initiator mass spectrometry (NIMS) imaging

    PubMed Central

    2013-01-01

    Background Tissue imaging of treatment-induced metabolic changes is useful for optimizing cancer therapies, but commonly used methods require trade-offs between assay sensitivity and spatial resolution. Nanostructure-Initiator Mass Spectrometry imaging (NIMS) permits quantitative co-localization of drugs and treatment response biomarkers in cells and tissues with relatively high resolution. The present feasibility studies use NIMS to monitor phosphorylation of 3′-deoxy-3′-fluorothymidine (FLT) to FLT-MP in lymphoma cells and solid tumors as an indicator of drug exposure and pharmacodynamic responses. Methods NIMS analytical sensitivity and spatial resolution were examined in cultured Burkitt’s lymphoma cells treated briefly with Rapamycin or FLT. Sample aliquots were dispersed on NIMS surfaces for single cell imaging and metabolic profiling, or extracted in parallel for LC-MS/MS analysis. Docetaxel-induced changes in FLT metabolism were also monitored in tissues and tissue extracts from mice bearing drug-sensitive tumor xenografts. To correct for variations in FLT disposition, the ratio of FLT-MP to FLT was used as a measure of TK1 thymidine kinase activity in NIMS images. TK1 and tumor-specific luciferase were measured in adjacent tissue sections using immuno-fluorescence microscopy. Results NIMS and LC-MS/MS yielded consistent results. FLT, FLT-MP, and Rapamycin were readily detected at the single cell level using NIMS. Rapid changes in endogenous metabolism were detected in drug-treated cells, and rapid accumulation of FLT-MP was seen in most, but not all imaged cells. FLT-MP accumulation in xenograft tumors was shown to be sensitive to Docetaxel treatment, and TK1 immunoreactivity co-localized with tumor-specific antigens in xenograft tumors, supporting a role for xenograft-derived TK1 activity in tumor FLT metabolism. Conclusions NIMS is suitable for monitoring drug exposure and metabolite biotransformation with essentially single cell resolution, and

  16. Single Cell Assay for Molecular Diagnostics and Medicine: Monitoring Intracellular Concentrations of Macromolecules by Two-photon Fluorescence Lifetime Imaging.

    PubMed

    Pliss, Artem; Peng, Xiao; Liu, Lixin; Kuzmin, Andrey; Wang, Yan; Qu, Junle; Li, Yuee; Prasad, Paras N

    2015-01-01

    Molecular organization of a cell is dynamically transformed along the course of cellular physiological processes, pathologic developments or derived from interactions with drugs. The capability to measure and monitor concentrations of macromolecules in a single cell would greatly enhance studies of cellular processes in heterogeneous populations. In this communication, we introduce and experimentally validate a bio-analytical single-cell assay, wherein the overall concentration of macromolecules is estimated in specific subcellular domains, such as structure-function compartments of the cell nucleus as well as in nucleoplasm. We describe quantitative mapping of local biomolecular concentrations, either intrinsic relating to the functional and physiological state of a cell, or altered by a therapeutic drug action, using two-photon excited fluorescence lifetime imaging (FLIM). The proposed assay utilizes a correlation between the fluorescence lifetime of fluorophore and the refractive index of its microenvironment varying due to changes in the concentrations of macromolecules, mainly proteins. Two-photon excitation in Near-Infra Red biological transparency window reduced the photo-toxicity in live cells, as compared with a conventional single-photon approach. Using this new assay, we estimated average concentrations of proteins in the compartments of nuclear speckles and in the nucleoplasm at ~150 mg/ml, and in the nucleolus at ~284 mg/ml. Furthermore, we show a profound influence of pharmaceutical inhibitors of RNA synthesis on intracellular protein density. The approach proposed here will significantly advance theranostics, and studies of drug-cell interactions at the single-cell level, aiding development of personal molecular medicine. PMID:26155309

  17. Single Cell Assay for Molecular Diagnostics and Medicine: Monitoring Intracellular Concentrations of Macromolecules by Two-photon Fluorescence Lifetime Imaging

    PubMed Central

    Pliss, Artem; Peng, Xiao; Liu, Lixin; Kuzmin, Andrey; Wang, Yan; Qu, Junle; Li, Yuee; Prasad, Paras N

    2015-01-01

    Molecular organization of a cell is dynamically transformed along the course of cellular physiological processes, pathologic developments or derived from interactions with drugs. The capability to measure and monitor concentrations of macromolecules in a single cell would greatly enhance studies of cellular processes in heterogeneous populations. In this communication, we introduce and experimentally validate a bio-analytical single-cell assay, wherein the overall concentration of macromolecules is estimated in specific subcellular domains, such as structure-function compartments of the cell nucleus as well as in nucleoplasm. We describe quantitative mapping of local biomolecular concentrations, either intrinsic relating to the functional and physiological state of a cell, or altered by a therapeutic drug action, using two-photon excited fluorescence lifetime imaging (FLIM). The proposed assay utilizes a correlation between the fluorescence lifetime of fluorophore and the refractive index of its microenvironment varying due to changes in the concentrations of macromolecules, mainly proteins. Two-photon excitation in Near-Infra Red biological transparency window reduced the photo-toxicity in live cells, as compared with a conventional single-photon approach. Using this new assay, we estimated average concentrations of proteins in the compartments of nuclear speckles and in the nucleoplasm at ~150 mg/ml, and in the nucleolus at ~284 mg/ml. Furthermore, we show a profound influence of pharmaceutical inhibitors of RNA synthesis on intracellular protein density. The approach proposed here will significantly advance theranostics, and studies of drug-cell interactions at the single-cell level, aiding development of personal molecular medicine. PMID:26155309

  18. Functional imaging of a single cell: far-field infrared super-resolution microscopy using autofluorescence detection

    NASA Astrophysics Data System (ADS)

    Ohmori, Tsutomu; Inoue, Keiichi; Sakai, Makoto; Fujii, Masaaki; Ishihara, Miya; Kikuchi, Makoto

    2009-02-01

    We demonstrated cell imaging without any stain by far-field 2-color infrared (IR) super-resolution microscopy, combining laser fluorescence microscope and picosecond transient fluorescence detected IR (TFD-IR) spectroscopy. TFD-IR spectroscopy detects IR absorption by monitoring fluorescence due to an electronic transition from a vibrational excited level by an additional visible light. By using the IR microscopy based on TFD-IR spectroscopy, the spatial resolution of the image can be increased to the visible diffraction limit of sub-μm, i.e., the IR is super-resolved. Cell auto-fluorescence due to flavin molecules was monitored for label-free detection of the cellular components. The fluorescence image of an A549 cell was obtained by introducing both an IR light at 3300 nm and a visible light at 560 nm. The spatial resolution of the image was estimated to be 1.6 μm. This is about 2.5-times higher resolution than the diffraction limit of IR light. The fluorescence intensity of the images at 3448 nm was smaller than that at 3300 nm, corresponding to the smaller IR absorption. Therefore, IR spectral imaging of a single cell was achieved with superresolution.

  19. Atomic force microscopy imaging and 3-D reconstructions of serial thin sections of a single cell and its interior structures

    PubMed Central

    Chen, Yong; Cai, Jiye; Zhao, Tao; Wang, Chenxi; Dong, Shuo; Luo, Shuqian; Chen, Zheng W.

    2010-01-01

    The thin sectioning has been widely applied in electron microscopy (EM), and successfully used for an in situ observation of inner ultrastructure of cells. This powerful technique has recently been extended to the research field of atomic force microscopy (AFM). However, there have been no reports describing AFM imaging of serial thin sections and three-dimensional (3-D) reconstruction of cells and their inner structures. In the present study, we used AFM to scan serial thin sections approximately 60nm thick of a mouse embryonic stem (ES) cell, and to observe the in situ inner ultrastructure including cell membrane, cytoplasm, mitochondria, nucleus membrane, and linear chromatin. The high-magnification AFM imaging of single mitochondria clearly demonstrated the outer membrane, inner boundary membrane and cristal membrane of mitochondria in the cellular compartment. Importantly, AFM imaging on six serial thin sections of a single mouse ES cell showed that mitochondria underwent sequential changes in the number, morphology and distribution. These nanoscale images allowed us to perform 3-D surface reconstruction of interested interior structures in cells. Based on the serial in situ images, 3-D models of morphological characteristics, numbers and distributions of interior structures of the single ES cells were validated and reconstructed. Our results suggest that the combined AFM and serial-thin-section technique is useful for the nanoscale imaging and 3-D reconstruction of single cells and their inner structures. This technique may facilitate studies of proliferating and differentiating stages of stem cells or somatic cells at a nanoscale. PMID:15850704

  20. Imaging single cells in a beam of live cyanobacteria with an X-ray laser

    DOE Data Explorer

    Schot, Gijs, vander

    2015-02-10

    This entry contains ten diffraction patterns, and reconstructions images, of individual living Cyanobium gracile cells, imaged using 517 eV X-rays from the LCLS XFEL. The Hawk software package was used for phasing. The Uppsala aerosol injector was used for sample injection, assuring very low noise levels. The cells come from various stages of the cell cycle, and were imaged in random orientations.

  1. An automated programmable platform enabling multiplex dynamic stimuli delivery and cellular response monitoring for high-throughput suspension single-cell signaling studies.

    PubMed

    He, Luye; Kniss, Ariel; San-Miguel, Adriana; Rouse, Tel; Kemp, Melissa L; Lu, Hang

    2015-03-21

    Cell signaling events are orchestrated by dynamic external biochemical cues. By rapidly perturbing cells with dynamic inputs and examining the output from these systems, one could study the structure and dynamic properties of a cellular signaling network. Conventional experimental techniques limit the implementation of these systematic approaches due to the lack of sophistication in manipulating individual cells and the fluid microenvironment around them; existing microfluidic technologies thus far are mainly targeting adherent cells. In this paper we present an automated platform to interrogate suspension cells with dynamic stimuli while simultaneously monitoring cellular responses in a high-throughput manner at single-cell resolution. We demonstrate the use of this platform in an experiment to measure Jurkat T cells in response to distinct dynamic patterns of stimuli; we find cells exhibit highly heterogeneous responses under each stimulation condition. More interestingly, these cells act as low-pass filters, only entrained to the low frequency stimulus signals. We also demonstrate that this platform can be easily programmed to actively generate arbitrary dynamic signals. We envision our platform to be useful in other contexts to study cellular signaling dynamics, which may be difficult using conventional experimental methods. PMID:25609410

  2. Imaging of single cells and tissue using MeV ions

    NASA Astrophysics Data System (ADS)

    Watt, F.; Bettiol, A. A.; van Kan, J. A.; Ynsa, M. D.; Minqin, Ren; Rajendran, R.; Huifang, Cui; Fwu-Shen, Sheu; Jenner, A. M.

    2009-06-01

    With the attainment of sub-100 nm high energy (MeV) ion beams, comes the opportunity to image cells and tissue at nano-dimensions. The advantage of MeV ion imaging is that the ions will penetrate whole cells, or relatively thick tissue sections, without any significant loss of resolution. In this paper, we demonstrate that whole cells (cultured N2A neuroblastoma cells ATCC) and tissue sections (rabbit pancreas tissue) can be imaged at sub-100 nm resolutions using scanning transmission ion microscopy (STIM), and that sub-cellular structural details can be identified. In addition to STIM imaging we have also demonstrated for the first time, that sub-cellular proton induced fluorescence imaging (on cultured N2A neuroblastoma cells ATCC) can also be carried out at resolutions of 200 nm, compared with 300-400 nm resolutions achieved by conventional optical fluorescence imaging. The combination of both techniques offers a potentially powerful tool in the quest for elucidating cell function, particularly when it should be possible in the near future to image down to sub-50 nm.

  3. Software for Automated Image-to-Image Co-registration

    NASA Technical Reports Server (NTRS)

    Benkelman, Cody A.; Hughes, Heidi

    2007-01-01

    The project objectives are: a) Develop software to fine-tune image-to-image co-registration, presuming images are orthorectified prior to input; b) Create a reusable software development kit (SDK) to enable incorporation of these tools into other software; d) provide automated testing for quantitative analysis; and e) Develop software that applies multiple techniques to achieve subpixel precision in the co-registration of image pairs.

  4. Fibered confocal fluorescence microscopy for imaging apoptotic DNA fragmentation at the single-cell level in vivo

    SciTech Connect

    Al-Gubory, Kais H. . E-mail: kais.algubory@jouy.inra.fr

    2005-11-01

    The major characteristic of cell death by apoptosis is the loss of nuclear DNA integrity by endonucleases, resulting in the formation of small DNA fragments. The application of confocal imaging to in vivo monitoring of dynamic cellular events, like apoptosis, within internal organs and tissues has been limited by the accessibility to these sites. Therefore, the aim of the present study was to test the feasibility of fibered confocal fluorescence microscopy (FCFM) to image in situ apoptotic DNA fragmentation in surgically exteriorized sheep corpus luteum in the living animal. Following intra-luteal administration of a fluorescent DNA-staining dye, YO-PRO-1, DNA cleavage within nuclei of apoptotic cells was serially imaged at the single-cell level by FCFM. This imaging technology is sufficiently simple and rapid to allow time series in situ detection and visualization of cells undergoing apoptosis in the intact animal. Combined with endoscope, this approach can be used for minimally invasive detection of fluorescent signals and visualization of cellular events within internal organs and tissues and thereby provides the opportunity to study biological processes in the natural physiological environment of the cell in living animals.

  5. Single cell imaging of Bruton's Tyrosine Kinase using an irreversible inhibitor

    NASA Astrophysics Data System (ADS)

    Turetsky, Anna; Kim, Eunha; Kohler, Rainer H.; Miller, Miles A.; Weissleder, Ralph

    2014-04-01

    A number of Bruton's tyrosine kinase (BTK) inhibitors are currently in development, yet it has been difficult to visualize BTK expression and pharmacological inhibition in vivo in real time. We synthesized a fluorescent, irreversible BTK binder based on the drug Ibrutinib and characterized its behavior in cells and in vivo. We show a 200 nM affinity of the imaging agent, high selectivity, and irreversible binding to its target following initial washout, resulting in surprisingly high target-to-background ratios. In vivo, the imaging agent rapidly distributed to BTK expressing tumor cells, but also to BTK-positive tumor-associated host cells.

  6. Automated Image Data Exploitation Final Report

    SciTech Connect

    Kamath, C; Poland, D; Sengupta, S K; Futterman, J H

    2004-01-26

    The automated production of maps of human settlement from recent satellite images is essential to detailed studies of urbanization, population movement, and the like. Commercial satellite imagery is becoming available with sufficient spectral and spatial resolution to apply computer vision techniques previously considered only for laboratory (high resolution, low noise) images. In this project, we extracted the boundaries of human settlements from IKONOS 4-band and panchromatic images using spectral segmentation together with a form of generalized second-order statistics and detection of edges and corners.

  7. Plenoptic Imager for Automated Surface Navigation

    NASA Technical Reports Server (NTRS)

    Zollar, Byron; Milder, Andrew; Milder, Andrew; Mayo, Michael

    2010-01-01

    An electro-optical imaging device is capable of autonomously determining the range to objects in a scene without the use of active emitters or multiple apertures. The novel, automated, low-power imaging system is based on a plenoptic camera design that was constructed as a breadboard system. Nanohmics proved feasibility of the concept by designing an optical system for a prototype plenoptic camera, developing simulated plenoptic images and range-calculation algorithms, constructing a breadboard prototype plenoptic camera, and processing images (including range calculations) from the prototype system. The breadboard demonstration included an optical subsystem comprised of a main aperture lens, a mechanical structure that holds an array of micro lenses at the focal distance from the main lens, and a structure that mates a CMOS imaging sensor the correct distance from the micro lenses. The demonstrator also featured embedded electronics for camera readout, and a post-processor executing image-processing algorithms to provide ranging information.

  8. Semi-automated Image Processing for Preclinical Bioluminescent Imaging

    PubMed Central

    Slavine, Nikolai V; McColl, Roderick W

    2015-01-01

    Objective Bioluminescent imaging is a valuable noninvasive technique for investigating tumor dynamics and specific biological molecular events in living animals to better understand the effects of human disease in animal models. The purpose of this study was to develop and test a strategy behind automated methods for bioluminescence image processing from the data acquisition to obtaining 3D images. Methods In order to optimize this procedure a semi-automated image processing approach with multi-modality image handling environment was developed. To identify a bioluminescent source location and strength we used the light flux detected on the surface of the imaged object by CCD cameras. For phantom calibration tests and object surface reconstruction we used MLEM algorithm. For internal bioluminescent sources we used the diffusion approximation with balancing the internal and external intensities on the boundary of the media and then determined an initial order approximation for the photon fluence we subsequently applied a novel iterative deconvolution method to obtain the final reconstruction result. Results We find that the reconstruction techniques successfully used the depth-dependent light transport approach and semi-automated image processing to provide a realistic 3D model of the lung tumor. Our image processing software can optimize and decrease the time of the volumetric imaging and quantitative assessment. Conclusion The data obtained from light phantom and lung mouse tumor images demonstrate the utility of the image reconstruction algorithms and semi-automated approach for bioluminescent image processing procedure. We suggest that the developed image processing approach can be applied to preclinical imaging studies: characteristics of tumor growth, identify metastases, and potentially determine the effectiveness of cancer treatment. PMID:26618187

  9. Estimation of single cell volume from 3D confocal images using automatic data processing

    NASA Astrophysics Data System (ADS)

    Chorvatova, A.; Cagalinec, M.; Mateasik, A.; Chorvat, D., Jr.

    2012-06-01

    Cardiac cells are highly structured with a non-uniform morphology. Although precise estimation of their volume is essential for correct evaluation of hypertrophic changes of the heart, simple and unified techniques that allow determination of the single cardiomyocyte volume with sufficient precision are still limited. Here, we describe a novel approach to assess the cell volume from confocal microscopy 3D images of living cardiac myocytes. We propose a fast procedure based on segementation using active deformable contours. This technique is independent on laser gain and/or pinhole settings and it is also applicable on images of cells stained with low fluorescence markers. Presented approach is a promising new tool to investigate changes in the cell volume during normal, as well as pathological growth, as we demonstrate in the case of cell enlargement during hypertension in rats.

  10. Imaging single cells in a beam of live cyanobacteria with an X-ray laser.

    PubMed

    van der Schot, Gijs; Svenda, Martin; Maia, Filipe R N C; Hantke, Max; DePonte, Daniel P; Seibert, M Marvin; Aquila, Andrew; Schulz, Joachim; Kirian, Richard; Liang, Mengning; Stellato, Francesco; Iwan, Bianca; Andreasson, Jakob; Timneanu, Nicusor; Westphal, Daniel; Almeida, F Nunes; Odic, Dusko; Hasse, Dirk; Carlsson, Gunilla H; Larsson, Daniel S D; Barty, Anton; Martin, Andrew V; Schorb, Sebastian; Bostedt, Christoph; Bozek, John D; Rolles, Daniel; Rudenko, Artem; Epp, Sascha; Foucar, Lutz; Rudek, Benedikt; Hartmann, Robert; Kimmel, Nils; Holl, Peter; Englert, Lars; Duane Loh, Ne-Te; Chapman, Henry N; Andersson, Inger; Hajdu, Janos; Ekeberg, Tomas

    2015-01-01

    There exists a conspicuous gap of knowledge about the organization of life at mesoscopic levels. Ultra-fast coherent diffractive imaging with X-ray free-electron lasers can probe structures at the relevant length scales and may reach sub-nanometer resolution on micron-sized living cells. Here we show that we can introduce a beam of aerosolised cyanobacteria into the focus of the Linac Coherent Light Source and record diffraction patterns from individual living cells at very low noise levels and at high hit ratios. We obtain two-dimensional projection images directly from the diffraction patterns, and present the results as synthetic X-ray Nomarski images calculated from the complex-valued reconstructions. We further demonstrate that it is possible to record diffraction data to nanometer resolution on live cells with X-ray lasers. Extension to sub-nanometer resolution is within reach, although improvements in pulse parameters and X-ray area detectors will be necessary to unlock this potential. PMID:25669616

  11. Spatial and temporal single-cell volume estimation by a fluorescence imaging technique with application to astrocytes in primary culture

    NASA Astrophysics Data System (ADS)

    Khatibi, Siamak; Allansson, Louise; Gustavsson, Tomas; Blomstrand, Fredrik; Hansson, Elisabeth; Olsson, Torsten

    1999-05-01

    Cell volume changes are often associated with important physiological and pathological processes in the cell. These changes may be the means by which the cell interacts with its surrounding. Astroglial cells change their volume and shape under several circumstances that affect the central nervous system. Following an incidence of brain damage, such as a stroke or a traumatic brain injury, one of the first events seen is swelling of the astroglial cells. In order to study this and other similar phenomena, it is desirable to develop technical instrumentation and analysis methods capable of detecting and characterizing dynamic cell shape changes in a quantitative and robust way. We have developed a technique to monitor and to quantify the spatial and temporal volume changes in a single cell in primary culture. The technique is based on two- and three-dimensional fluorescence imaging. The temporal information is obtained from a sequence of microscope images, which are analyzed in real time. The spatial data is collected in a sequence of images from the microscope, which is automatically focused up and down through the specimen. The analysis of spatial data is performed off-line and consists of photobleaching compensation, focus restoration, filtering, segmentation and spatial volume estimation.

  12. Single-cell resolution fluorescence imaging of circadian rhythms detected with a Nipkow spinning disk confocal system.

    PubMed

    Enoki, Ryosuke; Ono, Daisuke; Hasan, Mazahir T; Honma, Sato; Honma, Ken-Ichi

    2012-05-30

    Single-point laser scanning confocal imaging produces signals with high spatial resolution in living organisms. However, photo-induced toxicity, bleaching, and focus drift remain challenges, especially when recording over several days for monitoring circadian rhythms. Bioluminescence imaging is a tool widely used for this purpose, and does not cause photo-induced difficulties. However, bioluminescence signals are dimmer than fluorescence signals, and are potentially affected by levels of cofactors, including ATP, O(2), and the substrate, luciferin. Here we describe a novel time-lapse confocal imaging technique to monitor circadian rhythms in living tissues. The imaging system comprises a multipoint scanning Nipkow spinning disk confocal unit and a high-sensitivity EM-CCD camera mounted on an inverted microscope with auto-focusing function. Brain slices of the suprachiasmatic nucleus (SCN), the central circadian clock, were prepared from transgenic mice expressing a clock gene, Period 1 (Per1), and fluorescence reporter protein (Per1::d2EGFP). The SCN slices were cut out together with membrane, flipped over, and transferred to the collagen-coated glass dishes to obtain signals with a high signal-to-noise ratio and to minimize focus drift. The imaging technique and improved culture method enabled us to monitor the circadian rhythm of Per1::d2EGFP from optically confirmed single SCN neurons without noticeable photo-induced effects or focus drift. Using recombinant adeno-associated virus carrying a genetically encoded calcium indicator, we also monitored calcium circadian rhythms at a single-cell level in a large population of SCN neurons. Thus, the Nipkow spinning disk confocal imaging system developed here facilitates long-term visualization of circadian rhythms in living cells. PMID:22480987

  13. Automated zone correction in bitmapped document images

    NASA Astrophysics Data System (ADS)

    Hauser, Susan E.; Le, Daniel X.; Thoma, George R.

    1999-12-01

    The optical character recognition system (OCR) selected by the National Library of Medicine (NLM) as part of its system for automating the production of MEDLINER records frequently segments the scanned page images into zones which are inappropriate for NLM's application. Software has been created in-house to correct the zones using character coordinate and character attribute information provided as part of the OCR output data. The software correctly delineates over 97% of the zones of interest tested to date.

  14. Imaging single cells in a beam of live cyanobacteria with an X-ray laser

    DOE Data Explorer

    Schot, Gijs, vander

    2015-02-10

    Diffraction pattern of a micron-sized S. elongatus cell at 1,100 eV photon energy (1.13 nm wavelength) with ~10^11 photons per square micron on the sample in ~70 fs. The signal to noise ratio at 4 nm resolution is 3.7 with 0.24 photons per Nyquist pixel. The cell was alive at the time of the exposure. The central region of the pattern (dark red) is saturated and this prevented reliable image reconstruction.

  15. In Situ Probing of Cholesterol in Astrocytes at the Single Cell Level using Laser Desorption Ionization Mass Spectrometric Imaging with Colloidal Silver

    SciTech Connect

    Perdian, D.C.; Cha, Sangwon; Oh, Jisun; Sakaguchi, Donald S.; Yeung, Edward S.; and Lee, Young Jin

    2010-03-18

    Mass spectrometric imaging has been utilized to localize individual astrocytes and to obtain cholesterol populations at the single-cell level in laser desorption ionization (LDI) with colloidal silver. The silver ion adduct of membrane-bound cholesterol was monitored to detect individual cells. Good correlation between mass spectrometric and optical images at different cell densities indicates the ability to perform single-cell studies of cholesterol abundance. The feasibility of quantification is confirmed by the agreement between the LDI-MS ion signals and the results from a traditional enzymatic fluorometric assay. We propose that this approach could be an effective tool to study chemical populations at the cellular level.

  16. Single-cell imaging tools for brain energy metabolism: a review

    PubMed Central

    San Martín, Alejandro; Sotelo-Hitschfeld, Tamara; Lerchundi, Rodrigo; Fernández-Moncada, Ignacio; Ceballo, Sebastian; Valdebenito, Rocío; Baeza-Lehnert, Felipe; Alegría, Karin; Contreras-Baeza, Yasna; Garrido-Gerter, Pamela; Romero-Gómez, Ignacio; Barros, L. Felipe

    2014-01-01

    Abstract. Neurophotonics comes to light at a time in which advances in microscopy and improved calcium reporters are paving the way toward high-resolution functional mapping of the brain. This review relates to a parallel revolution in metabolism. We argue that metabolism needs to be approached both in vitro and in vivo, and that it does not just exist as a low-level platform but is also a relevant player in information processing. In recent years, genetically encoded fluorescent nanosensors have been introduced to measure glucose, glutamate, ATP, NADH, lactate, and pyruvate in mammalian cells. Reporting relative metabolite levels, absolute concentrations, and metabolic fluxes, these sensors are instrumental for the discovery of new molecular mechanisms. Sensors continue to be developed, which together with a continued improvement in protein expression strategies and new imaging technologies, herald an exciting era of high-resolution characterization of metabolism in the brain and other organs. PMID:26157964

  17. Optoelectronic tweezers system for single cell manipulation and fluorescence imaging of live immune cells.

    PubMed

    Jeorrett, Abigail H; Neale, Steven L; Massoubre, David; Gu, Erdan; Henderson, Robert K; Millington, Owain; Mathieson, Keith; Dawson, Martin D

    2014-01-27

    A compact optoelectronic tweezers system for combined cell manipulation and analysis is presented. CMOS-controlled gallium nitride micro-LED arrays are used to provide simultaneous spatio-temporal control of dielectrophoresis traps within an optoelectronic tweezers device and fluorescence imaging of contrasting dye labelled cells. This capability provides direct identification, selection and controlled interaction of single T-lymphocytes and dendritic cells. The trap strength and profile for two emission wavelengths of micro-LED array have been measured and a maximum trapping force of 13.1 and 7.6 pN was achieved for projected micro-LED devices emitting at λmax 520 and 450 nm, respectively. A potential application in biological research is demonstrated through the controlled interaction of live immune cells where there is potential for this method of OET to be implemented as a compact device. PMID:24515144

  18. Automated image analysis of uterine cervical images

    NASA Astrophysics Data System (ADS)

    Li, Wenjing; Gu, Jia; Ferris, Daron; Poirson, Allen

    2007-03-01

    Cervical Cancer is the second most common cancer among women worldwide and the leading cause of cancer mortality of women in developing countries. If detected early and treated adequately, cervical cancer can be virtually prevented. Cervical precursor lesions and invasive cancer exhibit certain morphologic features that can be identified during a visual inspection exam. Digital imaging technologies allow us to assist the physician with a Computer-Aided Diagnosis (CAD) system. In colposcopy, epithelium that turns white after application of acetic acid is called acetowhite epithelium. Acetowhite epithelium is one of the major diagnostic features observed in detecting cancer and pre-cancerous regions. Automatic extraction of acetowhite regions from cervical images has been a challenging task due to specular reflection, various illumination conditions, and most importantly, large intra-patient variation. This paper presents a multi-step acetowhite region detection system to analyze the acetowhite lesions in cervical images automatically. First, the system calibrates the color of the cervical images to be independent of screening devices. Second, the anatomy of the uterine cervix is analyzed in terms of cervix region, external os region, columnar region, and squamous region. Third, the squamous region is further analyzed and subregions based on three levels of acetowhite are identified. The extracted acetowhite regions are accompanied by color scores to indicate the different levels of acetowhite. The system has been evaluated by 40 human subjects' data and demonstrates high correlation with experts' annotations.

  19. Non-invasive single-cell biomechanical analysis using live-imaging datasets.

    PubMed

    Pearson, Yanthe E; Lund, Amanda W; Lin, Alex W H; Ng, Chee P; Alsuwaidi, Aysha; Azzeh, Sara; Gater, Deborah L; Teo, Jeremy C M

    2016-09-01

    The physiological state of a cell is governed by a multitude of processes and can be described by a combination of mechanical, spatial and temporal properties. Quantifying cell dynamics at multiple scales is essential for comprehensive studies of cellular function, and remains a challenge for traditional end-point assays. We introduce an efficient, non-invasive computational tool that takes time-lapse images as input to automatically detect, segment and analyze unlabeled live cells; the program then outputs kinematic cellular shape and migration parameters, while simultaneously measuring cellular stiffness and viscosity. We demonstrate the capabilities of the program by testing it on human mesenchymal stem cells (huMSCs) induced to differentiate towards the osteoblastic (huOB) lineage, and T-lymphocyte cells (T cells) of naïve and stimulated phenotypes. The program detected relative cellular stiffness differences in huMSCs and huOBs that were comparable to those obtained with studies that utilize atomic force microscopy; it further distinguished naïve from stimulated T cells, based on characteristics necessary to invoke an immune response. In summary, we introduce an integrated tool to decipher spatiotemporal and intracellular dynamics of cells, providing a new and alternative approach for cell characterization. PMID:27422102

  20. Single-Cell Imaging and Spectroscopic Analyses of Cr(VI) Reduction on the Surface of Bacterial Cells

    PubMed Central

    Wang, Yuanmin; Sevinc, Papatya C.; Balchik, Sara M.; Fridrickson, Jim; Shi, Liang; Lu, H. Peter

    2013-01-01

    We investigate single-cell reduction of toxic Cr(VI) by the dissimilatory metal-reducing bacterium Shewanella oneidensis MR-1 (MR-1), an important bioremediation process, using Raman spectroscopy and scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDX). Our experiments indicate that the toxic and highly soluble Cr(VI) can be efficiently reduced to the less toxic and non-soluble Cr2O3 nanoparticles by MR-1. Cr2O3 is observed to emerge as nanoparticles adsorbed on the cell surface and its chemical nature is identified by EDX imaging and Raman spectroscopy. Co-localization of Cr2O3 and cytochromes by EDX imaging and Raman spectroscopy suggests a terminal reductase role for MR-1 surface-exposed cytochromes MtrC and OmcA. Our experiments revealed that the cooperation of surface proteins OmcA and MtrC makes the reduction reaction most efficient, and the sequence of the reducing reactivity of the MR-1 is: wild type > single mutant ΔmtrC or mutant ΔomcA > double mutant (ΔomcA-ΔmtrC). Moreover, our results also suggest that the direct microbial Cr(VI) reduction and Fe(II) (hematite)-mediated Cr(VI) reduction mechanisms may co-exist in the reduction processes. PMID:23249294

  1. Single-Cell Imaging and Spectroscopic Analyses of Cr(VI) Reduction on the Surface of Bacterial Cells

    SciTech Connect

    Wang, Yuanmin; Sevinc, Papatya C.; Belchik, Sara M.; Fredrickson, Jim K.; Shi, Liang; Lu, H. Peter

    2013-01-22

    We investigate single-cell reduction of toxic Cr(VI) by the dissimilatory metal-reducing bacterium Shewanella oneidensis MR-1 (MR-1), an important bioremediation process, using Raman spectroscopy and scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDX). Our experiments indicate that the toxic and highly soluble Cr(VI) can be efficiently reduced to the less toxic and non-soluble Cr2O3 nanoparticles by MR-1. Cr2O3 is observed to emerge as nanoparticles adsorbed on the cell surface and its chemical nature is identified by EDX imaging and Raman spectroscopy. Co-localization of Cr2O3 and cytochromes by EDX imaging and Raman spectroscopy suggests a terminal reductase role for MR-1 surface-exposed cytochromes MtrC and OmcA. Our experiments revealed that the cooperation of surface proteins OmcA and MtrC makes the reduction reaction most efficient, and the sequence of the reducing reactivity of the MR-1 is: wild type > single mutant @mtrC or mutant @omcA > double mutant (@omcA-@mtrC). Moreover, our results also suggest that the direct microbial Cr(VI) reduction and Fe(II) (hematite)-mediated Cr(VI) reduction mechanisms may co-exist in the reduction processes.

  2. Multicolor bioluminescence boosts malaria research: quantitative dual-color assay and single-cell imaging in Plasmodium falciparum parasites.

    PubMed

    Cevenini, Luca; Camarda, Grazia; Michelini, Elisa; Siciliano, Giulia; Calabretta, Maria Maddalena; Bona, Roberta; Kumar, T R Santha; Cara, Andrea; Branchini, Bruce R; Fidock, David A; Roda, Aldo; Alano, Pietro

    2014-09-01

    New reliable and cost-effective antimalarial drug screening assays are urgently needed to identify drugs acting on different stages of the parasite Plasmodium falciparum, and particularly those responsible for human-to-mosquito transmission, that is, the P. falciparum gametocytes. Low Z' factors, narrow dynamic ranges, and/or extended assay times are commonly reported in current gametocyte assays measuring gametocyte-expressed fluorescent or luciferase reporters, endogenous ATP levels, activity of gametocyte enzymes, or redox-dependent dye fluorescence. We hereby report on a dual-luciferase gametocyte assay with immature and mature P. falciparum gametocyte stages expressing red and green-emitting luciferases from Pyrophorus plagiophthalamus under the control of the parasite sexual stage-specific pfs16 gene promoter. The assay was validated with reference antimalarial drugs and allowed to quantitatively and simultaneously measure stage-specific drug effects on parasites at different developmental stages. The optimized assay, requiring only 48 h incubation with drugs and using a cost-effective luminogenic substrate, significantly reduces assay cost and time in comparison to state-of-the-art analogous assays. The assay had a Z' factor of 0.71 ± 0.03, and it is suitable for implementation in 96- and 384-well microplate formats. Moreover, the use of a nonlysing D-luciferin substrate significantly improved the reliability of the assay and allowed one to perform, for the first time, P. falciparum bioluminescence imaging at single-cell level. PMID:25102353

  3. Automated tissue characterization in MR imaging

    NASA Astrophysics Data System (ADS)

    Braun, Juergen; Bernarding, Johannes; Koennecke, Hans-Christian; Wolf, Karl J.; Tolxdorff, Thomas

    2000-04-01

    A histogram-based segmentation technique was extended to exploit information acquired by manifold MRI techniques. An automated method was used to combine T2-weighted imaging, diffusion-weighted imaging (DWI), and derived maps of the quantitative apparent diffusion coefficient (ADC). DWI allows the early detection of cerebral ischemia, and the calculated ADC value may provide information on pathophysiologic changes. Different optionally shaped clusters were characterized as separate local density maxima in the resultant 3D histogram. Cluster borders were determined by detecting density minima. Distinct but related clusters could be merged in the histogram using the Euclidian distance and a score describing the spatial neighborhood of pixels in the image. In healthy volunteers, gray matter, white matter, muscle, skin, adipose tissue, and cerebrospinal fluid were clearly identified by the automated analysis. In stroke patients, ischemic regions were reliably segmented irrespective of shape, size, and location. The time course of relative ADC changes in ischemic lesions was determined. Results were confirmed by a radiologist. The proposed automatic segmentation algorithm can be used without restrictions for the fast analysis of any multidimensional dataset. The method has proved to be reliable for determining quantities containing information on the physiologic state of tissue, such as the ADC.

  4. Automated landmark-guided deformable image registration.

    PubMed

    Kearney, Vasant; Chen, Susie; Gu, Xuejun; Chiu, Tsuicheng; Liu, Honghuan; Jiang, Lan; Wang, Jing; Yordy, John; Nedzi, Lucien; Mao, Weihua

    2015-01-01

    The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency. PMID:25479095

  5. Automated landmark-guided deformable image registration

    NASA Astrophysics Data System (ADS)

    Kearney, Vasant; Chen, Susie; Gu, Xuejun; Chiu, Tsuicheng; Liu, Honghuan; Jiang, Lan; Wang, Jing; Yordy, John; Nedzi, Lucien; Mao, Weihua

    2015-01-01

    The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency.

  6. Automated eXpert Spectral Image Analysis

    Energy Science and Technology Software Center (ESTSC)

    2003-11-25

    AXSIA performs automated factor analysis of hyperspectral images. In such images, a complete spectrum is collected an each point in a 1-, 2- or 3- dimensional spatial array. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful information. Multivariate factor analysis techniques have proven effective for extracting the essential information from high dimensional data sets into a limtedmore » number of factors that describe the spectral characteristics and spatial distributions of the pure components comprising the sample. AXSIA provides tools to estimate different types of factor models including Singular Value Decomposition (SVD), Principal Component Analysis (PCA), PCA with factor rotation, and Alternating Least Squares-based Multivariate Curve Resolution (MCR-ALS). As part of the analysis process, AXSIA can automatically estimate the number of pure components that comprise the data and can scale the data to account for Poisson noise. The data analysis methods are fundamentally based on eigenanalysis of the data crossproduct matrix coupled with orthogonal eigenvector rotation and constrained alternating least squares refinement. A novel method for automatically determining the number of significant components, which is based on the eigenvalues of the crossproduct matrix, has also been devised and implemented. The data can be compressed spectrally via PCA and spatially through wavelet transforms, and algorithms have been developed that perform factor analysis in the transform domain while retaining full spatial and spectral resolution in the final result. These latter innovations enable the analysis of larger-than core-memory spectrum-images. AXSIA was designed to perform automated chemical phase analysis of spectrum-images acquired by a variety of chemical imaging techniques. Successful applications include Energy Dispersive X-ray Spectroscopy, X

  7. From Quantitative Microscopy to Automated Image Understanding

    PubMed Central

    Huang, Kai; Murphy, Robert F.

    2005-01-01

    Quantitative microscopy has been extensively used in biomedical research and has provided significant insights into structure and dynamics at the cell and tissue level. The entire procedure of quantitative microscopy is comprised of specimen preparation, light absorption/reflection/emission from the specimen, microscope optical processing, optical/electrical conversion by a camera or detector, and computational processing of digitized images. Although many of the latest digital signal processing techniques have been successfully applied to compress, restore, and register digital microscope images, automated approaches for recognition and understanding of complex subcellular patterns in light microscope images have been far less widely used. In this review, we describe a systematic approach for interpreting protein subcellular distributions using various sets of Subcellular Location Features (SLF) in combination with supervised classification and unsupervised clustering methods. These methods can handle complex patterns in digital microscope images and the features can be applied for other purposes such as objectively choosing a representative image from a collection and performing statistical comparison of image sets. PMID:15447010

  8. Automated lesion detectors in retinal fundus images.

    PubMed

    Figueiredo, I N; Kumar, S; Oliveira, C M; Ramos, J D; Engquist, B

    2015-11-01

    Diabetic retinopathy (DR) is a sight-threatening condition occurring in persons with diabetes, which causes progressive damage to the retina. The early detection and diagnosis of DR is vital for saving the vision of diabetic persons. The early signs of DR which appear on the surface of the retina are the dark lesions such as microaneurysms (MAs) and hemorrhages (HEMs), and bright lesions (BLs) such as exudates. In this paper, we propose a novel automated system for the detection and diagnosis of these retinal lesions by processing retinal fundus images. We devise appropriate binary classifiers for these three different types of lesions. Some novel contextual/numerical features are derived, for each lesion type, depending on its inherent properties. This is performed by analysing several wavelet bands (resulting from the isotropic undecimated wavelet transform decomposition of the retinal image green channel) and by using an appropriate combination of Hessian multiscale analysis, variational segmentation and cartoon+texture decomposition. The proposed methodology has been validated on several medical datasets, with a total of 45,770 images, using standard performance measures such as sensitivity and specificity. The individual performance, per frame, of the MA detector is 93% sensitivity and 89% specificity, of the HEM detector is 86% sensitivity and 90% specificity, and of the BL detector is 90% sensitivity and 97% specificity. Regarding the collective performance of these binary detectors, as an automated screening system for DR (meaning that a patient is considered to have DR if it is a positive patient for at least one of the detectors) it achieves an average 95-100% of sensitivity and 70% of specificity at a per patient basis. Furthermore, evaluation conducted on publicly available datasets, for comparison with other existing techniques, shows the promising potential of the proposed detectors. PMID:26378502

  9. Automated vertebra identification in CT images

    NASA Astrophysics Data System (ADS)

    Ehm, Matthias; Klinder, Tobias; Kneser, Reinhard; Lorenz, Cristian

    2009-02-01

    In this paper, we describe and compare methods for automatically identifying individual vertebrae in arbitrary CT images. The identification is an essential precondition for a subsequent model-based segmentation, which is used in a wide field of orthopedic, neurological, and oncological applications, e.g., spinal biopsies or the insertion of pedicle screws. Since adjacent vertebrae show similar characteristics, an automated labeling of the spine column is a very challenging task, especially if no surrounding reference structures can be taken into account. Furthermore, vertebra identification is complicated due to the fact that many images are bounded to a very limited field of view and may contain only few vertebrae. We propose and evaluate two methods for automatically labeling the spine column by evaluating similarities between given models and vertebral objects. In one method, object boundary information is taken into account by applying a Generalized Hough Transform (GHT) for each vertebral object. In the other method, appearance models containing mean gray value information are registered to each vertebral object using cross and local correlation as similarity measures for the optimization function. The GHT is advantageous in terms of computational performance but cuts back concerning the identification rate. A correct labeling of the vertebral column has been successfully performed on 93% of the test set consisting of 63 disparate input images using rigid image registration with local correlation as similarity measure.

  10. Multiparameter fluorescence imaging for quantification of TH-1 and TH-2 cytokines at the single-cell level

    NASA Astrophysics Data System (ADS)

    Fekkar, Hakim; Benbernou, N.; Esnault, S.; Shin, H. C.; Guenounou, Moncef

    1998-04-01

    Immune responses are strongly influenced by the cytokines following antigenic stimulation. Distinct cytokine-producing T cell subsets are well known to play a major role in immune responses and to be differentially regulated during immunological disorders, although the characterization and quantification of the TH-1/TH-2 cytokine pattern in T cells remained not clearly defined. Expression of cytokines by T lymphocytes is a highly balanced process, involving stimulatory and inhibitory intracellular signaling pathways. The aim of this study was (1) to quantify the cytokine expression in T cells at the single cell level using optical imaging, (2) and to analyze the influence of cyclic AMP- dependent signal transduction pathway in the balance between the TH-1 and TH-2 cytokine profile. We attempted to study several cytokines (IL-2, IFN-(gamma) , IL-4, IL-10 and IL-13) in peripheral blood mononuclear cells. Cells were prestimulated in vitro using phytohemagglutinin and phorbol ester for 36h, and then further cultured for 8h in the presence of monensin. Cells were permeabilized and then simple-, double- or triple-labeled with the corresponding specific fluorescent monoclonal antibodies. The cell phenotype was also determined by analyzing the expression of each of CD4, CD8, CD45RO and CD45RA with the cytokine expression. Conventional images of cells were recorded with a Peltier- cooled CCD camera (B/W C5985, Hamamatsu photonics) through an inverted microscope equipped with epi-fluorescence (Diaphot 300, Nikon). Images were digitalized using an acquisition video interface (Oculus TCX Coreco) in 762 by 570 pixels coded in 8 bits (256 gray levels), and analyzed thereafter in an IBM PC computer based on an intel pentium processor with an adequate software (Visilog 4, Noesis). The first image processing step is the extraction of cell areas using an edge detection and a binary thresholding method. In order to reduce the background noise of fluorescence, we performed an opening

  11. The Comet Assay: Automated Imaging Methods for Improved Analysis and Reproducibility.

    PubMed

    Braafladt, Signe; Reipa, Vytas; Atha, Donald H

    2016-01-01

    Sources of variability in the comet assay include variations in the protocol used to process the cells, the microscope imaging system and the software used in the computerized analysis of the images. Here we focus on the effect of variations in the microscope imaging system and software analysis using fixed preparations of cells and a single cell processing protocol. To determine the effect of the microscope imaging and analysis on the measured percentage of damaged DNA (% DNA in tail), we used preparations of mammalian cells treated with etoposide or electrochemically induced DNA damage conditions and varied the settings of the automated microscope, camera, and commercial image analysis software. Manual image analysis revealed measurement variations in percent DNA in tail as high as 40% due to microscope focus, camera exposure time and the software image intensity threshold level. Automated image analysis reduced these variations as much as three-fold, but only within a narrow range of focus and exposure settings. The magnitude of variation, observed using both analysis methods, was highly dependent on the overall extent of DNA damage in the particular sample. Mitigating these sources of variability with optimal instrument settings facilitates an accurate evaluation of cell biological variability. PMID:27581626

  12. The Comet Assay: Automated Imaging Methods for Improved Analysis and Reproducibility

    PubMed Central

    Braafladt, Signe; Reipa, Vytas; Atha, Donald H.

    2016-01-01

    Sources of variability in the comet assay include variations in the protocol used to process the cells, the microscope imaging system and the software used in the computerized analysis of the images. Here we focus on the effect of variations in the microscope imaging system and software analysis using fixed preparations of cells and a single cell processing protocol. To determine the effect of the microscope imaging and analysis on the measured percentage of damaged DNA (% DNA in tail), we used preparations of mammalian cells treated with etoposide or electrochemically induced DNA damage conditions and varied the settings of the automated microscope, camera, and commercial image analysis software. Manual image analysis revealed measurement variations in percent DNA in tail as high as 40% due to microscope focus, camera exposure time and the software image intensity threshold level. Automated image analysis reduced these variations as much as three-fold, but only within a narrow range of focus and exposure settings. The magnitude of variation, observed using both analysis methods, was highly dependent on the overall extent of DNA damage in the particular sample. Mitigating these sources of variability with optimal instrument settings facilitates an accurate evaluation of cell biological variability. PMID:27581626

  13. A High-Throughput Automated Microfluidic Platform for Calcium Imaging of Taste Sensing.

    PubMed

    Hsiao, Yi-Hsing; Hsu, Chia-Hsien; Chen, Chihchen

    2016-01-01

    The human enteroendocrine L cell line NCI-H716, expressing taste receptors and taste signaling elements, constitutes a unique model for the studies of cellular responses to glucose, appetite regulation, gastrointestinal motility, and insulin secretion. Targeting these gut taste receptors may provide novel treatments for diabetes and obesity. However, NCI-H716 cells are cultured in suspension and tend to form multicellular aggregates, preventing high-throughput calcium imaging due to interferences caused by laborious immobilization and stimulus delivery procedures. Here, we have developed an automated microfluidic platform that is capable of trapping more than 500 single cells into microwells with a loading efficiency of 77% within two minutes, delivering multiple chemical stimuli and performing calcium imaging with enhanced spatial and temporal resolutions when compared to bath perfusion systems. Results revealed the presence of heterogeneity in cellular responses to the type, concentration, and order of applied sweet and bitter stimuli. Sucralose and denatonium benzoate elicited robust increases in the intracellular Ca(2+) concentration. However, glucose evoked a rapid elevation of intracellular Ca(2+) followed by reduced responses to subsequent glucose stimulation. Using Gymnema sylvestre as a blocking agent for the sweet taste receptor confirmed that different taste receptors were utilized for sweet and bitter tastes. This automated microfluidic platform is cost-effective, easy to fabricate and operate, and may be generally applicable for high-throughput and high-content single-cell analysis and drug screening. PMID:27399663

  14. Single cell wound repair

    PubMed Central

    Abreu-Blanco, Maria Teresa; Verboon, Jeffrey M

    2011-01-01

    Cell wounding is a common event in the life of many cell types, and the capacity of the cell to repair day-to-day wear-and-tear injuries, as well as traumatic ones, is fundamental for maintaining tissue integrity. Cell wounding is most frequent in tissues exposed to high levels of stress. Survival of such plasma membrane disruptions requires rapid resealing to prevent the loss of cytosolic components, to block Ca2+ influx and to avoid cell death. In addition to patching the torn membrane, plasma membrane and cortical cytoskeleton remodeling are required to restore cell function. Although a general understanding of the cell wound repair process is in place, the underlying mechanisms of each step of this response are not yet known. We have developed a model to study single cell wound repair using the early Drosophila embryo. Our system combines genetics and live imaging tools, allowing us to dissect in vivo the dynamics of the single cell wound response. We have shown that cell wound repair in Drosophila requires the coordinated activities of plasma membrane and cytoskeleton components. Furthermore, we identified an unexpected role for E-cadherin as a link between the contractile actomyosin ring and the newly formed plasma membrane plug. PMID:21922041

  15. Single-cell pharmacokinetic imaging reveals a therapeutic strategy to overcome drug resistance to the microtubule inhibitor eribulin

    PubMed Central

    Laughney, Ashley M.; Kim, Eunha; Sprachman, Melissa M.; Miller, Miles A.; Kohler, Rainer H.; Yang, Katy S.; Orth, James D.; Mitchison, Timothy J.; Weissleder, Ralph

    2015-01-01

    Eribulin mesylate was developed as a potent microtubule-targeting cytotoxic agent to treat taxane-resistant cancers, but recent clinical trials have shown that it eventually fails in many patient sub-populations for unclear reasons. To investigate its resistance mechanisms, we developed a fluorescent analog of eribulin with pharmacokinetic (PK) properties and cytotoxic activity across a human cell line panel that are sufficiently similar to the parent drug to study its cellular PK and tissue distribution. Using intravital imaging and automated tracking of cellular dynamics, we found that resistance to eribulin and the fluorescent analog depended directly on the multidrug resistance protein 1 (MDR1). Intravital imaging allowed for real-time analysis of in vivo pharmacokinetics in tumors that were engineered to be spatially heterogeneous for taxane resistance, whereby an MDR1-mApple fusion protein distinguished resistant cells fluorescently. In vivo, MDR1-mediated drug efflux and the three-dimensional tumor vascular architecture were discovered to be critical determinants of drug accumulation in tumor cells. We furthermore show that standard intravenous administration of a third-generation MDR1 inhibitor, HM30181, failed to rescue drug accumulation; however, the same MDR1 inhibitor encapsulated within a nanoparticle delivery system reversed the multidrug-resistant phenotype and potentiated the eribulin effect in vitro and in vivo in mice. Our work demonstrates that in vivo assessment of cellular PK of an anticancer drug is a powerful strategy for elucidating mechanisms of drug resistance in heterogeneous tumors and evaluating strategies to overcome this resistance. PMID:25378644

  16. Automated object detection for astronomical images

    NASA Astrophysics Data System (ADS)

    Orellana, Sonny; Zhao, Lei; Boussalis, Helen; Liu, Charles; Rad, Khosrow; Dong, Jane

    2005-10-01

    Sponsored by the National Aeronautical Space Association (NASA), the Synergetic Education and Research in Enabling NASA-centered Academic Development of Engineers and Space Scientists (SERENADES) Laboratory was established at California State University, Los Angeles (CSULA). An important on-going research activity in this lab is to develop an easy-to-use image analysis software with the capability of automated object detection to facilitate astronomical research. This paper presented a fast object detection algorithm based on the characteristics of astronomical images. This algorithm consists of three steps. First, the foreground and background are separated using histogram-based approach. Second, connectivity analysis is conducted to extract individual object. The final step is post processing which refines the detection results. To improve the detection accuracy when some objects are blocked by clouds, top-hat transform is employed to split the sky into cloudy region and non-cloudy region. A multi-level thresholding algorithm is developed to select the optimal threshold for different regions. Experimental results show that our proposed approach can successfully detect the blocked objects by clouds.

  17. Automated image segmentation using support vector machines

    NASA Astrophysics Data System (ADS)

    Powell, Stephanie; Magnotta, Vincent A.; Andreasen, Nancy C.

    2007-03-01

    Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.

  18. Automated image based prominent nucleoli detection

    PubMed Central

    Yap, Choon K.; Kalaw, Emarene M.; Singh, Malay; Chong, Kian T.; Giron, Danilo M.; Huang, Chao-Hui; Cheng, Li; Law, Yan N.; Lee, Hwee Kuan

    2015-01-01

    Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings. PMID:26167383

  19. An automated digital imaging system for environmental monitoring applications

    USGS Publications Warehouse

    Bogle, Rian; Velasco, Miguel; Vogel, John

    2013-01-01

    Recent improvements in the affordability and availability of high-resolution digital cameras, data loggers, embedded computers, and radio/cellular modems have advanced the development of sophisticated automated systems for remote imaging. Researchers have successfully placed and operated automated digital cameras in remote locations and in extremes of temperature and humidity, ranging from the islands of the South Pacific to the Mojave Desert and the Grand Canyon. With the integration of environmental sensors, these automated systems are able to respond to local conditions and modify their imaging regimes as needed. In this report we describe in detail the design of one type of automated imaging system developed by our group. It is easily replicated, low-cost, highly robust, and is a stand-alone automated camera designed to be placed in remote locations, without wireless connectivity.

  20. Toward Automated Feature Detection in UAVSAR Images

    NASA Astrophysics Data System (ADS)

    Parker, J. W.; Donnellan, A.; Glasscoe, M. T.

    2014-12-01

    Edge detection identifies seismic or aseismic fault motion, as demonstrated in repeat-pass inteferograms obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) program. But this identification is not robust at present: it requires a flattened background image, interpolation into missing data (holes) and outliers, and background noise that is either sufficiently small or roughly white Gaussian. Identification and mitigation of nongaussian background image noise is essential to creating a robust, automated system to search for such features. Clearly a robust method is needed for machine scanning of the thousands of UAVSAR repeat-pass interferograms for evidence of fault slip, landslides, and other local features.Empirical examination of detrended noise based on 20 km east-west profiles through desert terrain with little tectonic deformation for a suite of flight interferograms shows nongaussian characteristics. Statistical measurement of curvature with varying length scale (Allan variance) shows nearly white behavior (Allan variance slope with spatial distance from roughly -1.76 to -2) from 25 to 400 meters, deviations from -2 suggesting short-range differences (such as used in detecting edges) are often freer of noise than longer-range differences. At distances longer than 400 m the Allan variance flattens out without consistency from one interferogram to another. We attribute this additional noise afflicting difference estimates at longer distances to atmospheric water vapor and uncompensated aircraft motion.Paradoxically, California interferograms made with increasing time intervals before and after the El Mayor Cucapah earthquake (2008, M7.2, Mexico) show visually stronger and more interesting edges, but edge detection methods developed for the first year do not produce reliable results over the first two years, because longer time spans suffer reduced coherence in the interferogram. The changes over time are reflecting fault slip and block

  1. Effect of the surfactant tween 80 on the detachment and dispersal of Salmonella enterica serovar Thompson single cells and aggregates from cilantro leaves as revealed by image analysis.

    PubMed

    Brandl, Maria T; Huynh, Steven

    2014-08-01

    Salmonella enterica has the ability to form biofilms and large aggregates on produce surfaces, including on cilantro leaves. Aggregates of S. enterica serovar Thompson that remained attached to cilantro leaves after rigorous washing and that were present free or bound to dislodged leaf tissue in the wash suspension were observed by confocal microscopy. Measurement of S. Thompson population sizes in the leaf washes by plate counts failed to show an effect of 0.05% Tween 80 on the removal of the pathogen from cilantro leaves 2 and 6 days after inoculation. On the contrary, digital image analysis of micrographs of single cells and aggregates of green fluorescent protein (GFP)-S. Thompson present in cilantro leaf washes revealed that single cells represented 13.7% of the cell assemblages in leaf washes containing Tween 80, versus 9.3% in those without the surfactant. Moreover, Tween 80 decreased the percentage of the total S. Thompson cell population located in aggregates equal to or larger than 64 cells from 9.8% to 4.4% (P < 0.05). Regression analysis of the frequency distribution of aggregate size in leaf washes with and without Tween 80 showed that the surfactant promoted the dispersal of cells from large aggregates into smaller ones and into single cells (P < 0.05). Our study underlines the importance of investigating bacterial behavior at the scale of single cells in order to uncover trends undetectable at the population level by bacterial plate counts. Such an approach may provide valuable information to devise strategies aimed at enhancing the efficacy of produce sanitization treatments. PMID:24907336

  2. Procedures for the quantification of whole-tissue immunofluorescence images obtained at single-cell resolution during murine tubular organ development.

    PubMed

    Hirashima, Tsuyoshi; Adachi, Taiji

    2015-01-01

    Whole-tissue quantification at single-cell resolution has become an inevitable approach for further quantitative understanding of morphogenesis in organ development. The feasibility of the approach has been dramatically increased by recent technological improvements in optical tissue clearing and microscopy. However, the series of procedures required for this approach to lead to successful whole-tissue quantification is far from developed. To provide the appropriate procedure, we here show tips for each critical step of the entire process, including fixation for immunofluorescence, optical clearing, and digital image processing, using developing murine internal organs such as epididymis, kidney, and lung as an example. Through comparison of fixative solutions and of clearing methods, we found optimal conditions to achieve clearer deep-tissue imaging of specific immunolabeled targets and explain what methods result in vivid volume imaging. In addition, we demonstrated that three-dimensional digital image processing after optical clearing produces objective quantitative data for the whole-tissue analysis, focusing on the spatial distribution of mitotic cells in the epididymal tubule. The procedure for the whole-tissue quantification shown in this article should contribute to systematic measurements of cellular processes in developing organs, accelerating the further understanding of morphogenesis at the single cell level. PMID:26258587

  3. Optical manipulation for single-cell studies.

    PubMed

    Ramser, Kerstin; Hanstorp, Dag

    2010-04-01

    In the last decade optical manipulation has evolved from a field of interest for physicists to a versatile tool widely used within life sciences. This has been made possible in particular due to the development of a large variety of imaging techniques that allow detailed information to be gained from investigations of single cells. The use of multiple optical traps has high potential within single-cell analysis since parallel measurements provide good statistics. Multifunctional optical tweezers are, for instance, used to study cell heterogeneity in an ensemble, and force measurements are used to investigate the mechanical properties of individual cells. Investigations of molecular motors and forces on the single-molecule level have led to discoveries that would have been difficult to make with other techniques. Optical manipulation has prospects within the field of cell signalling and tissue engineering. When combined with microfluidic systems the chemical environment of cells can be precisely controlled. Hence the influence of pH, salt concentration, drugs and temperature can be investigated in real time. Fast advancing technical developments of automated and user-friendly optical manipulation tools and cross-disciplinary collaboration will contribute to the routinely use of optical manipulation techniques within the life sciences. PMID:19718682

  4. Automated feature extraction and classification from image sources

    USGS Publications Warehouse

    U.S. Geological Survey

    1995-01-01

    The U.S. Department of the Interior, U.S. Geological Survey (USGS), and Unisys Corporation have completed a cooperative research and development agreement (CRADA) to explore automated feature extraction and classification from image sources. The CRADA helped the USGS define the spectral and spatial resolution characteristics of airborne and satellite imaging sensors necessary to meet base cartographic and land use and land cover feature classification requirements and help develop future automated geographic and cartographic data production capabilities. The USGS is seeking a new commercial partner to continue automated feature extraction and classification research and development.

  5. Image segmentation for automated dental identification

    NASA Astrophysics Data System (ADS)

    Haj Said, Eyad; Nassar, Diaa Eldin M.; Ammar, Hany H.

    2006-02-01

    Dental features are one of few biometric identifiers that qualify for postmortem identification; therefore, creation of an Automated Dental Identification System (ADIS) with goals and objectives similar to the Automated Fingerprint Identification System (AFIS) has received increased attention. As a part of ADIS, teeth segmentation from dental radiographs films is an essential step in the identification process. In this paper, we introduce a fully automated approach for teeth segmentation with goal to extract at least one tooth from the dental radiograph film. We evaluate our approach based on theoretical and empirical basis, and we compare its performance with the performance of other approaches introduced in the literature. The results show that our approach exhibits the lowest failure rate and the highest optimality among all full automated approaches introduced in the literature.

  6. Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation.

    PubMed

    Beijbom, Oscar; Edmunds, Peter J; Roelfsema, Chris; Smith, Jennifer; Kline, David I; Neal, Benjamin P; Dunlap, Matthew J; Moriarty, Vincent; Fan, Tung-Yung; Tan, Chih-Jui; Chan, Stephen; Treibitz, Tali; Gamst, Anthony; Mitchell, B Greg; Kriegman, David

    2015-01-01

    Global climate change and other anthropogenic stressors have heightened the need to rapidly characterize ecological changes in marine benthic communities across large scales. Digital photography enables rapid collection of survey images to meet this need, but the subsequent image annotation is typically a time consuming, manual task. We investigated the feasibility of using automated point-annotation to expedite cover estimation of the 17 dominant benthic categories from survey-images captured at four Pacific coral reefs. Inter- and intra- annotator variability among six human experts was quantified and compared to semi- and fully- automated annotation methods, which are made available at coralnet.ucsd.edu. Our results indicate high expert agreement for identification of coral genera, but lower agreement for algal functional groups, in particular between turf algae and crustose coralline algae. This indicates the need for unequivocal definitions of algal groups, careful training of multiple annotators, and enhanced imaging technology. Semi-automated annotation, where 50% of the annotation decisions were performed automatically, yielded cover estimate errors comparable to those of the human experts. Furthermore, fully-automated annotation yielded rapid, unbiased cover estimates but with increased variance. These results show that automated annotation can increase spatial coverage and decrease time and financial outlay for image-based reef surveys. PMID:26154157

  7. Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation

    PubMed Central

    Beijbom, Oscar; Edmunds, Peter J.; Roelfsema, Chris; Smith, Jennifer; Kline, David I.; Neal, Benjamin P.; Dunlap, Matthew J.; Moriarty, Vincent; Fan, Tung-Yung; Tan, Chih-Jui; Chan, Stephen; Treibitz, Tali; Gamst, Anthony; Mitchell, B. Greg; Kriegman, David

    2015-01-01

    Global climate change and other anthropogenic stressors have heightened the need to rapidly characterize ecological changes in marine benthic communities across large scales. Digital photography enables rapid collection of survey images to meet this need, but the subsequent image annotation is typically a time consuming, manual task. We investigated the feasibility of using automated point-annotation to expedite cover estimation of the 17 dominant benthic categories from survey-images captured at four Pacific coral reefs. Inter- and intra- annotator variability among six human experts was quantified and compared to semi- and fully- automated annotation methods, which are made available at coralnet.ucsd.edu. Our results indicate high expert agreement for identification of coral genera, but lower agreement for algal functional groups, in particular between turf algae and crustose coralline algae. This indicates the need for unequivocal definitions of algal groups, careful training of multiple annotators, and enhanced imaging technology. Semi-automated annotation, where 50% of the annotation decisions were performed automatically, yielded cover estimate errors comparable to those of the human experts. Furthermore, fully-automated annotation yielded rapid, unbiased cover estimates but with increased variance. These results show that automated annotation can increase spatial coverage and decrease time and financial outlay for image-based reef surveys. PMID:26154157

  8. Chemical imaging of molecular changes in a hydrated single cell by dynamic secondary ion mass spectrometry and super-resolution microscopy.

    PubMed

    Hua, Xin; Szymanski, Craig; Wang, Zhaoying; Zhou, Yufan; Ma, Xiang; Yu, Jiachao; Evans, James; Orr, Galya; Liu, Songqin; Zhu, Zihua; Yu, Xiao-Ying

    2016-05-16

    Chemical imaging of single cells at the molecular level is important in capturing biological dynamics. Single cell correlative imaging is realized between super-resolution microscopy, namely, structured illumination microscopy (SIM), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) using a multimodal microreactor (i.e., System for Analysis at the Liquid Vacuum Interface, SALVI). SIM characterized cells and guided subsequent ToF-SIMS analysis. Lipid fragments were identified in the cell membrane via dynamic ToF-SIMS depth profiling. Positive SIMS spectra show intracellular potassium and sodium ion transport due to exposure to nanoparticles. Spectral principal component analysis elucidates differences in chemical composition among healthy alveolar epithelial mouse lung C10 cells, cells that uptake zinc oxide nanoparticles, and various wet and dry control samples. The observation of Zn(+) gives the first direct evidence of ZnO NP uptake and dissolution by the cell membrane. Our results provide submicron chemical mapping for investigating cell dynamics at the molecular level. PMID:27053104

  9. Quantitative chemical imaging of the intracellular spatial distribution of fundamental elements and light metals in single cells.

    PubMed

    Malucelli, Emil; Iotti, Stefano; Gianoncelli, Alessandra; Fratini, Michela; Merolle, Lucia; Notargiacomo, Andrea; Marraccini, Chiara; Sargenti, Azzurra; Cappadone, Concettina; Farruggia, Giovanna; Bukreeva, Inna; Lombardo, Marco; Trombini, Claudio; Maier, Jeanette A; Lagomarsino, Stefano

    2014-05-20

    We report a method that allows a complete quantitative characterization of whole single cells, assessing the total amount of carbon, nitrogen, oxygen, sodium, and magnesium and providing submicrometer maps of element molar concentration, cell density, mass, and volume. This approach allows quantifying elements down to 10(6) atoms/μm(3). This result was obtained by applying a multimodal fusion approach that combines synchrotron radiation microscopy techniques with off-line atomic force microscopy. The method proposed permits us to find the element concentration in addition to the mass fraction and provides a deeper and more complete knowledge of cell composition. We performed measurements on LoVo human colon cancer cells sensitive (LoVo-S) and resistant (LoVo-R) to doxorubicin. The comparison of LoVo-S and LoVo-R revealed different patterns in the maps of Mg concentration with higher values within the nucleus in LoVo-R and in the perinuclear region in LoVo-S cells. This feature was not so evident for the other elements, suggesting that Mg compartmentalization could be a significant trait of the drug-resistant cells. PMID:24734900

  10. Automating proliferation rate estimation from Ki-67 histology images

    NASA Astrophysics Data System (ADS)

    Al-Lahham, Heba Z.; Alomari, Raja S.; Hiary, Hazem; Chaudhary, Vipin

    2012-03-01

    Breast cancer is the second cause of women death and the most diagnosed female cancer in the US. Proliferation rate estimation (PRE) is one of the prognostic indicators that guide the treatment protocols and it is clinically performed from Ki-67 histopathology images. Automating PRE substantially increases the efficiency of the pathologists. Moreover, presenting a deterministic and reproducible proliferation rate value is crucial to reduce inter-observer variability. To that end, we propose a fully automated CAD system for PRE from the Ki-67 histopathology images. This CAD system is based on a model of three steps: image pre-processing, image clustering, and nuclei segmentation and counting that are finally followed by PRE. The first step is based on customized color modification and color-space transformation. Then, image pixels are clustered by K-Means depending on the features extracted from the images derived from the first step. Finally, nuclei are segmented and counted using global thresholding, mathematical morphology and connected component analysis. Our experimental results on fifty Ki-67-stained histopathology images show a significant agreement between our CAD's automated PRE and the gold standard's one, where the latter is an average between two observers' estimates. The Paired T-Test, for the automated and manual estimates, shows ρ = 0.86, 0.45, 0.8 for the brown nuclei count, blue nuclei count, and proliferation rate, respectively. Thus, our proposed CAD system is as reliable as the pathologist estimating the proliferation rate. Yet, its estimate is reproducible.

  11. Automated quality assurance for image-guided radiation therapy.

    PubMed

    Schreibmann, Eduard; Elder, Eric; Fox, Tim

    2009-01-01

    The use of image-guided patient positioning requires fast and reliable Quality Assurance (QA) methods to ensure the megavoltage (MV) treatment beam coincides with the integrated kilovoltage (kV) or volumetric cone-beam CT (CBCT) imaging and guidance systems. Current QA protocol is based on visually observing deviations of certain features in acquired kV in-room treatment images such as markers, distances, or HU values from phantom specifications. This is a time-consuming and subjective task because these features are identified by human operators. The method implemented in this study automated an IGRT QA protocol by using specific image processing algorithms that rigorously detected phantom features and performed all measurements involved in a classical QA protocol. The algorithm was tested on four different IGRT QA phantoms. Image analysis algorithms were able to detect QA features with the same accuracy as the manual approach but significantly faster. All described tests were performed in a single procedure, with acquisition of the images taking approximately 5 minutes, and the automated software analysis taking less than 1 minute. The study showed that the automated image analysis based procedure may be used as a daily QA procedure because it is completely automated and uses a single phantom setup. PMID:19223842

  12. Comparison of automated and manual segmentation of hippocampus MR images

    NASA Astrophysics Data System (ADS)

    Haller, John W.; Christensen, Gary E.; Miller, Michael I.; Joshi, Sarang C.; Gado, Mokhtar; Csernansky, John G.; Vannier, Michael W.

    1995-05-01

    The precision and accuracy of area estimates from magnetic resonance (MR) brain images and using manual and automated segmentation methods are determined. Areas of the human hippocampus were measured to compare a new automatic method of segmentation with regions of interest drawn by an expert. MR images of nine normal subjects and nine schizophrenic patients were acquired with a 1.5-T unit (Siemens Medical Systems, Inc., Iselin, New Jersey). From each individual MPRAGE 3D volume image a single comparable 2-D slice (matrix equals 256 X 256) was chosen which corresponds to the same coronal slice of the hippocampus. The hippocampus was first manually segmented, then segmented using high dimensional transformations of a digital brain atlas to individual brain MR images. The repeatability of a trained rater was assessed by comparing two measurements from each individual subject. Variability was also compared within and between subject groups of schizophrenics and normal subjects. Finally, the precision and accuracy of automated segmentation of hippocampal areas were determined by comparing automated measurements to manual segmentation measurements made by the trained rater on MR and brain slice images. The results demonstrate the high repeatability of area measurement from MR images of the human hippocampus. Automated segmentation using high dimensional transformations from a digital brain atlas provides repeatability superior to that of manual segmentation. Furthermore, the validity of automated measurements was demonstrated by a high correlation with manual segmentation measurements made by a trained rater. Quantitative morphometry of brain substructures (e.g. hippocampus) is feasible by use of a high dimensional transformation of a digital brain atlas to an individual MR image. This method automates the search for neuromorphological correlates of schizophrenia by a new mathematically robust method with unprecedented sensitivity to small local and regional differences.

  13. Automated Registration Of Images From Multiple Sensors

    NASA Technical Reports Server (NTRS)

    Rignot, Eric J. M.; Kwok, Ronald; Curlander, John C.; Pang, Shirley S. N.

    1994-01-01

    Images of terrain scanned in common by multiple Earth-orbiting remote sensors registered automatically with each other and, where possible, on geographic coordinate grid. Simulated image of terrain viewed by sensor computed from ancillary data, viewing geometry, and mathematical model of physics of imaging. In proposed registration algorithm, simulated and actual sensor images matched by area-correlation technique.

  14. Patch-Clamp Recordings and Calcium Imaging Followed by Single-Cell PCR Reveal the Developmental Profile of 13 Genes in iPSC-Derived Human Neurons

    PubMed Central

    Belinsky, Glenn S.; Rich, Matthew T.; Sirois, Carissa L.; Short, Shaina M.; Pedrosa, Erika; Lachman, Herbert M.; Antic, Srdjan D.

    2013-01-01

    Molecular genetic studies are typically performed on homogenized biological samples, resulting in contamination from non-neuronal cells. To improve expression profiling of neurons we combined patch recordings with single-cell PCR. Two iPSC lines (healthy subject and 22q11.2 deletion), were differentiated into neurons. Patch electrode recordings were performed on 229 human cells from Day-13 to Day-88, followed by capture and single-cell PCR for 13 genes: ACTB, HPRT, vGLUT1, βTUBIII, COMT, DISC1, GAD1, PAX6, DTNBP1, ERBB4, FOXP1, FOXP2, and GIRK2. Neurons derived from both iPSC lines expressed βTUBIII, fired action potentials, and experienced spontaneous depolarizations (UP states) ~2 weeks before vGLUT1, GAD1 and GIRK2 appeared. Multisite calcium imaging revealed that these UP states were not synchronized among hESC-H9-derived neurons. The expression of FOXP1, FOXP2 and vGLUT1 was lost after 50 days in culture, in contrast to other continuously expressed genes. When gene expression was combined with electrophysiology, two subsets of genes were apparent; those irrelevant to spontaneous depolarizations (including vGLUT1, GIRK2, FOXP2 and DISC1) and those associated with spontaneous depolarizations (GAD1 and ERBB4). The results demonstrate that in the earliest stages of neuron development, it is useful to combine genetic analysis with physiological characterizations, on a cell-to-cell basis. PMID:24157591

  15. Automation of Cassini Support Imaging Uplink Command Development

    NASA Technical Reports Server (NTRS)

    Ly-Hollins, Lisa; Breneman, Herbert H.; Brooks, Robert

    2010-01-01

    "Support imaging" is imagery requested by other Cassini science teams to aid in the interpretation of their data. The generation of the spacecraft command sequences for these images is performed by the Cassini Instrument Operations Team. The process initially established for doing this was very labor-intensive, tedious and prone to human error. Team management recognized this process as one that could easily benefit from automation. Team members were tasked to document the existing manual process, develop a plan and strategy to automate the process, implement the plan and strategy, test and validate the new automated process, and deliver the new software tools and documentation to Flight Operations for use during the Cassini extended mission. In addition to the goals of higher efficiency and lower risk in the processing of support imaging requests, an effort was made to maximize adaptability of the process to accommodate uplink procedure changes and the potential addition of new capabilities outside the scope of the initial effort.

  16. Research relative to automated multisensor image registration

    NASA Technical Reports Server (NTRS)

    Kanal, L. N.

    1983-01-01

    The basic aproaches to image registration are surveyed. Three image models are presented as models of the subpixel problem. A variety of approaches to the analysis of subpixel analysis are presented using these models.

  17. Quantification of Dynamic Morphological Drug Responses in 3D Organotypic Cell Cultures by Automated Image Analysis

    PubMed Central

    Härmä, Ville; Schukov, Hannu-Pekka; Happonen, Antti; Ahonen, Ilmari; Virtanen, Johannes; Siitari, Harri; Åkerfelt, Malin; Lötjönen, Jyrki; Nees, Matthias

    2014-01-01

    Glandular epithelial cells differentiate into complex multicellular or acinar structures, when embedded in three-dimensional (3D) extracellular matrix. The spectrum of different multicellular morphologies formed in 3D is a sensitive indicator for the differentiation potential of normal, non-transformed cells compared to different stages of malignant progression. In addition, single cells or cell aggregates may actively invade the matrix, utilizing epithelial, mesenchymal or mixed modes of motility. Dynamic phenotypic changes involved in 3D tumor cell invasion are sensitive to specific small-molecule inhibitors that target the actin cytoskeleton. We have used a panel of inhibitors to demonstrate the power of automated image analysis as a phenotypic or morphometric readout in cell-based assays. We introduce a streamlined stand-alone software solution that supports large-scale high-content screens, based on complex and organotypic cultures. AMIDA (Automated Morphometric Image Data Analysis) allows quantitative measurements of large numbers of images and structures, with a multitude of different spheroid shapes, sizes, and textures. AMIDA supports an automated workflow, and can be combined with quality control and statistical tools for data interpretation and visualization. We have used a representative panel of 12 prostate and breast cancer lines that display a broad spectrum of different spheroid morphologies and modes of invasion, challenged by a library of 19 direct or indirect modulators of the actin cytoskeleton which induce systematic changes in spheroid morphology and differentiation versus invasion. These results were independently validated by 2D proliferation, apoptosis and cell motility assays. We identified three drugs that primarily attenuated the invasion and formation of invasive processes in 3D, without affecting proliferation or apoptosis. Two of these compounds block Rac signalling, one affects cellular cAMP/cGMP accumulation. Our approach supports

  18. Automated cardiac sarcomere analysis from second harmonic generation images

    NASA Astrophysics Data System (ADS)

    Garcia-Canadilla, Patricia; Gonzalez-Tendero, Anna; Iruretagoyena, Igor; Crispi, Fatima; Torre, Iratxe; Amat-Roldan, Ivan; Bijnens, Bart H.; Gratacos, Eduard

    2014-05-01

    Automatic quantification of cardiac muscle properties in tissue sections might provide important information related to different types of diseases. Second harmonic generation (SHG) imaging provides a stain-free microscopy approach to image cardiac fibers that, combined with our methodology of the automated measurement of the ultrastructure of muscle fibers, computes a reliable set of quantitative image features (sarcomere length, A-band length, thick-thin interaction length, and fiber orientation). We evaluated the performance of our methodology in computer-generated muscle fibers modeling some artifacts that are present during the image acquisition. Then, we also evaluated it by comparing it to manual measurements in SHG images from cardiac tissue of fetal and adult rabbits. The results showed a good performance of our methodology at high signal-to-noise ratio of 20 dB. We conclude that our automated measurements enable reliable characterization of cardiac fiber tissues to systematically study cardiac tissue in a wide range of conditions.

  19. Analyzing the effect of absorption and refractive index on image formation in high numerical aperture transmission microscopy of single cells

    NASA Astrophysics Data System (ADS)

    Coe, Ryan L.; Seibel, Eric J.

    2013-02-01

    Transmission bright-field microscopy is the clinical mainstay for disease diagnosis where image contrast is provided by absorptive and refractive index differences between tissue and the surrounding media. Different microscopy techniques often assume one of the two contrast mechanisms is negligible as a means to better understand the tissue scattering processes. This particular work provides better understanding of the role of refractive index and absorption within Optical Projection Tomographic Microscopy (OPTM) through the development of a generalized computational model based upon the Finite-Difference Time-Domain method. The model mimics OPTM by simulating axial scanning of the objective focal plane through the cell to produce projection images. These projection images, acquired from circumferential positions around the cell, are reconstructed into isometric three-dimensional images using the filtered backprojection normally employed in Computed Tomography (CT). The model provides a platform to analyze all aspects of bright-field microscopes, such as the degree of refractive index matching and the numerical aperture, which can be varied from air-immersion to high NA oil-immersion. In this preliminary work, the model is used to understand the effects of absorption and refraction on image formation using micro-shells and idealized nuclei. Analysis of absorption and refractive index separately provides the opportunity to better assess their role together as a complex refractive index for improved interpretation of bright-field scattering, essential for OPTM image reconstruction. This simulation, as well as ones in the future looking at other effects, will be used to optimize OPTM imaging parameters and triage efforts to further improve the overall system design.

  20. Fuzzy emotional semantic analysis and automated annotation of scene images.

    PubMed

    Cao, Jianfang; Chen, Lichao

    2015-01-01

    With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP) neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance. PMID:25838818

  1. Fully Automated Image Orientation in the Absence of Targets

    NASA Astrophysics Data System (ADS)

    Stamatopoulos, C.; Chuang, T. Y.; Fraser, C. S.; Lu, Y. Y.

    2012-07-01

    Automated close-range photogrammetric network orientation has traditionally been associated with the use of coded targets in the object space to allow for an initial relative orientation (RO) and subsequent spatial resection of the images. Over the past decade, automated orientation via feature-based matching (FBM) techniques has attracted renewed research attention in both the photogrammetry and computer vision (CV) communities. This is largely due to advances made towards the goal of automated relative orientation of multi-image networks covering untargetted (markerless) objects. There are now a number of CV-based algorithms, with accompanying open-source software, that can achieve multi-image orientation within narrow-baseline networks. From a photogrammetric standpoint, the results are typically disappointing as the metric integrity of the resulting models is generally poor, or even unknown, while the number of outliers within the image matching and triangulation is large, and generally too large to allow relative orientation (RO) via the commonly used coplanarity equations. On the other hand, there are few examples within the photogrammetric research field of automated markerless camera calibration to metric tolerances, and these too are restricted to narrow-baseline, low-convergence imaging geometry. The objective addressed in this paper is markerless automatic multi-image orientation, maintaining metric integrity, within networks that incorporate wide-baseline imagery. By wide-baseline we imply convergent multi-image configurations with convergence angles of up to around 90°. An associated aim is provision of a fast, fully automated process, which can be performed without user intervention. For this purpose, various algorithms require optimisation to allow parallel processing utilising multiple PC cores and graphics processing units (GPUs).

  2. SINGLE CELL GENOME SEQUENCING

    PubMed Central

    Yilmaz, Suzan; Singh, Anup K.

    2011-01-01

    Whole genome amplification and next-generation sequencing of single cells has become a powerful approach for studying uncultivated microorganisms that represent 90–99 % of all environmental microbes. Single cell sequencing enables not only the identification of microbes but also linking of functions to species, a feat not achievable by metagenomic techniques. Moreover, it allows the analysis of low abundance species that may be missed in community-based analyses. It has also proved very useful in complementing metagenomics in the assembly and binning of single genomes. With the advent of drastically cheaper and higher throughput sequencing technologies, it is expected that single cell sequencing will become a standard tool in studying the genome and transcriptome of microbial communities. PMID:22154471

  3. Single Cell Oncogenesis

    NASA Astrophysics Data System (ADS)

    Lu, Xin

    It is believed that cancer originates from a single cell that has gone through generations of evolution of genetic and epigenetic changes that associate with the hallmarks of cancer. In some cancers such as various types of leukemia, cancer is clonal. Yet in other cancers like glioblastoma (GBM), there is tremendous tumor heterogeneity that is likely to be caused by simultaneous evolution of multiple subclones within the same tissue. It is obvious that understanding how a single cell develops into a clonal tumor upon genetic alterations, at molecular and cellular levels, holds the key to the real appreciation of tumor etiology and ultimate solution for therapeutics. Surprisingly very little is known about the process of spontaneous tumorigenesis from single cells in human or vertebrate animal models. The main reason is the lack of technology to track the natural process of single cell changes from a homeostatic state to a progressively cancerous state. Recently, we developed a patented compound, photoactivatable (''caged'') tamoxifen analogue 4-OHC and associated technique called optochemogenetic switch (OCG switch), which we believe opens the opportunity to address this urgent biological as well as clinical question about cancer. We propose to combine OCG switch with genetically engineered mouse models of head and neck squamous cell carcinoma and high grade astrocytoma (including GBM) to study how single cells, when transformed through acute loss of tumor suppressor genes PTEN and TP53 and gain of oncogenic KRAS, can develop into tumor colonies with cellular and molecular heterogeneity in these tissues. The abstract is for my invited talk in session ``Beyond Darwin: Evolution in Single Cells'' 3/18/2016 11:15 AM.

  4. Automated live cell imaging systems reveal dynamic cell behavior.

    PubMed

    Chirieleison, Steven M; Bissell, Taylor A; Scelfo, Christopher C; Anderson, Jordan E; Li, Yong; Koebler, Doug J; Deasy, Bridget M

    2011-07-01

    Automated time-lapsed microscopy provides unique research opportunities to visualize cells and subcellular components in experiments with time-dependent parameters. As accessibility to these systems is increasing, we review here their use in cell science with a focus on stem cell research. Although the use of time-lapsed imaging to answer biological questions dates back nearly 150 years, only recently have the use of an environmentally controlled chamber and robotic stage controllers allowed for high-throughput continuous imaging over long periods at the cell and subcellular levels. Numerous automated imaging systems are now available from both companies that specialize in live cell imaging and from major microscope manufacturers. We discuss the key components of robots used for time-lapsed live microscopic imaging, and the unique data that can be obtained from image analysis. We show how automated features enhance experimentation by providing examples of uniquely quantified proliferation and migration live cell imaging data. In addition to providing an efficient system that drastically reduces man-hours and consumes fewer laboratory resources, this technology greatly enhances cell science by providing a unique dataset of temporal changes in cell activity. PMID:21692197

  5. Digital Microfluidics for Manipulation and Analysis of a Single Cell

    PubMed Central

    He, Jie-Long; Chen, An-Te; Lee, Jyong-Huei; Fan, Shih-Kang

    2015-01-01

    The basic structural and functional unit of a living organism is a single cell. To understand the variability and to improve the biomedical requirement of a single cell, its analysis has become a key technique in biological and biomedical research. With a physical boundary of microchannels and microstructures, single cells are efficiently captured and analyzed, whereas electric forces sort and position single cells. Various microfluidic techniques have been exploited to manipulate single cells through hydrodynamic and electric forces. Digital microfluidics (DMF), the manipulation of individual droplets holding minute reagents and cells of interest by electric forces, has received more attention recently. Because of ease of fabrication, compactness and prospective automation, DMF has become a powerful approach for biological application. We review recent developments of various microfluidic chips for analysis of a single cell and for efficient genetic screening. In addition, perspectives to develop analysis of single cells based on DMF and emerging functionality with high throughput are discussed. PMID:26389890

  6. Digital Microfluidics for Manipulation and Analysis of a Single Cell.

    PubMed

    He, Jie-Long; Chen, An-Te; Lee, Jyong-Huei; Fan, Shih-Kang

    2015-01-01

    The basic structural and functional unit of a living organism is a single cell. To understand the variability and to improve the biomedical requirement of a single cell, its analysis has become a key technique in biological and biomedical research. With a physical boundary of microchannels and microstructures, single cells are efficiently captured and analyzed, whereas electric forces sort and position single cells. Various microfluidic techniques have been exploited to manipulate single cells through hydrodynamic and electric forces. Digital microfluidics (DMF), the manipulation of individual droplets holding minute reagents and cells of interest by electric forces, has received more attention recently. Because of ease of fabrication, compactness and prospective automation, DMF has become a powerful approach for biological application. We review recent developments of various microfluidic chips for analysis of a single cell and for efficient genetic screening. In addition, perspectives to develop analysis of single cells based on DMF and emerging functionality with high throughput are discussed. PMID:26389890

  7. Image Understanding for Automated Retinal Diagnosis

    PubMed Central

    Goldbaum, M.H.; Katz, N.P.; Chaudhuri, S.; Nelson, M.

    1989-01-01

    Interpretation of images of the ocular fundus by the STARE (STructured Analysis of the REtina) system requires many steps, including image enhancement, object segmentation, object identification, and scene analysis. We describe how these steps are performed and linked, and we demonstrate some success with the STARE system in each of these steps. We are currently able to segment the blood vessels, optic nerve, fovea, bright lesions, and dark lesions automatically. We describe the methods for these tasks and the development underway to complete the production of a database of objects that forms a coded description of the image. For the final step in interpreting the image, we found the backpropagation neural network to be able to learn to diagnose a set of diseases from the type of information in the coded description of the image.

  8. Automating the assessment of citrus canker symptoms with image analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Citrus canker (CC, caused by Xanthomonas citri) is a serious disease of citrus in Florida and other citrus-growing regions. Severity of symptoms can be estimated by visual rating, but there is inter- and intra-rater variation. Automated image analysis (IA) may offer a way of reducing some of ...

  9. Automated Risk Prediction for Esophageal Optical Endomicroscopic Images

    PubMed Central

    Kothari, Sonal; Wu, Hang; Tong, Li; Woods, Kevin E.; Wang, May D.

    2016-01-01

    Biomedical in vivo imaging has been playing an essential role in diagnoses and treatment in modern medicine. However, compared with the fast development of medical imaging systems, the medical imaging informatics, especially automated prediction, has not been fully explored. In our paper, we compared different feature extraction and classification methods for prediction pipeline to analyze in vivo endomicroscopic images, obtained from patients who are at risks for the development of gastric disease, esophageal adenocarcionoma. Extensive experiment results show that the selected feature representation and prediction algorithms achieved high accuracy in both binary and multi-class prediction tasks. PMID:27532065

  10. Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution: Toward Organism-Level Systems Biology in Mammals.

    PubMed

    Susaki, Etsuo A; Ueda, Hiroki R

    2016-01-21

    Organism-level systems biology aims to identify, analyze, control and design cellular circuits in organisms. Many experimental and computational approaches have been developed over the years to allow us to conduct these studies. Some of the most powerful methods are based on using optical imaging in combination with fluorescent labeling, and for those one of the long-standing stumbling blocks has been tissue opacity. Recently, the solutions to this problem have started to emerge based on whole-body and whole-organ clearing techniques that employ innovative tissue-clearing chemistry. Here, we review these advancements and discuss how combining new clearing techniques with high-performing fluorescent proteins or small molecule tags, rapid volume imaging and efficient image informatics is resulting in comprehensive and quantitative organ-wide, single-cell resolution experimental data. These technologies are starting to yield information on connectivity and dynamics in cellular circuits at unprecedented resolution, and bring us closer to system-level understanding of physiology and diseases of complex mammalian systems. PMID:26933741

  11. Quantitative Automated Image Analysis System with Automated Debris Filtering for the Detection of Breast Carcinoma Cells

    PubMed Central

    Martin, David T.; Sandoval, Sergio; Ta, Casey N.; Ruidiaz, Manuel E.; Cortes-Mateos, Maria Jose; Messmer, Davorka; Kummel, Andrew C.; Blair, Sarah L.; Wang-Rodriguez, Jessica

    2011-01-01

    Objective To develop an intraoperative method for margin status evaluation during breast conservation therapy (BCT) using an automated analysis of imprint cytology specimens. Study Design Imprint cytology samples were prospectively taken from 47 patients undergoing either BCT or breast reduction surgery. Touch preparations from BCT patients were taken on cut sections through the tumor to generate positive margin controls. For breast reduction patients, slide imprints were taken at cuts through the center of excised tissue. Analysis results from the presented technique were compared against standard pathologic diagnosis. Slides were stained with cytokeratin and Hoechst, imaged with an automated fluorescent microscope, and analyzed with a fast algorithm to automate discrimination between epithelial cells and noncellular debris. Results The accuracy of the automated analysis was 95% for identifying invasive cancers compared against final pathologic diagnosis. The overall sensitivity was 87% while specificity was 100% (no false positives). This is comparable to the best reported results from manual examination of intraoperative imprint cytology slides while reducing the need for direct input from a cytopathologist. Conclusion This work demonstrates a proof of concept for developing a highly accurate and automated system for the intraoperative evaluation of margin status to guide surgical decisions and lower positive margin rates. PMID:21525740

  12. Automated quality assessment in three-dimensional breast ultrasound images.

    PubMed

    Schwaab, Julia; Diez, Yago; Oliver, Arnau; Martí, Robert; van Zelst, Jan; Gubern-Mérida, Albert; Mourri, Ahmed Bensouda; Gregori, Johannes; Günther, Matthias

    2016-04-01

    Automated three-dimensional breast ultrasound (ABUS) is a valuable adjunct to x-ray mammography for breast cancer screening of women with dense breasts. High image quality is essential for proper diagnostics and computer-aided detection. We propose an automated image quality assessment system for ABUS images that detects artifacts at the time of acquisition. Therefore, we study three aspects that can corrupt ABUS images: the nipple position relative to the rest of the breast, the shadow caused by the nipple, and the shape of the breast contour on the image. Image processing and machine learning algorithms are combined to detect these artifacts based on 368 clinical ABUS images that have been rated manually by two experienced clinicians. At a specificity of 0.99, 55% of the images that were rated as low quality are detected by the proposed algorithms. The areas under the ROC curves of the single classifiers are 0.99 for the nipple position, 0.84 for the nipple shadow, and 0.89 for the breast contour shape. The proposed algorithms work fast and reliably, which makes them adequate for online evaluation of image quality during acquisition. The presented concept may be extended to further image modalities and quality aspects. PMID:27158633

  13. Automated hybridization/imaging device for fluorescent multiplex DNA sequencing

    DOEpatents

    Weiss, Robert B.; Kimball, Alvin W.; Gesteland, Raymond F.; Ferguson, F. Mark; Dunn, Diane M.; Di Sera, Leonard J.; Cherry, Joshua L.

    1995-01-01

    A method is disclosed for automated multiplex sequencing of DNA with an integrated automated imaging hybridization chamber system. This system comprises an hybridization chamber device for mounting a membrane containing size-fractionated multiplex sequencing reaction products, apparatus for fluid delivery to the chamber device, imaging apparatus for light delivery to the membrane and image recording of fluorescence emanating from the membrane while in the chamber device, and programmable controller apparatus for controlling operation of the system. The multiplex reaction products are hybridized with a probe, then an enzyme (such as alkaline phosphatase) is bound to a binding moiety on the probe, and a fluorogenic substrate (such as a benzothiazole derivative) is introduced into the chamber device by the fluid delivery apparatus. The enzyme converts the fluorogenic substrate into a fluorescent product which, when illuminated in the chamber device with a beam of light from the imaging apparatus, excites fluorescence of the fluorescent product to produce a pattern of hybridization. The pattern of hybridization is imaged by a CCD camera component of the imaging apparatus to obtain a series of digital signals. These signals are converted by the controller apparatus into a string of nucleotides corresponding to the nucleotide sequence an automated sequence reader. The method and apparatus are also applicable to other membrane-based applications such as colony and plaque hybridization and Southern, Northern, and Western blots.

  14. Automated hybridization/imaging device for fluorescent multiplex DNA sequencing

    DOEpatents

    Weiss, R.B.; Kimball, A.W.; Gesteland, R.F.; Ferguson, F.M.; Dunn, D.M.; Di Sera, L.J.; Cherry, J.L.

    1995-11-28

    A method is disclosed for automated multiplex sequencing of DNA with an integrated automated imaging hybridization chamber system. This system comprises an hybridization chamber device for mounting a membrane containing size-fractionated multiplex sequencing reaction products, apparatus for fluid delivery to the chamber device, imaging apparatus for light delivery to the membrane and image recording of fluorescence emanating from the membrane while in the chamber device, and programmable controller apparatus for controlling operation of the system. The multiplex reaction products are hybridized with a probe, the enzyme (such as alkaline phosphatase) is bound to a binding moiety on the probe, and a fluorogenic substrate (such as a benzothiazole derivative) is introduced into the chamber device by the fluid delivery apparatus. The enzyme converts the fluorogenic substrate into a fluorescent product which, when illuminated in the chamber device with a beam of light from the imaging apparatus, excites fluorescence of the fluorescent product to produce a pattern of hybridization. The pattern of hybridization is imaged by a CCD camera component of the imaging apparatus to obtain a series of digital signals. These signals are converted by the controller apparatus into a string of nucleotides corresponding to the nucleotide sequence an automated sequence reader. The method and apparatus are also applicable to other membrane-based applications such as colony and plaque hybridization and Southern, Northern, and Western blots. 9 figs.

  15. Quantitative imaging of magnesium distribution at single-cell resolution in brain tumors and infiltrating tumor cells with secondary ion mass spectrometry (SIMS).

    PubMed

    Chandra, Subhash; Parker, Dylan J; Barth, Rolf F; Pannullo, Susan C

    2016-03-01

    Glioblastoma multiforme (GBM) is one of the deadliest forms of human brain tumors. The infiltrative pattern of growth of these tumors includes the spread of individual and/or clusters of tumor cells at some distance from the main tumor mass in parts of the brain protected by an intact blood-brain-barrier. Pathophysiological studies of GBM could be greatly enhanced by analytical techniques capable of in situ single-cell resolution measurements of infiltrating tumor cells. Magnesium homeostasis is an area of active investigation in high grade gliomas. In the present study, we have used the F98 rat glioma as a model of human GBM and an elemental/isotopic imaging technique of secondary ion mass spectrometry, a CAMECA IMS-3f ion microscope, for studying Mg distribution with single-cell resolution in freeze-dried brain tissue cryosections. Quantitative observations were made on tumor cells in the main tumor mass, contiguous brain tissue, and infiltrating tumor cells in adjacent normal brain. The brain tissue contained a significantly lower total Mg concentration of 4.70 ± 0.93 mmol/kg wet weight (mean ± SD) in comparison to 11.64 ± 1.96 mmol/kg wet weight in tumor cells of the main tumor mass and 10.72 ± 1.76 mmol/kg wet weight in infiltrating tumor cells (p < 0.05). The nucleus of individual tumor cells contained elevated levels of bound Mg. These observations have established that there was enhanced influx and increased binding of Mg in tumor cells. They provide strong support for further investigation of altered Mg homeostasis and activation of Mg-transporting channels in GBMs as possible therapeutic targets. PMID:26703785

  16. Automated planning of breast radiotherapy using cone beam CT imaging

    SciTech Connect

    Amit, Guy; Purdie, Thomas G.

    2015-02-15

    Purpose: Develop and clinically validate a methodology for using cone beam computed tomography (CBCT) imaging in an automated treatment planning framework for breast IMRT. Methods: A technique for intensity correction of CBCT images was developed and evaluated. The technique is based on histogram matching of CBCT image sets, using information from “similar” planning CT image sets from a database of paired CBCT and CT image sets (n = 38). Automated treatment plans were generated for a testing subset (n = 15) on the planning CT and the corrected CBCT. The plans generated on the corrected CBCT were compared to the CT-based plans in terms of beam parameters, dosimetric indices, and dose distributions. Results: The corrected CBCT images showed considerable similarity to their corresponding planning CTs (average mutual information 1.0±0.1, average sum of absolute differences 185 ± 38). The automated CBCT-based plans were clinically acceptable, as well as equivalent to the CT-based plans with average gantry angle difference of 0.99°±1.1°, target volume overlap index (Dice) of 0.89±0.04 although with slightly higher maximum target doses (4482±90 vs 4560±84, P < 0.05). Gamma index analysis (3%, 3 mm) showed that the CBCT-based plans had the same dose distribution as plans calculated with the same beams on the registered planning CTs (average gamma index 0.12±0.04, gamma <1 in 99.4%±0.3%). Conclusions: The proposed method demonstrates the potential for a clinically feasible and efficient online adaptive breast IMRT planning method based on CBCT imaging, integrating automation.

  17. Endocytic Sorting of CFTR variants Monitored by Single Cell Fluorescence Ratio Image Analysis (FRIA) in Living Cells

    PubMed Central

    Barriere, H.; Apaja, P.; Okiyoneda, T.; Lukacs, G. L.

    2016-01-01

    Summary The wild-type CFTR channel undergoes constitutive internalization and recycling at the plasma membrane. This process is initiated by the recognition of the Tyr- and di-Leu-based endocytic motifs of CFTR by the AP-2 adaptor complex, leading to the formation of clathrin-coated vesicles and the channel delivery to sorting/recycling endosomes. Accumulating evidence suggests that conformationally defective mutant CFTRs (e.g. rescued ΔF508 and glycosylation-deficient channel) are unstable at the plasma membrane and undergo augmented ubiquitination in post-Golgi compartments. Ubiquitination conceivably accounts for the metabolic instability at cell surface by provoking accelerated internalization, as well as rerouting the channel from recycling towards lysosomal degradation. We developed an in vivo fluorescence ratio imaging assay (FRIA) that in concert with genetic manipulation can be utilized to establish the post-endocytic fate and sorting determinants of mutant CFTRs. PMID:21594793

  18. An Automated Image Processing System for Concrete Evaluation

    SciTech Connect

    Baumgart, C.W.; Cave, S.P.; Linder, K.E.

    1998-11-23

    AlliedSignal Federal Manufacturing & Technologies (FM&T) was asked to perform a proof-of-concept study for the Missouri Highway and Transportation Department (MHTD), Research Division, in June 1997. The goal of this proof-of-concept study was to ascertain if automated scanning and imaging techniques might be applied effectively to the problem of concrete evaluation. In the current evaluation process, a concrete sample core is manually scanned under a microscope. Voids (or air spaces) within the concrete are then detected visually by a human operator by incrementing the sample under the cross-hairs of a microscope and by counting the number of "pixels" which fall within a void. Automation of the scanning and image analysis processes is desired to improve the speed of the scanning process, to improve evaluation consistency, and to reduce operator fatigue. An initial, proof-of-concept image analysis approach was successfully developed and demonstrated using acquired black and white imagery of concrete samples. In this paper, the automated scanning and image capture system currently under development will be described and the image processing approach developed for the proof-of-concept study will be demonstrated. A development update and plans for future enhancements are also presented.

  19. Automated Image Analysis for Determination of Antibody Titers Against Occupational Bacterial Antigens Using Indirect Immunofluorescence.

    PubMed

    Brauner, Paul; Jäckel, Udo

    2016-06-01

    Employees who are exposed to high concentrations of microorganisms in bioaerosols frequently suffer from respiratory disorders. However, etiology and in particular potential roles of microorganisms in pathogenesis still need to be elucidated. Thus, determination of employees' antibody titers against specific occupational microbial antigens may lead to identification of potentially harmful species. Since indirect immunofluorescence (IIF) is easy to implement, we used this technique to analyze immunoreactions in human sera. In order to address disadvantageous inter-observer variations as well as the absence of quantifiable fluorescence data in conventional titer determination by eye, we specifically developed a software tool for automated image analysis. The 'Fluorolyzer' software is able to reliably quantify fluorescence intensities of antibody-bound bacterial cells on digital images. Subsequently, fluorescence values of single cells have been used to calculate non-discrete IgG titers. We tested this approach on multiple bacterial workplace isolates and determined titers in sera from 20 volunteers. Furthermore, we compared image-based results with the conventional manual readout and found significant correlation as well as statistically confirmed reproducibility. In conclusion, we successfully employed 'Fluorolyzer' for determination of titers against various bacterial species and demonstrated its applicability as a useful tool for reliable and efficient analysis of immune response toward occupational exposure to bioaerosols. PMID:27026659

  20. Automated Pointing of Cardiac Imaging Catheters

    PubMed Central

    Loschak, Paul M.; Brattain, Laura J.; Howe, Robert D.

    2013-01-01

    Intracardiac echocardiography (ICE) catheters enable high-quality ultrasound imaging within the heart, but their use in guiding procedures is limited due to the difficulty of manually pointing them at structures of interest. This paper presents the design and testing of a catheter steering model for robotic control of commercial ICE catheters. The four actuated degrees of freedom (4-DOF) are two catheter handle knobs to produce bi-directional bending in combination with rotation and translation of the handle. An extra degree of freedom in the system allows the imaging plane (dependent on orientation) to be directed at an object of interest. A closed form solution for forward and inverse kinematics enables control of the catheter tip position and the imaging plane orientation. The proposed algorithms were validated with a robotic test bed using electromagnetic sensor tracking of the catheter tip. The ability to automatically acquire imaging targets in the heart may improve the efficiency and effectiveness of intracardiac catheter interventions by allowing visualization of soft tissue structures that are not visible using standard fluoroscopic guidance. Although the system has been developed and tested for manipulating ICE catheters, the methods described here are applicable to any long thin tendon-driven tool (with single or bi-directional bending) requiring accurate tip position and orientation control. PMID:24683501

  1. Automated vasculature extraction from placenta images

    NASA Astrophysics Data System (ADS)

    Almoussa, Nizar; Dutra, Brittany; Lampe, Bryce; Getreuer, Pascal; Wittman, Todd; Salafia, Carolyn; Vese, Luminita

    2011-03-01

    Recent research in perinatal pathology argues that analyzing properties of the placenta may reveal important information on how certain diseases progress. One important property is the structure of the placental blood vessels, which supply a fetus with all of its oxygen and nutrition. An essential step in the analysis of the vascular network pattern is the extraction of the blood vessels, which has only been done manually through a costly and time-consuming process. There is no existing method to automatically detect placental blood vessels; in addition, the large variation in the shape, color, and texture of the placenta makes it difficult to apply standard edge-detection algorithms. We describe a method to automatically detect and extract blood vessels from a given image by using image processing techniques and neural networks. We evaluate several local features for every pixel, in addition to a novel modification to an existing road detector. Pixels belonging to blood vessel regions have recognizable responses; hence, we use an artificial neural network to identify the pattern of blood vessels. A set of images where blood vessels are manually highlighted is used to train the network. We then apply the neural network to recognize blood vessels in new images. The network is effective in capturing the most prominent vascular structures of the placenta.

  2. Automated Pointing of Cardiac Imaging Catheters.

    PubMed

    Loschak, Paul M; Brattain, Laura J; Howe, Robert D

    2013-12-31

    Intracardiac echocardiography (ICE) catheters enable high-quality ultrasound imaging within the heart, but their use in guiding procedures is limited due to the difficulty of manually pointing them at structures of interest. This paper presents the design and testing of a catheter steering model for robotic control of commercial ICE catheters. The four actuated degrees of freedom (4-DOF) are two catheter handle knobs to produce bi-directional bending in combination with rotation and translation of the handle. An extra degree of freedom in the system allows the imaging plane (dependent on orientation) to be directed at an object of interest. A closed form solution for forward and inverse kinematics enables control of the catheter tip position and the imaging plane orientation. The proposed algorithms were validated with a robotic test bed using electromagnetic sensor tracking of the catheter tip. The ability to automatically acquire imaging targets in the heart may improve the efficiency and effectiveness of intracardiac catheter interventions by allowing visualization of soft tissue structures that are not visible using standard fluoroscopic guidance. Although the system has been developed and tested for manipulating ICE catheters, the methods described here are applicable to any long thin tendon-driven tool (with single or bi-directional bending) requiring accurate tip position and orientation control. PMID:24683501

  3. Automated Analysis of Mammography Phantom Images

    NASA Astrophysics Data System (ADS)

    Brooks, Kenneth Wesley

    The present work stems from the hypothesis that humans are inconsistent when making subjective analyses of images and that human decisions for moderately complex images may be performed by a computer with complete objectivity, once a human acceptance level has been established. The following goals were established to test the hypothesis: (1) investigate observer variability within the standard mammographic phantom evaluation process; (2) evaluate options for high-resolution image digitization and utilize the most appropriate technology for standard mammographic phantom film digitization; (3) develop a machine-based vision system for evaluating standard mammographic phantom images to eliminate effects of human variabilities; and (4) demonstrate the completed system's performance against human observers for accreditation and for manufacturing quality control of standard mammographic phantom images. The following methods and procedures were followed to achieve the goals of the research: (1) human variabilities in the American College of Radiology accreditation process were simulated by observer studies involving 30 medical physicists and these were compared to the same number of diagnostic radiologists and untrained control group of observers; (2) current digitization technologies were presented and performance test procedures were developed; three devices were tested which represented commercially available high, intermediate and low-end contrast and spatial resolution capabilities; (3) optimal image processing schemes were applied and tested which performed low, intermediate and high-level computer vision tasks; and (4) the completed system's performance was tested against human observers for accreditation and for manufacturing quality control of standard mammographic phantom images. The results from application of the procedures were as follows: (1) the simulated American College of Radiology mammography accreditation program phantom evaluation process demonstrated

  4. Automated Image Registration Using Morphological Region of Interest Feature Extraction

    NASA Technical Reports Server (NTRS)

    Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

    2005-01-01

    With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

  5. Fast methods for analysis of neurotransmitters from single cell and monitoring their releases in central nervous system by capillary electrophoresis, fluorescence microscopy and luminescence imaging

    SciTech Connect

    Wang, Ziqiang

    1999-12-10

    Fast methods for separation and detection of important neurotransmitters and the releases in central nervous system (CNS) were developed. Enzyme based immunoassay combined with capillary electrophoresis was used to analyze the contents of amino acid neurotransmitters from single neuron cells. The release of amino acid neurotransmitters from neuron cultures was monitored by laser induced fluorescence imaging method. The release and signal transduction of adenosine triphosphate (ATP) in CNS was studied with sensitive luminescence imaging method. A new dual-enzyme on-column reaction method combined with capillary electrophoresis has been developed for determining the glutamate content in single cells. Detection was based on monitoring the laser-induced fluorescence of the reaction product NADH, and the measured fluorescence intensity was related to the concentration of glutamate in each cell. The detection limit of glutamate is down to 10{sup {minus}8} M level, which is 1 order of magnitude lower than the previously reported detection limit based on similar detection methods. The mass detection limit of a few attomoles is far superior to that of any other reports. Selectivity for glutamate is excellent over most of amino acids. The glutamate content in single human erythrocyte and baby rat brain neurons were determined with this method and results agreed well with literature values.

  6. Single cell analysis: the new frontier in 'Omics'

    SciTech Connect

    Wang, Daojing; Bodovitz, Steven

    2010-01-14

    Cellular heterogeneity arising from stochastic expression of genes, proteins, and metabolites is a fundamental principle of cell biology, but single cell analysis has been beyond the capabilities of 'Omics' technologies. This is rapidly changing with the recent examples of single cell genomics, transcriptomics, proteomics, and metabolomics. The rate of change is expected to accelerate owing to emerging technologies that range from micro/nanofluidics to microfabricated interfaces for mass spectrometry to third- and fourth-generation automated DNA sequencers. As described in this review, single cell analysis is the new frontier in Omics, and single cell Omics has the potential to transform systems biology through new discoveries derived from cellular heterogeneity.

  7. SAND: Automated VLBI imaging and analyzing pipeline

    NASA Astrophysics Data System (ADS)

    Zhang, Ming

    2016-05-01

    The Search And Non-Destroy (SAND) is a VLBI data reduction pipeline composed of a set of Python programs based on the AIPS interface provided by ObitTalk. It is designed for the massive data reduction of multi-epoch VLBI monitoring research. It can automatically investigate calibrated visibility data, search all the radio emissions above a given noise floor and do the model fitting either on the CLEANed image or directly on the uv data. It then digests the model-fitting results, intelligently identifies the multi-epoch jet component correspondence, and recognizes the linear or non-linear proper motion patterns. The outputs including CLEANed image catalogue with polarization maps, animation cube, proper motion fitting and core light curves. For uncalibrated data, a user can easily add inline modules to do the calibration and self-calibration in a batch for a specific array.

  8. Automated Imaging Techniques for Biosignature Detection in Geologic Samples

    NASA Astrophysics Data System (ADS)

    Williford, K. H.

    2015-12-01

    Robust biosignature detection in geologic samples typically requires the integration of morphological/textural data with biogeochemical data across a variety of scales. We present new automated imaging and coordinated biogeochemical analysis techniques developed at the JPL Astrobiogeochemistry Laboratory (abcLab) in support of biosignature detection in terrestrial samples as well as those that may eventually be returned from Mars. Automated gigapixel mosaic imaging of petrographic thin sections in transmitted and incident light (including UV epifluorescence) is supported by a microscopy platform with a digital XYZ stage. Images are acquired, processed, and co-registered using multiple software platforms at JPL and can be displayed and shared using Gigapan, a freely available, web-based toolset (e.g. . Automated large area (cm-scale) elemental mapping at sub-micrometer spatial resolution is enabled by a variable pressure scanning electron microscope (SEM) with a large (150 mm2) silicon drift energy dispersive spectroscopy (EDS) detector system. The abcLab light and electron microscopy techniques are augmented by additional elemental chemistry, mineralogy and organic detection/classification using laboratory Micro-XRF and UV Raman/fluorescence systems, precursors to the PIXL and SHERLOC instrument platforms selected for flight on the NASA Mars 2020 rover mission. A workflow including careful sample preparation followed by iterative gigapixel imaging, SEM/EDS, Micro-XRF and UV fluorescence/Raman in support of organic, mineralogic, and elemental biosignature target identification and follow up analysis with other techniques including secondary ion mass spectrometry (SIMS) will be discussed.

  9. Automated delineation of stroke lesions using brain CT images

    PubMed Central

    Gillebert, Céline R.; Humphreys, Glyn W.; Mantini, Dante

    2014-01-01

    Computed tomographic (CT) images are widely used for the identification of abnormal brain tissue following infarct and hemorrhage in stroke. Manual lesion delineation is currently the standard approach, but is both time-consuming and operator-dependent. To address these issues, we present a method that can automatically delineate infarct and hemorrhage in stroke CT images. The key elements of this method are the accurate normalization of CT images from stroke patients into template space and the subsequent voxelwise comparison with a group of control CT images for defining areas with hypo- or hyper-intense signals. Our validation, using simulated and actual lesions, shows that our approach is effective in reconstructing lesions resulting from both infarct and hemorrhage and yields lesion maps spatially consistent with those produced manually by expert operators. A limitation is that, relative to manual delineation, there is reduced sensitivity of the automated method in regions close to the ventricles and the brain contours. However, the automated method presents a number of benefits in terms of offering significant time savings and the elimination of the inter-operator differences inherent to manual tracing approaches. These factors are relevant for the creation of large-scale lesion databases for neuropsychological research. The automated delineation of stroke lesions from CT scans may also enable longitudinal studies to quantify changes in damaged tissue in an objective and reproducible manner. PMID:24818079

  10. Automated image processing and fusion for remote sensing applications

    NASA Astrophysics Data System (ADS)

    Zabuawala, Sakina; Wei, Hai; Raju, Chaitanya; Ray, Nilanjan; Yadegar, Jacob

    2009-02-01

    The ever increasing volumes and resolutions of remote sensing imagery have not only boosted the value of image-based analysis and visualization in scientific research and commercial sectors, but also introduced new challenges. Specifically, processing large volumes of newly acquired high-resolution imagery as well as fusing them against existing imagery (for correction, update, and visualization) still remain highly subjective and labor-intensive tasks, which has not been fully automated by the existing GIS software tools. This calls for the development of novel computational algorithms to automate the routine image processing tasks involved in various remote sensing based applications. In this paper, a suite of efficient and automated computational algorithms has been proposed and developed to address the aforementioned challenge. It includes a segmentation algorithm to achieve the automatic "cleaning" (i.e. segmenting out the valid pixels) of any newly acquired ortho-photo image, automatic feature point extraction, image alignment by maximization of mutual information and finally smoothing/feathering the edges of the imagery at the join zone. The proposed algorithms have been implemented and tested using practical large-scale GIS imagery/data. The experimental results demonstrate the efficiency and effectiveness of the proposed algorithms and the corresponding capability of fully automated segmentation, registration and fusion, which allows the end-user to bring together image of heterogeneous resolution, projection, datum, and sources for analysis and visualization. The potential benefits of the proposed algorithms include great reduction of the production time, more accurate and reliable results, and user consistency within and across organizations.

  11. Quantifying biodiversity using digital cameras and automated image analysis.

    NASA Astrophysics Data System (ADS)

    Roadknight, C. M.; Rose, R. J.; Barber, M. L.; Price, M. C.; Marshall, I. W.

    2009-04-01

    Monitoring the effects on biodiversity of extensive grazing in complex semi-natural habitats is labour intensive. There are also concerns about the standardization of semi-quantitative data collection. We have chosen to focus initially on automating the most time consuming aspect - the image analysis. The advent of cheaper and more sophisticated digital camera technology has lead to a sudden increase in the number of habitat monitoring images and information that is being collected. We report on the use of automated trail cameras (designed for the game hunting market) to continuously capture images of grazer activity in a variety of habitats at Moor House National Nature Reserve, which is situated in the North of England at an average altitude of over 600m. Rainfall is high, and in most areas the soil consists of deep peat (1m to 3m), populated by a mix of heather, mosses and sedges. The cameras have been continuously in operation over a 6 month period, daylight images are in full colour and night images (IR flash) are black and white. We have developed artificial intelligence based methods to assist in the analysis of the large number of images collected, generating alert states for new or unusual image conditions. This paper describes the data collection techniques, outlines the quantitative and qualitative data collected and proposes online and offline systems that can reduce the manpower overheads and increase focus on important subsets in the collected data. By converting digital image data into statistical composite data it can be handled in a similar way to other biodiversity statistics thus improving the scalability of monitoring experiments. Unsupervised feature detection methods and supervised neural methods were tested and offered solutions to simplifying the process. Accurate (85 to 95%) categorization of faunal content can be obtained, requiring human intervention for only those images containing rare animals or unusual (undecidable) conditions, and

  12. Automated techniques for quality assurance of radiological image modalities

    NASA Astrophysics Data System (ADS)

    Goodenough, David J.; Atkins, Frank B.; Dyer, Stephen M.

    1991-05-01

    This paper will attempt to identify many of the important issues for quality assurance (QA) of radiological modalities. It is of course to be realized that QA can span many aspects of the diagnostic decision making process. These issues range from physical image performance levels to and through the diagnostic decision of the radiologist. We will use as a model for automated approaches a program we have developed to work with computed tomography (CT) images. In an attempt to unburden the user, and in an effort to facilitate the performance of QA, we have been studying automated approaches. The ultimate utility of the system is its ability to render in a safe and efficacious manner, decisions that are accurate, sensitive, specific and which are possible within the economic constraints of modern health care delivery.

  13. Note: An automated image analysis method for high-throughput classification of surface-bound bacterial cell motions

    NASA Astrophysics Data System (ADS)

    Shen, Simon; Syal, Karan; Tao, Nongjian; Wang, Shaopeng

    2015-12-01

    We present a Single-Cell Motion Characterization System (SiCMoCS) to automatically extract bacterial cell morphological features from microscope images and use those features to automatically classify cell motion for rod shaped motile bacterial cells. In some imaging based studies, bacteria cells need to be attached to the surface for time-lapse observation of cellular processes such as cell membrane-protein interactions and membrane elasticity. These studies often generate large volumes of images. Extracting accurate bacterial cell morphology features from these images is critical for quantitative assessment. Using SiCMoCS, we demonstrated simultaneous and automated motion tracking and classification of hundreds of individual cells in an image sequence of several hundred frames. This is a significant improvement from traditional manual and semi-automated approaches to segmenting bacterial cells based on empirical thresholds, and a first attempt to automatically classify bacterial motion types for motile rod shaped bacterial cells, which enables rapid and quantitative analysis of various types of bacterial motion.

  14. Note: An automated image analysis method for high-throughput classification of surface-bound bacterial cell motions.

    PubMed

    Shen, Simon; Syal, Karan; Tao, Nongjian; Wang, Shaopeng

    2015-12-01

    We present a Single-Cell Motion Characterization System (SiCMoCS) to automatically extract bacterial cell morphological features from microscope images and use those features to automatically classify cell motion for rod shaped motile bacterial cells. In some imaging based studies, bacteria cells need to be attached to the surface for time-lapse observation of cellular processes such as cell membrane-protein interactions and membrane elasticity. These studies often generate large volumes of images. Extracting accurate bacterial cell morphology features from these images is critical for quantitative assessment. Using SiCMoCS, we demonstrated simultaneous and automated motion tracking and classification of hundreds of individual cells in an image sequence of several hundred frames. This is a significant improvement from traditional manual and semi-automated approaches to segmenting bacterial cells based on empirical thresholds, and a first attempt to automatically classify bacterial motion types for motile rod shaped bacterial cells, which enables rapid and quantitative analysis of various types of bacterial motion. PMID:26724085

  15. Image pre-processing for optimizing automated photogrammetry performances

    NASA Astrophysics Data System (ADS)

    Guidi, G.; Gonizzi, S.; Micoli, L. L.

    2014-05-01

    The purpose of this paper is to analyze how optical pre-processing with polarizing filters and digital pre-processing with HDR imaging, may improve the automated 3D modeling pipeline based on SFM and Image Matching, with special emphasis on optically non-cooperative surfaces of shiny or dark materials. Because of the automatic detection of homologous points, the presence of highlights due to shiny materials, or nearly uniform dark patches produced by low reflectance materials, may produce erroneous matching involving wrong 3D point estimations, and consequently holes and topological errors on the mesh originated by the associated dense 3D cloud. This is due to the limited dynamic range of the 8 bit digital images that are matched each other for generating 3D data. The same 256 levels can be more usefully employed if the actual dynamic range is compressed, avoiding luminance clipping on the darker and lighter image areas. Such approach is here considered both using optical filtering and HDR processing with tone mapping, with experimental evaluation on different Cultural Heritage objects characterized by non-cooperative optical behavior. Three test images of each object have been captured from different positions, changing the shooting conditions (filter/no-filter) and the image processing (no processing/HDR processing), in order to have the same 3 camera orientations with different optical and digital pre-processing, and applying the same automated process to each photo set.

  16. Quantitative Determination and Subcellular Imaging of Cu in Single Cells via Laser Ablation-ICP-Mass Spectrometry Using High-Density Microarray Gelatin Standards.

    PubMed

    Van Malderen, Stijn J M; Vergucht, Eva; De Rijcke, Maarten; Janssen, Colin; Vincze, Laszlo; Vanhaecke, Frank

    2016-06-01

    This manuscript describes the development and characterization of a high-density microarray calibration standard, manufactured in-house and designed to overcome the limitations in precision, accuracy, and throughput of current calibration approaches for the quantification of elemental concentrations on the cellular level using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICPMS). As a case study, the accumulation of Cu in the model organism Scrippsiella trochoidea resulting from transition metal exposure (ranging from 0.5 to 100 μg/L) was evaluated. After the Cu exposure, cells of this photosynthetic dinoflagellate were treated with a critical point drying protocol, transferred to a carbon stub, and sputter-coated with a Au layer for scanning electron microscopy (SEM) analysis. In subsequent LA-ICPMS analysis, approximately 100 cells of each population were individually ablated. This approach permitted the evaluation of the mean concentration of Cu in the cell population across different exposure levels and also allowed the examination of the cellular distribution of Cu within the populations. In a cross-validation exercise, subcellular LA-ICPMS imaging was demonstrated to corroborate synchrotron radiation confocal X-ray fluorescence (SR-XRF) microimaging of single cells investigated under in vivo conditions. PMID:27149342

  17. Automated FMV image quality assessment based on power spectrum statistics

    NASA Astrophysics Data System (ADS)

    Kalukin, Andrew

    2015-05-01

    Factors that degrade image quality in video and other sensor collections, such as noise, blurring, and poor resolution, also affect the spatial power spectrum of imagery. Prior research in human vision and image science from the last few decades has shown that the image power spectrum can be useful for assessing the quality of static images. The research in this article explores the possibility of using the image power spectrum to automatically evaluate full-motion video (FMV) imagery frame by frame. This procedure makes it possible to identify anomalous images and scene changes, and to keep track of gradual changes in quality as collection progresses. This article will describe a method to apply power spectral image quality metrics for images subjected to simulated blurring, blocking, and noise. As a preliminary test on videos from multiple sources, image quality measurements for image frames from 185 videos are compared to analyst ratings based on ground sampling distance. The goal of the research is to develop an automated system for tracking image quality during real-time collection, and to assign ratings to video clips for long-term storage, calibrated to standards such as the National Imagery Interpretability Rating System (NIIRS).

  18. Image mosaicing for automated pipe scanning

    SciTech Connect

    Summan, Rahul Dobie, Gordon Guarato, Francesco MacLeod, Charles Marshall, Stephen Pierce, Gareth; Forrester, Cailean; Bolton, Gary

    2015-03-31

    Remote visual inspection (RVI) is critical for the inspection of the interior condition of pipelines particularly in the nuclear and oil and gas industries. Conventional RVI equipment produces a video which is analysed online by a trained inspector employing expert knowledge. Due to the potentially disorientating nature of the footage, this is a time intensive and difficult activity. In this paper a new probe for such visual inspections is presented. The device employs a catadioptric lens coupled with feature based structure from motion to create a 3D model of the interior surface of a pipeline. Reliance upon the availability of image features is mitigated through orientation and distance estimates from an inertial measurement unit and encoder respectively. Such a model affords a global view of the data thus permitting a greater appreciation of the nature and extent of defects. Furthermore, the technique estimates the 3D position and orientation of the probe thus providing information to direct remedial action. Results are presented for both synthetic and real pipe sections. The former enables the accuracy of the generated model to be assessed while the latter demonstrates the efficacy of the technique in a practice.

  19. Image mosaicing for automated pipe scanning

    NASA Astrophysics Data System (ADS)

    Summan, Rahul; Dobie, Gordon; Guarato, Francesco; MacLeod, Charles; Marshall, Stephen; Forrester, Cailean; Pierce, Gareth; Bolton, Gary

    2015-03-01

    Remote visual inspection (RVI) is critical for the inspection of the interior condition of pipelines particularly in the nuclear and oil and gas industries. Conventional RVI equipment produces a video which is analysed online by a trained inspector employing expert knowledge. Due to the potentially disorientating nature of the footage, this is a time intensive and difficult activity. In this paper a new probe for such visual inspections is presented. The device employs a catadioptric lens coupled with feature based structure from motion to create a 3D model of the interior surface of a pipeline. Reliance upon the availability of image features is mitigated through orientation and distance estimates from an inertial measurement unit and encoder respectively. Such a model affords a global view of the data thus permitting a greater appreciation of the nature and extent of defects. Furthermore, the technique estimates the 3D position and orientation of the probe thus providing information to direct remedial action. Results are presented for both synthetic and real pipe sections. The former enables the accuracy of the generated model to be assessed while the latter demonstrates the efficacy of the technique in a practice.

  20. Automated Reduction of Data from Images and Holograms

    NASA Technical Reports Server (NTRS)

    Lee, G. (Editor); Trolinger, James D. (Editor); Yu, Y. H. (Editor)

    1987-01-01

    Laser techniques are widely used for the diagnostics of aerodynamic flow and particle fields. The storage capability of holograms has made this technique an even more powerful. Over 60 researchers in the field of holography, particle sizing and image processing convened to discuss these topics. The research program of ten government laboratories, several universities, industry and foreign countries were presented. A number of papers on holographic interferometry with applications to fluid mechanics were given. Several papers on combustion and particle sizing, speckle velocimetry and speckle interferometry were given. A session on image processing and automated fringe data reduction techniques and the type of facilities for fringe reduction was held.

  1. Single-cell proteins

    SciTech Connect

    Litchfield, J.H.

    1983-02-11

    Both photosynthetic and nonphotosynthetic microorganisms, grown on various carbon and energy sources, are used in fermentation processes for the production of single-cell proteins. Commercial-scale production has been limited to two algal processes, one bacterial process, and several yeast and fungal processes. High capital and operating costs and the need for extensive nutritional and toxicological assessments have limited the development and commercialization of new processes. Any increase in commercial-scale production appears to be limited to those regions of the world where low-cost carbon and energy sources are available and conventional animal feedstuff proteins, such as soybean meal or fish meal, are in short supply. (Refs. 59).

  2. Discriminating enumeration of subseafloor life using automated fluorescent image analysis

    NASA Astrophysics Data System (ADS)

    Morono, Y.; Terada, T.; Masui, N.; Inagaki, F.

    2008-12-01

    Enumeration of microbial cells in marine subsurface sediments has provided fundamental information for understanding the extent of life and deep-biosphere on Earth. The microbial population has been manually evaluated by direct cell count under the microscopy because the recognition of cell-derived fluorescent signals has been extremely difficult. Here, we improved the conventional method by removing the non- specific fluorescent backgrounds and enumerated the cell population in sediments using a newly developed automated microscopic imaging system. Although SYBR Green I is known to specifically bind to the double strand DNA (Lunau et al., 2005), we still observed some SYBR-stainable particulate matters (SYBR-SPAMs) in the heat-sterilized control sediments (450°C, 6h), which assumed to be silicates or mineralized organic matters. Newly developed acid-wash treatments with hydrofluoric acid (HF) followed by image analysis successfully removed these background objects and yielded artifact-free microscopic images. To obtain statistically meaningful fluorescent images, we constructed a computer-assisted automated cell counting system. Given the comparative data set of cell abundance in acid-washed marine sediments evaluated by SYBR Green I- and acridine orange (AO)-stain with and without the image analysis, our protocol could provide the statistically meaningful absolute numbers of discriminating cell-derived fluorescent signals.

  3. Magnetic levitation of single cells.

    PubMed

    Durmus, Naside Gozde; Tekin, H Cumhur; Guven, Sinan; Sridhar, Kaushik; Arslan Yildiz, Ahu; Calibasi, Gizem; Ghiran, Ionita; Davis, Ronald W; Steinmetz, Lars M; Demirci, Utkan

    2015-07-14

    Several cellular events cause permanent or transient changes in inherent magnetic and density properties of cells. Characterizing these changes in cell populations is crucial to understand cellular heterogeneity in cancer, immune response, infectious diseases, drug resistance, and evolution. Although magnetic levitation has previously been used for macroscale objects, its use in life sciences has been hindered by the inability to levitate microscale objects and by the toxicity of metal salts previously applied for levitation. Here, we use magnetic levitation principles for biological characterization and monitoring of cells and cellular events. We demonstrate that each cell type (i.e., cancer, blood, bacteria, and yeast) has a characteristic levitation profile, which we distinguish at an unprecedented resolution of 1 × 10(-4) g ⋅ mL(-1). We have identified unique differences in levitation and density blueprints between breast, esophageal, colorectal, and nonsmall cell lung cancer cell lines, as well as heterogeneity within these seemingly homogenous cell populations. Furthermore, we demonstrate that changes in cellular density and levitation profiles can be monitored in real time at single-cell resolution, allowing quantification of heterogeneous temporal responses of each cell to environmental stressors. These data establish density as a powerful biomarker for investigating living systems and their responses. Thereby, our method enables rapid, density-based imaging and profiling of single cells with intriguing applications, such as label-free identification and monitoring of heterogeneous biological changes under various physiological conditions, including antibiotic or cancer treatment in personalized medicine. PMID:26124131

  4. Magnetic levitation of single cells

    PubMed Central

    Durmus, Naside Gozde; Tekin, H. Cumhur; Guven, Sinan; Sridhar, Kaushik; Arslan Yildiz, Ahu; Calibasi, Gizem; Davis, Ronald W.; Steinmetz, Lars M.; Demirci, Utkan

    2015-01-01

    Several cellular events cause permanent or transient changes in inherent magnetic and density properties of cells. Characterizing these changes in cell populations is crucial to understand cellular heterogeneity in cancer, immune response, infectious diseases, drug resistance, and evolution. Although magnetic levitation has previously been used for macroscale objects, its use in life sciences has been hindered by the inability to levitate microscale objects and by the toxicity of metal salts previously applied for levitation. Here, we use magnetic levitation principles for biological characterization and monitoring of cells and cellular events. We demonstrate that each cell type (i.e., cancer, blood, bacteria, and yeast) has a characteristic levitation profile, which we distinguish at an unprecedented resolution of 1 × 10−4 g⋅mL−1. We have identified unique differences in levitation and density blueprints between breast, esophageal, colorectal, and nonsmall cell lung cancer cell lines, as well as heterogeneity within these seemingly homogenous cell populations. Furthermore, we demonstrate that changes in cellular density and levitation profiles can be monitored in real time at single-cell resolution, allowing quantification of heterogeneous temporal responses of each cell to environmental stressors. These data establish density as a powerful biomarker for investigating living systems and their responses. Thereby, our method enables rapid, density-based imaging and profiling of single cells with intriguing applications, such as label-free identification and monitoring of heterogeneous biological changes under various physiological conditions, including antibiotic or cancer treatment in personalized medicine. PMID:26124131

  5. Semi-Automated Identification of Rocks in Images

    NASA Technical Reports Server (NTRS)

    Bornstein, Benjamin; Castano, Andres; Anderson, Robert

    2006-01-01

    Rock Identification Toolkit Suite is a computer program that assists users in identifying and characterizing rocks shown in images returned by the Mars Explorer Rover mission. Included in the program are components for automated finding of rocks, interactive adjustments of outlines of rocks, active contouring of rocks, and automated analysis of shapes in two dimensions. The program assists users in evaluating the surface properties of rocks and soil and reports basic properties of rocks. The program requires either the Mac OS X operating system running on a G4 (or more capable) processor or a Linux operating system running on a Pentium (or more capable) processor, plus at least 128MB of random-access memory.

  6. Automated retinal image analysis for diabetic retinopathy in telemedicine.

    PubMed

    Sim, Dawn A; Keane, Pearse A; Tufail, Adnan; Egan, Catherine A; Aiello, Lloyd Paul; Silva, Paolo S

    2015-03-01

    There will be an estimated 552 million persons with diabetes globally by the year 2030. Over half of these individuals will develop diabetic retinopathy, representing a nearly insurmountable burden for providing diabetes eye care. Telemedicine programmes have the capability to distribute quality eye care to virtually any location and address the lack of access to ophthalmic services. In most programmes, there is currently a heavy reliance on specially trained retinal image graders, a resource in short supply worldwide. These factors necessitate an image grading automation process to increase the speed of retinal image evaluation while maintaining accuracy and cost effectiveness. Several automatic retinal image analysis systems designed for use in telemedicine have recently become commercially available. Such systems have the potential to substantially improve the manner by which diabetes eye care is delivered by providing automated real-time evaluation to expedite diagnosis and referral if required. Furthermore, integration with electronic medical records may allow a more accurate prognostication for individual patients and may provide predictive modelling of medical risk factors based on broad population data. PMID:25697773

  7. Automated curved planar reformation of 3D spine images

    NASA Astrophysics Data System (ADS)

    Vrtovec, Tomaz; Likar, Bostjan; Pernus, Franjo

    2005-10-01

    Traditional techniques for visualizing anatomical structures are based on planar cross-sections from volume images, such as images obtained by computed tomography (CT) or magnetic resonance imaging (MRI). However, planar cross-sections taken in the coordinate system of the 3D image often do not provide sufficient or qualitative enough diagnostic information, because planar cross-sections cannot follow curved anatomical structures (e.g. arteries, colon, spine, etc). Therefore, not all of the important details can be shown simultaneously in any planar cross-section. To overcome this problem, reformatted images in the coordinate system of the inspected structure must be created. This operation is usually referred to as curved planar reformation (CPR). In this paper we propose an automated method for CPR of 3D spine images, which is based on the image transformation from the standard image-based to a novel spine-based coordinate system. The axes of the proposed spine-based coordinate system are determined on the curve that represents the vertebral column, and the rotation of the vertebrae around the spine curve, both of which are described by polynomial models. The optimal polynomial parameters are obtained in an image analysis based optimization framework. The proposed method was qualitatively and quantitatively evaluated on five CT spine images. The method performed well on both normal and pathological cases and was consistent with manually obtained ground truth data. The proposed spine-based CPR benefits from reduced structural complexity in favour of improved feature perception of the spine. The reformatted images are diagnostically valuable and enable easier navigation, manipulation and orientation in 3D space. Moreover, reformatted images may prove useful for segmentation and other image analysis tasks.

  8. A case for automated tape in clinical imaging.

    PubMed

    Bookman, G; Baune, D

    1998-08-01

    Electronic archiving of radiology images over many years will require many terabytes of storage with a need for rapid retrieval of these images. As more large PACS installations are installed and implemented, a data crisis occurs. The ability to store this large amount of data using the traditional method of optical jukeboxes or online disk alone becomes an unworkable solution. The amount of floor space number of optical jukeboxes, and off-line shelf storage required to store the images becomes unmanageable. With the recent advances in tape and tape drives, the use of tape for long term storage of PACS data has become the preferred alternative. A PACS system consisting of a centrally managed system of RAID disk, software and at the heart of the system, tape, presents a solution that for the first time solves the problems of multi-modality high end PACS, non-DICOM image, electronic medical record and ADT data storage. This paper will examine the installation of the University of Utah, Department of Radiology PACS system and the integration of automated tape archive. The tape archive is also capable of storing data other than traditional PACS data. The implementation of an automated data archive to serve the many other needs of a large hospital will also be discussed. This will include the integration of a filmless cardiology department and the backup/archival needs of a traditional MIS department. The need for high bandwidth to tape with a large RAID cache will be examined and how with an interface to a RIS pre-fetch engine, tape can be a superior solution to optical platters or other archival solutions. The data management software will be discussed in detail. The performance and cost of RAID disk cache and automated tape compared to a solution that includes optical will be examined. PMID:9735431

  9. Automated 3D renal segmentation based on image partitioning

    NASA Astrophysics Data System (ADS)

    Yeghiazaryan, Varduhi; Voiculescu, Irina D.

    2016-03-01

    Despite several decades of research into segmentation techniques, automated medical image segmentation is barely usable in a clinical context, and still at vast user time expense. This paper illustrates unsupervised organ segmentation through the use of a novel automated labelling approximation algorithm followed by a hypersurface front propagation method. The approximation stage relies on a pre-computed image partition forest obtained directly from CT scan data. We have implemented all procedures to operate directly on 3D volumes, rather than slice-by-slice, because our algorithms are dimensionality-independent. The results picture segmentations which identify kidneys, but can easily be extrapolated to other body parts. Quantitative analysis of our automated segmentation compared against hand-segmented gold standards indicates an average Dice similarity coefficient of 90%. Results were obtained over volumes of CT data with 9 kidneys, computing both volume-based similarity measures (such as the Dice and Jaccard coefficients, true positive volume fraction) and size-based measures (such as the relative volume difference). The analysis considered both healthy and diseased kidneys, although extreme pathological cases were excluded from the overall count. Such cases are difficult to segment both manually and automatically due to the large amplitude of Hounsfield unit distribution in the scan, and the wide spread of the tumorous tissue inside the abdomen. In the case of kidneys that have maintained their shape, the similarity range lies around the values obtained for inter-operator variability. Whilst the procedure is fully automated, our tools also provide a light level of manual editing.

  10. Automated Processing of Zebrafish Imaging Data: A Survey

    PubMed Central

    Dickmeis, Thomas; Driever, Wolfgang; Geurts, Pierre; Hamprecht, Fred A.; Kausler, Bernhard X.; Ledesma-Carbayo, María J.; Marée, Raphaël; Mikula, Karol; Pantazis, Periklis; Ronneberger, Olaf; Santos, Andres; Stotzka, Rainer; Strähle, Uwe; Peyriéras, Nadine

    2013-01-01

    Abstract Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines. PMID:23758125

  11. Automated Dsm Extraction from Uav Images and Performance Analysis

    NASA Astrophysics Data System (ADS)

    Rhee, S.; Kim, T.

    2015-08-01

    As technology evolves, unmanned aerial vehicles (UAVs) imagery is being used from simple applications such as image acquisition to complicated applications such as 3D spatial information extraction. Spatial information is usually provided in the form of a DSM or point cloud. It is important to generate very dense tie points automatically from stereo images. In this paper, we tried to apply stereo image-based matching technique developed for satellite/aerial images to UAV images, propose processing steps for automated DSM generation and to analyse the possibility of DSM generation. For DSM generation from UAV images, firstly, exterior orientation parameters (EOPs) for each dataset were adjusted. Secondly, optimum matching pairs were determined. Thirdly, stereo image matching was performed with each pair. Developed matching algorithm is based on grey-level correlation on pixels applied along epipolar lines. Finally, the extracted match results were united with one result and the final DSM was made. Generated DSM was compared with a reference DSM from Lidar. Overall accuracy was 1.5 m in NMAD. However, several problems have to be solved in future, including obtaining precise EOPs, handling occlusion and image blurring problems. More effective interpolation technique needs to be developed in the future.

  12. Single Cell Physiology

    NASA Astrophysics Data System (ADS)

    Neveu, Pierre; Sinha, Deepak Kumar; Kettunen, Petronella; Vriz, Sophie; Jullien, Ludovic; Bensimon, David

    The possibility to control at specific times and specific places the activity of biomolecules (enzymes, transcription factors, RNA, hormones, etc.) is opening up new opportunities in the study of physiological processes at the single cell level in a live organism. Most existing gene expression systems allow for tissue specific induction upon feeding the organism with exogenous inducers (e.g., tetracycline). Local genetic control has earlier been achieved by micro-injection of the relevant inducer/repressor molecule, but this is an invasive and possibly traumatic technique. In this chapter, we present the requirements for a noninvasive optical control of the activity of biomolecules and review the recent advances in this new field of research.

  13. Single cell optical transfection.

    PubMed

    Stevenson, David J; Gunn-Moore, Frank J; Campbell, Paul; Dholakia, Kishan

    2010-06-01

    The plasma membrane of a eukaryotic cell is impermeable to most hydrophilic substances, yet the insertion of these materials into cells is an extremely important and universal requirement for the cell biologist. To address this need, many transfection techniques have been developed including viral, lipoplex, polyplex, capillary microinjection, gene gun and electroporation. The current discussion explores a procedure called optical injection, where a laser field transiently increases the membrane permeability to allow species to be internalized. If the internalized substance is a nucleic acid, such as DNA, RNA or small interfering RNA (siRNA), then the process is called optical transfection. This contactless, aseptic, single cell transfection method provides a key nanosurgical tool to the microscopist-the intracellular delivery of reagents and single nanoscopic objects. The experimental possibilities enabled by this technology are only beginning to be realized. A review of optical transfection is presented, along with a forecast of future applications of this rapidly developing and exciting technology. PMID:20064901

  14. Automated shimming of B0 for spectroscopic imaging

    NASA Astrophysics Data System (ADS)

    Tropp, James; Derby, Kevin A.; Hawryszko, Christine; Sugiura, Satoshi; Yamagata, Hitoshi

    A method for automated shimming of B0 in whole body imaging magnets is demonstrated. The method employs spectroscopic imaging [ T. R. Brown, B. M. Kincaid, and K. Ugurbil, Proc. Natl. Acad. Sci. USA, 79, 3532, 1982 ] of 1H to map the B0 field of the region of interest with the subject in situ. Correction currents for the shims, based on prior measurement of shim fields versus shim currents, are then calculated and applied by computer. The chief application of the method is shimming for multivoxel spectroscopy examinations in vivo wherein it is shown to produce marked improvement of 31P spectra from spectroscopic imaging exams of brain and calf muscle, in normal and diseased subjects.

  15. Datamining the NOAO NVO Portal: Automated Image Classification

    NASA Astrophysics Data System (ADS)

    Vaswani, Pooja; Miller, C. J.; Barg, I.; Smith, R. C.

    2006-12-01

    Image metadata describes the properties of an image and can be used for classification, e.g., galactic, extra-galactic, solar system, standard star, among others. We are developing a data mining application to automate such a classification process based on supervised learning using decision trees. We are applying this application to the NOAO NVO Portal (www.nvo.noao.edu). The core concepts of Quinlan's C4.5 decision tree induction algorithm are used to train, build a decision tree, and generate classification rules. These rules are then used to classify previously unseen image metadata. We utilize a collection of decision trees instead of a single classifier and average the classification probabilities. The concept of ``Bagging'' was used to create the collection of classifiers. The classification algorithm also facilitates the addition of weights to the probability estimate of the classes when prior knowledge of the class distribution is known.

  16. Automated rice leaf disease detection using color image analysis

    NASA Astrophysics Data System (ADS)

    Pugoy, Reinald Adrian D. L.; Mariano, Vladimir Y.

    2011-06-01

    In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as a manual eyeball exercise, is important to come up with good nutrient and disease management strategies. In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based K-means clustering algorithm to group related regions into clusters. Then, these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf.

  17. Automated computational aberration correction method for broadband interferometric imaging techniques.

    PubMed

    Pande, Paritosh; Liu, Yuan-Zhi; South, Fredrick A; Boppart, Stephen A

    2016-07-15

    Numerical correction of optical aberrations provides an inexpensive and simpler alternative to the traditionally used hardware-based adaptive optics techniques. In this Letter, we present an automated computational aberration correction method for broadband interferometric imaging techniques. In the proposed method, the process of aberration correction is modeled as a filtering operation on the aberrant image using a phase filter in the Fourier domain. The phase filter is expressed as a linear combination of Zernike polynomials with unknown coefficients, which are estimated through an iterative optimization scheme based on maximizing an image sharpness metric. The method is validated on both simulated data and experimental data obtained from a tissue phantom, an ex vivo tissue sample, and an in vivo photoreceptor layer of the human retina. PMID:27420526

  18. Automated localization of vertebra landmarks in MRI images

    NASA Astrophysics Data System (ADS)

    Pai, Akshay; Narasimhamurthy, Anand; Rao, V. S. Veeravasarapu; Vaidya, Vivek

    2011-03-01

    The identification of key landmark points in an MR spine image is an important step for tasks such as vertebra counting. In this paper, we propose a template matching based approach for automatic detection of two key landmark points, namely the second cervical vertebra (C2) and the sacrum from sagittal MR images. The approach is comprised of an approximate localization of vertebral column followed by matching with appropriate templates in order to detect/localize the landmarks. A straightforward extension of the work described here is an automated classification of spine section(s). It also serves as a useful building block for further automatic processing such as extraction of regions of interest for subsequent image processing and also in aiding the counting of vertebra.

  19. Automated spectroscopic imaging of oxygen saturation in human retinal vessels

    NASA Astrophysics Data System (ADS)

    Nakamura, D.; Sueda, S.; Matsuoka, N.; Yoshinaga, Y.; Enaida, H.; Okada, T.; Ishibashi, T.

    2009-02-01

    A new automatic visualization procedure for the oxygen saturation imaging from multi-spectral imaging of human retinal vessels has been proposed. Two-wavelength retinal fundus images at 545 and 560 nm, which were oxygen insensitive and oxygen sensitive, respectively, were captured by CCD cameras simultaneously through a beam splitter and interference filters. We applied a morphological processing technique to presume a distribution of incident light including the vessel parts and an optical density (OD) image of each wavelength image. And the OD ratio (OD560/OD545) image was calculated as a relative indicator of oxygen saturation. Furthermore, processing of line convergence index filter was adopted to identify the retinal vessels. Clear difference between retinal arteries and veins was observed in the automated imaging method. In addition, the decrease of oxygen saturation in the retinal artery without breathing could be monitored by the ODR. This method is possible to be applied to real-time monitoring for oxygen saturation of retinal vessels.

  20. Automating the Photogrammetric Bridging Based on MMS Image Sequence Processing

    NASA Astrophysics Data System (ADS)

    Silva, J. F. C.; Lemes Neto, M. C.; Blasechi, V.

    2014-11-01

    The photogrammetric bridging or traverse is a special bundle block adjustment (BBA) for connecting a sequence of stereo-pairs and of determining the exterior orientation parameters (EOP). An object point must be imaged in more than one stereo-pair. In each stereo-pair the distance ratio between an object and its corresponding image point varies significantly. We propose to automate the photogrammetric bridging based on a fully automatic extraction of homologous points in stereo-pairs and on an arbitrary Cartesian datum to refer the EOP and tie points. The technique uses SIFT algorithm and the keypoint matching is given by similarity descriptors of each keypoint based on the smallest distance. All the matched points are used as tie points. The technique was applied initially to two pairs. The block formed by four images was treated by BBA. The process follows up to the end of the sequence and it is semiautomatic because each block is processed independently and the transition from one block to the next depends on the operator. Besides four image blocks (two pairs), we experimented other arrangements with block sizes of six, eight, and up to twenty images (respectively, three, four, five and up to ten bases). After the whole image pairs sequence had sequentially been adjusted in each experiment, a simultaneous BBA was run so to estimate the EOP set of each image. The results for classical ("normal case") pairs were analyzed based on standard statistics regularly applied to phototriangulation, and they show figures to validate the process.

  1. Automated Identification of Fiducial Points on 3D Torso Images

    PubMed Central

    Kawale, Manas M; Reece, Gregory P; Crosby, Melissa A; Beahm, Elisabeth K; Fingeret, Michelle C; Markey, Mia K; Merchant, Fatima A

    2013-01-01

    Breast reconstruction is an important part of the breast cancer treatment process for many women. Recently, 2D and 3D images have been used by plastic surgeons for evaluating surgical outcomes. Distances between different fiducial points are frequently used as quantitative measures for characterizing breast morphology. Fiducial points can be directly marked on subjects for direct anthropometry, or can be manually marked on images. This paper introduces novel algorithms to automate the identification of fiducial points in 3D images. Automating the process will make measurements of breast morphology more reliable, reducing the inter- and intra-observer bias. Algorithms to identify three fiducial points, the nipples, sternal notch, and umbilicus, are described. The algorithms used for localization of these fiducial points are formulated using a combination of surface curvature and 2D color information. Comparison of the 3D co-ordinates of automatically detected fiducial points and those identified manually, and geodesic distances between the fiducial points are used to validate algorithm performance. The algorithms reliably identified the location of all three of the fiducial points. We dedicate this article to our late colleague and friend, Dr. Elisabeth K. Beahm. Elisabeth was both a talented plastic surgeon and physician-scientist; we deeply miss her insight and her fellowship. PMID:25288903

  2. Improved digital breast tomosynthesis images using automated ultrasound

    PubMed Central

    Zhang, Xing; Yuan, Jie; Du, Sidan; Kripfgans, Oliver D.; Wang, Xueding; Carson, Paul L.; Liu, Xiaojun

    2014-01-01

    Purpose: Digital breast tomosynthesis (DBT) offers poor image quality along the depth direction. This paper presents a new method that improves the image quality of DBT considerably through the a priori information from automated ultrasound (AUS) images. Methods: DBT and AUS images of a complex breast-mimicking phantom are acquired by a DBT/AUS dual-modality system. The AUS images are taken in the same geometry as the DBT images and the gradient information of the in-slice AUS images is adopted into the new loss functional during the DBT reconstruction process. The additional data allow for new iterative equations through solving the optimization problem utilizing the gradient descent method. Both visual comparison and quantitative analysis are employed to evaluate the improvement on DBT images. Normalized line profiles of lesions are obtained to compare the edges of the DBT and AUS-corrected DBT images. Additionally, image quality metrics such as signal difference to noise ratio (SDNR) and artifact spread function (ASF) are calculated to quantify the effectiveness of the proposed method. Results: In traditional DBT image reconstructions, serious artifacts can be found along the depth direction (Z direction), resulting in the blurring of lesion edges in the off-focus planes parallel to the detector. However, by applying the proposed method, the quality of the reconstructed DBT images is greatly improved. Visually, the AUS-corrected DBT images have much clearer borders in both in-focus and off-focus planes, fewer Z direction artifacts and reduced overlapping effect compared to the conventional DBT images. Quantitatively, the corrected DBT images have better ASF, indicating a great reduction in Z direction artifacts as well as better Z resolution. The sharper line profiles along the Y direction show enhancement on the edges. Besides, noise is also reduced, evidenced by the obviously improved SDNR values. Conclusions: The proposed method provides great improvement on

  3. An automated 3D reconstruction method of UAV images

    NASA Astrophysics Data System (ADS)

    Liu, Jun; Wang, He; Liu, Xiaoyang; Li, Feng; Sun, Guangtong; Song, Ping

    2015-10-01

    In this paper a novel fully automated 3D reconstruction approach based on low-altitude unmanned aerial vehicle system (UAVs) images will be presented, which does not require previous camera calibration or any other external prior knowledge. Dense 3D point clouds are generated by integrating orderly feature extraction, image matching, structure from motion (SfM) and multi-view stereo (MVS) algorithms, overcoming many of the cost, time limitations of rigorous photogrammetry techniques. An image topology analysis strategy is introduced to speed up large scene reconstruction by taking advantage of the flight-control data acquired by UAV. Image topology map can significantly reduce the running time of feature matching by limiting the combination of images. A high-resolution digital surface model of the study area is produced base on UAV point clouds by constructing the triangular irregular network. Experimental results show that the proposed approach is robust and feasible for automatic 3D reconstruction of low-altitude UAV images, and has great potential for the acquisition of spatial information at large scales mapping, especially suitable for rapid response and precise modelling in disaster emergency.

  4. Digital microfluidic immunocytochemistry in single cells.

    PubMed

    Ng, Alphonsus H C; Dean Chamberlain, M; Situ, Haozhong; Lee, Victor; Wheeler, Aaron R

    2015-01-01

    We report a new technique called Digital microfluidic Immunocytochemistry in Single Cells (DISC). DISC automates protocols for cell culture, stimulation and immunocytochemistry, enabling the interrogation of protein phosphorylation on pulsing with stimulus for as little as 3 s. DISC was used to probe the phosphorylation states of platelet-derived growth factor receptor (PDGFR) and the downstream signalling protein, Akt, to evaluate concentration- and time-dependent effects of stimulation. The high time resolution of the technique allowed for surprising new observations-for example, a 10 s pulse stimulus of a low concentration of PDGF is sufficient to cause >30% of adherent fibroblasts to commit to Akt activation. With the ability to quantitatively probe signalling events with high time resolution at the single-cell level, we propose that DISC may be an important new technique for a wide range of applications, especially for screening signalling responses of a heterogeneous cell population. PMID:26104298

  5. Automated fine structure image analysis method for discrimination of diabetic retinopathy stage using conjunctival microvasculature images

    PubMed Central

    Khansari, Maziyar M; O’Neill, William; Penn, Richard; Chau, Felix; Blair, Norman P; Shahidi, Mahnaz

    2016-01-01

    The conjunctiva is a densely vascularized mucus membrane covering the sclera of the eye with a unique advantage of accessibility for direct visualization and non-invasive imaging. The purpose of this study is to apply an automated quantitative method for discrimination of different stages of diabetic retinopathy (DR) using conjunctival microvasculature images. Fine structural analysis of conjunctival microvasculature images was performed by ordinary least square regression and Fisher linear discriminant analysis. Conjunctival images between groups of non-diabetic and diabetic subjects at different stages of DR were discriminated. The automated method’s discriminate rates were higher than those determined by human observers. The method allowed sensitive and rapid discrimination by assessment of conjunctival microvasculature images and can be potentially useful for DR screening and monitoring. PMID:27446692

  6. Automated fine structure image analysis method for discrimination of diabetic retinopathy stage using conjunctival microvasculature images.

    PubMed

    Khansari, Maziyar M; O'Neill, William; Penn, Richard; Chau, Felix; Blair, Norman P; Shahidi, Mahnaz

    2016-07-01

    The conjunctiva is a densely vascularized mucus membrane covering the sclera of the eye with a unique advantage of accessibility for direct visualization and non-invasive imaging. The purpose of this study is to apply an automated quantitative method for discrimination of different stages of diabetic retinopathy (DR) using conjunctival microvasculature images. Fine structural analysis of conjunctival microvasculature images was performed by ordinary least square regression and Fisher linear discriminant analysis. Conjunctival images between groups of non-diabetic and diabetic subjects at different stages of DR were discriminated. The automated method's discriminate rates were higher than those determined by human observers. The method allowed sensitive and rapid discrimination by assessment of conjunctival microvasculature images and can be potentially useful for DR screening and monitoring. PMID:27446692

  7. Granulometric profiling of aeolian dust deposits by automated image analysis

    NASA Astrophysics Data System (ADS)

    Varga, György; Újvári, Gábor; Kovács, János; Jakab, Gergely; Kiss, Klaudia; Szalai, Zoltán

    2016-04-01

    Determination of granulometric parameters is of growing interest in the Earth sciences. Particle size data of sedimentary deposits provide insights into the physicochemical environment of transport, accumulation and post-depositional alterations of sedimentary particles, and are important proxies applied in paleoclimatic reconstructions. It is especially true for aeolian dust deposits with a fairly narrow grain size range as a consequence of the extremely selective nature of wind sediment transport. Therefore, various aspects of aeolian sedimentation (wind strength, distance to source(s), possible secondary source regions and modes of sedimentation and transport) can be reconstructed only from precise grain size data. As terrestrial wind-blown deposits are among the most important archives of past environmental changes, proper explanation of the proxy data is a mandatory issue. Automated imaging provides a unique technique to gather direct information on granulometric characteristics of sedimentary particles. Granulometric data obtained from automatic image analysis of Malvern Morphologi G3-ID is a rarely applied new technique for particle size and shape analyses in sedimentary geology. Size and shape data of several hundred thousand (or even million) individual particles were automatically recorded in this study from 15 loess and paleosoil samples from the captured high-resolution images. Several size (e.g. circle-equivalent diameter, major axis, length, width, area) and shape parameters (e.g. elongation, circularity, convexity) were calculated by the instrument software. At the same time, the mean light intensity after transmission through each particle is automatically collected by the system as a proxy of optical properties of the material. Intensity values are dependent on chemical composition and/or thickness of the particles. The results of the automated imaging were compared to particle size data determined by three different laser diffraction instruments

  8. Automated classification of colon polyps in endoscopic image data

    NASA Astrophysics Data System (ADS)

    Gross, Sebastian; Palm, Stephan; Tischendorf, Jens J. W.; Behrens, Alexander; Trautwein, Christian; Aach, Til

    2012-03-01

    Colon cancer is the third most commonly diagnosed type of cancer in the US. In recent years, however, early diagnosis and treatment have caused a significant rise in the five year survival rate. Preventive screening is often performed by colonoscopy (endoscopic inspection of the colon mucosa). Narrow Band Imaging (NBI) is a novel diagnostic approach highlighting blood vessel structures on polyps which are an indicator for future cancer risk. In this paper, we review our automated inter- and intra-observer independent system for the automated classification of polyps into hyperplasias and adenomas based on vessel structures to further improve the classification performance. To surpass the performance limitations we derive a novel vessel segmentation approach, extract 22 features to describe complex vessel topologies, and apply three feature selection strategies. Tests are conducted on 286 NBI images with diagnostically important and challenging polyps (10mm or smaller) taken from our representative polyp database. Evaluations are based on ground truth data determined by histopathological analysis. Feature selection by Simulated Annealing yields the best result with a prediction accuracy of 96.2% (sensitivity: 97.6%, specificity: 94.2%) using eight features. Future development aims at implementing a demonstrator platform to begin clinical trials at University Hospital Aachen.

  9. Automated angiogenesis quantification through advanced image processing techniques.

    PubMed

    Doukas, Charlampos N; Maglogiannis, Ilias; Chatziioannou, Aristotle; Papapetropoulos, Andreas

    2006-01-01

    Angiogenesis, the formation of blood vessels in tumors, is an interactive process between tumor, endothelial and stromal cells in order to create a network for oxygen and nutrients supply, necessary for tumor growth. According to this, angiogenic activity is considered a suitable method for both tumor growth or inhibition detection. The angiogenic potential is usually estimated by counting the number of blood vessels in particular sections. One of the most popular assay tissues to study the angiogenesis phenomenon is the developing chick embryo and its chorioallantoic membrane (CAM), which is a highly vascular structure lining the inner surface of the egg shell. The aim of this study was to develop and validate an automated image analysis method that would give an unbiased quantification of the micro-vessel density and growth in angiogenic CAM images. The presented method has been validated by comparing automated results to manual counts over a series of digital chick embryo photos. The results indicate the high accuracy of the tool, which has been thus extensively used for tumor growth detection at different stages of embryonic development. PMID:17946107

  10. Semi-automated object tracking methods in biological imaging.

    PubMed

    Davis, Michael A; Praěský, Ondřej; Sysko, Laura R

    2015-01-01

    Time-lapse imaging is a rich data source offering potential kinetic information of cellular activity and behavior. Tracking and extracting measurements of objects from time-lapse datasets are challenges that result from the complexity and dynamics of each object's motion and intensity or the appearance of new objects in the field of view. A wide range of strategies for proper data sampling, object detection, image analysis, and post-analysis interpretation are available. Theory and methods for single-particle tracking, spot detection, and object linking are discussed in this unit, as well as examples with step-by-step procedures for utilizing semi-automated software and visualization tools for achieving tracking results and interpreting this output. PMID:25559222

  11. Infrared Thermal Imaging for Automated Detection of Diabetic Foot Complications

    PubMed Central

    van Netten, Jaap J.; van Baal, Jeff G.; Liu, Chanjuan; van der Heijden, Ferdi; Bus, Sicco A.

    2013-01-01

    Background Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability of high-resolution infrared thermal imaging for noninvasive automated detection of signs of diabetic foot disease. Methods The plantar foot surfaces of 15 diabetes patients were imaged with an infrared camera (resolution, 1.2 mm/pixel): 5 patients had no visible signs of foot complications, 5 patients had local complications (e.g., abundant callus or neuropathic ulcer), and 5 patients had diffuse complications (e.g., Charcot foot, infected ulcer, or critical ischemia). Foot temperature was calculated as mean temperature across pixels for the whole foot and for specified regions of interest (ROIs). Results No differences in mean temperature >1.5 °C between the ipsilateral and the contralateral foot were found in patients without complications. In patients with local complications, mean temperatures of the ipsilateral and the contralateral foot were similar, but temperature at the ROI was >2 °C higher compared with the corresponding region in the contralateral foot and to the mean of the whole ipsilateral foot. In patients with diffuse complications, mean temperature differences of >3 °C between ipsilateral and contralateral foot were found. Conclusions With an algorithm based on parameters that can be captured and analyzed with a high-resolution infrared camera and a computer, it is possible to detect signs of diabetic foot disease and to discriminate between no, local, or diffuse diabetic foot complications. As such, an intelligent telemedicine monitoring system for noninvasive automated detection of signs of diabetic foot disease is one step closer. Future studies are essential to confirm and extend these promising early findings. PMID:24124937

  12. Fully automated liver segmentation from SPIR image series.

    PubMed

    Göçeri, Evgin; Gürcan, Metin N; Dicle, Oğuz

    2014-10-01

    Accurate liver segmentation is an important component of surgery planning for liver transplantation, which enables patients with liver disease a chance to survive. Spectral pre-saturation inversion recovery (SPIR) image sequences are useful for liver vessel segmentation because vascular structures in the liver are clearly visible in these sequences. Although level-set based segmentation techniques are frequently used in liver segmentation due to their flexibility to adapt to different problems by incorporating prior knowledge, the need to initialize the contours on each slice is a common drawback of such techniques. In this paper, we present a fully automated variational level set approach for liver segmentation from SPIR image sequences. Our approach is designed to be efficient while achieving high accuracy. The efficiency is achieved by (1) automatically defining an initial contour for each slice, and (2) automatically computing weight values of each term in the applied energy functional at each iteration during evolution. Automated detection and exclusion of spurious structures (e.g. cysts and other bright white regions on the skin) in the pre-processing stage increases the accuracy and robustness. We also present a novel approach to reduce computational cost by employing binary regularization of level set function. A signed pressure force function controls the evolution of the active contour. The method was applied to ten data sets. In each image, the performance of the algorithm was measured using the receiver operating characteristics method in terms of accuracy, sensitivity and specificity. The accuracy of the proposed method was 96%. Quantitative analyses of results indicate that the proposed method can accurately, efficiently and consistently segment liver images. PMID:25192606

  13. Semi-Automated Segmentation of Microbes in Color Images

    NASA Astrophysics Data System (ADS)

    Reddy, Chandankumar K.; Liu, Feng-I.; Dazzo, Frank B.

    2003-01-01

    The goal of this work is to develop a system that can semi-automate the detection of multicolored foreground objects in digitized color images that also contain complex and very noisy backgrounds. Although considered a general problem of color image segmentation, our application is microbiology where various colored stains are used to reveal information on the microbes without cultivation. Instead of providing a simple threshold, the proposed system offers an interactive environment whereby the user chooses multiple sample points to define the range of color pixels comprising the foreground microbes of interest. The system then uses the color and spatial distances of these target points to segment the microbes from the confusing background of pixels whose RGB values lie outside the newly defined range and finally finds each cell's boundary using region-growing and mathematical morphology. Some other image processing methods are also applied to enhance the resultant image containing the colored microbes against a noise-free background. The prototype performs with 98% accuracy on a test set compared to ground truth data. The system described here will have many applications in image processing and analysis where one needs to segment typical pixel regions of similar but non-identical colors.

  14. An automated deformable image registration evaluation of confidence tool.

    PubMed

    Kirby, Neil; Chen, Josephine; Kim, Hojin; Morin, Olivier; Nie, Ke; Pouliot, Jean

    2016-04-21

    Deformable image registration (DIR) is a powerful tool for radiation oncology, but it can produce errors. Beyond this, DIR accuracy is not a fixed quantity and varies on a case-by-case basis. The purpose of this study is to explore the possibility of an automated program to create a patient- and voxel-specific evaluation of DIR accuracy. AUTODIRECT is a software tool that was developed to perform this evaluation for the application of a clinical DIR algorithm to a set of patient images. In brief, AUTODIRECT uses algorithms to generate deformations and applies them to these images (along with processing) to generate sets of test images, with known deformations that are similar to the actual ones and with realistic noise properties. The clinical DIR algorithm is applied to these test image sets (currently 4). From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student's t distribution. In this study, four commercially available DIR algorithms were used to deform a dose distribution associated with a virtual pelvic phantom image set, and AUTODIRECT was used to generate dose uncertainty estimates for each deformation. The virtual phantom image set has a known ground-truth deformation, so the true dose-warping errors of the DIR algorithms were also known. AUTODIRECT predicted error patterns that closely matched the actual error spatial distribution. On average AUTODIRECT overestimated the magnitude of the dose errors, but tuning the AUTODIRECT algorithms should improve agreement. This proof-of-principle test demonstrates the potential for the AUTODIRECT algorithm as an empirical method to predict DIR errors. PMID:27025957

  15. An automated deformable image registration evaluation of confidence tool

    NASA Astrophysics Data System (ADS)

    Kirby, Neil; Chen, Josephine; Kim, Hojin; Morin, Olivier; Nie, Ke; Pouliot, Jean

    2016-04-01

    Deformable image registration (DIR) is a powerful tool for radiation oncology, but it can produce errors. Beyond this, DIR accuracy is not a fixed quantity and varies on a case-by-case basis. The purpose of this study is to explore the possibility of an automated program to create a patient- and voxel-specific evaluation of DIR accuracy. AUTODIRECT is a software tool that was developed to perform this evaluation for the application of a clinical DIR algorithm to a set of patient images. In brief, AUTODIRECT uses algorithms to generate deformations and applies them to these images (along with processing) to generate sets of test images, with known deformations that are similar to the actual ones and with realistic noise properties. The clinical DIR algorithm is applied to these test image sets (currently 4). From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student’s t distribution. In this study, four commercially available DIR algorithms were used to deform a dose distribution associated with a virtual pelvic phantom image set, and AUTODIRECT was used to generate dose uncertainty estimates for each deformation. The virtual phantom image set has a known ground-truth deformation, so the true dose-warping errors of the DIR algorithms were also known. AUTODIRECT predicted error patterns that closely matched the actual error spatial distribution. On average AUTODIRECT overestimated the magnitude of the dose errors, but tuning the AUTODIRECT algorithms should improve agreement. This proof-of-principle test demonstrates the potential for the AUTODIRECT algorithm as an empirical method to predict DIR errors.

  16. Scanning probe image wizard: A toolbox for automated scanning probe microscopy data analysis

    NASA Astrophysics Data System (ADS)

    Stirling, Julian; Woolley, Richard A. J.; Moriarty, Philip

    2013-11-01

    We describe SPIW (scanning probe image wizard), a new image processing toolbox for SPM (scanning probe microscope) images. SPIW can be used to automate many aspects of SPM data analysis, even for images with surface contamination and step edges present. Specialised routines are available for images with atomic or molecular resolution to improve image visualisation and generate statistical data on surface structure.

  17. An automated high-content screening image analysis pipeline for the identification of selective autophagic inducers in human cancer cell lines.

    PubMed

    Kriston-Vizi, Janos; Lim, Ching Aeng; Condron, Peter; Chua, Kelvin; Wasser, Martin; Flotow, Horst

    2010-08-01

    Automated image processing is a critical and often rate-limiting step in high-content screening (HCS) workflows. The authors describe an open-source imaging-statistical framework with emphasis on segmentation to identify novel selective pharmacological inducers of autophagy. They screened a human alveolar cancer cell line and evaluated images by both local adaptive and global segmentation. At an individual cell level, region-growing segmentation was compared with histogram-derived segmentation. The histogram approach allowed segmentation of a sporadic-pattern foreground and hence the attainment of pixel-level precision. Single-cell phenotypic features were measured and reduced after assessing assay quality control. Hit compounds selected by machine learning corresponded well to the subjective threshold-based hits determined by expert analysis. Histogram-derived segmentation displayed robustness against image noise, a factor adversely affecting region growing segmentation. PMID:20547532

  18. Automated extraction of chemical structure information from digital raster images

    PubMed Central

    Park, Jungkap; Rosania, Gus R; Shedden, Kerby A; Nguyen, Mandee; Lyu, Naesung; Saitou, Kazuhiro

    2009-01-01

    Background To search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated. Results This paper aims to provide critical reviews for these systems and also report our recent development of ChemReader – a fully automated tool for extracting chemical structure diagrams in research articles and converting them into standard, searchable chemical file formats. Basic algorithms for recognizing lines and letters representing bonds and atoms in chemical structure diagrams can be independently run in sequence from a graphical user interface-and the algorithm parameters can be readily changed-to facilitate additional development specifically tailored to a chemical database annotation scheme. Compared with existing software programs such as OSRA, Kekule, and CLiDE, our results indicate that ChemReader outperforms other software systems on several sets of sample images from diverse sources in terms of the rate of correct outputs and the accuracy on extracting molecular substructure patterns. Conclusion The availability of ChemReader as a cheminformatic tool for extracting chemical structure information from digital raster images allows research and development groups to enrich their chemical structure databases by annotating the entries with published research articles. Based on its stable performance and high accuracy, ChemReader may be sufficiently accurate for annotating the chemical database with links to scientific research

  19. Automated target recognition technique for image segmentation and scene analysis

    NASA Astrophysics Data System (ADS)

    Baumgart, Chris W.; Ciarcia, Christopher A.

    1994-03-01

    Automated target recognition (ATR) software has been designed to perform image segmentation and scene analysis. Specifically, this software was developed as a package for the Army's Minefield and Reconnaissance and Detector (MIRADOR) program. MIRADOR is an on/off road, remote control, multisensor system designed to detect buried and surface- emplaced metallic and nonmetallic antitank mines. The basic requirements for this ATR software were the following: (1) an ability to separate target objects from the background in low signal-noise conditions; (2) an ability to handle a relatively high dynamic range in imaging light levels; (3) the ability to compensate for or remove light source effects such as shadows; and (4) the ability to identify target objects as mines. The image segmentation and target evaluation was performed using an integrated and parallel processing approach. Three basic techniques (texture analysis, edge enhancement, and contrast enhancement) were used collectively to extract all potential mine target shapes from the basic image. Target evaluation was then performed using a combination of size, geometrical, and fractal characteristics, which resulted in a calculated probability for each target shape. Overall results with this algorithm were quite good, though there is a tradeoff between detection confidence and the number of false alarms. This technology also has applications in the areas of hazardous waste site remediation, archaeology, and law enforcement.

  20. Image-based path planning for automated virtual colonoscopy navigation

    NASA Astrophysics Data System (ADS)

    Hong, Wei

    2008-03-01

    Virtual colonoscopy (VC) is a noninvasive method for colonic polyp screening, by reconstructing three-dimensional models of the colon using computerized tomography (CT). In virtual colonoscopy fly-through navigation, it is crucial to generate an optimal camera path for efficient clinical examination. In conventional methods, the centerline of the colon lumen is usually used as the camera path. In order to extract colon centerline, some time consuming pre-processing algorithms must be performed before the fly-through navigation, such as colon segmentation, distance transformation, or topological thinning. In this paper, we present an efficient image-based path planning algorithm for automated virtual colonoscopy fly-through navigation without the requirement of any pre-processing. Our algorithm only needs the physician to provide a seed point as the starting camera position using 2D axial CT images. A wide angle fisheye camera model is used to generate a depth image from the current camera position. Two types of navigational landmarks, safe regions and target regions are extracted from the depth images. Camera position and its corresponding view direction are then determined using these landmarks. The experimental results show that the generated paths are accurate and increase the user comfort during the fly-through navigation. Moreover, because of the efficiency of our path planning algorithm and rendering algorithm, our VC fly-through navigation system can still guarantee 30 FPS.

  1. Automated pollen identification using microscopic imaging and texture analysis.

    PubMed

    Marcos, J Víctor; Nava, Rodrigo; Cristóbal, Gabriel; Redondo, Rafael; Escalante-Ramírez, Boris; Bueno, Gloria; Déniz, Óscar; González-Porto, Amelia; Pardo, Cristina; Chung, François; Rodríguez, Tomás

    2015-01-01

    Pollen identification is required in different scenarios such as prevention of allergic reactions, climate analysis or apiculture. However, it is a time-consuming task since experts are required to recognize each pollen grain through the microscope. In this study, we performed an exhaustive assessment on the utility of texture analysis for automated characterisation of pollen samples. A database composed of 1800 brightfield microscopy images of pollen grains from 15 different taxa was used for this purpose. A pattern recognition-based methodology was adopted to perform pollen classification. Four different methods were evaluated for texture feature extraction from the pollen image: Haralick's gray-level co-occurrence matrices (GLCM), log-Gabor filters (LGF), local binary patterns (LBP) and discrete Tchebichef moments (DTM). Fisher's discriminant analysis and k-nearest neighbour were subsequently applied to perform dimensionality reduction and multivariate classification, respectively. Our results reveal that LGF and DTM, which are based on the spectral properties of the image, outperformed GLCM and LBP in the proposed classification problem. Furthermore, we found that the combination of all the texture features resulted in the highest performance, yielding an accuracy of 95%. Therefore, thorough texture characterisation could be considered in further implementations of automatic pollen recognition systems based on image processing techniques. PMID:25259684

  2. Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

    PubMed Central

    Francis, Michael; Waldrup, Josh; Qian, Xun; Taylor, Mark S.

    2014-01-01

    Intracellular Ca2+ signals are commonly studied with fluorescent Ca2+ indicator dyes and microscopy techniques. However, quantitative analysis of Ca2+ imaging data is time consuming and subject to bias. Automated signal analysis algorithms based on region of interest (ROI) detection have been implemented for one-dimensional line scan measurements, but there is no current algorithm which integrates optimized identification and analysis of ROIs in two-dimensional image sequences. Here an algorithm for rapid acquisition and analysis of ROIs in image sequences is described. It utilizes ellipses fit to noise filtered signals in order to determine optimal ROI placement, and computes Ca2+ signal parameters of amplitude, duration and spatial spread. This algorithm was implemented as a freely available plugin for ImageJ (NIH) software. Together with analysis scripts written for the open source statistical processing software R, this approach provides a high-capacity pipeline for performing quick statistical analysis of experimental output. The authors suggest that use of this analysis protocol will lead to a more complete and unbiased characterization of physiologic Ca2+ signaling. PMID:24962784

  3. Automated radar image analysis research in support of military needs

    NASA Astrophysics Data System (ADS)

    Rohde, Frederick W.; Chen, Pi-Fuay; Hevenor, Richard A.

    1986-10-01

    Synthetic aperture radars (SAR) are high resolution radars that can be used for reconnaissance, surveillance, and terrain analysis. The high resolution in range and azimuth is achieved by pulse compression and phase history processing, respectively. SAR images have much in common with optical images such as aerial photographs. Both are characterized by tones, patterns, shapes, and shadows. There are, however, significant differences between SAR and optical images due to the differences in the wavelengths and in the illumination and reflection of the targets. Cloud cover presents an obstacle to optical imagery but not to SAR imagery because radar waves can penetrate cloud cover. Optical imagery provides more detailed information than SAR imagery because of its higher resolution. The resolution of optical imagery decreases with distance whereas the resolution of SAR imagery is independent of distance. For large distances, for example from satellites to the surface of the Earth, the resolution of SAR imagery approaches the resolution of optical imagery. These properties make SAR a very useful tool for military purposes. SAR systems can collect large quantities of imagery. For the timely and economic analysis and interpretation of SAR imagery there is a need for the development of automated and interactive capabilities that will reduce the dependency on and requirements for highly trained image analysts.

  4. Texture-Based Automated Lithological Classification Using Aeromagenetic Anomaly Images

    USGS Publications Warehouse

    Shankar, Vivek

    2009-01-01

    This report consists of a thesis submitted to the faculty of the Department of Electrical and Computer Engineering, in partial fulfillment of the requirements for the degree of Master of Science, Graduate College, The University of Arizona, 2004 Aeromagnetic anomaly images are geophysical prospecting tools frequently used in the exploration of metalliferous minerals and hydrocarbons. The amplitude and texture content of these images provide a wealth of information to geophysicists who attempt to delineate the nature of the Earth's upper crust. These images prove to be extremely useful in remote areas and locations where the minerals of interest are concealed by basin fill. Typically, geophysicists compile a suite of aeromagnetic anomaly images, derived from amplitude and texture measurement operations, in order to obtain a qualitative interpretation of the lithological (rock) structure. Texture measures have proven to be especially capable of capturing the magnetic anomaly signature of unique lithological units. We performed a quantitative study to explore the possibility of using texture measures as input to a machine vision system in order to achieve automated classification of lithological units. This work demonstrated a significant improvement in classification accuracy over random guessing based on a priori probabilities. Additionally, a quantitative comparison between the performances of five classes of texture measures in their ability to discriminate lithological units was achieved.

  5. Improvement of automated image stitching system for DR X-ray images.

    PubMed

    Yang, Fan; He, Yan; Deng, Zhen Sheng; Yan, Ang

    2016-04-01

    The full bone structure of X-ray images cannot be captured in a single scan with Digital radiography (DR) system. The stitching method of X-ray images is very important for scoliosis or lower limb malformation diagnosing and pre-surgical planning. Based on the image registration technology, this paper proposes a new automated image stitching method for full-spine and lower limb X-ray images. The stitching method utilized down-sampling to decrease the size of image and reduce the amount of computation; improved phase correlation algorithm was adopted to find the overlapping region; correlation coefficient was used to evaluate the similarity of overlapping region; weighted blending is brought in to produce a panorama image. The performance of the proposed method was evaluated by 40 pairs of images from patients with scoliosis or lower limb malformation. The stitching method was fully automated without any user input required. The experimental results were compared with previous methods by analyzing the same database. It is demonstrated that the improved phase correlation has higher accuracy and shorter average stitching time than previous methods. It could tackle problems including image translation, rotation and small overlapping in image stitching. PMID:26914239

  6. Chemical Cytometry: Fluorescence-Based Single-Cell Analysis

    NASA Astrophysics Data System (ADS)

    Cohen, Daniella; Dickerson, Jane A.; Whitmore, Colin D.; Turner, Emily H.; Palcic, Monica M.; Hindsgaul, Ole; Dovichi, Norman J.

    2008-07-01

    Cytometry deals with the analysis of the composition of single cells. Flow and image cytometry employ antibody-based stains to characterize a handful of components in single cells. Chemical cytometry, in contrast, employs a suite of powerful analytical tools to characterize a large number of components. Tools have been developed to characterize nucleic acids, proteins, and metabolites in single cells. Whereas nucleic acid analysis employs powerful polymerase chain reaction-based amplification techniques, protein and metabolite analysis tends to employ capillary electrophoresis separation and ultrasensitive laser-induced fluorescence detection. It is now possible to detect yoctomole amounts of many analytes in single cells.

  7. Chemical Analysis of Single Cells

    NASA Astrophysics Data System (ADS)

    Borland, Laura M.; Kottegoda, Sumith; Phillips, K. Scott; Allbritton, Nancy L.

    2008-07-01

    Chemical analysis of single cells requires methods for quickly and quantitatively detecting a diverse array of analytes from extremely small volumes (femtoliters to nanoliters) with very high sensitivity and selectivity. Microelectrophoretic separations, using both traditional capillary electrophoresis and emerging microfluidic methods, are well suited for handling the unique size of single cells and limited numbers of intracellular molecules. Numerous analytes, ranging from small molecules such as amino acids and neurotransmitters to large proteins and subcellular organelles, have been quantified in single cells using microelectrophoretic separation techniques. Microseparation techniques, coupled to varying detection schemes including absorbance and fluorescence detection, electrochemical detection, and mass spectrometry, have allowed researchers to examine a number of processes inside single cells. This review also touches on a promising direction in single cell cytometry: the development of microfluidics for integrated cellular manipulation, chemical processing, and separation of cellular contents.

  8. Automated Recognition of 3D Features in GPIR Images

    NASA Technical Reports Server (NTRS)

    Park, Han; Stough, Timothy; Fijany, Amir

    2007-01-01

    A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a

  9. Automated detection of open magnetic field regions in EUV images

    NASA Astrophysics Data System (ADS)

    Krista, Larisza Diana; Reinard, Alysha

    2016-05-01

    Open magnetic regions on the Sun are either long-lived (coronal holes) or transient (dimmings) in nature, but both appear as dark regions in EUV images. For this reason their detection can be done in a similar way. As coronal holes are often large and long-lived in comparison to dimmings, their detection is more straightforward. The Coronal Hole Automated Recognition and Monitoring (CHARM) algorithm detects coronal holes using EUV images and a magnetogram. The EUV images are used to identify dark regions, and the magnetogam allows us to determine if the dark region is unipolar – a characteristic of coronal holes. There is no temporal sensitivity in this process, since coronal hole lifetimes span days to months. Dimming regions, however, emerge and disappear within hours. Hence, the time and location of a dimming emergence need to be known to successfully identify them and distinguish them from regular coronal holes. Currently, the Coronal Dimming Tracker (CoDiT) algorithm is semi-automated – it requires the dimming emergence time and location as an input. With those inputs we can identify the dimming and track it through its lifetime. CoDIT has also been developed to allow the tracking of dimmings that split or merge – a typical feature of dimmings.The advantage of these particular algorithms is their ability to adapt to detecting different types of open field regions. For coronal hole detection, each full-disk solar image is processed individually to determine a threshold for the image, hence, we are not limited to a single pre-determined threshold. For dimming regions we also allow individual thresholds for each dimming, as they can differ substantially. This flexibility is necessary for a subjective analysis of the studied regions. These algorithms were developed with the goal to allow us better understand the processes that give rise to eruptive and non-eruptive open field regions. We aim to study how these regions evolve over time and what environmental

  10. Automated image analysis in the study of collagenous colitis

    PubMed Central

    Fiehn, Anne-Marie Kanstrup; Kristensson, Martin; Engel, Ulla; Munck, Lars Kristian; Holck, Susanne; Engel, Peter Johan Heiberg

    2016-01-01

    Purpose The aim of this study was to develop an automated image analysis software to measure the thickness of the subepithelial collagenous band in colon biopsies with collagenous colitis (CC) and incomplete CC (CCi). The software measures the thickness of the collagenous band on microscopic slides stained with Van Gieson (VG). Patients and methods A training set consisting of ten biopsies diagnosed as CC, CCi, and normal colon mucosa was used to develop the automated image analysis (VG app) to match the assessment by a pathologist. The study set consisted of biopsies from 75 patients. Twenty-five cases were primarily diagnosed as CC, 25 as CCi, and 25 as normal or near-normal colonic mucosa. Four pathologists individually reassessed the biopsies and categorized all into one of the abovementioned three categories. The result of the VG app was correlated with the diagnosis provided by the four pathologists. Results The interobserver agreement for each pair of pathologists ranged from κ-values of 0.56–0.81, while the κ-value for the VG app vs each of the pathologists varied from 0.63 to 0.79. The overall agreement between the four pathologists was κ=0.69, while the overall agreement between the four pathologists and the VG app was κ=0.71. Conclusion In conclusion, the Visiopharm VG app is able to measure the thickness of a sub-epithelial collagenous band in colon biopsies with an accuracy comparable to the performance of a pathologist and thereby provides a promising supplementary tool for the diagnosis of CC and CCi and in particular for research. PMID:27114713

  11. Automated Imaging and Analysis of the Hemagglutination Inhibition Assay.

    PubMed

    Nguyen, Michael; Fries, Katherine; Khoury, Rawia; Zheng, Lingyi; Hu, Branda; Hildreth, Stephen W; Parkhill, Robert; Warren, William

    2016-04-01

    The hemagglutination inhibition (HAI) assay quantifies the level of strain-specific influenza virus antibody present in serum and is the standard by which influenza vaccine immunogenicity is measured. The HAI assay endpoint requires real-time monitoring of rapidly evolving red blood cell (RBC) patterns for signs of agglutination at a rate of potentially thousands of patterns per day to meet the throughput needs for clinical testing. This analysis is typically performed manually through visual inspection by highly trained individuals. However, concordant HAI results across different labs are challenging to demonstrate due to analyst bias and variability in analysis methods. To address these issues, we have developed a bench-top, standalone, high-throughput imaging solution that automatically determines the agglutination states of up to 9600 HAI assay wells per hour and assigns HAI titers to 400 samples in a single unattended 30-min run. Images of the tilted plates are acquired as a function of time and analyzed using algorithms that were developed through comprehensive examination of manual classifications. Concordance testing of the imaging system with eight different influenza antigens demonstrates 100% agreement between automated and manual titer determination with a percent difference of ≤3.4% for all cases. PMID:26464422

  12. Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo

    PubMed Central

    2010-01-01

    Background Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual C. elegans genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (i.e., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours. Results In this paper, we reduce the time required to correct errors made by StarryNite. We target one of the most frequent error types (movements annotated as divisions) and train a support vector machine (SVM) classifier to decide whether a division call made by StarryNite is correct or not. We show, via cross-validation experiments on several benchmark data sets, that the SVM successfully identifies this type of error significantly. A new version of StarryNite that includes the trained SVM classifier is available at http://starrynite.sourceforge.net. Conclusions We demonstrate the utility of a machine learning approach to error annotation for StarryNite. In the process, we also provide some general methodologies for developing and validating a classifier with respect to a given pattern recognition task. PMID:20146825

  13. Automated transient detection in the STEREO Heliospheric Imagers.

    NASA Astrophysics Data System (ADS)

    Barnard, Luke; Scott, Chris; Owens, Mat; Lockwood, Mike; Tucker-Hood, Kim; Davies, Jackie

    2014-05-01

    Since the launch of the twin STEREO satellites, the heliospheric imagers (HI) have been used, with good results, in tracking transients of solar origin, such as Coronal Mass Ejections (CMEs), out far into the heliosphere. A frequently used approach is to build a "J-map", in which multiple elongation profiles along a constant position angle are stacked in time, building an image in which radially propagating transients form curved tracks in the J-map. From this the time-elongation profile of a solar transient can be manually identified. This is a time consuming and laborious process, and the results are subjective, depending on the skill and expertise of the investigator. Therefore, it is desirable to develop an automated algorithm for the detection and tracking of the transient features observed in HI data. This is to some extent previously covered ground, as similar problems have been encountered in the analysis of coronagraph data and have led to the development of products such as CACtus etc. We present the results of our investigation into the automated detection of solar transients observed in J-maps formed from HI data. We use edge and line detection methods to identify transients in the J-maps, and then use kinematic models of the solar transient propagation (such as the fixed-phi and harmonic mean geometric models) to estimate the solar transients properties, such as transient speed and propagation direction, from the time-elongation profile. The effectiveness of this process is assessed by comparison of our results with a set of manually identified CMEs, extracted and analysed by the Solar Storm Watch Project. Solar Storm Watch is a citizen science project in which solar transients are identified in J-maps formed from HI data and tracked multiple times by different users. This allows the calculation of a consensus time-elongation profile for each event, and therefore does not suffer from the potential subjectivity of an individual researcher tracking an

  14. Automating sky object classification in astronomical survey images

    NASA Technical Reports Server (NTRS)

    Fayyad, Usama M.; Doyle, Richard J.; Weir, Nicholas; Djorgovski, S. G.

    1992-01-01

    We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomer Observatory Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 10(exp 7) galaxies and 10(exp 8) stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. The size of this data set precludes manual analysis. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3* and O-BTree, two inductive learning techniques, learn classification decision trees from examples. These classifiers will be used to process the rest of the data. This paper gives an overview of the machine learning techniques used, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our approach is well-suited to the problem. The primary benefits of the approach are increased data reduction throughput and consistency of classification. The classification rules which are the product of the inductive learning techniques will form an object, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically cataloged

  15. Automated 3D ultrasound image segmentation to aid breast cancer image interpretation.

    PubMed

    Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A; Yuan, Jie; Wang, Xueding; Carson, Paul L

    2016-02-01

    Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer. PMID:26547117

  16. Automated retinal vessel type classification in color fundus images

    NASA Astrophysics Data System (ADS)

    Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.

    2013-02-01

    Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

  17. Automated Image Retrieval of Chest CT Images Based on Local Grey Scale Invariant Features.

    PubMed

    Arrais Porto, Marcelo; Cordeiro d'Ornellas, Marcos

    2015-01-01

    Textual-based tools are regularly employed to retrieve medical images for reading and interpretation using current retrieval Picture Archiving and Communication Systems (PACS) but pose some drawbacks. All-purpose content-based image retrieval (CBIR) systems are limited when dealing with medical images and do not fit well into PACS workflow and clinical practice. This paper presents an automated image retrieval approach for chest CT images based local grey scale invariant features from a local database. Performance was measured in terms of precision and recall, average retrieval precision (ARP), and average retrieval rate (ARR). Preliminary results have shown the effectiveness of the proposed approach. The prototype is also a useful tool for radiology research and education, providing valuable information to the medical and broader healthcare community. PMID:26262345

  18. Automated counting of bacterial colonies by image analysis.

    PubMed

    Chiang, Pei-Ju; Tseng, Min-Jen; He, Zong-Sian; Li, Chia-Hsun

    2015-01-01

    Research on microorganisms often involves culturing as a means to determine the survival and proliferation of bacteria. The number of colonies in a culture is counted to calculate the concentration of bacteria in the original broth; however, manual counting can be time-consuming and imprecise. To save time and prevent inconsistencies, this study proposes a fully automated counting system using image processing methods. To accurately estimate the number of viable bacteria in a known volume of suspension, colonies distributing over the whole surface area of a plate, including the central and rim areas of a Petri dish are taken into account. The performance of the proposed system is compared with verified manual counts, as well as with two freely available counting software programs. Comparisons show that the proposed system is an effective method with excellent accuracy with mean value of absolute percentage error of 3.37%. A user-friendly graphical user interface is also developed and freely available for download, providing researchers in biomedicine with a more convenient instrument for the enumeration of bacterial colonies. PMID:25451456

  19. Automated diagnosis of dry eye using infrared thermography images

    NASA Astrophysics Data System (ADS)

    Acharya, U. Rajendra; Tan, Jen Hong; Koh, Joel E. W.; Sudarshan, Vidya K.; Yeo, Sharon; Too, Cheah Loon; Chua, Chua Kuang; Ng, E. Y. K.; Tong, Louis

    2015-07-01

    Dry Eye (DE) is a condition of either decreased tear production or increased tear film evaporation. Prolonged DE damages the cornea causing the corneal scarring, thinning and perforation. There is no single uniform diagnosis test available to date; combinations of diagnostic tests are to be performed to diagnose DE. The current diagnostic methods available are subjective, uncomfortable and invasive. Hence in this paper, we have developed an efficient, fast and non-invasive technique for the automated identification of normal and DE classes using infrared thermography images. The features are extracted from nonlinear method called Higher Order Spectra (HOS). Features are ranked using t-test ranking strategy. These ranked features are fed to various classifiers namely, K-Nearest Neighbor (KNN), Nave Bayesian Classifier (NBC), Decision Tree (DT), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM) to select the best classifier using minimum number of features. Our proposed system is able to identify the DE and normal classes automatically with classification accuracy of 99.8%, sensitivity of 99.8%, and specificity if 99.8% for left eye using PNN and KNN classifiers. And we have reported classification accuracy of 99.8%, sensitivity of 99.9%, and specificity if 99.4% for right eye using SVM classifier with polynomial order 2 kernel.

  20. Automated Detection of Firearms and Knives in a CCTV Image

    PubMed Central

    Grega, Michał; Matiolański, Andrzej; Guzik, Piotr; Leszczuk, Mikołaj

    2016-01-01

    Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims. PMID:26729128

  1. Exploiting Image Registration for Automated Resonance Assignment in NMR

    PubMed Central

    Strickland, Madeleine; Stephens, Thomas; Liu, Jian; Tjandra, Nico

    2015-01-01

    Summary Analysis of protein NMR data involves the assignment of resonance peaks in a number of multidimensional data sets. To establish resonance assignment a three-dimensional search is used to match a pair of common variables, such as chemical shifts of the same spin system, in different NMR spectra. We show that by displaying the variables to be compared in two-dimensional plots the process can be simplified. Moreover, by utilizing a fast Fourier transform (FFT) cross-correlation algorithm, more common to the field of image registration or pattern matching, we can automate this process. Here, we use sequential NMR backbone assignment as an example to show that the combination of correlation plots and segmented pattern matching establishes fast backbone assignment in fifteen proteins of varying sizes. For example, the 265-residue RalBP1 protein was 95.4% correctly assigned in 10 seconds. The same concept can be applied to any multidimensional NMR data set where analysis comprises the comparison of two variables. This modular and robust approach offers high efficiency with excellent computational scalability and could be easily incorporated into existing assignment software. PMID:25828257

  2. Automated Detection of Firearms and Knives in a CCTV Image.

    PubMed

    Grega, Michał; Matiolański, Andrzej; Guzik, Piotr; Leszczuk, Mikołaj

    2016-01-01

    Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims. PMID:26729128

  3. Single-cell western blotting

    PubMed Central

    Hughes, Alex J.; Spelke, Dawn P.; Xu, Zhuchen; Kang, Chi-Chih; Schaffer, David V.; Herr, Amy E.

    2014-01-01

    To measure cell-to-cell variation in protein-mediated functions — a hallmark of biological processes — we developed an approach to conduct ~103 concurrent single-cell western blots (scWesterns) in ~4 hours. A microscope slide supporting a 30 µm-thick photoactive polyacrylamide gel enables western blotting comprised of: settling of single cells into microwells, lysis in situ, gel electrophoresis, photoinitiated blotting to immobilize proteins, and antibody probing. We apply this scWestern to monitor single rat neural stem cell differentiation and responses to mitogen stimulation. The scWestern quantifies target proteins even with off-target antibody binding, multiplexes to 11 protein targets per single cell with detection thresholds of <30,000 molecules, and supports analyses of low starting cell numbers (~200) when integrated with fluorescence activated cell sorting. The scWestern thus overcomes limitations in single-cell protein analysis (i.e., antibody fidelity, sensitivity, and starting cell number) and constitutes a versatile tool for the study of complex cell populations at single-cell resolution. PMID:24880876

  4. Single Cell Electrical Characterization Techniques

    PubMed Central

    Mansor, Muhammad Asraf; Ahmad, Mohd Ridzuan

    2015-01-01

    Electrical properties of living cells have been proven to play significant roles in understanding of various biological activities including disease progression both at the cellular and molecular levels. Since two decades ago, many researchers have developed tools to analyze the cell’s electrical states especially in single cell analysis (SCA). In depth analysis and more fully described activities of cell differentiation and cancer can only be accomplished with single cell analysis. This growing interest was supported by the emergence of various microfluidic techniques to fulfill high precisions screening, reduced equipment cost and low analysis time for characterization of the single cell’s electrical properties, as compared to classical bulky technique. This paper presents a historical review of single cell electrical properties analysis development from classical techniques to recent advances in microfluidic techniques. Technical details of the different microfluidic techniques are highlighted, and the advantages and limitations of various microfluidic devices are discussed. PMID:26053399

  5. Automated endoscopic navigation and advisory system from medical image

    NASA Astrophysics Data System (ADS)

    Kwoh, Chee K.; Khan, Gul N.; Gillies, Duncan F.

    1999-05-01

    , which is developed to obtain the relative depth of the colon surface in the image by assuming a point light source very close to the camera. If we assume the colon has a shape similar to a tube, then a reasonable approximation of the position of the center of the colon (lumen) will be a function of the direction in which the majority of the normal vectors of shape are pointing. The second layer is the control layer and at this level, a decision model must be built for endoscope navigation and advisory system. The system that we built is the models of probabilistic networks that create a basic, artificial intelligence system for navigation in the colon. We have constructed the probabilistic networks from correlated objective data using the maximum weighted spanning tree algorithm. In the construction of a probabilistic network, it is always assumed that the variables starting from the same parent are conditionally independent. However, this may not hold and will give rise to incorrect inferences. In these cases, we proposed the creation of a hidden node to modify the network topology, which in effect models the dependency of correlated variables, to solve the problem. The conditional probability matrices linking the hidden node to its neighbors are determined using a gradient descent method which minimizing the objective cost function. The error gradients can be treated as updating messages and ca be propagated in any direction throughout any singly connected network to adjust the network parameters. With the above two- level approach, we have been able to build an automated endoscope navigation and advisory system successfully.

  6. Application of automated image analysis to coal petrography

    USGS Publications Warehouse

    Chao, E.C.T.; Minkin, J.A.; Thompson, C.L.

    1982-01-01

    The coal petrologist seeks to determine the petrographic characteristics of organic and inorganic coal constituents and their lateral and vertical variations within a single coal bed or different coal beds of a particular coal field. Definitive descriptions of coal characteristics and coal facies provide the basis for interpretation of depositional environments, diagenetic changes, and burial history and determination of the degree of coalification or metamorphism. Numerous coal core or columnar samples must be studied in detail in order to adequately describe and define coal microlithotypes, lithotypes, and lithologic facies and their variations. The large amount of petrographic information required can be obtained rapidly and quantitatively by use of an automated image-analysis system (AIAS). An AIAS can be used to generate quantitative megascopic and microscopic modal analyses for the lithologic units of an entire columnar section of a coal bed. In our scheme for megascopic analysis, distinctive bands 2 mm or more thick are first demarcated by visual inspection. These bands consist of either nearly pure microlithotypes or lithotypes such as vitrite/vitrain or fusite/fusain, or assemblages of microlithotypes. Megascopic analysis with the aid of the AIAS is next performed to determine volume percentages of vitrite, inertite, minerals, and microlithotype mixtures in bands 0.5 to 2 mm thick. The microlithotype mixtures are analyzed microscopically by use of the AIAS to determine their modal composition in terms of maceral and optically observable mineral components. Megascopic and microscopic data are combined to describe the coal unit quantitatively in terms of (V) for vitrite, (E) for liptite, (I) for inertite or fusite, (M) for mineral components other than iron sulfide, (S) for iron sulfide, and (VEIM) for the composition of the mixed phases (Xi) i = 1,2, etc. in terms of the maceral groups vitrinite V, exinite E, inertinite I, and optically observable mineral

  7. Measuring Intracellular Ca2+ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

    PubMed Central

    Mata-Martínez, Esperanza; José, Omar; Torres-Rodríguez, Paulina; Solís-López, Alejandra; Sánchez-Tusie, Ana A.; Sánchez-Guevara, Yoloxochitl; Treviño, Marcela B.; Treviño, Claudia L.

    2013-01-01

    Spermatozoa are male reproductive cells especially designed to reach, recognize and fuse with the egg. To perform these tasks, sperm cells must be prepared to face a constantly changing environment and to overcome several physical barriers. Being in essence transcriptionally and translationally silent, these motile cells rely profoundly on diverse signaling mechanisms to orient themselves and swim in a directed fashion, and to contend with challenging environmental conditions during their journey to find the egg. In particular, Ca2+-mediated signaling is pivotal for several sperm functions: activation of motility, capacitation (a complex process that prepares sperm for the acrosome reaction) and the acrosome reaction (an exocytotic event that allows sperm-egg fusion). The use of fluorescent dyes to track intracellular fluctuations of this ion is of remarkable importance due to their ease of application, sensitivity, and versatility of detection. Using one single dye-loading protocol we utilize four different fluorometric techniques to monitor sperm Ca2+ dynamics. Each technique provides distinct information that enables spatial and/or temporal resolution, generating data both at single cell and cell population levels. PMID:23728309

  8. Single cell elemental analysis using nuclear microscopy

    NASA Astrophysics Data System (ADS)

    Ren, M. Q.; Thong, P. S. P.; Kara, U.; Watt, F.

    1999-04-01

    The use of Particle Induced X-ray Emission (PIXE), Rutherford Backscattering Spectrometry (RBS) and Scanning Transmission Ion Microscopy (STIM) to provide quantitative elemental analysis of single cells is an area which has high potential, particularly when the trace elements such as Ca, Fe, Zn and Cu can be monitored. We describe the methodology of sample preparation for two cell types, the procedures of cell imaging using STIM, and the quantitative elemental analysis of single cells using RBS and PIXE. Recent work on single cells at the Nuclear Microscopy Research Centre,National University of Singapore has centred around two research areas: (a) Apoptosis (programmed cell death), which has been recently implicated in a wide range of pathological conditions such as cancer, Parkinson's disease etc, and (b) Malaria (infection of red blood cells by the malaria parasite). Firstly we present results on the elemental analysis of human Chang liver cells (ATTCC CCL 13) where vanadium ions were used to trigger apoptosis, and demonstrate that nuclear microscopy has the capability of monitoring vanadium loading within individual cells. Secondly we present the results of elemental changes taking place in individual mouse red blood cells which have been infected with the malaria parasite and treated with the anti-malaria drug Qinghaosu (QHS).

  9. Evaluation of automated target detection using image fusion

    NASA Astrophysics Data System (ADS)

    Irvine, John M.; Abramson, Susan; Mossing, John

    2003-09-01

    Reliance on Automated Target Recognition (ATR) technology is essential to the future success of Intelligence, Surveillance, and Reconnaissance (ISR) missions. Although benefits may be realized through ATR processing of a single data source, fusion of information across multiple images and multiple sensors promises significant performance gains. A major challenge, as ATR fusion technologies mature, is the establishment of sound methods for evaluating ATR performance in the context of data fusion. The Deputy Under Secretary of Defense for Science and Technology (DUSD/S&T), as part of their ongoing ATR Program, has sponsored an effort to develop and demonstrate methods for evaluating ATR algorithms that utilize multiple data source, i.e., fusion-based ATR. This paper presents results from this program, focusing on the target detection and cueing aspect of the problem. The first step in assessing target detection performance is to relate the ground truth to the ATR decisions. Once the ATR decisions have been mapped to ground truth, the second step in the evaluation is to characterize ATR performance. A common approach is to vary the confidence threshold of the ATR and compute the Probability of Detection (PD) and the False Alarm Rate (FAR) associated with each threshold. Varying the threshold, therefore, produces an empirical performance curve relating detection performance to false alarms. Various statistical methods have been developed, largely in the medical imaging literature, to model this curve so that statistical inferences are possible. One approach, based on signal detection theory, generalizes the Receiver Operator Characteristic (ROC) curve. Under this approach, the Free Response Operating Characteristic (FROC) curve models performance for search problems. The FROC model is appropriate when multiple detections are possible and the number of false alarms is unconstrained. The parameterization of the FROC model provides a natural method for characterizing both

  10. Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging.

    PubMed

    Peterson, David E; Chen, Steven D; Calabrese, Evan; White, Leonard E; Provenzale, James M

    2016-02-01

    The goal of this study was to apply image registration-based automated segmentation methods to measure diffusion tensor imaging (DTI) metrics within the canine brain. Specifically, we hypothesized that this method could measure DTI metrics within the canine brain with greater reproducibility than with hand-drawn region of interest (ROI) methods. We performed high-resolution post-mortem DTI imaging on two canine brains on a 7 T MR scanner. We designated the two brains as brain 1 and brain 2. We measured DTI metrics within the corpus callosum of brain 1 using a hand-drawn ROI method and an automated segmentation method in which ROIs from brain 2 were transformed into the space of brain 1. We repeated both methods in order to measure their reliability. Mean differences between the two sets of hand-drawn ROIs ranged from 4% to 10%. Mean differences between the hand-drawn ROIs and the automated ROIs were less than 3%. The mean differences between the first and second automated ROIs were all less than 0.25%. Our findings indicate that the image registration-based automated segmentation method was clearly the more reproducible method. These results provide the groundwork for using image registration-based automated segmentation methods to measure DTI metrics within the canine brain. Such methods will facilitate the study of white matter pathology in canine models of neurologic disease. PMID:26577603

  11. Twelve automated thresholding methods for segmentation of PET images: a phantom study

    NASA Astrophysics Data System (ADS)

    Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M.

    2012-06-01

    Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical 18F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

  12. Twelve automated thresholding methods for segmentation of PET images: a phantom study.

    PubMed

    Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M

    2012-06-21

    Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical (18)F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools. PMID:22647928

  13. Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

    DOE PAGESBeta

    Venkatraman, S.; Doktycz, M. J.; Qi, H.; Morrell-Falvey, J. L.

    2006-01-01

    The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction.more » Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.« less

  14. An automated image segmentation and classification algorithm for immunohistochemically stained tumor cell nuclei

    NASA Astrophysics Data System (ADS)

    Yeo, Hangu; Sheinin, Vadim; Sheinin, Yuri

    2009-02-01

    As medical image data sets are digitized and the number of data sets is increasing exponentially, there is a need for automated image processing and analysis technique. Most medical imaging methods require human visual inspection and manual measurement which are labor intensive and often produce inconsistent results. In this paper, we propose an automated image segmentation and classification method that identifies tumor cell nuclei in medical images and classifies these nuclei into two categories, stained and unstained tumor cell nuclei. The proposed method segments and labels individual tumor cell nuclei, separates nuclei clusters, and produces stained and unstained tumor cell nuclei counts. The representative fields of view have been chosen by a pathologist from a known diagnosis (clear cell renal cell carcinoma), and the automated results are compared with the hand-counted results by a pathologist.

  15. Automated detection of a prostate Ni-Ti stent in electronic portal images

    SciTech Connect

    Carl, Jesper; Nielsen, Henning; Nielsen, Jane; Lund, Bente; Larsen, Erik Hoejkjaer

    2006-12-15

    Planning target volumes (PTV) in fractionated radiotherapy still have to be outlined with wide margins to the clinical target volume due to uncertainties arising from daily shift of the prostate position. A recently proposed new method of visualization of the prostate is based on insertion of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is based on the Ni-Ti stent having a cylindrical shape with a fixed diameter, which was used as the basis for an automated detection algorithm. The automated method uses enhancement of lines combined with a grayscale morphology operation that looks for enhanced pixels separated with a distance similar to the diameter of the stent. The images in this study are all from prostate cancer patients treated with radiotherapy in a previous study. Images of a stent inserted in a humanoid phantom demonstrated a localization accuracy of 0.4-0.7 mm which equals the pixel size in the image. The automated detection of the stent was compared to manual detection in 71 pairs of orthogonal images taken in nine patients. The algorithm was successful in 67 of 71 pairs of images. The method is fast, has a high success rate, good accuracy, and has a potential for unsupervised localization of the prostate before radiotherapy, which would enable automated repositioning before treatment and allow for the use of very tight PTV margins.

  16. Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images.

    PubMed

    Kim, Kwang-Min; Son, Kilho; Palmore, G Tayhas R

    2015-01-01

    Image analysis software is an essential tool used in neuroscience and neural engineering to evaluate changes in neuronal structure following extracellular stimuli. Both manual and automated methods in current use are severely inadequate at detecting and quantifying changes in neuronal morphology when the images analyzed have a low signal-to-noise ratio (SNR). This inadequacy derives from the fact that these methods often include data from non-neuronal structures or artifacts by simply tracing pixels with high intensity. In this paper, we describe Neuron Image Analyzer (NIA), a novel algorithm that overcomes these inadequacies by employing Laplacian of Gaussian filter and graphical models (i.e., Hidden Markov Model, Fully Connected Chain Model) to specifically extract relational pixel information corresponding to neuronal structures (i.e., soma, neurite). As such, NIA that is based on vector representation is less likely to detect false signals (i.e., non-neuronal structures) or generate artifact signals (i.e., deformation of original structures) than current image analysis algorithms that are based on raster representation. We demonstrate that NIA enables precise quantification of neuronal processes (e.g., length and orientation of neurites) in low quality images with a significant increase in the accuracy of detecting neuronal changes post-stimulation. PMID:26593337

  17. Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images

    PubMed Central

    Kim, Kwang-Min; Son, Kilho; Palmore, G. Tayhas R.

    2015-01-01

    Image analysis software is an essential tool used in neuroscience and neural engineering to evaluate changes in neuronal structure following extracellular stimuli. Both manual and automated methods in current use are severely inadequate at detecting and quantifying changes in neuronal morphology when the images analyzed have a low signal-to-noise ratio (SNR). This inadequacy derives from the fact that these methods often include data from non-neuronal structures or artifacts by simply tracing pixels with high intensity. In this paper, we describe Neuron Image Analyzer (NIA), a novel algorithm that overcomes these inadequacies by employing Laplacian of Gaussian filter and graphical models (i.e., Hidden Markov Model, Fully Connected Chain Model) to specifically extract relational pixel information corresponding to neuronal structures (i.e., soma, neurite). As such, NIA that is based on vector representation is less likely to detect false signals (i.e., non-neuronal structures) or generate artifact signals (i.e., deformation of original structures) than current image analysis algorithms that are based on raster representation. We demonstrate that NIA enables precise quantification of neuronal processes (e.g., length and orientation of neurites) in low quality images with a significant increase in the accuracy of detecting neuronal changes post-stimulation. PMID:26593337

  18. Automated analysis of investigational near-infrared fluorescence lymphatic imaging in humans

    PubMed Central

    Zhang, Jingdan; Zhou, Shaohua Kevin; Xiang, Xiaoyan; Bautista, Merrick L.; Niccum, Blake A.; Dickinson, Gabriel S.; Tan, I-Chih; Chan, Wenyaw; Sevick-Muraca, Eva M.; Rasmussen, John C.

    2012-01-01

    ALFIA (Automated Lymphatic Function Imaging Analysis), an algorithm providing quantitative analysis of investigational near-infrared fluorescence lymphatic images, is described. Images from nine human subjects were analyzed for apparent lymphatic propagation velocities and propulsion periods using manual analysis and ALFIA. While lymphatic propulsion was more easily detected using ALFIA than with manual analysis, statistical analyses indicate no significant difference in the apparent lymphatic velocities although ALFIA tended to calculate longer propulsion periods. With the base ALFIA algorithms validated, further automation can now proceed to provide a clinically relevant analytic tool for quantitatively assessing lymphatic function in humans. PMID:22808440

  19. Knowledge Acquisition, Validation, and Maintenance in a Planning System for Automated Image Processing

    NASA Technical Reports Server (NTRS)

    Chien, Steve A.

    1996-01-01

    A key obstacle hampering fielding of AI planning applications is the considerable expense of developing, verifying, updating, and maintainting the planning knowledge base (KB). Planning systems must be able to compare favorably in terms of software lifecycle costs to other means of automation such as scripts or rule-based expert systems. This paper describes a planning application of automated imaging processing and our overall approach to knowledge acquisition for this application.

  20. Validation of Supervised Automated Algorithm for Fast Quantitative Evaluation of Organ Motion on Magnetic Resonance Imaging

    SciTech Connect

    Prakash, Varuna; Stainsby, Jeffrey A.; Satkunasingham, Janakan; Craig, Tim; Catton, Charles; Chan, Philip; Dawson, Laura; Hensel, Jennifer; Jaffray, David; Milosevic, Michael; Nichol, Alan; Sussman, Marshall S.; Lockwood, Gina; Menard, Cynthia

    2008-07-15

    Purpose: To validate a correlation coefficient template-matching algorithm applied to the supervised automated quantification of abdominal-pelvic organ motion captured on time-resolved magnetic resonance imaging. Methods and Materials: Magnetic resonance images of 21 patients across four anatomic sites were analyzed. Representative anatomic points of interest were chosen as surrogates for organ motion. The point of interest displacements across each image frame relative to baseline were quantified manually and through the use of a template-matching software tool, termed 'Motiontrack.' Automated and manually acquired displacement measures, as well as the standard deviation of intrafraction motion, were compared for each image frame and for each patient. Results: Discrepancies between the automated and manual displacements of {>=}2 mm were uncommon, ranging in frequency of 0-9.7% (liver and prostate, respectively). The standard deviations of intrafraction motion measured with each method correlated highly (r = 0.99). Considerable interpatient variability in organ motion was demonstrated by a wide range of standard deviations in the liver (1.4-7.5 mm), uterus (1.1-8.4 mm), and prostate gland (0.8-2.7 mm). The automated algorithm performed successfully in all patients but 1 and substantially improved efficiency compared with manual quantification techniques (5 min vs. 60-90 min). Conclusion: Supervised automated quantification of organ motion captured on magnetic resonance imaging using a correlation coefficient template-matching algorithm was efficient, accurate, and may play an important role in off-line adaptive approaches to intrafraction motion management.

  1. Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers

    PubMed Central

    Karaçalı, Bilge; Vamvakidou, Alexandra P; Tözeren, Aydın

    2007-01-01

    Background Three-dimensional in vitro culture of cancer cells are used to predict the effects of prospective anti-cancer drugs in vivo. In this study, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images. Methods Histologic cross sections of breast tumoroids developed in co-culture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using k-means clustering. Automated segmentation was used to identify image regions composed of cells expressing a given biomarker. Synthesized images were created to check the accuracy of the image processing system. Results Accuracy of automated segmentation was over 95% in identifying regions of interest in synthesized images. Image analysis of adjacent histology slides stained, respectively, for Ecad and PR, accurately predicted regions of different cell phenotypes. Image analysis of tumoroid cross sections from different tumoroids obtained under the same co-culture conditions indicated the variation of cellular composition from one tumoroid to another. Variations in the compositions of cross sections obtained from the same tumoroid were established by parallel analysis of Ecad and PR-stained cross section images. Conclusion Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development. PMID:17822559

  2. Quantitative Analysis of Mouse Retinal Layers Using Automated Segmentation of Spectral Domain Optical Coherence Tomography Images

    PubMed Central

    Dysli, Chantal; Enzmann, Volker; Sznitman, Raphael; Zinkernagel, Martin S.

    2015-01-01

    Purpose Quantification of retinal layers using automated segmentation of optical coherence tomography (OCT) images allows for longitudinal studies of retinal and neurological disorders in mice. The purpose of this study was to compare the performance of automated retinal layer segmentation algorithms with data from manual segmentation in mice using the Spectralis OCT. Methods Spectral domain OCT images from 55 mice from three different mouse strains were analyzed in total. The OCT scans from 22 C57Bl/6, 22 BALBc, and 11 C3A.Cg-Pde6b+Prph2Rd2/J mice were automatically segmented using three commercially available automated retinal segmentation algorithms and compared to manual segmentation. Results Fully automated segmentation performed well in mice and showed coefficients of variation (CV) of below 5% for the total retinal volume. However, all three automated segmentation algorithms yielded much thicker total retinal thickness values compared to manual segmentation data (P < 0.0001) due to segmentation errors in the basement membrane. Conclusions Whereas the automated retinal segmentation algorithms performed well for the inner layers, the retinal pigmentation epithelium (RPE) was delineated within the sclera, leading to consistently thicker measurements of the photoreceptor layer and the total retina. Translational Relevance The introduction of spectral domain OCT allows for accurate imaging of the mouse retina. Exact quantification of retinal layer thicknesses in mice is important to study layers of interest under various pathological conditions. PMID:26336634

  3. A New Approach for Measuring Single-Cell Oxygen Consumption Rates

    PubMed Central

    Molter, Timothy W.; McQuaide, Sarah C.; Holl, Mark R.; Meldrum, Deirdre R.; Dragavon, Joseph M.; Anderson, Judith B.; Young, A. Cody; Burgess, Lloyd W.; Lidstrom, Mary E.

    2010-01-01

    A novel system that has enabled the measurement of single-cell oxygen consumption rates is presented. The experimental apparatus includes a temperature controlled environmental chamber, an array of microwells etched in glass, and a lid actuator used to seal cells in the microwells. Each microwell contains an oxygen sensitive platinum phosphor sensor used to monitor the cellular metabolic rates. Custom automation software controls the digital image data collection for oxygen sensor measurements, which are analyzed using an image-processing program to yield the oxygen concentration within each microwell versus time. Two proof-of-concept experiments produced oxygen consumption rate measurements for A549 human epithelial lung cancer cells of 5.39 and 5.27 fmol/min/cell, closely matching published oxygen consumption rates for bulk A549 populations. PMID:21057593

  4. Bioinformatics approaches to single-cell analysis in developmental biology.

    PubMed

    Yalcin, Dicle; Hakguder, Zeynep M; Otu, Hasan H

    2016-03-01

    Individual cells within the same population show various degrees of heterogeneity, which may be better handled with single-cell analysis to address biological and clinical questions. Single-cell analysis is especially important in developmental biology as subtle spatial and temporal differences in cells have significant associations with cell fate decisions during differentiation and with the description of a particular state of a cell exhibiting an aberrant phenotype. Biotechnological advances, especially in the area of microfluidics, have led to a robust, massively parallel and multi-dimensional capturing, sorting, and lysis of single-cells and amplification of related macromolecules, which have enabled the use of imaging and omics techniques on single cells. There have been improvements in computational single-cell image analysis in developmental biology regarding feature extraction, segmentation, image enhancement and machine learning, handling limitations of optical resolution to gain new perspectives from the raw microscopy images. Omics approaches, such as transcriptomics, genomics and epigenomics, targeting gene and small RNA expression, single nucleotide and structural variations and methylation and histone modifications, rely heavily on high-throughput sequencing technologies. Although there are well-established bioinformatics methods for analysis of sequence data, there are limited bioinformatics approaches which address experimental design, sample size considerations, amplification bias, normalization, differential expression, coverage, clustering and classification issues, specifically applied at the single-cell level. In this review, we summarize biological and technological advancements, discuss challenges faced in the aforementioned data acquisition and analysis issues and present future prospects for application of single-cell analyses to developmental biology. PMID:26358759

  5. Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology.

    PubMed

    Roy, Mohendra; Seo, Dongmin; Oh, Sangwoo; Chae, Yeonghun; Nam, Myung-Hyun; Seo, Sungkyu

    2016-01-01

    Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, and HepG2, HeLa, and MCF7 cells. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings. PMID:27164146

  6. Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology

    PubMed Central

    Roy, Mohendra; Seo, Dongmin; Oh, Sangwoo; Chae, Yeonghun; Nam, Myung-Hyun; Seo, Sungkyu

    2016-01-01

    Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, HepG2, HeLa, and MCF7 cells lines. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings. PMID:27164146

  7. An automated method for gridding and clustering-based segmentation of cDNA microarray images.

    PubMed

    Giannakeas, Nikolaos; Fotiadis, Dimitrios I

    2009-01-01

    Microarrays are widely used to quantify gene expression levels. Microarray image analysis is one of the tools, which are necessary when dealing with vast amounts of biological data. In this work we propose a new method for the automated analysis of microarray images. The proposed method consists of two stages: gridding and segmentation. Initially, the microarray images are preprocessed using template matching, and block and spot finding takes place. Then, the non-expressed spots are detected and a grid is fit on the image using a Voronoi diagram. In the segmentation stage, K-means and Fuzzy C means (FCM) clustering are employed. The proposed method was evaluated using images from the Stanford Microarray Database (SMD). The results that are presented in the segmentation stage show the efficiency of our Fuzzy C means-based work compared to the two already developed K-means-based methods. The proposed method can handle images with artefacts and it is fully automated. PMID:19046850

  8. A Semi-Automated Image Analysis Procedure for In Situ Plankton Imaging Systems

    PubMed Central

    Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C.; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M.

    2015-01-01

    Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the

  9. A semi-automated image analysis procedure for in situ plankton imaging systems.

    PubMed

    Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M

    2015-01-01

    Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the

  10. Automated synthesis of image processing procedures using AI planning techniques

    NASA Technical Reports Server (NTRS)

    Chien, Steve; Mortensen, Helen

    1994-01-01

    This paper describes the Multimission VICAR (Video Image Communication and Retrieval) Planner (MVP) (Chien 1994) system, which uses artificial intelligence planning techniques (Iwasaki & Friedland, 1985, Pemberthy & Weld, 1992, Stefik, 1981) to automatically construct executable complex image processing procedures (using models of the smaller constituent image processing subprograms) in response to image processing requests made to the JPL Multimission Image Processing Laboratory (MIPL). The MVP system allows the user to specify the image processing requirements in terms of the various types of correction required. Given this information, MVP derives unspecified required processing steps and determines appropriate image processing programs and parameters to achieve the specified image processing goals. This information is output as an executable image processing program which can then be executed to fill the processing request.

  11. Chimenea and other tools: Automated imaging of multi-epoch radio-synthesis data with CASA

    NASA Astrophysics Data System (ADS)

    Staley, T. D.; Anderson, G. E.

    2015-11-01

    In preparing the way for the Square Kilometre Array and its pathfinders, there is a pressing need to begin probing the transient sky in a fully robotic fashion using the current generation of radio telescopes. Effective exploitation of such surveys requires a largely automated data-reduction process. This paper introduces an end-to-end automated reduction pipeline, AMIsurvey, used for calibrating and imaging data from the Arcminute Microkelvin Imager Large Array. AMIsurvey makes use of several component libraries which have been packaged separately for open-source release. The most scientifically significant of these is chimenea, which implements a telescope-agnostic algorithm for automated imaging of pre-calibrated multi-epoch radio-synthesis data, of the sort typically acquired for transient surveys or follow-up. The algorithm aims to improve upon standard imaging pipelines by utilizing iterative RMS-estimation and automated source-detection to avoid so called 'Clean-bias', and makes use of CASA subroutines for the underlying image-synthesis operations. At a lower level, AMIsurvey relies upon two libraries, drive-ami and drive-casa, built to allow use of mature radio-astronomy software packages from within Python scripts. While targeted at automated imaging, the drive-casa interface can also be used to automate interaction with any of the CASA subroutines from a generic Python process. Additionally, these packages may be of wider technical interest beyond radio-astronomy, since they demonstrate use of the Python library pexpect to emulate terminal interaction with an external process. This approach allows for rapid development of a Python interface to any legacy or externally-maintained pipeline which accepts command-line input, without requiring alterations to the original code.

  12. Digital Transplantation Pathology: Combining Whole Slide Imaging, Multiplex Staining, and Automated Image Analysis

    PubMed Central

    Isse, Kumiko; Lesniak, Andrew; Grama, Kedar; Roysam, Badrinath; Minervini, Martha I.; Demetris, Anthony J

    2013-01-01

    Conventional histopathology is the gold standard for allograft monitoring, but its value proposition is increasingly questioned. “-Omics” analysis of tissues, peripheral blood and fluids and targeted serologic studies provide mechanistic insights into allograft injury not currently provided by conventional histology. Microscopic biopsy analysis, however, provides valuable and unique information: a) spatial-temporal relationships; b) rare events/cells; c) complex structural context; and d) integration into a “systems” model. Nevertheless, except for immunostaining, no transformative advancements have “modernized” routine microscopy in over 100 years. Pathologists now team with hardware and software engineers to exploit remarkable developments in digital imaging, nanoparticle multiplex staining, and computational image analysis software to bridge the traditional histology - global “–omic” analyses gap. Included are side-by-side comparisons, objective biopsy finding quantification, multiplexing, automated image analysis, and electronic data and resource sharing. Current utilization for teaching, quality assurance, conferencing, consultations, research and clinical trials is evolving toward implementation for low-volume, high-complexity clinical services like transplantation pathology. Cost, complexities of implementation, fluid/evolving standards, and unsettled medical/legal and regulatory issues remain as challenges. Regardless, challenges will be overcome and these technologies will enable transplant pathologists to increase information extraction from tissue specimens and contribute to cross-platform biomarker discovery for improved outcomes. PMID:22053785

  13. Submicron mass spectrometry imaging of single cells by combined use of mega electron volt time-of-flight secondary ion mass spectrometry and scanning transmission ion microscopy

    SciTech Connect

    Siketić, Zdravko; Bogdanović Radović, Ivančica; Jakšić, Milko; Popović Hadžija, Marijana; Hadžija, Mirko

    2015-08-31

    In order to better understand biochemical processes inside an individual cell, it is important to measure the molecular composition at the submicron level. One of the promising mass spectrometry imaging techniques that may be used to accomplish this is Time-of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS), using MeV energy heavy ions for excitation. MeV ions have the ability to desorb large intact molecules with a yield that is several orders of magnitude higher than conventional SIMS using keV ions. In order to increase the spatial resolution of the MeV TOF-SIMS system, we propose an independent TOF trigger using a STIM (scanning transmission ion microscopy) detector that is placed just behind the thin transmission target. This arrangement is suitable for biological samples in which the STIM detector simultaneously measures the mass distribution in scanned samples. The capability of the MeV TOF-SIMS setup was demonstrated by imaging the chemical composition of CaCo-2 cells.

  14. Apoptosis induction-related cytosolic calcium responses revealed by the dual FRET imaging of calcium signals and caspase-3 activation in a single cell.

    PubMed

    Miyamoto, Akitoshi; Miyauchi, Hiroshi; Kogure, Takako; Miyawaki, Atsushi; Michikawa, Takayuki; Mikoshiba, Katsuhiko

    2015-04-24

    Stimulus-induced changes in the intracellular Ca(2+) concentration control cell fate decision, including apoptosis. However, the precise patterns of the cytosolic Ca(2+) signals that are associated with apoptotic induction remain unknown. We have developed a novel genetically encoded sensor of activated caspase-3 that can be applied in combination with a genetically encoded sensor of the Ca(2+) concentration and have established a dual imaging system that enables the imaging of both cytosolic Ca(2+) signals and caspase-3 activation, which is an indicator of apoptosis, in the same cell. Using this system, we identified differences in the cytosolic Ca(2+) signals of apoptotic and surviving DT40 B lymphocytes after B cell receptor (BCR) stimulation. In surviving cells, BCR stimulation evoked larger initial Ca(2+) spikes followed by a larger sustained elevation of the Ca(2+) concentration than those in apoptotic cells; BCR stimulation also resulted in repetitive transient Ca(2+) spikes, which were mediated by the influx of Ca(2+) from the extracellular space. Our results indicate that the observation of both Ca(2+) signals and cells fate in same cell is crucial to gain an accurate understanding of the function of intracellular Ca(2+) signals in apoptotic induction. PMID:25998736

  15. Automated Photogrammetric Image Matching with Sift Algorithm and Delaunay Triangulation

    NASA Astrophysics Data System (ADS)

    Karagiannis, Georgios; Antón Castro, Francesc; Mioc, Darka

    2016-06-01

    An algorithm for image matching of multi-sensor and multi-temporal satellite images is developed. The method is based on the SIFT feature detector proposed by Lowe in (Lowe, 1999). First, SIFT feature points are detected independently in two images (reference and sensed image). The features detected are invariant to image rotations, translations, scaling and also to changes in illumination, brightness and 3-dimensional viewpoint. Afterwards, each feature of the reference image is matched with one in the sensed image if, and only if, the distance between them multiplied by a threshold is shorter than the distances between the point and all the other points in the sensed image. Then, the matched features are used to compute the parameters of the homography that transforms the coordinate system of the sensed image to the coordinate system of the reference image. The Delaunay triangulations of each feature set for each image are computed. The isomorphism of the Delaunay triangulations is determined to guarantee the quality of the image matching. The algorithm is implemented in Matlab and tested on World-View 2, SPOT6 and TerraSAR-X image patches.

  16. Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies.

    PubMed

    Welikala, R A; Fraz, M M; Foster, P J; Whincup, P H; Rudnicka, A R; Owen, C G; Strachan, D P; Barman, S A

    2016-04-01

    Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost. PMID:26894596

  17. Improving Automated Annotation of Benthic Survey Images Using Wide-band Fluorescence.

    PubMed

    Beijbom, Oscar; Treibitz, Tali; Kline, David I; Eyal, Gal; Khen, Adi; Neal, Benjamin; Loya, Yossi; Mitchell, B Greg; Kriegman, David

    2016-01-01

    Large-scale imaging techniques are used increasingly for ecological surveys. However, manual analysis can be prohibitively expensive, creating a bottleneck between collected images and desired data-products. This bottleneck is particularly severe for benthic surveys, where millions of images are obtained each year. Recent automated annotation methods may provide a solution, but reflectance images do not always contain sufficient information for adequate classification accuracy. In this work, the FluorIS, a low-cost modified consumer camera, was used to capture wide-band wide-field-of-view fluorescence images during a field deployment in Eilat, Israel. The fluorescence images were registered with standard reflectance images, and an automated annotation method based on convolutional neural networks was developed. Our results demonstrate a 22% reduction of classification error-rate when using both images types compared to only using reflectance images. The improvements were large, in particular, for coral reef genera Platygyra, Acropora and Millepora, where classification recall improved by 38%, 33%, and 41%, respectively. We conclude that convolutional neural networks can be used to combine reflectance and fluorescence imagery in order to significantly improve automated annotation accuracy and reduce the manual annotation bottleneck. PMID:27021133

  18. Improving Automated Annotation of Benthic Survey Images Using Wide-band Fluorescence

    PubMed Central

    Beijbom, Oscar; Treibitz, Tali; Kline, David I.; Eyal, Gal; Khen, Adi; Neal, Benjamin; Loya, Yossi; Mitchell, B. Greg; Kriegman, David

    2016-01-01

    Large-scale imaging techniques are used increasingly for ecological surveys. However, manual analysis can be prohibitively expensive, creating a bottleneck between collected images and desired data-products. This bottleneck is particularly severe for benthic surveys, where millions of images are obtained each year. Recent automated annotation methods may provide a solution, but reflectance images do not always contain sufficient information for adequate classification accuracy. In this work, the FluorIS, a low-cost modified consumer camera, was used to capture wide-band wide-field-of-view fluorescence images during a field deployment in Eilat, Israel. The fluorescence images were registered with standard reflectance images, and an automated annotation method based on convolutional neural networks was developed. Our results demonstrate a 22% reduction of classification error-rate when using both images types compared to only using reflectance images. The improvements were large, in particular, for coral reef genera Platygyra, Acropora and Millepora, where classification recall improved by 38%, 33%, and 41%, respectively. We conclude that convolutional neural networks can be used to combine reflectance and fluorescence imagery in order to significantly improve automated annotation accuracy and reduce the manual annotation bottleneck. PMID:27021133

  19. Improving Automated Annotation of Benthic Survey Images Using Wide-band Fluorescence

    NASA Astrophysics Data System (ADS)

    Beijbom, Oscar; Treibitz, Tali; Kline, David I.; Eyal, Gal; Khen, Adi; Neal, Benjamin; Loya, Yossi; Mitchell, B. Greg; Kriegman, David

    2016-03-01

    Large-scale imaging techniques are used increasingly for ecological surveys. However, manual analysis can be prohibitively expensive, creating a bottleneck between collected images and desired data-products. This bottleneck is particularly severe for benthic surveys, where millions of images are obtained each year. Recent automated annotation methods may provide a solution, but reflectance images do not always contain sufficient information for adequate classification accuracy. In this work, the FluorIS, a low-cost modified consumer camera, was used to capture wide-band wide-field-of-view fluorescence images during a field deployment in Eilat, Israel. The fluorescence images were registered with standard reflectance images, and an automated annotation method based on convolutional neural networks was developed. Our results demonstrate a 22% reduction of classification error-rate when using both images types compared to only using reflectance images. The improvements were large, in particular, for coral reef genera Platygyra, Acropora and Millepora, where classification recall improved by 38%, 33%, and 41%, respectively. We conclude that convolutional neural networks can be used to combine reflectance and fluorescence imagery in order to significantly improve automated annotation accuracy and reduce the manual annotation bottleneck.

  20. Double regions growing algorithm for automated satellite image mosaicking

    NASA Astrophysics Data System (ADS)

    Tan, Yihua; Chen, Chen; Tian, Jinwen

    2011-12-01

    Feathering is a most widely used method in seamless satellite image mosaicking. A simple but effective algorithm - double regions growing (DRG) algorithm, which utilizes the shape content of images' valid regions, is proposed for generating robust feathering-line before feathering. It works without any human intervention, and experiment on real satellite images shows the advantages of the proposed method.

  1. Syntrophic interactions and mechanisms underpinning anaerobic methane oxidation: targeted metaproteogenomics, single-cell protein detection and quantitative isotope imaging of microbial consortia

    SciTech Connect

    Orphan, Victoria Jeanne

    2014-11-26

    Syntrophy and mutualism play a central role in carbon and nutrient cycling by microorganisms. Yet, our ability to effectively study symbionts in culture has been hindered by the inherent interdependence of syntrophic associations, their dynamic behavior, and their frequent existence at thermodynamic limits. Now solutions to these challenges are emerging in the form of new methodologies. Developing strategies that establish links between the identity of microorganisms and their metabolic potential, as well as techniques that can probe metabolic networks on a scale that captures individual molecule exchange and processing, is at the forefront of microbial ecology. Understanding the interactions between microorganisms on this level, at a resolution previously intractable, will lead to our greater understanding of carbon turnover and microbial community resilience to environmental perturbations. In this project, we studied an enigmatic syntrophic association between uncultured methane-oxidizing archaea and sulfate-reducing bacteria. This environmental archaeal-bacterial partnership represents a globally important sink for methane in anoxic environments. The specific goals of this project were organized into 3 major tasks designed to address questions relating to the ecophysiology of these syntrophic organisms under changing environmental conditions (e.g. different electron acceptors and nutrients), primarily through the development of microanalytical imaging methods which enable the visualization of the spatial distribution of the partners within aggregates, consumption and exchange of isotopically labeled substrates, and expression of targeted proteins identified via metaproteomics. The advanced tool set developed here to collect, correlate, and analyze these high resolution image and isotope-based datasets from methane-oxidizing consortia has the potential to be widely applicable for studying and modeling patterns of activity and interactions across a broad range of

  2. Automated method and system for the alignment and correlation of images from two different modalities

    DOEpatents

    Giger, Maryellen L.; Chen, Chin-Tu; Armato, Samuel; Doi, Kunio

    1999-10-26

    A method and system for the computerized registration of radionuclide images with radiographic images, including generating image data from radiographic and radionuclide images of the thorax. Techniques include contouring the lung regions in each type of chest image, scaling and registration of the contours based on location of lung apices, and superimposition after appropriate shifting of the images. Specific applications are given for the automated registration of radionuclide lungs scans with chest radiographs. The method in the example given yields a system that spatially registers and correlates digitized chest radiographs with V/Q scans in order to correlate V/Q functional information with the greater structural detail of chest radiographs. Final output could be the computer-determined contours from each type of image superimposed on any of the original images, or superimposition of the radionuclide image data, which contains high activity, onto the radiographic chest image.

  3. Single Cell Chromatography, LDRD Feasibility Study

    SciTech Connect

    Knize, M G; Bailey, C G

    2007-02-22

    A limitation in the mass spectrometry of biological materials is the reduced ion formation caused by sample complexity. We proposed to develop an enabling technology, single cell planar chromatography, which will greatly increase the amount of chemical information that can be obtained from single biological cells when using imaging mass spectrometry or other surface analysis methods. The sample preparation methods were developed for the time-of-flight secondary mass spectrometer (ToF-SIMS) at LLNL. This instrument has a measured zeptomole (10{sup -21} mole, 600 atoms) limit-of-detection for a molecule with a mass to charge ratio of 225[1]. Our goal was to use planar chromatographic separation to approach similar low limits of detection even with the chemically complex contents of a single cell. The process was proposed to reduce ion suppression and at the same time expose more of the cell contents to the ion beam. The method of work was to deposit biological cells on a silicon chip with suitable chromatographic and electrical properties, dissolve the cell with a droplet of solvent, allow the solvent to evaporate, and then allow the movement of cell contents laterally by immersing an edge of the chip in to a chromatographic solvent, that then moves through the chromatographic matrix allowing the components to interact with, and be separated by, the chromatographic substrate. This process is a miniaturized version of thin layer chromatography with detection by surface mass spectrometry.

  4. Nanofountain Probe Electroporation of Single Cells

    PubMed Central

    Kang, Wonmo; Yavari, Fazel; Minary-Jolandan, Majid; Giraldo-Vela, Juan P.; Safi, Asmahan; McNaughton, Rebecca L.; Parpoil, Victor; Espinosa, Horacio D.

    2013-01-01

    The ability to precisely deliver molecules into single cells is of great interest to biotechnology researchers for advancing applications in therapeutics, diagnostics, and drug delivery toward the promise of personalized medicine. The use of bulk electroporation techniques for cell transfection has increased significantly in the last decade, but the technique is nonspecific and requires high voltage, resulting in variable efficiency and low cell viability. We have developed a new tool for electroporation using nanofountain probe (NFP) technology, which can deliver molecules into cells in a manner that is highly efficient and gentler to cells than bulk electroporation or microinjection. Here we demonstrate NFP electroporation (NFP-E) of single HeLa cells within a population by transfecting them with fluorescently labeled dextran and imaging the cells to evaluate the transfection efficiency and cell viability. Our theoretical analysis of the mechanism of NFP-E reveals that application of the voltage creates a localized electric field between the NFP cantilever tip and the region of the cell membrane in contact with the tip. Therefore, NFP-E can deliver molecules to a target cell with minimal effect of the electric potential on the cell. Our experiments on HeLa cells confirm that NFP-E offers single cell selectivity, high transfection efficiency (>95%), qualitative dosage control, and very high viability (92%) of transfected cells. PMID:23650871

  5. An automated, video tape-based image archiving system.

    PubMed

    Vesely, I; Eickmeier, B; Campbell, G

    1991-01-01

    We have developed an image storage and retrieval system that makes use of a Super-VHS video tape recorder, and a personal computer fitted with an interface board and a video frame grabber. Under PC control, video images are acquired into the frame grabber, a numeric bar code is graphically superimposed for identification purposes, and the composite images are recorded on video tape. During retrieval, the bar code is decoded in real-time and the desired images are automatically retrieved. This video tape-based system, enables the images to be previewed and retrieved much faster than if stored in digital format. PMID:1769220

  6. A review of automated image understanding within 3D baggage computed tomography security screening.

    PubMed

    Mouton, Andre; Breckon, Toby P

    2015-01-01

    Baggage inspection is the principal safeguard against the transportation of prohibited and potentially dangerous materials at airport security checkpoints. Although traditionally performed by 2D X-ray based scanning, increasingly stringent security regulations have led to a growing demand for more advanced imaging technologies. The role of X-ray Computed Tomography is thus rapidly expanding beyond the traditional materials-based detection of explosives. The development of computer vision and image processing techniques for the automated understanding of 3D baggage-CT imagery is however, complicated by poor image resolutions, image clutter and high levels of noise and artefacts. We discuss the recent and most pertinent advancements and identify topics for future research within the challenging domain of automated image understanding for baggage security screening CT. PMID:26409422

  7. Monitoring UVR induced damage in single cells and isolated nuclei using SR-FTIR microspectroscopy and 3D confocal Raman imaging.

    PubMed

    Lipiec, Ewelina; Bambery, Keith R; Heraud, Philip; Kwiatek, Wojciech M; McNaughton, Don; Tobin, Mark J; Vogel, Christian; Wood, Bayden R

    2014-09-01

    SR-FTIR in combination with Principal Component Analysis (PCA) was applied to investigate macromolecular changes in a population of melanocytes and their extracted nuclei induced by environmentally relevant fluxes of UVR (Ultraviolet Radiation). Living cells and isolated cellular nuclei were investigated post-irradiation for three different irradiation dosages (130, 1505, 15,052 Jm(-2) UVR, weighted) after either 24 or 48 hours of incubation. DNA conformational changes were observed in cells exposed to an artificial UVR solar-simulator source as evidenced by a shift in the DNA asymmetric phosphodiester vibration from 1236 cm(-1) to 1242 cm(-1) in the case of the exposed cells and from 1225 cm(-1) to 1242 cm(-1) for irradiated nuclei. PCA Scores plots revealed distinct clustering of spectra from irradiated cells and nuclei from non-irradiated controls in response to the range of applied UVR radiation doses. 3D Raman confocal imaging in combination with k-means cluster analysis was applied to study the effect of the UVR radiation exposure on cellular nuclei. Chemical changes associated with apoptosis were detected and included intra-nuclear lipid deposition along with chromatin condensation. The results reported here demonstrate the utility of SR-FTIR and Raman spectroscopy to probe in situ DNA damage in cell nuclei resulting from UVR exposure. These results are in agreement with the increasing body of evidence that lipid accumulation is a characteristic of aggressive cancer cells, and are involved in the production of membranes for rapid cell proliferation. PMID:24995477

  8. Fully automated corneal endothelial morphometry of images captured by clinical specular microscopy

    NASA Astrophysics Data System (ADS)

    Bucht, Curry; Söderberg, Per; Manneberg, Göran

    2010-02-01

    The corneal endothelium serves as the posterior barrier of the cornea. Factors such as clarity and refractive properties of the cornea are in direct relationship to the quality of the endothelium. The endothelial cell density is considered the most important morphological factor of the corneal endothelium. Pathological conditions and physical trauma may threaten the endothelial cell density to such an extent that the optical property of the cornea and thus clear eyesight is threatened. Diagnosis of the corneal endothelium through morphometry is an important part of several clinical applications. Morphometry of the corneal endothelium is presently carried out by semi automated analysis of pictures captured by a Clinical Specular Microscope (CSM). Because of the occasional need of operator involvement, this process can be tedious, having a negative impact on sampling size. This study was dedicated to the development and use of fully automated analysis of a very large range of images of the corneal endothelium, captured by CSM, using Fourier analysis. Software was developed in the mathematical programming language Matlab. Pictures of the corneal endothelium, captured by CSM, were read into the analysis software. The software automatically performed digital enhancement of the images, normalizing lights and contrasts. The digitally enhanced images of the corneal endothelium were Fourier transformed, using the fast Fourier transform (FFT) and stored as new images. Tools were developed and applied for identification and analysis of relevant characteristics of the Fourier transformed images. The data obtained from each Fourier transformed image was used to calculate the mean cell density of its corresponding corneal endothelium. The calculation was based on well known diffraction theory. Results in form of estimated cell density of the corneal endothelium were obtained, using fully automated analysis software on 292 images captured by CSM. The cell density obtained by the

  9. A feasibility assessment of automated FISH image and signal analysis to assist cervical cancer detection

    NASA Astrophysics Data System (ADS)

    Wang, Xingwei; Li, Yuhua; Liu, Hong; Li, Shibo; Zhang, Roy R.; Zheng, Bin

    2012-02-01

    Fluorescence in situ hybridization (FISH) technology provides a promising molecular imaging tool to detect cervical cancer. Since manual FISH analysis is difficult, time-consuming, and inconsistent, the automated FISH image scanning systems have been developed. Due to limited focal depth of scanned microscopic image, a FISH-probed specimen needs to be scanned in multiple layers that generate huge image data. To improve diagnostic efficiency of using automated FISH image analysis, we developed a computer-aided detection (CAD) scheme. In this experiment, four pap-smear specimen slides were scanned by a dual-detector fluorescence image scanning system that acquired two spectrum images simultaneously, which represent images of interphase cells and FISH-probed chromosome X. During image scanning, once detecting a cell signal, system captured nine image slides by automatically adjusting optical focus. Based on the sharpness index and maximum intensity measurement, cells and FISH signals distributed in 3-D space were projected into a 2-D con-focal image. CAD scheme was applied to each con-focal image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm and detect FISH-probed signals using a top-hat transform. The ratio of abnormal cells was calculated to detect positive cases. In four scanned specimen slides, CAD generated 1676 con-focal images that depicted analyzable cells. FISH-probed signals were independently detected by our CAD algorithm and an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots. The study demonstrated the feasibility of applying automated FISH image and signal analysis to assist cyto-geneticists in detecting cervical cancers.

  10. Automated thematic mapping and change detection of ERTS-1 images

    NASA Technical Reports Server (NTRS)

    Gramenopoulos, N. (Principal Investigator); Alpaugh, H.

    1972-01-01

    The author has identified the following significant results. An ERTS-1 image was compared to aircraft photography and maps of an area near Brownsville, Texas. In the coastal region of Cameron County, natural and cultural detail were identified in the ERTS-1 image. In Hidalgo County, ground truth was located on the ERTS-1 image. Haze and 50% cloud cover over Hidalgo County reduced the usefulness of multispectral techniques for recognizing crops.

  11. Automated thermal mapping techniques using chromatic image analysis

    NASA Technical Reports Server (NTRS)

    Buck, Gregory M.

    1989-01-01

    Thermal imaging techniques are introduced using a chromatic image analysis system and temperature sensitive coatings. These techniques are used for thermal mapping and surface heat transfer measurements on aerothermodynamic test models in hypersonic wind tunnels. Measurements are made on complex vehicle configurations in a timely manner and at minimal expense. The image analysis system uses separate wavelength filtered images to analyze surface spectral intensity data. The system was initially developed for quantitative surface temperature mapping using two-color thermographic phosphors but was found useful in interpreting phase change paint and liquid crystal data as well.

  12. Automated detection of cardiac phase from intracoronary ultrasound image sequences.

    PubMed

    Sun, Zheng; Dong, Yi; Li, Mengchan

    2015-01-01

    Intracoronary ultrasound (ICUS) is a widely used interventional imaging modality in clinical diagnosis and treatment of cardiac vessel diseases. Due to cyclic cardiac motion and pulsatile blood flow within the lumen, there exist changes of coronary arterial dimensions and relative motion between the imaging catheter and the lumen during continuous pullback of the catheter. The action subsequently causes cyclic changes to the image intensity of the acquired image sequence. Information on cardiac phases is implied in a non-gated ICUS image sequence. A 1-D phase signal reflecting cardiac cycles was extracted according to cyclical changes in local gray-levels in ICUS images. The local extrema of the signal were then detected to retrieve cardiac phases and to retrospectively gate the image sequence. Results of clinically acquired in vivo image data showed that the average inter-frame dissimilarity of lower than 0.1 was achievable with our technique. In terms of computational efficiency and complexity, the proposed method was shown to be competitive when compared with the current methods. The average frame processing time was lower than 30 ms. We effectively reduced the effect of image noises, useless textures, and non-vessel region on the phase signal detection by discarding signal components caused by non-cardiac factors. PMID:26406038

  13. SU-E-I-94: Automated Image Quality Assessment of Radiographic Systems Using An Anthropomorphic Phantom

    SciTech Connect

    Wells, J; Wilson, J; Zhang, Y; Samei, E; Ravin, Carl E.

    2014-06-01

    Purpose: In a large, academic medical center, consistent radiographic imaging performance is difficult to routinely monitor and maintain, especially for a fleet consisting of multiple vendors, models, software versions, and numerous imaging protocols. Thus, an automated image quality control methodology has been implemented using routine image quality assessment with a physical, stylized anthropomorphic chest phantom. Methods: The “Duke” Phantom (Digital Phantom 07-646, Supertech, Elkhart, IN) was imaged twice on each of 13 radiographic units from a variety of vendors at 13 primary care clinics. The first acquisition used the clinical PA chest protocol to acquire the post-processed “FOR PRESENTATION” image. The second image was acquired without an antiscatter grid followed by collection of the “FOR PROCESSING” image. Manual CNR measurements were made from the largest and thickest contrast-detail inserts in the lung, heart, and abdominal regions of the phantom in each image. An automated image registration algorithm was used to estimate the CNR of the same insert using similar ROIs. Automated measurements were then compared to the manual measurements. Results: Automatic and manual CNR measurements obtained from “FOR PRESENTATION” images had average percent differences of 0.42%±5.18%, −3.44%±4.85%, and 1.04%±3.15% in the lung, heart, and abdominal regions, respectively; measurements obtained from “FOR PROCESSING” images had average percent differences of -0.63%±6.66%, −0.97%±3.92%, and −0.53%±4.18%, respectively. The maximum absolute difference in CNR was 15.78%, 10.89%, and 8.73% in the respective regions. In addition to CNR assessment of the largest and thickest contrast-detail inserts, the automated method also provided CNR estimates for all 75 contrast-detail inserts in each phantom image. Conclusion: Automated analysis of a radiographic phantom has been shown to be a fast, robust, and objective means for assessing radiographic

  14. Automating Image Import for Google Sky using Virtual Observatory Tools

    NASA Astrophysics Data System (ADS)

    Crossley, Jared H.; DuPlain, R.; Radziwill, N. M.

    2009-01-01

    We have developed a prototype web service that brings the wealth of Virtual Observatory image data to the Google Sky desktop client. The web service, "KML Now!," presents the user with a simple web interface and requires no specialized knowledge of image conversion, coordinate system conversion, or Google Sky's KML metadata format. KML Now! makes use of Virtual Observatory Simple Image Access Services to acquire images based on user-input search coordinates. Once images are acquired, open source conversion software is used to generate Sky-compatible image and metadata files; the files are cached on the server for reuse. A "launcher" KML file pointing to all applicable server-side data is returned to the user, and when opened in Google Sky, all images are automatically placed within the desktop client. KML Now! can also operate directly on a user-specified image, without the need for Virtual Observatory interaction. A KML Now! query is coded in URL arguments, which allows it to be easily called from within Google Sky, a feature to be added in future developments. Funding for this project is provided by the National Radio Astronomy Observatory and the National Virtual Observatory, both supported by the National Science Foundation.

  15. An automated method for gridding in microarray images.

    PubMed

    Giannakeas, Nikolaos; Fotiadis, Dimitrios I; Politou, Anastasia S

    2006-01-01

    Microarray technology is a powerful tool for analyzing the expression of a large number of genes in parallel. A typical microarray image consists of a few thousands of spots which determine the level of gene expression in the sample. In this paper we propose a method which automatically addresses each spot area in the image. Initially, a preliminary segmentation of the image is produced using a template matching algorithm. Next, grid and spot finding are realized. The position of non-expressed spots is located and finally a Voronoi diagram is employed to fit the grid on the image. Our method has been evaluated in a set of five images consisting of 45960 spots, from the Stanford microarray database and the reported accuracy for spot detection was 93% PMID:17946343

  16. MOSK - AUTOMATED MOSAICKING ALGORITHM FOR GENERATION OF SPACEBORNE SAR IMAGE

    NASA Technical Reports Server (NTRS)

    Curlander, J. C.

    1994-01-01

    Spaceborne Synthetic Aperture Radar (SAR) images are useful for planetary mapping and Earth sciences investigations. However, swath widths rarely exceed 100 Kilometers, and images must be patched together to create a mosaic in order to analyze larger areas. The primary function of this program is to generate large digital mosaics of SAR imagery without manually marked tiepoints. MOSK can produce multiframe mosaics by combining images in the along-track, adjacent cross-track swaths, or ascending and descending passes. Geocoded map registered images, such as the ones produced by MAPJTC (NPO-17718), are required as input. The output is a geocoded mosaic on a standard map grid which permits easy registration with other geocoded data sets. Mosaicking of geocoded SAR imagery involves three steps. First, a match point is selected at the center of the overlapping area, then an image patch around the match point is extracted from both images and cross-correlation is done on this area. Then, images with their refined match points are merged together to form a mosaic. To handle the large data volume of overlapping intermediate stages, large mosaics are divided into equal size quadrants with each quadrant cut from an intermediate mosaic. The full mosaic can then be assembled from the individual quadrants. Finally, radiometric disparities at the image seams are smoothed by a "feathering" technique. The automatic mosaic system generates output with minimal operator interaction. However, manual tiepointing is required in cases of a large registration error or two images with smooth surfaces such as ocean images. MOSK is implemented on a DEC VAX 11/785 running VMS 4.5. Most subroutines are in FORTRAN, but three are in MAXL and one is in APAL. The program requires 1 Mb of memory and a Floating Point Systems AP-5210 array processor. The system memory usage is approximately 1000 pages and the requirement of page file size is 2000 blocks. MOSK was developed in 1988.

  17. To the development of an automated system of assessment of radiological images of joints

    NASA Astrophysics Data System (ADS)

    Grechikhin, A. I.; Grunina, E. A.; Karetnikova, I. R.

    2008-03-01

    An algorithm developed for the adaptive automated computer processing of radiological images of hands and feet in order to assess the degree of bone and cartilage destruction in rheumatoid arthritis is described. A set of new numeral signs was proposed in order to assess a degree of arthritis radiological progression.

  18. Automated High-Throughput Fluorescence Lifetime Imaging Microscopy to Detect Protein-Protein Interactions.

    PubMed

    Guzmán, Camilo; Oetken-Lindholm, Christina; Abankwa, Daniel

    2016-04-01

    Fluorescence resonance energy transfer (FRET) is widely used to study conformational changes of macromolecules and protein-protein, protein-nucleic acid, and protein-small molecule interactions. FRET biosensors can serve as valuable secondary assays in drug discovery and for target validation in mammalian cells. Fluorescence lifetime imaging microscopy (FLIM) allows precise quantification of the FRET efficiency in intact cells, as FLIM is independent of fluorophore concentration, detection efficiency, and fluorescence intensity. We have developed an automated FLIM system using a commercial frequency domain FLIM attachment (Lambert Instruments) for wide-field imaging. Our automated FLIM system is capable of imaging and analyzing up to 50 different positions of a slide in less than 4 min, or the inner 60 wells of a 96-well plate in less than 20 min. Automation is achieved using a motorized stage and controller (Prior Scientific) coupled with a Zeiss Axio Observer body and full integration into the Lambert Instruments FLIM acquisition software. As an application example, we analyze the interaction of the oncoprotein Ras and its effector Raf after drug treatment. In conclusion, our automated FLIM imaging system requires only commercial components and may therefore allow for a broader use of this technique in chemogenomics projects. PMID:26384400

  19. Fully automated 3D prostate central gland segmentation in MR images: a LOGISMOS based approach

    NASA Astrophysics Data System (ADS)

    Yin, Yin; Fotin, Sergei V.; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Turkbey, Baris; Choyke, Peter

    2012-02-01

    One widely accepted classification of a prostate is by a central gland (CG) and a peripheral zone (PZ). In some clinical applications, separating CG and PZ from the whole prostate is useful. For instance, in prostate cancer detection, radiologist wants to know in which zone the cancer occurs. Another application is for multiparametric MR tissue characterization. In prostate T2 MR images, due to the high intensity variation between CG and PZ, automated differentiation of CG and PZ is difficult. Previously, we developed an automated prostate boundary segmentation system, which tested on large datasets and showed good performance. Using the results of the pre-segmented prostate boundary, in this paper, we proposed an automated CG segmentation algorithm based on Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces (LOGISMOS). The designed LOGISMOS model contained both shape and topology information during deformation. We generated graph cost by training classifiers and used coarse-to-fine search. The LOGISMOS framework guarantees optimal solution regarding to cost and shape constraint. A five-fold cross-validation approach was applied to training dataset containing 261 images to optimize the system performance and compare with a voxel classification based reference approach. After the best parameter settings were found, the system was tested on a dataset containing another 261 images. The mean DSC of 0.81 for the test set indicates that our approach is promising for automated CG segmentation. Running time for the system is about 15 seconds.

  20. Automated Image Registration Using Geometrically Invariant Parameter Space Clustering (GIPSC)

    SciTech Connect

    Seedahmed, Gamal H.; Martucci, Louis M.

    2002-09-01

    Accurate, robust, and automatic image registration is a critical task in many typical applications, which employ multi-sensor and/or multi-date imagery information. In this paper we present a new approach to automatic image registration, which obviates the need for feature matching and solves for the registration parameters in a Hough-like approach. The basic idea underpinning, GIPSC methodology is to pair each data element belonging to two overlapping images, with all other data in each image, through a mathematical transformation. The results of pairing are encoded and exploited in histogram-like arrays as clusters of votes. Geometrically invariant features are adopted in this approach to reduce the computational complexity generated by the high dimensionality of the mathematical transformation. In this way, the problem of image registration is characterized, not by spatial or radiometric properties, but by the mathematical transformation that describes the geometrical relationship between the two images or more. While this approach does not require feature matching, it does permit recovery of matched features (e.g., points) as a useful by-product. The developed methodology incorporates uncertainty modeling using a least squares solution. Successful and promising experimental results of multi-date automatic image registration are reported in this paper.

  1. Automated spatial drift correction for EFTEM image series.

    PubMed

    Schaffer, Bernhard; Grogger, Werner; Kothleitner, Gerald

    2004-12-01

    Energy filtering transmission electron microscopy (EFTEM) is a widely used technique in many areas of scientific research. Image contrast in energy-filtered images arises from specific scattering events such as the ionization of atoms. By combining a set of two or more images, relative sample thickness maps or elemental distribution maps can be easily created. It is also possible to acquire a whole series of energy-filtered images to do more complex data analysis. However, whenever several images are combined to extract certain information, problems are introduced due to sample drift between the exposures. In order to obtain artifact-free information, this spatial drift has to be taken care of. Manual alignment by overlaying and shifting the images to find the best overlap is usually very accurate but extremely time consuming for larger data sets. When large amounts of images are recorded in an EFTEM series, manual correction is no longer a reasonable option. Hence, automatic routines have been developed that are mostly based on the cross-correlation algorithm. Existing routines, however, sometimes fail and again make time consuming manual adjustments necessary. In this paper we describe a new approach to the drift correction problem by incorporating a statistical treatment of the data and we present our statistically determined spatial drift (SDSD) correction program. We show its improved performance by applying it to a typical EFTEM series data block. PMID:15556698

  2. Fractional Proliferation: A method to deconvolve cell population dynamics from single-cell data

    PubMed Central

    Tyson, Darren R.; Garbett, Shawn P.; Frick, Peter L.; Quaranta, Vito

    2012-01-01

    We present an integrated method that exploits extended time-lapse automated imaging to quantify dynamics of cell proliferation. Cell counts are fit with a Quiescence-Growth model that estimates rates of cell division, entry into quiescence and death. The model is constrained with rates extracted experimentally from the behavior of tracked single cells over time. We visualize the output of the analysis in Fractional Proliferation graphs, which deconvolve dynamic proliferative responses to perturbations into the relative contributions of dividing, quiescent (non-dividing) and dead cells. The method reveals that the response of “oncogene-addicted” human cancer cells to tyrosine kinase inhibitors is a composite of altered rates of division, death and entry into quiescence, challenging the notion that such cells simply ‘die’ in response to oncogene-targeted therapy. PMID:22886092

  3. Automated registration of multispectral MR vessel wall images of the carotid artery

    SciTech Connect

    Klooster, R. van 't; Staring, M.; Reiber, J. H. C.; Lelieveldt, B. P. F.; Geest, R. J. van der; Klein, S.; Kwee, R. M.; Kooi, M. E.

    2013-12-15

    Purpose: Atherosclerosis is the primary cause of heart disease and stroke. The detailed assessment of atherosclerosis of the carotid artery requires high resolution imaging of the vessel wall using multiple MR sequences with different contrast weightings. These images allow manual or automated classification of plaque components inside the vessel wall. Automated classification requires all sequences to be in alignment, which is hampered by patient motion. In clinical practice, correction of this motion is performed manually. Previous studies applied automated image registration to correct for motion using only nondeformable transformation models and did not perform a detailed quantitative validation. The purpose of this study is to develop an automated accurate 3D registration method, and to extensively validate this method on a large set of patient data. In addition, the authors quantified patient motion during scanning to investigate the need for correction. Methods: MR imaging studies (1.5T, dedicated carotid surface coil, Philips) from 55 TIA/stroke patients with ipsilateral <70% carotid artery stenosis were randomly selected from a larger cohort. Five MR pulse sequences were acquired around the carotid bifurcation, each containing nine transverse slices: T1-weighted turbo field echo, time of flight, T2-weighted turbo spin-echo, and pre- and postcontrast T1-weighted turbo spin-echo images (T1W TSE). The images were manually segmented by delineating the lumen contour in each vessel wall sequence and were manually aligned by applying throughplane and inplane translations to the images. To find the optimal automatic image registration method, different masks, choice of the fixed image, different types of the mutual information image similarity metric, and transformation models including 3D deformable transformation models, were evaluated. Evaluation of the automatic registration results was performed by comparing the lumen segmentations of the fixed image and

  4. An image-processing program for automated counting

    USGS Publications Warehouse

    Cunningham, D.J.; Anderson, W.H.; Anthony, R.M.

    1996-01-01

    An image-processing program developed by the National Institute of Health, IMAGE, was modified in a cooperative project between remote sensing specialists at the Ohio State University Center for Mapping and scientists at the Alaska Science Center to facilitate estimating numbers of black brant (Branta bernicla nigricans) in flocks at Izembek National Wildlife Refuge. The modified program, DUCK HUNT, runs on Apple computers. Modifications provide users with a pull down menu that optimizes image quality; identifies objects of interest (e.g., brant) by spectral, morphometric, and spatial parameters defined interactively by users; counts and labels objects of interest; and produces summary tables. Images from digitized photography, videography, and high- resolution digital photography have been used with this program to count various species of waterfowl.

  5. Progress in the robust automated segmentation of real cell images

    NASA Astrophysics Data System (ADS)

    Bamford, P.; Jackway, P.; Lovell, Brian

    1999-07-01

    We propose a collection of robust algorithms for the segmentation of cell images from Papanicolaou stained cervical smears (`Pap' smears). This problem is deceptively difficult and often results on laboratory datasets do not carry over to real world data. Our approach is in 3 parts. First, we segment the cytoplasm from the background using a novel method based on the Wilson and Spann multi-resolution framework. Second, we segment the nucleus from the cytoplasm using an active contour method, where the best contour is found by a global minimization method. Third, we implement a method to determine a confidence measure for the segmentation of each object. This uses a stability criterion over the regularization parameter (lambda) in the active contour. We present the results of thorough testing of the algorithms on large numbers of cell images. A database of 20,120 images is used for the segmentation tests and 18,718 images for the robustness tests.

  6. Observation of sea-ice dynamics using synthetic aperture radar images: Automated analysis

    NASA Technical Reports Server (NTRS)

    Vesecky, John F.; Samadani, Ramin; Smith, Martha P.; Daida, Jason M.; Bracewell, Ronald N.

    1988-01-01

    The European Space Agency's ERS-1 satellite, as well as others planned to follow, is expected to carry synthetic-aperture radars (SARs) over the polar regions beginning in 1989. A key component in utilization of these SAR data is an automated scheme for extracting the sea-ice velocity field from a time sequence of SAR images of the same geographical region. Two techniques for automated sea-ice tracking, image pyramid area correlation (hierarchical correlation) and feature tracking, are described. Each technique is applied to a pair of Seasat SAR sea-ice images. The results compare well with each other and with manually tracked estimates of the ice velocity. The advantages and disadvantages of these automated methods are pointed out. Using these ice velocity field estimates it is possible to construct one sea-ice image from the other member of the pair. Comparing the reconstructed image with the observed image, errors in the estimated velocity field can be recognized and a useful probable error display created automatically to accompany ice velocity estimates. It is suggested that this error display may be useful in segmenting the sea ice observed into regions that move as rigid plates of significant ice velocity shear and distortion.

  7. VirtualShave: automated hair removal from digital dermatoscopic images.

    PubMed

    Fiorese, M; Peserico, E; Silletti, A

    2011-01-01

    VirtualShave is a novel tool to remove hair from digital dermatoscopic images. First, individual hairs are identified using a top-hat filter followed by morphological postprocessing. Then, they are replaced through PDE-based inpainting with an estimate of the underlying occluded skin. VirtualShave's performance is comparable to that of a human operator removing hair manually, and the resulting images are almost indistinguishable from those of hair-free skin. PMID:22255497

  8. ASTRiDE: Automated Streak Detection for Astronomical Images

    NASA Astrophysics Data System (ADS)

    Kim, Dae-Won

    2016-05-01

    ASTRiDE detects streaks in astronomical images using a "border" of each object (i.e. "boundary-tracing" or "contour-tracing") and their morphological parameters. Fast moving objects such as meteors, satellites, near-Earth objects (NEOs), or even cosmic rays can leave streak-like traces in the images; ASTRiDE can detect not only long streaks but also relatively short or curved streaks.

  9. The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images

    PubMed Central

    2014-01-01

    Background Two-dimensional projection radiographs have been traditionally considered the modality of choice for cephalometric analysis. To overcome the shortcomings of two-dimensional images, three-dimensional computed tomography (CT) has been used to evaluate craniofacial structures. However, manual landmark detection depends on medical expertise, and the process is time-consuming. The present study was designed to produce software capable of automated localization of craniofacial landmarks on cone beam (CB) CT images based on image registration and to evaluate its accuracy. Methods The software was designed using MATLAB programming language. The technique was a combination of feature-based (principal axes registration) and voxel similarity-based methods for image registration. A total of 8 CBCT images were selected as our reference images for creating a head atlas. Then, 20 CBCT images were randomly selected as the test images for evaluating the method. Three experts twice located 14 landmarks in all 28 CBCT images during two examinations set 6 weeks apart. The differences in the distances of coordinates of each landmark on each image between manual and automated detection methods were calculated and reported as mean errors. Results The combined intraclass correlation coefficient for intraobserver reliability was 0.89 and for interobserver reliability 0.87 (95% confidence interval, 0.82 to 0.93). The mean errors of all 14 landmarks were <4 mm. Additionally, 63.57% of landmarks had a mean error of <3 mm compared with manual detection (gold standard method). Conclusion The accuracy of our approach for automated localization of craniofacial landmarks, which was based on combining feature-based and voxel similarity-based methods for image registration, was acceptable. Nevertheless we recommend repetition of this study using other techniques, such as intensity-based methods. PMID:25223399

  10. Multispectral Image Road Extraction Based Upon Automated Map Conflation

    NASA Astrophysics Data System (ADS)

    Chen, Bin

    Road network extraction from remotely sensed imagery enables many important and diverse applications such as vehicle tracking, drone navigation, and intelligent transportation studies. There are, however, a number of challenges to road detection from an image. Road pavement material, width, direction, and topology vary across a scene. Complete or partial occlusions caused by nearby buildings, trees, and the shadows cast by them, make maintaining road connectivity difficult. The problems posed by occlusions are exacerbated with the increasing use of oblique imagery from aerial and satellite platforms. Further, common objects such as rooftops and parking lots are made of materials similar or identical to road pavements. This problem of common materials is a classic case of a single land cover material existing for different land use scenarios. This work addresses these problems in road extraction from geo-referenced imagery by leveraging the OpenStreetMap digital road map to guide image-based road extraction. The crowd-sourced cartography has the advantages of worldwide coverage that is constantly updated. The derived road vectors follow only roads and so can serve to guide image-based road extraction with minimal confusion from occlusions and changes in road material. On the other hand, the vector road map has no information on road widths and misalignments between the vector map and the geo-referenced image are small but nonsystematic. Properly correcting misalignment between two geospatial datasets, also known as map conflation, is an essential step. A generic framework requiring minimal human intervention is described for multispectral image road extraction and automatic road map conflation. The approach relies on the road feature generation of a binary mask and a corresponding curvilinear image. A method for generating the binary road mask from the image by applying a spectral measure is presented. The spectral measure, called anisotropy-tunable distance (ATD

  11. Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Fotin, Sergei V.; Yin, Yin; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Cornud, François; Turkbey, Baris; Choyke, Peter L.

    2012-02-01

    Fully automated prostate segmentation helps to address several problems in prostate cancer diagnosis and treatment: it can assist in objective evaluation of multiparametric MR imagery, provides a prostate contour for MR-ultrasound (or CT) image fusion for computer-assisted image-guided biopsy or therapy planning, may facilitate reporting and enables direct prostate volume calculation. Among the challenges in automated analysis of MR images of the prostate are the variations of overall image intensities across scanners, the presence of nonuniform multiplicative bias field within scans and differences in acquisition setup. Furthermore, images acquired with the presence of an endorectal coil suffer from localized high-intensity artifacts at the posterior part of the prostate. In this work, a three-dimensional method for fast automated prostate detection based on normalized gradient fields cross-correlation, insensitive to intensity variations and coil-induced artifacts, is presented and evaluated. The components of the method, offline template learning and the localization algorithm, are described in detail. The method was validated on a dataset of 522 T2-weighted MR images acquired at the National Cancer Institute, USA that was split in two halves for development and testing. In addition, second dataset of 29 MR exams from Centre d'Imagerie Médicale Tourville, France were used to test the algorithm. The 95% confidence intervals for the mean Euclidean distance between automatically and manually identified prostate centroids were 4.06 +/- 0.33 mm and 3.10 +/- 0.43 mm for the first and second test datasets respectively. Moreover, the algorithm provided the centroid within the true prostate volume in 100% of images from both datasets. Obtained results demonstrate high utility of the detection method for a fully automated prostate segmentation.

  12. Automated analysis of image mammogram for breast cancer diagnosis

    NASA Astrophysics Data System (ADS)

    Nurhasanah, Sampurno, Joko; Faryuni, Irfana Diah; Ivansyah, Okto

    2016-03-01

    Medical imaging help doctors in diagnosing and detecting diseases that attack the inside of the body without surgery. Mammogram image is a medical image of the inner breast imaging. Diagnosis of breast cancer needs to be done in detail and as soon as possible for determination of next medical treatment. The aim of this work is to increase the objectivity of clinical diagnostic by using fractal analysis. This study applies fractal method based on 2D Fourier analysis to determine the density of normal and abnormal and applying the segmentation technique based on K-Means clustering algorithm to image abnormal for determine the boundary of the organ and calculate the area of organ segmentation results. The results show fractal method based on 2D Fourier analysis can be used to distinguish between the normal and abnormal breast and segmentation techniques with K-Means Clustering algorithm is able to generate the boundaries of normal and abnormal tissue organs, so area of the abnormal tissue can be determined.

  13. Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images

    PubMed Central

    2015-01-01

    Biological assays formatted as microarrays have become a critical tool for the generation of the comprehensive data sets required for systems-level understanding of biological processes. Manual annotation of data extracted from images of microarrays, however, remains a significant bottleneck, particularly for protein microarrays due to the sensitivity of this technology to weak artifact signal. In order to automate the extraction and curation of data from protein microarrays, we describe an algorithm called Crossword that logically combines information from multiple approaches to fully automate microarray segmentation. Automated artifact removal is also accomplished by segregating structured pixels from the background noise using iterative clustering and pixel connectivity. Correlation of the location of structured pixels across image channels is used to identify and remove artifact pixels from the image prior to data extraction. This component improves the accuracy of data sets while reducing the requirement for time-consuming visual inspection of the data. Crossword enables a fully automated protocol that is robust to significant spatial and intensity aberrations. Overall, the average amount of user intervention is reduced by an order of magnitude and the data quality is increased through artifact removal and reduced user variability. The increase in throughput should aid the further implementation of microarray technologies in clinical studies. PMID:24417579

  14. An Automated Platform for High-Resolution Tissue Imaging Using Nanospray Desorption Electrospray Ionization Mass Spectrometry

    SciTech Connect

    Lanekoff, Ingela T.; Heath, Brandi S.; Liyu, Andrey V.; Thomas, Mathew; Carson, James P.; Laskin, Julia

    2012-10-02

    An automated platform has been developed for acquisition and visualization of mass spectrometry imaging (MSI) data using nanospray desorption electrospray ionization (nano-DESI). The new system enables robust operation of the nano-DESI imaging source over many hours. This is achieved by controlling the distance between the sample and the probe by mounting the sample holder onto an automated XYZ stage and defining the tilt of the sample plane. This approach is useful for imaging of relatively flat samples such as thin tissue sections. Custom software called MSI QuickView was developed for visualization of large data sets generated in imaging experiments. MSI QuickView enables fast visualization of the imaging data during data acquisition and detailed processing after the entire image is acquired. The performance of the system is demonstrated by imaging rat brain tissue sections. High resolution mass analysis combined with MS/MS experiments enabled identification of lipids and metabolites in the tissue section. In addition, high dynamic range and sensitivity of the technique allowed us to generate ion images of low-abundance isobaric lipids. High-spatial resolution image acquired over a small region of the tissue section revealed the spatial distribution of an abundant brain metabolite, creatine, in the white and gray matter that is consistent with the literature data obtained using magnetic resonance spectroscopy.

  15. Datamining approach for automation of diagnosis of breast cancer in immunohistochemically stained tissue microarray images.

    PubMed

    Prasad, Keerthana; Zimmermann, Bernhard; Prabhu, Gopalakrishna; Pai, Muktha

    2010-01-01

    Cancer of the breast is the second most common human neoplasm, accounting for approximately one quarter of all cancers in females after cervical carcinoma. Estrogen receptor (ER), Progesteron receptor and human epidermal growth factor receptor (HER-2/neu) expressions play an important role in diagnosis and prognosis of breast carcinoma. Tissue microarray (TMA) technique is a high throughput technique which provides a standardized set of images which are uniformly stained, facilitating effective automation of the evaluation of the specimen images. TMA technique is widely used to evaluate hormone expression for diagnosis of breast cancer. If one considers the time taken for each of the steps in the tissue microarray process workflow, it can be observed that the maximum amount of time is taken by the analysis step. Hence, automated analysis will significantly reduce the overall time required to complete the study. Many tools are available for automated digital acquisition of images of the spots from the microarray slide. Each of these images needs to be evaluated by a pathologist to assign a score based on the staining intensity to represent the hormone expression, to classify them into negative or positive cases. Our work aims to develop a system for automated evaluation of sets of images generated through tissue microarray technique, representing the ER expression images and HER-2/neu expression images. Our study is based on the Tissue Microarray Database portal of Stanford university at http://tma.stanford.edu/cgi-bin/cx?n=her1, which has made huge number of images available to researchers. We used 171 images corresponding to ER expression and 214 images corresponding to HER-2/neu expression of breast carcinoma. Out of the 171 images corresponding to ER expression, 104 were negative and 67 were representing positive cases. Out of the 214 images corresponding to HER-2/neu expression, 112 were negative and 102 were representing positive cases. Our method has 92

  16. A new toolbox for assessing single cells.

    PubMed

    Tsioris, Konstantinos; Torres, Alexis J; Douce, Thomas B; Love, J Christopher

    2014-01-01

    Unprecedented access to the biology of single cells is now feasible, enabled by recent technological advancements that allow us to manipulate and measure sparse samples and achieve a new level of resolution in space and time. This review focuses on advances in tools to study single cells for specific areas of biology. We examine both mature and nascent techniques to study single cells at the genomics, transcriptomics, and proteomics level. In addition, we provide an overview of tools that are well suited for following biological responses to defined perturbations with single-cell resolution. Techniques to analyze and manipulate single cells through soluble and chemical ligands, the microenvironment, and cell-cell interactions are provided. For each of these topics, we highlight the biological motivation, applications, methods, recent advances, and opportunities for improvement. The toolbox presented in this review can function as a starting point for the design of single-cell experiments. PMID:24910919

  17. Automated detection of meteors in observed image sequence

    NASA Astrophysics Data System (ADS)

    Šimberová, Stanislava; Suk, Tomáš

    2015-12-01

    We propose a new detection technique based on statistical characteristics of images in the video sequence. These characteristics displayed in time enable to catch any bright track during the whole sequence. We applied our method to the image datacubes that are created from camera pictures of the night sky. Meteor flying through the Earth's atmosphere leaves a light trail lasting a few seconds on the sky background. We developed a special technique to recognize this event automatically in the complete observed video sequence. For further analysis leading to the precise recognition of object we suggest to apply Fourier and Hough transformations.

  18. System and method for automated object detection in an image

    DOEpatents

    Kenyon, Garrett T.; Brumby, Steven P.; George, John S.; Paiton, Dylan M.; Schultz, Peter F.

    2015-10-06

    A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.

  19. Automated parasite faecal egg counting using fluorescence labelling, smartphone image capture and computational image analysis.

    PubMed

    Slusarewicz, Paul; Pagano, Stefanie; Mills, Christopher; Popa, Gabriel; Chow, K Martin; Mendenhall, Michael; Rodgers, David W; Nielsen, Martin K

    2016-07-01

    Intestinal parasites are a concern in veterinary medicine worldwide and for human health in the developing world. Infections are identified by microscopic visualisation of parasite eggs in faeces, which is time-consuming, requires technical expertise and is impractical for use on-site. For these reasons, recommendations for parasite surveillance are not widely adopted and parasite control is based on administration of rote prophylactic treatments with anthelmintic drugs. This approach is known to promote anthelmintic resistance, so there is a pronounced need for a convenient egg counting assay to promote good clinical practice. Using a fluorescent chitin-binding protein, we show that this structural carbohydrate is present and accessible in shells of ova of strongyle, ascarid, trichurid and coccidian parasites. Furthermore, we show that a cellular smartphone can be used as an inexpensive device to image fluorescent eggs and, by harnessing the computational power of the phone, to perform image analysis to count the eggs. Strongyle egg counts generated by the smartphone system had a significant linear correlation with manual McMaster counts (R(2)=0.98), but with a significantly lower coefficient of variation (P=0.0177). Furthermore, the system was capable of differentiating equine strongyle and ascarid eggs similar to the McMaster method, but with significantly lower coefficients of variation (P<0.0001). This demonstrates the feasibility of a simple, automated on-site test to detect and/or enumerate parasite eggs in mammalian faeces without the need for a laboratory microscope, and highlights the potential of smartphones as relatively sophisticated, inexpensive and portable medical diagnostic devices. PMID:27025771

  20. Single-Cell Genomics for Virology.

    PubMed

    Ciuffi, Angela; Rato, Sylvie; Telenti, Amalio

    2016-01-01

    Single-cell sequencing technologies, i.e., single cell analysis followed by deep sequencing investigate cellular heterogeneity in many biological settings. It was only in the past year that single-cell sequencing analyses has been applied in the field of virology, providing new ways to explore viral diversity and cell response to viral infection, which are summarized in the present review. PMID:27153082

  1. Single-Cell Genomics for Virology

    PubMed Central

    Ciuffi, Angela; Rato, Sylvie; Telenti, Amalio

    2016-01-01

    Single-cell sequencing technologies, i.e., single cell analysis followed by deep sequencing investigate cellular heterogeneity in many biological settings. It was only in the past year that single-cell sequencing analyses has been applied in the field of virology, providing new ways to explore viral diversity and cell response to viral infection, which are summarized in the present review. PMID:27153082

  2. Redefining Signaling Pathways with an Expanding Single-Cell Toolbox.

    PubMed

    Gaudet, Suzanne; Miller-Jensen, Kathryn

    2016-06-01

    Genetically identical cells respond heterogeneously to uniform environmental stimuli. Consequently, investigating the signaling networks that control these cell responses using 'average' bulk cell measurements can obscure underlying mechanisms and misses information emerging from cell-to-cell variability. Here we review recent technological advances including live-cell fluorescence imaging-based approaches and microfluidic devices that enable measurements of signaling networks, dynamics, and responses in single cells. We discuss how these single-cell tools have uncovered novel mechanistic insights for canonical signaling pathways that control cell proliferation (ERK), DNA-damage responses (p53), and innate immune and stress responses (NF-κB). Future improvements in throughput and multiplexing, analytical pipelines, and in vivo applicability will all significantly expand the biological information gained from single-cell measurements of signaling pathways. PMID:26968612

  3. Automated Bulk Uploading of Images and Metadata to Flickr

    ERIC Educational Resources Information Center

    Michel, Jason Paul; Tzoc, Elias

    2010-01-01

    The Digital Initiatives department at Miami University, like most digital initiatives and special collections departments, has a large number of rich digital image collections, stored primarily in a third-party database. Typically, these databases are not findable to the average Web user. From a desire to expose these collections to the wider Web…

  4. Automated design of image operators that detect interest points.

    PubMed

    Trujillo, Leonardo; Olague, Gustavo

    2008-01-01

    This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research. PMID:19053496

  5. Automated target recognition of humans in infrared images

    NASA Astrophysics Data System (ADS)

    Bankman, Daniel; Neighoff, Todd

    2007-04-01

    The ability to automatically detect humans in infrared images has value in military and civilian applications. Robots and unattended ground stations equipped with real-time human ATR capability can operate as scouts, perform reconnaissance for military units, and serve to locate humans in remote or hazardous sites. With the algorithm proposed in this study, human targets can be detected in infrared images based on the structure and radiance of the human head. The algorithm works in a three step process. First, the infrared image is segmented primarily based on edges and secondarily based on intensity of pixels. Once the regions of interest have been determined, the segments undergo feature extraction, in which they are characterized based on circularity and smoothness. The final step of the algorithm uses a k-Nearest Neighbor classifier to match the segment's features to a database, determining whether the segment is a head or not. This algorithm operates best in environments in which contrast between the human and the background is high, such as nighttime or indoors. Tests show that 82% accuracy in identification of human heads is possible for a single still image. After analyzing two uncorrelated frames viewing the same scene, the likelihood of correctly classifying a human head that appears in both frames is 97%.

  6. Automated Coronal Loop Identification Using Digital Image Processing Techniques

    NASA Technical Reports Server (NTRS)

    Lee, Jong K.; Gary, G. Allen; Newman, Timothy S.

    2003-01-01

    The results of a master thesis project on a study of computer algorithms for automatic identification of optical-thin, 3-dimensional solar coronal loop centers from extreme ultraviolet and X-ray 2-dimensional images will be presented. These center splines are proxies of associated magnetic field lines. The project is pattern recognition problems in which there are no unique shapes or edges and in which photon and detector noise heavily influence the images. The study explores extraction techniques using: (1) linear feature recognition of local patterns (related to the inertia-tensor concept), (2) parametric space via the Hough transform, and (3) topological adaptive contours (snakes) that constrains curvature and continuity as possible candidates for digital loop detection schemes. We have developed synthesized images for the coronal loops to test the various loop identification algorithms. Since the topology of these solar features is dominated by the magnetic field structure, a first-order magnetic field approximation using multiple dipoles provides a priori information in the identification process. Results from both synthesized and solar images will be presented.

  7. Automated identification of retained surgical items in radiological images

    NASA Astrophysics Data System (ADS)

    Agam, Gady; Gan, Lin; Moric, Mario; Gluncic, Vicko

    2015-03-01

    Retained surgical items (RSIs) in patients is a major operating room (OR) patient safety concern. An RSI is any surgical tool, sponge, needle or other item inadvertently left in a patients body during the course of surgery. If left undetected, RSIs may lead to serious negative health consequences such as sepsis, internal bleeding, and even death. To help physicians efficiently and effectively detect RSIs, we are developing computer-aided detection (CADe) software for X-ray (XR) image analysis, utilizing large amounts of currently available image data to produce a clinically effective RSI detection system. Physician analysis of XRs for the purpose of RSI detection is a relatively lengthy process that may take up to 45 minutes to complete. It is also error prone due to the relatively low acuity of the human eye for RSIs in XR images. The system we are developing is based on computer vision and machine learning algorithms. We address the problem of low incidence by proposing synthesis algorithms. The CADe software we are developing may be integrated into a picture archiving and communication system (PACS), be implemented as a stand-alone software application, or be integrated into portable XR machine software through application programming interfaces. Preliminary experimental results on actual XR images demonstrate the effectiveness of the proposed approach.

  8. An automated programmable platform enabling multiplex dynamic stimuli delivery and cellular response monitoring for high-throughput suspension single-cell signaling studies† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c4lc01070a Click here for additional data file.

    PubMed Central

    He, Luye; Kniss, Ariel; San-Miguel, Adriana; Rouse, Tel; Kemp, Melissa L.

    2015-01-01

    Cell signaling events are orchestrated by dynamic external biochemical cues. By rapidly perturbing cells with dynamic inputs and examining the output from these systems, one could study the structure and dynamic properties of a cellular signaling network. Conventional experimental techniques limit the implementation of these systematic approaches due to the lack of sophistication in manipulating individual cells and the fluid microenvironment around them; existing microfluidic technologies thus far are mainly targeting adherent cells. In this paper we present an automated platform to interrogate suspension cells with dynamic stimuli while simultaneously monitoring cellular responses in a high-throughput manner at single-cell resolution. We demonstrate the use of this platform in an experiment to measure Jurkat T cells in response to distinct dynamic patterns of stimuli; we find cells exhibit highly heterogeneous responses under each stimulation condition. More interestingly, these cells act as low-pass filters, only entrained to the low frequency stimulus signals. We also demonstrate that this platform can be easily programmed to actively generate arbitrary dynamic signals. We envision our platform to be useful in other contexts to study cellular signaling dynamics, which may be difficult using conventional experimental methods. PMID:25609410

  9. Automated Drusen Segmentation and Quantification in SD-OCT Images

    PubMed Central

    Chen, Qiang; Leng, Theodore; Zheng, Luoluo; Kutzscher, Lauren; Ma, Jeffrey; de Sisternes, Luis; Rubin, Daniel L.

    2013-01-01

    Spectral domain optical coherence tomography (SD-OCT) is a useful tool for the visualization of drusen, a retinal abnormality seen in patients with age-related macular degeneration (AMD); however, objective assessment of drusen is thwarted by the lack of a method to robustly quantify these lesions on serial OCT images. Here, we describe an automatic drusen segmentation method for SD-OCT retinal images, which leverages a priori knowledge of normal retinal morphology and anatomical features. The highly reflective and locally connected pixels located below the retinal nerve fiber layer (RNFL) are used to generate a segmentation of the retinal pigment epithelium (RPE) layer. The observed and expected contours of the RPE layer are obtained by interpolating and fitting the shape of the segmented RPE layer, respectively. The areas located between the interpolated and fitted RPE shapes (which have nonzero area when drusen occurs) are marked as drusen. To enhance drusen quantification, we also developed a novel method of retinal projection to generate an en face retinal image based on the RPE extraction, which improves the quality of drusen visualization over the current approach to producing retinal projections from SD-OCT images based on a summed-voxel projection (SVP), and it provides a means of obtaining quantitative features of drusen in the en face projection. Visualization of the segmented drusen is refined through several post-processing steps, drusen detection to eliminate false positive detections on consecutive slices, drusen refinement on a projection view of drusen, and drusen smoothing. Experimental evaluation results demonstrate that our method is effective for drusen segmentation. In a preliminary analysis of the potential clinical utility of our methods, quantitative drusen measurements, such as area and volume, can be correlated with the drusen progression in non-exudative AMD, suggesting that our approach may produce useful quantitative imaging biomarkers

  10. Evaluation of automated brain MR image segmentation and volumetry methods.

    PubMed

    Klauschen, Frederick; Goldman, Aaron; Barra, Vincent; Meyer-Lindenberg, Andreas; Lundervold, Arvid

    2009-04-01

    We compare three widely used brain volumetry methods available in the software packages FSL, SPM5, and FreeSurfer and evaluate their performance using simulated and real MR brain data sets. We analyze the accuracy of gray and white matter volume measurements and their robustness against changes of image quality using the BrainWeb MRI database. These images are based on "gold-standard" reference brain templates. This allows us to assess between- (same data set, different method) and also within-segmenter (same method, variation of image quality) comparability, for both of which we find pronounced variations in segmentation results for gray and white matter volumes. The calculated volumes deviate up to >10% from the reference values for gray and white matter depending on method and image quality. Sensitivity is best for SPM5, volumetric accuracy for gray and white matter was similar in SPM5 and FSL and better than in FreeSurfer. FSL showed the highest stability for white (<5%), FreeSurfer (6.2%) for gray matter for constant image quality BrainWeb data. Between-segmenter comparisons show discrepancies of up to >20% for the simulated data and 24% on average for the real data sets, whereas within-method performance analysis uncovered volume differences of up to >15%. Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies. PMID:18537111

  11. An Imaging System for Automated Characteristic Length Measurement of Debrisat Fragments

    NASA Technical Reports Server (NTRS)

    Moraguez, Mathew; Patankar, Kunal; Fitz-Coy, Norman; Liou, J.-C.; Sorge, Marlon; Cowardin, Heather; Opiela, John; Krisko, Paula H.

    2015-01-01

    The debris fragments generated by DebriSat's hypervelocity impact test are currently being processed and characterized through an effort of NASA and USAF. The debris characteristics will be used to update satellite breakup models. In particular, the physical dimensions of the debris fragments must be measured to provide characteristic lengths for use in these models. Calipers and commercial 3D scanners were considered as measurement options, but an automated imaging system was ultimately developed to measure debris fragments. By automating the entire process, the measurement results are made repeatable and the human factor associated with calipers and 3D scanning is eliminated. Unlike using calipers to measure, the imaging system obtains non-contact measurements to avoid damaging delicate fragments. Furthermore, this fully automated measurement system minimizes fragment handling, which reduces the potential for fragment damage during the characterization process. In addition, the imaging system reduces the time required to determine the characteristic length of the debris fragment. In this way, the imaging system can measure the tens of thousands of DebriSat fragments at a rate of about six minutes per fragment, compared to hours per fragment in NASA's current 3D scanning measurement approach. The imaging system utilizes a space carving algorithm to generate a 3D point cloud of the article being measured and a custom developed algorithm then extracts the characteristic length from the point cloud. This paper describes the measurement process, results, challenges, and future work of the imaging system used for automated characteristic length measurement of DebriSat fragments.

  12. Towards A Fully Automated High-Throughput Phototransfection System

    PubMed Central

    Cappelleri, David J.; Halasz, Adam; Sul, Jai-Yoon; Kim, Tae Kyung; Eberwine, James; Kumar, Vijay

    2010-01-01

    We have designed and implemented a framework for creating a fully automated high-throughput phototransfection system. Integrated image processing, laser target position calculation, and stage movements show a throughput increase of > 23X over the current manual phototransfection method while the potential for even greater throughput improvements (> 110X) is described. A software tool for automated off-line single cell morphological measurements, as well as real-time image segmentation analysis, has also been constructed and shown to be able quantify changes in the cell before and after the process, successfully characterizing them, using metrics such as cell perimeter, area, major and minor axis length, and eccentricity values. PMID:20706617

  13. Automated marker tracking using noisy X-ray images degraded by the treatment beam.

    PubMed

    Wisotzky, E; Fast, M F; Oelfke, U; Nill, S

    2015-06-01

    This study demonstrates the feasibility of automated marker tracking for the real-time detection of intrafractional target motion using noisy kilovoltage (kV) X-ray images degraded by the megavoltage (MV) treatment beam. The authors previously introduced the in-line imaging geometry, in which the flat-panel detector (FPD) is mounted directly underneath the treatment head of the linear accelerator. They found that the 121 kVp image quality was severely compromised by the 6 MV beam passing through the FPD at the same time. Specific MV-induced artefacts present a considerable challenge for automated marker detection algorithms. For this study, the authors developed a new imaging geometry by re-positioning the FPD and the X-ray tube. This improved the contrast-to-noise-ratio between 40% and 72% at the 1.2 mAs/image exposure setting. The increase in image quality clearly facilitates the quick and stable detection of motion with the aid of a template matching algorithm. The setup was tested with an anthropomorphic lung phantom (including an artificial lung tumour). In the tumour one or three Calypso beacons were embedded to achieve better contrast during MV radiation. For a single beacon, image acquisition and automated marker detection typically took around 76 ± 6 ms. The success rate was found to be highly dependent on imaging dose and gantry angle. To eliminate possible false detections, the authors implemented a training phase prior to treatment beam irradiation and also introduced speed limits for motion between subsequent images. PMID:25280891

  14. Assessment of Automated Image Analysis of Breast Cancer Tissue Microarrays for Epidemiologic Studies

    PubMed Central

    Bolton, Kelly L.; Garcia-Closas, Montserrat; Pfeiffer, Ruth M.; Duggan, Máire A.; Howat, William J.; Hewitt, Stephen M.; Yang, Xiaohong R.; Cornelison, Robert; Anzick, Sarah L.; Meltzer, Paul; Davis, Sean; Lenz, Petra; Figueroa, Jonine D.; Pharoah, Paul D.P.; Sherman, Mark E.

    2010-01-01

    A major challenge in studies of etiologic heterogeneity in breast cancer has been the limited throughput, accuracy and reproducibility of measuring tissue markers. Computerized image analysis systems may help address these concerns but published reports of their use are limited. We assessed agreement between automated and pathologist scores of a diverse set of immunohistochemical (IHC) assays performed on breast cancer TMAs. TMAs of 440 breast cancers previously stained for ER-α, PR, HER-2, ER-β and aromatase were independently scored by two pathologists and three automated systems (TMALabII, TMAx, Ariol). Agreement between automated and pathologist scores of negative/positive was measured using the area under the receiver operator characteristics curve (AUC) and weighted kappa statistics (κ) for categorical scores. We also investigated the correlation between IHC scores and mRNA expression levels. Agreement between pathologist and automated negative/positive and categorical scores was excellent for ER-α and PR (AUC range =0.98-0.99; κ range =0.86-0.91). Lower levels of agreement were seen for ER-β categorical scores (AUC=0.99-1.0; κ=0.80-0.86) and both negative/positive and categorical scores for aromatase (AUC=0.85-0.96; κ=0.41-0.67) and HER2 (AUC=0.94-0.97; κ=0.53-0.72). For ER-α and PR, there was strong correlation between mRNA levels and automated (ρ=0.67-0.74) and pathologist IHC scores (ρ=0.67-0.77). HER2 mRNA levels were more strongly correlated with pathologist (ρ=0.63) than automated IHC scores (ρ=0.41-0.49). Automated analysis of IHC markers is a promising approach for scoring large numbers of breast cancer tissues in epidemiologic investigations. This would facilitate studies of etiologic heterogeneity which ultimately may allow improved risk prediction and better prevention approaches. PMID:20332278

  15. Phenotype classification of single cells using SRS microscopy, RNA sequencing, and microfluidics (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Streets, Aaron M.; Cao, Chen; Zhang, Xiannian; Huang, Yanyi

    2016-03-01

    Phenotype classification of single cells reveals biological variation that is masked in ensemble measurement. This heterogeneity is found in gene and protein expression as well as in cell morphology. Many techniques are available to probe phenotypic heterogeneity at the single cell level, for example quantitative imaging and single-cell RNA sequencing, but it is difficult to perform multiple assays on the same single cell. In order to directly track correlation between morphology and gene expression at the single cell level, we developed a microfluidic platform for quantitative coherent Raman imaging and immediate RNA sequencing (RNA-Seq) of single cells. With this device we actively sort and trap cells for analysis with stimulated Raman scattering microscopy (SRS). The cells are then processed in parallel pipelines for lysis, and preparation of cDNA for high-throughput transcriptome sequencing. SRS microscopy offers three-dimensional imaging with chemical specificity for quantitative analysis of protein and lipid distribution in single cells. Meanwhile, the microfluidic platform facilitates single-cell manipulation, minimizes contamination, and furthermore, provides improved RNA-Seq detection sensitivity and measurement precision, which is necessary for differentiating biological variability from technical noise. By combining coherent Raman microscopy with RNA sequencing, we can better understand the relationship between cellular morphology and gene expression at the single-cell level.

  16. Automated 3D ultrasound image segmentation for assistant diagnosis of breast cancer

    NASA Astrophysics Data System (ADS)

    Wang, Yuxin; Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A.; Du, Sidan; Yuan, Jie; Wang, Xueding; Carson, Paul L.

    2016-04-01

    Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

  17. Automated Dermoscopy Image Analysis of Pigmented Skin Lesions

    PubMed Central

    Baldi, Alfonso; Quartulli, Marco; Murace, Raffaele; Dragonetti, Emanuele; Manganaro, Mario; Guerra, Oscar; Bizzi, Stefano

    2010-01-01

    Dermoscopy (dermatoscopy, epiluminescence microscopy) is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions (PSLs), allowing a better visualization of surface and subsurface structures (from the epidermis to the papillary dermis). This diagnostic tool permits the recognition of morphologic structures not visible by the naked eye, thus opening a new dimension in the analysis of the clinical morphologic features of PSLs. In order to reduce the learning-curve of non-expert clinicians and to mitigate problems inherent in the reliability and reproducibility of the diagnostic criteria used in pattern analysis, several indicative methods based on diagnostic algorithms have been introduced in the last few years. Recently, numerous systems designed to provide computer-aided analysis of digital images obtained by dermoscopy have been reported in the literature. The goal of this article is to review these systems, focusing on the most recent approaches based on content-based image retrieval systems (CBIR). PMID:24281070

  18. Automated image magnification for CO2 laser glass processing system (LZM-100 LazerMaster)

    NASA Astrophysics Data System (ADS)

    Harju, Michael E.; Sugawara, Hiroshi; Mizushima, Toshirou

    2014-03-01

    A novel Automated Image Magnification system has been developed for a CO2 laser glass processing system (LZM-100 LazerMaster) enabling precise and accurate alignment of optical fiber and fiber based components. This system measures the size of the fiber image and adjusts the imaging optical path based on the fiber size and process requirement. By automatically adjusting the magnification, we can process fibers and components requiring a large field of view (<2500um) and those with micron or submicron features without having to modify the machine configuration.

  19. Automated analysis of OCT images of the crystalline lens

    NASA Astrophysics Data System (ADS)

    Kim, Eon; Ehrmann, Klaus; Uhlhorn, Stephen; Borja, David; Parel, Jean-Marie

    2009-02-01

    Presbyopia is the age related, gradual loss of accommodation, mainly due to changes in the crystalline lens. As part of research efforts to understand and cure this condition, ex vivo, cross-sectional OCT images of crystalline lenses were obtained and analyzed to extract their physical and optical properties. The raw OCT images are distorted, as the probing beam passing through media of different refractive indices and refraction on curved surfaces. In a first step, various filters, edge detection and pattern matching methods are applied to isolate the edge contour. An ellipse is fitted to the lens outline to obtain central reference point for transforming the pixel data into the analysis coordinate system. This allows for the fitting of high order equation to obtain a mathematical description of the edge contour, which obeys constraints of continuity as well as zero to infinite surface slopes from apex to equator. Robustness of these algorithms are tested by analyzing the images at various contrast levels. Gradient refractive index of the lens is determined and the physical shape is reconstructed. In a further refinement, the refraction on the curved anterior surface is compensated to obtain the actual shape of the posterior surface. Once the physical shape is fully reconstructed, the optical properties are determined by fitting conic sections to both surfaces and calculating the power profile across the lens. The relative contribution of each of these refinement steps is investigated by comparing their influence on the effective power of the lens.

  20. Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

    PubMed Central

    Gruenberger, Alexander; Probst, Christopher; Heyer, Antonia; Wiechert, Wolfgang; Frunzke, Julia; Kohlheyer, Dietrich

    2013-01-01

    In this protocol the fabrication, experimental setup and basic operation of the recently introduced microfluidic picoliter bioreactor (PLBR) is described in detail. The PLBR can be utilized for the analysis of single bacteria and microcolonies to investigate biotechnological and microbiological related questions concerning, e.g. cell growth, morphology, stress response, and metabolite or protein production on single-cell level. The device features continuous media flow enabling constant environmental conditions for perturbation studies, but in addition allows fast medium changes as well as oscillating conditions to mimic any desired environmental situation. To fabricate the single use devices, a silicon wafer containing sub micrometer sized SU-8 structures served as the replication mold for rapid polydimethylsiloxane casting. Chips were cut, assembled, connected, and set up onto a high resolution and fully automated microscope suited for time-lapse imaging, a powerful tool for spatio-temporal cell analysis. Here, the biotechnological platform organism Corynebacterium glutamicum was seeded into the PLBR and cell growth and intracellular fluorescence were followed over several hours unraveling time dependent population heterogeneity on single-cell level, not possible with conventional analysis methods such as flow cytometry. Besides insights into device fabrication, furthermore, the preparation of the preculture, loading, trapping of bacteria, and the PLBR cultivation of single cells and colonies is demonstrated. These devices will add a new dimension in microbiological research to analyze time dependent phenomena of single bacteria under tight environmental control. Due to the simple and relatively short fabrication process the technology can be easily adapted at any microfluidics lab and simply tailored towards specific needs. PMID:24336165

  1. A three-dimensional image processing program for accurate, rapid, and semi-automated segmentation of neuronal somata with dense neurite outgrowth

    PubMed Central

    Ross, James D.; Cullen, D. Kacy; Harris, James P.; LaPlaca, Michelle C.; DeWeerth, Stephen P.

    2015-01-01

    Three-dimensional (3-D) image analysis techniques provide a powerful means to rapidly and accurately assess complex morphological and functional interactions between neural cells. Current software-based identification methods of neural cells generally fall into two applications: (1) segmentation of cell nuclei in high-density constructs or (2) tracing of cell neurites in single cell investigations. We have developed novel methodologies to permit the systematic identification of populations of neuronal somata possessing rich morphological detail and dense neurite arborization throughout thick tissue or 3-D in vitro constructs. The image analysis incorporates several novel automated features for the discrimination of neurites and somata by initially classifying features in 2-D and merging these classifications into 3-D objects; the 3-D reconstructions automatically identify and adjust for over and under segmentation errors. Additionally, the platform provides for software-assisted error corrections to further minimize error. These features attain very accurate cell boundary identifications to handle a wide range of morphological complexities. We validated these tools using confocal z-stacks from thick 3-D neural constructs where neuronal somata had varying degrees of neurite arborization and complexity, achieving an accuracy of ≥95%. We demonstrated the robustness of these algorithms in a more complex arena through the automated segmentation of neural cells in ex vivo brain slices. These novel methods surpass previous techniques by improving the robustness and accuracy by: (1) the ability to process neurites and somata, (2) bidirectional segmentation correction, and (3) validation via software-assisted user input. This 3-D image analysis platform provides valuable tools for the unbiased analysis of neural tissue or tissue surrogates within a 3-D context, appropriate for the study of multi-dimensional cell-cell and cell-extracellular matrix interactions. PMID

  2. Automated skin biopsy histopathological image annotation using multi-instance representation and learning.

    PubMed

    Zhang, Gang; Yin, Jian; Li, Ziping; Su, Xiangyang; Li, Guozheng; Zhang, Honglai

    2013-01-01

    With digitisation and the development of computer-aided diagnosis, histopathological image analysis has attracted considerable interest in recent years. In this article, we address the problem of the automated annotation of skin biopsy images, a special type of histopathological image analysis. In contrast to previous well-studied methods in histopathology, we propose a novel annotation method based on a multi-instance learning framework. The proposed framework first represents each skin biopsy image as a multi-instance sample using a graph cutting method, decomposing the image to a set of visually disjoint regions. Then, we construct two classification models using multi-instance learning algorithms, among which one provides determinate results and the other calculates a posterior probability. We evaluate the proposed annotation framework using a real dataset containing 6691 skin biopsy images, with 15 properties as target annotation terms. The results indicate that the proposed method is effective and medically acceptable. PMID:24565115

  3. Automation of disbond detection in aircraft fuselage through thermal image processing

    NASA Technical Reports Server (NTRS)

    Prabhu, D. R.; Winfree, W. P.

    1992-01-01

    A procedure for interpreting thermal images obtained during the nondestructive evaluation of aircraft bonded joints is presented. The procedure operates on time-derivative thermal images and resulted in a disbond image with disbonds highlighted. The size of the 'black clusters' in the output disbond image is a quantitative measure of disbond size. The procedure is illustrated using simulation data as well as data obtained through experimental testing of fabricated samples and aircraft panels. Good results are obtained, and, except in pathological cases, 'false calls' in the cases studied appeared only as noise in the output disbond image which was easily filtered out. The thermal detection technique coupled with an automated image interpretation capability will be a very fast and effective method for inspecting bonded joints in an aircraft structure.

  4. Toward omnidirectional and automated imaging system for measuring oceanic whitecap coverage.

    PubMed

    Al-Lashi, Raied S

    2016-08-01

    Accurate measurements of oceanic whitecap coverage from whitecap images are required for better understanding air-gas transfer and aerosol production processes. However, this is a challenging task because whitecap patches are formed immediately after a wave breaks and are spread over a wide area. The main challenges in designing whitecaps imaging instrument are the small field of view of the camera lens, processing huge numbers of images, recording data over long time periods, and deployment difficulties in stormy conditions. This paper describes the hardware design of a novel high-resolution optical instrument for imaging oceanic whitecaps and the automated algorithm processing the collected images. The instrument was successfully deployed in 2013 as part of the HiWINGS campaign in the North Atlantic Ocean. The instrument uses a fish-eye camera lens to image the whitecaps in a wide angle of view (180°). PMID:27505657

  5. Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone

    PubMed Central

    Ascenzi, Maria-Grazia; Du, Xia; Harding, James I; Beylerian, Emily N; de Silva, Brian M; Gross, Ben J; Kastein, Hannah K; Wang, Weiguang; Lyons, Karen M; Schaeffer, Hayden

    2014-01-01

    Microscopy imaging of mouse growth plates is extensively used in biology to understand the effect of specific molecules on various stages of normal bone development and on bone disease. Until now, such image analysis has been conducted by manual detection. In fact, when existing automated detection techniques were applied, morphological variations across the growth plate and heterogeneity of image background color, including the faint presence of cells (chondrocytes) located deeper in tissue away from the image’s plane of focus, and lack of cell-specific features, interfered with identification of cell. We propose the first method of automated detection and morphometry applicable to images of cells in the growth plate of long bone. Through ad hoc sequential application of the Retinex method, anisotropic diffusion and thresholding, our new cell detection algorithm (CDA) addresses these challenges on bright-field microscopy images of mouse growth plates. Five parameters, chosen by the user in respect of image characteristics, regulate our CDA. Our results demonstrate effectiveness of the proposed numerical method relative to manual methods. Our CDA confirms previously established results regarding chondrocytes’ number, area, orientation, height and shape of normal growth plates. Our CDA also confirms differences previously found between the genetic mutated mouse Smad1/5CKO and its control mouse on fluorescence images. The CDA aims to aid biomedical research by increasing efficiency and consistency of data collection regarding arrangement and characteristics of chondrocytes. Our results suggest that automated extraction of data from microscopy imaging of growth plates can assist in unlocking information on normal and pathological development, key to the underlying biological mechanisms of bone growth. PMID:25525552

  6. Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images

    PubMed Central

    Ughi, Giovanni Jacopo; Adriaenssens, Tom; Sinnaeve, Peter; Desmet, Walter; D’hooge, Jan

    2013-01-01

    Intravascular optical coherence tomography (IVOCT) is rapidly becoming the method of choice for the in vivo investigation of coronary artery disease. While IVOCT visualizes atherosclerotic plaques with a resolution <20µm, image analysis in terms of tissue composition is currently performed by a time-consuming manual procedure based on the qualitative interpretation of image features. We illustrate an algorithm for the automated and systematic characterization of IVOCT atherosclerotic tissue. The proposed method consists in a supervised classification of image pixels according to textural features combined with the estimated value of the optical attenuation coefficient. IVOCT images of 64 plaques, from 49 in vivo IVOCT data sets, constituted the algorithm’s training and testing data sets. Validation was obtained by comparing automated analysis results to the manual assessment of atherosclerotic plaques. An overall pixel-wise accuracy of 81.5% with a classification feasibility of 76.5% and per-class accuracy of 89.5%, 72.1% and 79.5% for fibrotic, calcified and lipid-rich tissue respectively, was found. Moreover, measured optical properties were in agreement with previous results reported in literature. As such, an algorithm for automated tissue characterization was developed and validated using in vivo human data, suggesting that it can be applied to clinical IVOCT data. This might be an important step towards the integration of IVOCT in cardiovascular research and routine clinical practice. PMID:23847728

  7. Development of automated image co-registration techniques: Part II - multisensor imagery

    SciTech Connect

    Lundeen, T.F.; Andrews, A.K.; Perry, E.M.; Whyatt, M.V.; Steinmaus, K.L.

    1996-10-01

    This is the second in a series of PNNL Multispectral Imagery (ST474D) reports on automated co-registration and rectification of multisensor imagery. In the first report, a semi-automated registration procedure was introduced based on methods proposed by Chen and Lee which emphasized registration of same sensor imagery. The Chen and Lee approach is outlined in Figure 1, and is described in detail in the first report. PNNL made several enhancements to the Chen and Lee approach; these modifications are outlined in Figure 2 and are also described in detail in the first report. The PNNL enhancements to the Chen and Lee approach introduced in the first phase have been named Multisensor Image Registration Automation (MIRA). These improvements increased computational efficiency and offered additional algorithms for coarse matching of disparate image types. In the MIRA approach, one set of optimum GCP locations are determined based on a Delaunay triangulation technique using an initial set of GCPs provided by the user, rather than repeating this step for each added control point as is proposed by Chen and Lee. The Chen and Lee approach uses an adjacent pixel difference algorithm for coarse matching patches of the reference image with the source image, while the MIRA approach adds other algorithms. Also the MIRA approach checks to determine if the a newly determined GCP fits the existing warping equation.

  8. Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images.

    PubMed

    Mookiah, Muthu Rama Krishnan; Acharya, U Rajendra; Koh, Joel E W; Chandran, Vinod; Chua, Chua Kuang; Tan, Jen Hong; Lim, Choo Min; Ng, E Y K; Noronha, Kevin; Tong, Louis; Laude, Augustinus

    2014-10-01

    Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that

  9. Imaging System for the Automated Determination of Microscopical Properties in Hardened Portland Concrete

    SciTech Connect

    Baumgart, C.W.; Cave, S.P.; Linder, K.E.

    2000-03-08

    During this CRADA, Honeywell FM and T and MoDOT personnel designed a unique scanning system (including both hardware and software) that can be used to perform an automated scan and evaluation of a concrete sample. The specific goals of the CRADA were: (1) Develop a combined system integration, image acquisition, and image analysis approach to mimic the manual scanning and evaluation process. Produce a prototype system which can: (a) automate the scanning process to improve its speed and efficiency; (b) reduce operator fatigue; and (c) improve the consistency of the evaluation process. (2) Capture and preserve the baseline knowledge used by the MoDOT experts in performing the evaluation process. At the present time, the evaluation expertise resides in two MoDOT personnel. Automation of the evaluation process will allow that knowledge to be captured, preserved, and used for training purposes. (3) Develop an approach for the image analysis which is flexible and extensible in order to accommodate the inevitable pathologies that arise in the evaluation process. Such pathologies include features such as cracks and fissures, voids filled with paste or debris, and multiple, overlapping voids. FM and T personnel used image processing, pattern recognition, and system integration skills developed for other Department of Energy applications to develop and test a prototype of an automated scanning system for concrete evaluation. MoDOT personnel provided all the basic hardware (microscope, camera, computer-controlled stage, etc.) for the prototype, supported FM and T in the acquisition of image data for software development, and provided their critical expert knowledge of the process of concrete evaluation. This combination of expertise was vital to the successful development of the prototype system.

  10. An automated system for numerically rating document image quality

    SciTech Connect

    Cannon, M.; Kelly, P.; Iyengar, S.S.; Brener, N.

    1997-04-01

    As part of the Department of Energy document declassification program, the authors have developed a numerical rating system to predict the OCR error rate that they expect to encounter when processing a particular document. The rating algorithm produces a vector containing scores for different document image attributes such as speckle and touching characters. The OCR error rate for a document is computed from a weighted sum of the elements of the corresponding quality vector. The predicted OCR error rate will be used to screen documents that would not be handled properly with existing document processing products.

  11. Automation of the Image Analysis for Thermographic Inspection

    NASA Technical Reports Server (NTRS)

    Plotnikov, Yuri A.; Winfree, William P.

    1998-01-01

    Several data processing procedures for the pulse thermal inspection require preliminary determination of an unflawed region. Typically, an initial analysis of the thermal images is performed by an operator to determine the locations of unflawed and the defective areas. In the present work an algorithm is developed for automatically determining a reference point corresponding to an unflawed region. Results are obtained for defects which are arbitrarily located in the inspection region. A comparison is presented of the distributions of derived values with right and wrong localization of the reference point. Different algorithms of automatic determination of the reference point are compared.

  12. Automation of image data processing. (Polish Title: Automatyzacja proces u przetwarzania danych obrazowych)

    NASA Astrophysics Data System (ADS)

    Preuss, R.

    2014-12-01

    This article discusses the current capabilities of automate processing of the image data on the example of using PhotoScan software by Agisoft. At present, image data obtained by various registration systems (metric and non - metric cameras) placed on airplanes, satellites, or more often on UAVs is used to create photogrammetric products. Multiple registrations of object or land area (large groups of photos are captured) are usually performed in order to eliminate obscured area as well as to raise the final accuracy of the photogrammetric product. Because of such a situation t he geometry of the resulting image blocks is far from the typical configuration of images. For fast images georeferencing automatic image matching algorithms are currently applied. They can create a model of a block in the local coordinate system or using initial exterior orientation and measured control points can provide image georeference in an external reference frame. In the case of non - metric image application, it is also possible to carry out self - calibration process at this stage. Image matching algorithm is also used in generation of dense point clouds reconstructing spatial shape of the object (area). In subsequent processing steps it is possible to obtain typical photogrammetric products such as orthomosaic, DSM or DTM and a photorealistic solid model of an object . All aforementioned processing steps are implemented in a single program in contrary to standard commercial software dividing all steps into dedicated modules. Image processing leading to final geo referenced products can be fully automated including sequential implementation of the processing steps at predetermined control parameters. The paper presents the practical results of the application fully automatic generation of othomosaic for both images obtained by a metric Vexell camera and a block of images acquired by a non - metric UAV system

  13. A Semi-Automated Single Day Image Differencing Technique to Identify Animals in Aerial Imagery

    PubMed Central

    Terletzky, Pat; Ramsey, Robert Douglas

    2014-01-01

    Our research presents a proof-of-concept that explores a new and innovative method to identify large animals in aerial imagery with single day image differencing. We acquired two aerial images of eight fenced pastures and conducted a principal component analysis of each image. We then subtracted the first principal component of the two pasture images followed by heuristic thresholding to generate polygons. The number of polygons represented the number of potential cattle (Bos taurus) and horses (Equus caballus) in the pasture. The process was considered semi-automated because we were not able to automate the identification of spatial or spectral thresholding values. Imagery was acquired concurrently with ground counts of animal numbers. Across the eight pastures, 82% of the animals were correctly identified, mean percent commission was 53%, and mean percent omission was 18%. The high commission error was due to small mis-alignments generated from image-to-image registration, misidentified shadows, and grouping behavior of animals. The high probability of correctly identifying animals suggests short time interval image differencing could provide a new technique to enumerate wild ungulates occupying grassland ecosystems, especially in isolated or difficult to access areas. To our knowledge, this was the first attempt to use standard change detection techniques to identify and enumerate large ungulates. PMID:24454827

  14. Automated Confocal Laser Scanning Microscopy and Semiautomated Image Processing for Analysis of Biofilms

    PubMed Central

    Kuehn, Martin; Hausner, Martina; Bungartz, Hans-Joachim; Wagner, Michael; Wilderer, Peter A.; Wuertz, Stefan

    1998-01-01

    The purpose of this study was to develop and apply a quantitative optical method suitable for routine measurements of biofilm structures under in situ conditions. A computer program was designed to perform automated investigations of biofilms by using image acquisition and image analysis techniques. To obtain a representative profile of a growing biofilm, a nondestructive procedure was created to study and quantify undisturbed microbial populations within the physical environment of a glass flow cell. Key components of the computer-controlled processing described in this paper are the on-line collection of confocal two-dimensional (2D) cross-sectional images from a preset 3D domain of interest followed by the off-line analysis of these 2D images. With the quantitative extraction of information contained in each image, a three-dimensional reconstruction of the principal biological events can be achieved. The program is convenient to handle and was generated to determine biovolumes and thus facilitate the examination of dynamic processes within biofilms. In the present study, Pseudomonas fluorescens or a green fluorescent protein-expressing Escherichia coli strain, EC12, was inoculated into glass flow cells and the respective monoculture biofilms were analyzed in three dimensions. In this paper we describe a method for the routine measurements of biofilms by using automated image acquisition and semiautomated image analysis. PMID:9797255

  15. Extended Field Laser Confocal Microscopy (EFLCM): Combining automated Gigapixel image capture with in silico virtual microscopy

    PubMed Central

    Flaberg, Emilie; Sabelström, Per; Strandh, Christer; Szekely, Laszlo

    2008-01-01

    Background Confocal laser scanning microscopy has revolutionized cell biology. However, the technique has major limitations in speed and sensitivity due to the fact that a single laser beam scans the sample, allowing only a few microseconds signal collection for each pixel. This limitation has been overcome by the introduction of parallel beam illumination techniques in combination with cold CCD camera based image capture. Methods Using the combination of microlens enhanced Nipkow spinning disc confocal illumination together with fully automated image capture and large scale in silico image processing we have developed a system allowing the acquisition, presentation and analysis of maximum resolution confocal panorama images of several Gigapixel size. We call the method Extended Field Laser Confocal Microscopy (EFLCM). Results We show using the EFLCM technique that it is possible to create a continuous confocal multi-colour mosaic from thousands of individually captured images. EFLCM can digitize and analyze histological slides, sections of entire rodent organ and full size embryos. It can also record hundreds of thousands cultured cells at multiple wavelength in single event or time-lapse fashion on fixed slides, in live cell imaging chambers or microtiter plates. Conclusion The observer independent image capture of EFLCM allows quantitative measurements of fluorescence intensities and morphological parameters on a large number of cells. EFLCM therefore bridges the gap between the mainly illustrative fluorescence microscopy and purely quantitative flow cytometry. EFLCM can also be used as high content analysis (HCA) instrument for automated screening processes. PMID:18627634

  16. Automated reconstruction of 3D scenes from sequences of images

    NASA Astrophysics Data System (ADS)

    Pollefeys, M.; Koch, R.; Vergauwen, M.; Van Gool, L.

    Modelling of 3D objects from image sequences is a challenging problem and has been an important research topic in the areas of photogrammetry and computer vision for many years. In this paper, a system is presented which automatically extracts a textured 3D surface model from a sequence of images of a scene. The system can deal with unknown camera settings. In addition, the parameters of this camera are allowed to change during acquisition (e.g., by zooming or focusing). No prior knowledge about the scene is necessary to build the 3D models. Therefore, this system offers a high degree of flexibility. The system is based on state-of-the-art algorithms recently developed in computer vision. The 3D modelling task is decomposed into a number of successive steps. Gradually, more knowledge of the scene and the camera setup is retrieved. At this point, the obtained accuracy is not yet at the level required for most metrology applications, but the visual quality is very convincing. This system has been applied to a number of applications in archaeology. The Roman site of Sagalassos (southwest Turkey) was used as a test case to illustrate the potential of this new approach.

  17. Automated detection of diabetic retinopathy in retinal images

    PubMed Central

    Valverde, Carmen; García, María; Hornero, Roberto; López-Gálvez, María I

    2016-01-01

    Diabetic retinopathy (DR) is a disease with an increasing prevalence and the main cause of blindness among working-age population. The risk of severe vision loss can be significantly reduced by timely diagnosis and treatment. Systematic screening for DR has been identified as a cost-effective way to save health services resources. Automatic retinal image analysis is emerging as an important screening tool for early DR detection, which can reduce the workload associated to manual grading as well as save diagnosis costs and time. Many research efforts in the last years have been devoted to developing automatic tools to help in the detection and evaluation of DR lesions. However, there is a large variability in the databases and evaluation criteria used in the literature, which hampers a direct comparison of the different studies. This work is aimed at summarizing the results of the available algorithms for the detection and classification of DR pathology. A detailed literature search was conducted using PubMed. Selected relevant studies in the last 10 years were scrutinized and included in the review. Furthermore, we will try to give an overview of the available commercial software for automatic retinal image analysis. PMID:26953020

  18. Automated Peripheral Neuropathy Assessment Using Optical Imaging and Foot Anthropometry.

    PubMed

    Siddiqui, Hafeez-U R; Spruce, Michelle; Alty, Stephen R; Dudley, Sandra

    2015-08-01

    A large proportion of individuals who live with type-2 diabetes suffer from plantar sensory neuropathy. Regular testing and assessment for the condition is required to avoid ulceration or other damage to patient's feet. Currently accepted practice involves a trained clinician testing a patient's feet manually with a hand-held nylon monofilament probe. The procedure is time consuming, labor intensive, requires special training, is prone to error, and repeatability is difficult. With the vast increase in type-2 diabetes, the number of plantar sensory neuropathy sufferers has already grown to such an extent as to make a traditional manual test problematic. This paper presents the first investigation of a novel approach to automatically identify the pressure points on a given patient's foot for the examination of sensory neuropathy via optical image processing incorporating plantar anthropometry. The method automatically selects suitable test points on the plantar surface that correspond to those repeatedly chosen by a trained podiatrist. The proposed system automatically identifies the specific pressure points at different locations, namely the toe (hallux), metatarsal heads and heel (Calcaneum) areas. The approach is generic and has shown 100% reliability on the available database used. The database consists of Chinese, Asian, African, and Caucasian foot images. PMID:26186748

  19. Integrated Electrowetting Nanoinjector for Single Cell Transfection

    PubMed Central

    Shekaramiz, Elaheh; Varadarajalu, Ganeshkumar; Day, Philip J.; Wickramasinghe, H. Kumar

    2016-01-01

    Single cell transfection techniques are essential to understand the heterogeneity between cells. We have developed an integrated electrowetting nanoinjector (INENI) to transfect single cells. The high transfection efficiency, controlled dosage delivery and ease of INENI fabrication promote the widespread application of the INENI in cell transfection assays. PMID:27374766

  20. Automated Breast Ultrasound: Dual-Sided Compared with Single-Sided Imaging.

    PubMed

    Larson, Eric D; Lee, Won-Mean; Roubidoux, Marilyn A; Goodsitt, Mitchel M; Lashbrook, Chris; Zafar, Fouzaan; Kripfgans, Oliver D; Thomenius, Kai; Carson, Paul L

    2016-09-01

    The design and performance of a mammographically configured, dual-sided, automated breast ultrasound (ABUS) 3-D imaging system are described. Dual-sided imaging (superior and inferior) is compared with single-sided imaging to aid decisions on clinical implementation of the more complex, but potentially higher-quality dual-sided imaging. Marked improvement in image quality and coverage of the breast is obtained in dual-sided ultrasound over single-sided ultrasound. Among hypo-echoic masses imaged, there are increases in the mean contrast-to-noise ratio of 57% and 79%, respectively, for spliced dual-sided versus superior or inferior single-sided imaging. The fractional breast volume coverage, defined as the percentage volume in the transducer field of view that is imaged with clinically acceptable quality, is improved from 59% in both superior and inferior single-sided imaging to 89% in dual-sided imaging. Applying acoustic coupling to the breast requires more effort or sophisticated methods in dual-sided imaging than in single-sided imaging. PMID:27264914

  1. Automated Analysis and Reannotation of Subcellular Locations in Confocal Images from the Human Protein Atlas

    PubMed Central

    Li, Jieyue; Newberg, Justin Y.; Uhlén, Mathias; Lundberg, Emma; Murphy, Robert F.

    2012-01-01

    The Human Protein Atlas contains immunofluorescence images showing subcellular locations for thousands of proteins. These are currently annotated by visual inspection. In this paper, we describe automated approaches to analyze the images and their use to improve annotation. We began by training classifiers to recognize the annotated patterns. By ranking proteins according to the confidence of the classifier, we generated a list of proteins that were strong candidates for reexamination. In parallel, we applied hierarchical clustering to group proteins and identified proteins whose annotations were inconsistent with the remainder of the proteins in their cluster. These proteins were reexamined by the original annotators, and a significant fraction had their annotations changed. The results demonstrate that automated approaches can provide an important complement to visual annotation. PMID:23226299

  2. Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

    PubMed Central

    Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D. Joshua

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi. PMID:21461364

  3. Automated classification of female facial beauty by image analysis and supervised learning

    NASA Astrophysics Data System (ADS)

    Gunes, Hatice; Piccardi, Massimo; Jan, Tony

    2004-01-01

    The fact that perception of facial beauty may be a universal concept has long been debated amongst psychologists and anthropologists. In this paper, we performed experiments to evaluate the extent of beauty universality by asking a number of diverse human referees to grade a same collection of female facial images. Results obtained show that the different individuals gave similar votes, thus well supporting the concept of beauty universality. We then trained an automated classifier using the human votes as the ground truth and used it to classify an independent test set of facial images. The high accuracy achieved proves that this classifier can be used as a general, automated tool for objective classification of female facial beauty. Potential applications exist in the entertainment industry and plastic surgery.

  4. Serial two-photon tomography: an automated method for ex-vivo mouse brain imaging

    PubMed Central

    Ragan, Timothy; Kadiri, Lolahon R.; Venkataraju, Kannan Umadevi; Bahlmann, Karsten; Sutin, Jason; Taranda, Julian; Arganda-Carreras, Ignacio; Kim, Yongsoo; Seung, H. Sebastian

    2011-01-01

    Here we describe an automated method, which we call serial two-photon (STP) tomography, that achieves high-throughput fluorescence imaging of mouse brains by integrating two-photon microscopy and tissue sectioning. STP tomography generates high-resolution datasets that are free of distortions and can be readily warped in 3D, for example, for comparing multiple anatomical tracings. This method opens the door to routine systematic studies of neuroanatomy in mouse models of human brain disorders. PMID:22245809

  5. [Automated imaging of the optic nerve and optic nerve fibers is essential to daily clinical practice].

    PubMed

    Lachkar, Y

    2004-06-01

    Chronic open-angle glaucoma is a progressive optical neuropathy. Automated imaging of the optic disc and optic nerve fibers provides reliable analysis of the optic nerve as well as long-term follow-up of neuropathy patients. HRT, GDX-VCC, and OCT, which analyze the optic disc and optical fibers, provide indisputable assistance in improving screening techniques and the follow-up of progressive glaucoma. PMID:15319758

  6. Automated semantic indexing of imaging reports to support retrieval of medical images in the multimedia electronic medical record.

    PubMed

    Lowe, H J; Antipov, I; Hersh, W; Smith, C A; Mailhot, M

    1999-12-01

    This paper describes preliminary work evaluating automated semantic indexing of radiology imaging reports to represent images stored in the Image Engine multimedia medical record system at the University of Pittsburgh Medical Center. The authors used the SAPHIRE indexing system to automatically identify important biomedical concepts within radiology reports and represent these concepts with terms from the 1998 edition of the U.S. National Library of Medicine's Unified Medical Language System (UMLS) Metathesaurus. This automated UMLS indexing was then compared with manual UMLS indexing of the same reports. Human indexing identified appropriate UMLS Metathesaurus descriptors for 81% of the important biomedical concepts contained in the report set. SAPHIRE automatically identified UMLS Metathesaurus descriptors for 64% of the important biomedical concepts contained in the report set. The overall conclusions of this pilot study were that the UMLS metathesaurus provided adequate coverage of the majority of the important concepts contained within the radiology report test set and that SAPHIRE could automatically identify and translate almost two thirds of these concepts into appropriate UMLS descriptors. Further work is required to improve both the recall and precision of this automated concept extraction process. PMID:10805018

  7. Automating PACS quality control with the Vanderbilt image processing enterprise resource

    NASA Astrophysics Data System (ADS)

    Esparza, Michael L.; Welch, E. Brian; Landman, Bennett A.

    2012-02-01

    Precise image acquisition is an integral part of modern patient care and medical imaging research. Periodic quality control using standardized protocols and phantoms ensures that scanners are operating according to specifications, yet such procedures do not ensure that individual datasets are free from corruption; for example due to patient motion, transient interference, or physiological variability. If unacceptable artifacts are noticed during scanning, a technologist can repeat a procedure. Yet, substantial delays may be incurred if a problematic scan is not noticed until a radiologist reads the scans or an automated algorithm fails. Given scores of slices in typical three-dimensional scans and widevariety of potential use cases, a technologist cannot practically be expected inspect all images. In large-scale research, automated pipeline systems have had great success in achieving high throughput. However, clinical and institutional workflows are largely based on DICOM and PACS technologies; these systems are not readily compatible with research systems due to security and privacy restrictions. Hence, quantitative quality control has been relegated to individual investigators and too often neglected. Herein, we propose a scalable system, the Vanderbilt Image Processing Enterprise Resource (VIPER) to integrate modular quality control and image analysis routines with a standard PACS configuration. This server unifies image processing routines across an institutional level and provides a simple interface so that investigators can collaborate to deploy new analysis technologies. VIPER integrates with high performance computing environments has successfully analyzed all standard scans from our institutional research center over the course of the last 18 months.

  8. Automated Formosat Image Processing System for Rapid Response to International Disasters

    NASA Astrophysics Data System (ADS)

    Cheng, M. C.; Chou, S. C.; Chen, Y. C.; Chen, B.; Liu, C.; Yu, S. J.

    2016-06-01

    FORMOSAT-2, Taiwan's first remote sensing satellite, was successfully launched in May of 2004 into the Sun-synchronous orbit at 891 kilometers of altitude. With the daily revisit feature, the 2-m panchromatic, 8-m multi-spectral resolution images captured have been used for researches and operations in various societal benefit areas. This paper details the orchestration of various tasks conducted in different institutions in Taiwan in the efforts responding to international disasters. The institutes involved including its space agency-National Space Organization (NSPO), Center for Satellite Remote Sensing Research of National Central University, GIS Center of Feng-Chia University, and the National Center for High-performance Computing. Since each institution has its own mandate, the coordinated tasks ranged from receiving emergency observation requests, scheduling and tasking of satellite operation, downlink to ground stations, images processing including data injection, ortho-rectification, to delivery of image products. With the lessons learned from working with international partners, the FORMOSAT Image Processing System has been extensively automated and streamlined with a goal to shorten the time between request and delivery in an efficient manner. The integrated team has developed an Application Interface to its system platform that provides functions of search in archive catalogue, request of data services, mission planning, inquiry of services status, and image download. This automated system enables timely image acquisition and substantially increases the value of data product. Example outcome of these efforts in recent response to support Sentinel Asia in Nepal Earthquake is demonstrated herein.

  9. Automated cell viability assessment using a microfluidics based portable imaging flow analyzer.

    PubMed

    Jagannadh, Veerendra Kalyan; Adhikari, Jayesh Vasudeva; Gorthi, Sai Siva

    2015-03-01

    In this work, we report a system-level integration of portable microscopy and microfluidics for the realization of optofluidic imaging flow analyzer with a throughput of 450 cells/s. With the use of a cellphone augmented with off-the-shelf optical components and custom designed microfluidics, we demonstrate a portable optofluidic imaging flow analyzer. A multiple microfluidic channel geometry was employed to demonstrate the enhancement of throughput in the context of low frame-rate imaging systems. Using the cell-phone based digital imaging flow analyzer, we have imaged yeast cells present in a suspension. By digitally processing the recorded videos of the flow stream on the cellphone, we demonstrated an automated cell viability assessment of the yeast cell population. In addition, we also demonstrate the suitability of the system for blood cell counting. PMID:26015835

  10. Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles.

    PubMed

    Lahmiri, Salim; Gargour, Christian S; Gabrea, Marcel

    2014-10-01

    An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity. PMID:26609393

  11. Automated cell viability assessment using a microfluidics based portable imaging flow analyzer

    PubMed Central

    Jagannadh, Veerendra Kalyan; Adhikari, Jayesh Vasudeva; Gorthi, Sai Siva

    2015-01-01

    In this work, we report a system-level integration of portable microscopy and microfluidics for the realization of optofluidic imaging flow analyzer with a throughput of 450 cells/s. With the use of a cellphone augmented with off-the-shelf optical components and custom designed microfluidics, we demonstrate a portable optofluidic imaging flow analyzer. A multiple microfluidic channel geometry was employed to demonstrate the enhancement of throughput in the context of low frame-rate imaging systems. Using the cell-phone based digital imaging flow analyzer, we have imaged yeast cells present in a suspension. By digitally processing the recorded videos of the flow stream on the cellphone, we demonstrated an automated cell viability assessment of the yeast cell population. In addition, we also demonstrate the suitability of the system for blood cell counting. PMID:26015835

  12. RootGraph: a graphic optimization tool for automated image analysis of plant roots

    PubMed Central

    Cai, Jinhai; Zeng, Zhanghui; Connor, Jason N.; Huang, Chun Yuan; Melino, Vanessa; Kumar, Pankaj; Miklavcic, Stanley J.

    2015-01-01

    This paper outlines a numerical scheme for accurate, detailed, and high-throughput image analysis of plant roots. In contrast to existing root image analysis tools that focus on root system-average traits, a novel, fully automated and robust approach for the detailed characterization of root traits, based on a graph optimization process is presented. The scheme, firstly, distinguishes primary roots from lateral roots and, secondly, quantifies a broad spectrum of root traits for each identified primary and lateral root. Thirdly, it associates lateral roots and their properties with the specific primary root from which the laterals emerge. The performance of this approach was evaluated through comparisons with other automated and semi-automated software solutions as well as against results based on manual measurements. The comparisons and subsequent application of the algorithm to an array of experimental data demonstrate that this method outperforms existing methods in terms of accuracy, robustness, and the ability to process root images under high-throughput conditions. PMID:26224880

  13. Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

    NASA Astrophysics Data System (ADS)

    Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

    2009-09-01

    We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

  14. An approach to automated acquisition of cryoEM images from lacey carbon grids

    PubMed Central

    Nicholson, William V.; White, Howard; Trinick, John

    2010-01-01

    An approach to automated acquisition of cryoEM image data from lacey carbon grids using the Leginon program is described. Automated liquid nitrogen top up of the specimen holder dewar was used as a step towards full automation, without operator intervention during the course of data collection. During cryoEM studies of actin labelled with myosin V, we have found it necessary to work with lacey grids rather than Quantifoil or C-flat grids due to interaction of myosin V with the support film. Lacey grids have irregular holes of variable shape and size, in contrast to Quantifoil or C-flat grids which have a regular array of similar circular holes on each grid square. Other laboratories also prefer to work with grids with irregular holes for a variety of reasons. Therefore, it was necessary to develop a different strategy from normal Leginon usage for working with lacey grids for targetting holes for image acquisition and suitable areas for focussing prior to image acquisition. This approach was implemented by using the extensible framework provided by Leginon and by developing a new MSI application within that framework which includes a new Leginon node (for a novel method for finding focus targets). PMID:20817100

  15. Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

    SciTech Connect

    Jurrus, Elizabeth R.; Watanabe, Shigeki; Giuly, Richard J.; Paiva, Antonio R.; Ellisman, Mark H.; Jorgensen, Erik M.; Tasdizen, Tolga

    2013-01-01

    Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.

  16. Data acceptance for automated leukocyte tracking through segmentation of spatiotemporal images.

    PubMed

    Ray, Nilanjan; Acton, Scott T

    2005-10-01

    A crucial task in inflammation research and inflammatory drug validation is leukocyte velocity data collection from microscopic video imagery. Since manual methods are bias-prone and extremely time consuming, automated tracking methods are required to compute cell velocities. However, an automated tracking method is of little practical use unless it is accompanied by a mechanism to validate the tracker output. In this paper, we propose a validation technique that accepts or rejects the output of automated tracking methods. The proposed method first generates a spatiotemporal image from the cell locations given by a tracking method; then, it segments the spatiotemporal image to detect the presence or absence of a leukocyte. For segmenting the spatiotemporal images, we employ an edge-direction sensitive nonlinear filter followed by an active contour based technique. The proposed nonlinear filter, the maximum absolute average directional derivative (MAADD), first computes the magnitude of the mean directional derivative over an oriented line segment and then chooses the maximum of all such values within a range of orientations of the line segment. The proposed active contour segmentation is obtained via growing contours controlled by a two-dimensional force field, which is constructed by imposing a Dirichlet boundary condition on the gradient vector flow (GVF) field equations. The performance of the proposed validation method is reported here for the outputs of three different tracking techniques: the method was successful in 97% of the trials using manual tracking, in 94% using correlation tracking and in 93% using active contour tracking. PMID:16235656

  17. Semi-automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

    PubMed Central

    Jurrus, Elizabeth; Watanabe, Shigeki; Giuly, Richard J.; Paiva, Antonio R. C.; Ellisman, Mark H.; Jorgensen, Erik M.; Tasdizen, Tolga

    2013-01-01

    Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes. PMID:22644867

  18. Automated construction of arterial and venous trees in retinal images.

    PubMed

    Hu, Qiao; Abràmoff, Michael D; Garvin, Mona K

    2015-10-01

    While many approaches exist to segment retinal vessels in fundus photographs, only a limited number focus on the construction and disambiguation of arterial and venous trees. Previous approaches are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to large vessels. We propose a more global framework to generate arteriovenous trees in retinal images, given a vessel segmentation. In particular, our approach consists of three stages. The first stage is to generate an overconnected vessel network, named the vessel potential connectivity map (VPCM), consisting of vessel segments and the potential connectivity between them. The second stage is to disambiguate the VPCM into multiple anatomical trees, using a graph-based metaheuristic algorithm. The third stage is to classify these trees into arterial or venous (A/V) trees. We evaluated our approach with a ground truth built based on a public database, showing a pixel-wise classification accuracy of 88.15% using a manual vessel segmentation as input, and 86.11% using an automatic vessel segmentation as input. PMID:26636114

  19. Single-cell transcriptome analysis of endometrial tissue

    PubMed Central

    Krjutškov, K.; Katayama, S.; Saare, M.; Vera-Rodriguez, M.; Lubenets, D.; Samuel, K.; Laisk-Podar, T.; Teder, H.; Einarsdottir, E.; Salumets, A.; Kere, J.

    2016-01-01

    STUDY QUESTION How can we study the full transcriptome of endometrial stromal and epithelial cells at the single-cell level? SUMMARY ANSWER By compiling and developing novel analytical tools for biopsy, tissue cryopreservation and disaggregation, single-cell sorting, library preparation, RNA sequencing (RNA-seq) and statistical data analysis. WHAT IS KNOWN ALREADY Although single-cell transcriptome analyses from various biopsied tissues have been published recently, corresponding protocols for human endometrium have not been described. STUDY DESIGN, SIZE, DURATION The frozen-thawed endometrial biopsies were fluorescence-activated cell sorted (FACS) to distinguish CD13-positive stromal and CD9-positive epithelial cells and single-cell transcriptome analysis performed from biopsied tissues without culturing the cells. We studied gene transcription, applying a modern and efficient RNA-seq protocol. In parallel, endometrial stromal cells were cultured and global expression profiles were compared with uncultured cells. PARTICIPANTS/MATERIALS, SETTING, METHODS For method validation, we used two endometrial biopsies, one from mid-secretory phase (Day 21, LH+8) and another from late-secretory phase (Day 25). The samples underwent single-cell FACS sorting, single-cell RNA-seq library preparation and Illumina sequencing. MAIN RESULTS AND THE ROLE OF CHANCE Here we present a complete pipeline for single-cell gene-expression studies, from clinical sampling to statistical data analysis. Tissue manipulation, starting from disaggregation and cell-type-specific labelling and ending with single-cell automated sorting, is managed within 90 min at low temperature to minimize changes in the gene expression profile. The single living stromal and epithelial cells were sorted using CD13- and CD9-specific antibodies, respectively. Of the 8622 detected genes, 2661 were more active in cultured stromal cells than in biopsy cells. In the comparison of biopsy versus cultured cells, 5603

  20. Scanner-based image quality measurement system for automated analysis of EP output

    NASA Astrophysics Data System (ADS)

    Kipman, Yair; Mehta, Prashant; Johnson, Kate

    2003-12-01

    Inspection of electrophotographic print cartridge quality and compatibility requires analysis of hundreds of pages on a wide population of printers and copiers. Although print quality inspection is often achieved through the use of anchor prints and densitometry, more comprehensive analysis and quantitative data is desired for performance tracking, benchmarking and failure mode analysis. Image quality measurement systems range in price and performance, image capture paths and levels of automation. In order to address the requirements of a specific application, careful consideration was made to print volume, budgetary limits, and the scope of the desired image quality measurements. A flatbed scanner-based image quality measurement system was selected to support high throughput, maximal automation, and sufficient flexibility for both measurement methods and image sampling rates. Using an automatic document feeder (ADF) for sample management, a half ream of prints can be measured automatically without operator intervention. The system includes optical character recognition (OCR) for automatic determination of target type for measurement suite selection. This capability also enables measurement of mixed stacks of targets since each sample is identified prior to measurement. In addition, OCR is used to read toner ID, machine ID, print count, and other pertinent information regarding the printing conditions and environment. This data is saved to a data file along with the measurement results for complete test documentation. Measurement methods were developed to replace current methods of visual inspection and densitometry. The features that were being analyzed visually could be addressed via standard measurement algorithms. Measurement of density proved to be less simple since the scanner is not a densitometer and anything short of an excellent estimation would be meaningless. In order to address the measurement of density, a transfer curve was built to translate the

  1. High resolution FTIR imaging provides automated discrimination and detection of single malaria parasite infected erythrocytes on glass.

    PubMed

    Perez-Guaita, David; Andrew, Dean; Heraud, Philip; Beeson, James; Anderson, David; Richards, Jack; Wood, Bayden R

    2016-06-23

    New highly sensitive tools for malaria diagnostics are urgently needed to enable the detection of infection in asymptomatic carriers and patients with low parasitemia. In pursuit of a highly sensitive diagnostic tool that can identify parasite infections at the single cell level, we have been exploring Fourier transform infrared (FTIR) microscopy using a Focal Plane Array (FPA) imaging detector. Here we report for the first time the application of a new optic configuration developed by Agilent that incorporates 25× condenser and objective Cassegrain optics with a high numerical aperture (NA = 0.81) along with additional high magnification optics within the microscope to provide 0.66 micron pixel resolution (total IR system magnification of 61×) to diagnose malaria parasites at the single cell level on a conventional glass microscope slide. The high quality images clearly resolve the parasite's digestive vacuole demonstrating sub-cellular resolution using this approach. Moreover, we have developed an algorithm that first detects the cells in the infrared image, and secondly extracts the average spectrum. The average spectrum is then run through a model based on Partial Least Squares-Discriminant Analysis (PLS-DA), which diagnoses unequivocally the infected from normal cells. The high quality images, and the fact this measurement can be achieved without a synchrotron source on a conventional glass slide, shows promise as a potential gold standard for malaria detection at the single cell level. PMID:27071693

  2. Semi-automated Digital Imaging and Processing System for Measuring Lake Ice Thickness

    NASA Astrophysics Data System (ADS)

    Singh, Preetpal

    Canada is home to thousands of freshwater lakes and rivers. Apart from being sources of infinite natural beauty, rivers and lakes are an important source of water, food and transportation. The northern hemisphere of Canada experiences extreme cold temperatures in the winter resulting in a freeze up of regional lakes and rivers. Frozen lakes and rivers tend to offer unique opportunities in terms of wildlife harvesting and winter transportation. Ice roads built on frozen rivers and lakes are vital supply lines for industrial operations in the remote north. Monitoring the ice freeze-up and break-up dates annually can help predict regional climatic changes. Lake ice impacts a variety of physical, ecological and economic processes. The construction and maintenance of a winter road can cost millions of dollars annually. A good understanding of ice mechanics is required to build and deem an ice road safe. A crucial factor in calculating load bearing capacity of ice sheets is the thickness of ice. Construction costs are mainly attributed to producing and maintaining a specific thickness and density of ice that can support different loads. Climate change is leading to warmer temperatures causing the ice to thin faster. At a certain point, a winter road may not be thick enough to support travel and transportation. There is considerable interest in monitoring winter road conditions given the high construction and maintenance costs involved. Remote sensing technologies such as Synthetic Aperture Radar have been successfully utilized to study the extent of ice covers and record freeze-up and break-up dates of ice on lakes and rivers across the north. Ice road builders often used Ultrasound equipment to measure ice thickness. However, an automated monitoring system, based on machine vision and image processing technology, which can measure ice thickness on lakes has not been thought of. Machine vision and image processing techniques have successfully been used in manufacturing

  3. Automated turbulences jitters correction with a dual ports imaging Fourier-transform spectrometer

    NASA Astrophysics Data System (ADS)

    Prel, Florent; Lantagne, Stéphane; Moreau, Louis; Roy, Claude

    2015-05-01

    When the scene observed by an imaging Fourier-Transform Spectrometer is not stable in amplitude or in position during the time it takes to acquire to spectrum, spectro-radiometric artifacts are generated. These artifacts reduce the radiometric accuracy and may also damage the spectral line shape. The displacements of the scene in the field of view can be due to air turbulence, platform jitters or scene jitters. We describe an automated correction process based on the information provided by the second output port of a two-port imaging FTS. Corrected and uncorrected data will be compared.

  4. Automated recognition and characterization of solar active regions based on the SOHO/MDI images

    NASA Technical Reports Server (NTRS)

    Pap, J. M.; Turmon, M.; Mukhtar, S.; Bogart, R.; Ulrich, R.; Froehlich, C.; Wehrli, C.

    1997-01-01

    The first results of a new method to identify and characterize the various surface structures on the sun, which may contribute to the changes in solar total and spectral irradiance, are shown. The full disk magnetograms (1024 x 1024 pixels) of the Michelson Doppler Imager (MDI) experiment onboard SOHO are analyzed. Use of a Bayesian inference scheme allows objective, uniform, automated processing of a long sequence of images. The main goal is to identify the solar magnetic features causing irradiance changes. The results presented are based on a pilot time interval of August 1996.

  5. Automated volume of interest delineation and rendering of cone beam CT images in interventional cardiology

    NASA Astrophysics Data System (ADS)

    Lorenz, Cristian; Schäfer, Dirk; Eshuis, Peter; Carroll, John; Grass, Michael

    2012-02-01

    Interventional C-arm systems allow the efficient acquisition of 3D cone beam CT images. They can be used for intervention planning, navigation, and outcome assessment. We present a fast and completely automated volume of interest (VOI) delineation for cardiac interventions, covering the whole visceral cavity including mediastinum and lungs but leaving out rib-cage and spine. The problem is addressed in a model based approach. The procedure has been evaluated on 22 patient cases and achieves an average surface error below 2mm. The method is able to cope with varying image intensities, varying truncations due to the limited reconstruction volume, and partially with heavy metal and motion artifacts.

  6. Automated collection of medical images for research from heterogeneous systems: trials and tribulations

    NASA Astrophysics Data System (ADS)

    Patel, M. N.; Looney, P.; Young, K.; Halling-Brown, M. D.

    2014-03-01

    Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. Over the past two decades both diagnostic and therapeutic imaging have undergone a rapid growth, the ability to be able to harness this large influx of medical images can provide an essential resource for research and training. Traditionally, the systematic collection of medical images for research from heterogeneous sites has not been commonplace within the NHS and is fraught with challenges including; data acquisition, storage, secure transfer and correct anonymisation. Here, we describe a semi-automated system, which comprehensively oversees the collection of both unprocessed and processed medical images from acquisition to a centralised database. The provision of unprocessed images within our repository enables a multitude of potential research possibilities that utilise the images. Furthermore, we have developed systems and software to integrate these data with their associated clinical data and annotations providing a centralised dataset for research. Currently we regularly collect digital mammography images from two sites and partially collect from a further three, with efforts to expand into other modalities and sites currently ongoing. At present we have collected 34,014 2D images from 2623 individuals. In this paper we describe our medical image collection system for research and discuss the wide spectrum of challenges faced during the design and implementation of such systems.

  7. Antiscatter stationary-grid artifacts automated detection and removal in projection radiography images

    NASA Astrophysics Data System (ADS)

    Belykh, Igor; Cornelius, Craig W.

    2001-07-01

    Antiscatter grids absorb scattered radiation and increase X- ray image contrast. Stationary grids leave line artifacts or create Moire patterns on resized digital images. Various grid designs were investigated to determine relevant physical properties that affect an image. A detection algorithm is based on grid peak determination in the image's averaged 1D Fourier spectrum. Grid artifact removal is based on frequency filtering in a spatial dimension orthogonal to the grid stripes. Different filter design algorithms were investigated to choose the transfer function that maximizes the suppression of grid artifacts with minimal image distortion. Algorithms were tested on synthetic data containing a variety of SNRs and grid spatial inclinations, on radiographic data containing phantoms with and without grids, and on a set of real CR images. Detector and filter performance were optimized by using Intel Signal Processing Library, resulting in a time of about 3 sec to process a 2Kx2.5K CR image on a Pentium II PC> There are no grid artifacts and no image blur revealed on processed images as evaluated by third party technical and medical experts. This automated grid artifact suppression method is built into a new version of Kodak PACS Link Medical Image Manager.

  8. Automated reconstruction of standing posture panoramas from multi-sector long limb x-ray images

    NASA Astrophysics Data System (ADS)

    Miller, Linzey; Trier, Caroline; Ben-Zikri, Yehuda K.; Linte, Cristian A.

    2016-03-01

    Due to the digital X-ray imaging system's limited field of view, several individual sector images are required to capture the posture of an individual in standing position. These images are then "stitched together" to reconstruct the standing posture. We have created an image processing application that automates the stitching, therefore minimizing user input, optimizing workflow, and reducing human error. The application begins with pre-processing the input images by removing artifacts, filtering out isolated noisy regions, and amplifying a seamless bone edge. The resulting binary images are then registered together using a rigid-body intensity based registration algorithm. The identified registration transformations are then used to map the original sector images into the panorama image. Our method focuses primarily on the use of the anatomical content of the images to generate the panoramas as opposed to using external markers employed to aid with the alignment process. Currently, results show robust edge detection prior to registration and we have tested our approach by comparing the resulting automatically-stitched panoramas to the manually stitched panoramas in terms of registration parameters, target registration error of homologous markers, and the homogeneity of the digitally subtracted automatically- and manually-stitched images using 26 patient datasets.

  9. Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging

    PubMed Central

    Roethke, Matthias C.; Kuru, Timur H.; Mueller-Wolf, Maya B.; Agterhuis, Erik; Edler, Christopher; Hohenfellner, Markus; Schlemmer, Heinz-Peter; Hadaschik, Boris A.

    2016-01-01

    Objective To evaluate the diagnostic performance of an automated analysis tool for the assessment of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI) of the prostate. Methods A fully automated analysis tool was used for a retrospective analysis of mpMRI sets (T2-weighted, T1-weighted dynamic contrast-enhanced, and diffusion-weighted sequences). The software provided a malignancy prediction value for each image pixel, defined as Malignancy Attention Index (MAI) that can be depicted as a colour map overlay on the original images. The malignancy maps were compared to histopathology derived from a combination of MRI-targeted and systematic transperineal MRI/TRUS-fusion biopsies. Results In total, mpMRI data of 45 patients were evaluated. With a sensitivity of 85.7% (with 95% CI of 65.4–95.0), a specificity of 87.5% (with 95% CI of 69.0–95.7) and a diagnostic accuracy of 86.7% (with 95% CI of 73.8–93.8) for detection of prostate cancer, the automated analysis results corresponded well with the reported diagnostic accuracies by human readers based on the PI-RADS system in the current literature. Conclusion The study revealed comparable diagnostic accuracies for the detection of prostate cancer of a user-independent MAI-based automated analysis tool and PI-RADS-scoring-based human reader analysis of mpMRI. Thus, the analysis tool could serve as a detection support system for less experienced readers. The results of the study also suggest the potential of MAI-based analysis for advanced lesion assessments, such as cancer extent and staging prediction. PMID:27454770

  10. A Collaborative Resource to Build Consensus for Automated Left Ventricular Segmentation of Cardiac MR Images

    PubMed Central

    Suinesiaputra, Avan; Cowan, Brett R.; Al-Agamy, Ahmed O.; AlAttar, Mustafa A.; Ayache, Nicholas; Fahmy, Ahmed S.; Khalifa, Ayman M.; Medrano-Gracia, Pau; Jolly, Marie-Pierre; Kadish, Alan H.; Lee, Daniel C.; Margeta, Ján; Warfield, Simon K.; Young, Alistair A.

    2013-01-01

    A collaborative framework was initiated to establish a community resource of ground truth segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising 95 patients (73 men, 22 women; mean age 62.73 ± 11.24 years) with coronary artery disease and prior myocardial infarction, were randomly selected from data made available by the Cardiac Atlas Project (Fonseca et al., 2011). Three semi- and two fully-automated raters segmented the left ventricular myocardium from short-axis cardiac MR images as part of a challenge introduced at the STACOM 2011 MICCAI workshop (Suinesiaputra et al., 2012). Consensus myocardium images were generated based on the Expectation-Maximization principle implemented by the STAPLE algorithm (Warfield et al., 2004). The mean sensitivity, specificity, positive predictive and negative predictive values ranged between 0.63-0.85, 0.60-0.98, 0.56-0.94 and 0.83-0.92, respectively, against the STAPLE consensus. Spatial and temporal agreement varied in different amounts for each rater. STAPLE produced high quality consensus images if the region of interest was limited to the area of discrepancy between raters. To maintain the quality of the consensus, an objective measure based on the candidate automated rater performance distribution is proposed. The consensus segmentation based on a combination of manual and automated raters were more consistent than any particular rater, even those with manual input. The consensus is expected to improve with the addition of new automated contributions. This resource is open for future contributions, and is available as a test bed for the evaluation of new segmentation algorithms, through the Cardiac Atlas Project (www.cardiacatlas.org). PMID:24091241

  11. Sfm_georef: Automating image measurement of ground control points for SfM-based projects

    NASA Astrophysics Data System (ADS)

    James, Mike R.

    2016-04-01

    Deriving accurate DEM and orthomosaic image products from UAV surveys generally involves the use of multiple ground control points (GCPs). Here, we demonstrate the automated collection of GCP image measurements for SfM-MVS processed projects, using sfm_georef software (James & Robson, 2012; http://www.lancaster.ac.uk/staff/jamesm/software/sfm_georef.htm). Sfm_georef was originally written to provide geo-referencing procedures for SfM-MVS projects. It has now been upgraded with a 3-D patch-based matching routine suitable for automating GCP image measurement in both aerial and ground-based (oblique) projects, with the aim of reducing the time required for accurate geo-referencing. Sfm_georef is compatible with a range of SfM-MVS software and imports the relevant files that describe the image network, including camera models and tie points. 3-D survey measurements of ground control are then provided, either for natural features or artificial targets distributed over the project area. Automated GCP image measurement is manually initiated through identifying a GCP position in an image by mouse click; the GCP is then represented by a square planar patch in 3-D, textured from the image and oriented parallel to the local topographic surface (as defined by the 3-D positions of nearby tie points). Other images are then automatically examined by projecting the patch into the images (to account for differences in viewing geometry) and carrying out a sub-pixel normalised cross-correlation search in the local area. With two or more observations of a GCP, its 3-D co-ordinates are then derived by ray intersection. With the 3-D positions of three or more GCPs identified, an initial geo-referencing transform can be derived to relate the SfM-MVS co-ordinate system to that of the GCPs. Then, if GCPs are symmetric and identical, image texture from one representative GCP can be used to search automatically for all others throughout the image set. Finally, the GCP observations can be

  12. ATOM - an OMERO add-on for automated import of image data

    PubMed Central

    2011-01-01

    Background Modern microscope platforms are able to generate multiple gigabytes of image data in a single experimental session. In a routine research laboratory workflow, these data are initially stored on the local acquisition computer from which files need to be transferred to the experimenter's (remote) image repository (e.g., DVDs, portable hard discs or server-based storage) because of limited local data storage. Although manual solutions for this migration, such as OMERO - a client-server software for visualising and managing large amounts of image data - exist, this import process may be a time-consuming and tedious task. Findings We have developed ATOM, a Java-based and thus platform-independent add-on for OMERO enabling automated transfer of image data from a wide variety of acquisition software packages into OMERO. ATOM provides a graphical user interface and allows pre-organisation of experimental data for the transfer. Conclusions ATOM is a convenient extension of the OMERO software system. An automated interface to OMERO will be a useful tool for scientists working with file formats supported by the Bio-Formats file format library, a platform-independent library for reading the most common file formats of microscope images. PMID:21978452

  13. Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets

    PubMed Central

    Bhikha, Charita; Andreasen, Arne; Christensen, Erik I.; Letts, Robyn F. R.; Pantanowitz, Adam; Rubin, David M.; Thomsen, Jesper S.; Zhai, Xiao-Yue

    2015-01-01

    An automated approach for tracking individual nephrons through three-dimensional histological image sets of mouse and rat kidneys is presented. In a previous study, the available images were tracked manually through the image sets in order to explore renal microarchitecture. The purpose of the current research is to reduce the time and effort required to manually trace nephrons by creating an automated, intelligent system as a standard tool for such datasets. The algorithm is robust enough to isolate closely packed nephrons and track their convoluted paths despite a number of nonideal, interfering conditions such as local image distortions, artefacts, and interstitial tissue interference. The system comprises image preprocessing, feature extraction, and a custom graph-based tracking algorithm, which is validated by a rule base and a machine learning algorithm. A study of a selection of automatically tracked nephrons, when compared with manual tracking, yields a 95% tracking accuracy for structures in the cortex, while those in the medulla have lower accuracy due to narrower diameter and higher density. Limited manual intervention is introduced to improve tracking, enabling full nephron paths to be obtained with an average of 17 manual corrections per mouse nephron and 58 manual corrections per rat nephron. PMID:26170896

  14. Bioimage informatics approach to automated meibomian gland analysis in infrared images of meibography

    PubMed Central

    Celik, Turgay; Lee, Hwee Kuan; Petznick, Andrea; Tong, Louis

    2013-01-01

    Background Infrared (IR) meibography is an imaging technique to capture the Meibomian glands in the eyelids. These ocular surface structures are responsible for producing the lipid layer of the tear film which helps to reduce tear evaporation. In a normal healthy eye, the glands have similar morphological features in terms of spatial width, in-plane elongation, length. On the other hand, eyes with Meibomian gland dysfunction show visible structural irregularities that help in the diagnosis and prognosis of the disease. However, currently there is no universally accepted algorithm for detection of these image features which will be clinically useful. We aim to develop a method of automated gland segmentation which allows images to be classified. Methods A set of 131 meibography images were acquired from patients from the Singapore National Eye Center. We used a method of automated gland segmentation using Gabor wavelets. Features of the imaged glands including orientation, width, length and curvature were extracted and the IR images enhanced. The images were classified as ‘healthy’, ‘intermediate’ or ‘unhealthy’, through the use of a support vector machine classifier (SVM). Half the images were used for training the SVM and the other half for validation. Independently of this procedure, the meibographs were classified by an expert clinician into the same 3 grades. Results The algorithm correctly detected 94% and 98% of mid-line pixels of gland and inter-gland regions, respectively, on healthy images. On intermediate images, correct detection rates of 92% and 97% of mid-line pixels of gland and inter-gland regions were achieved respectively. The true positive rate of detecting healthy images was 86%, and for intermediate images, 74%. The corresponding false positive rates were 15% and 31% respectively. Using the SVM, the proposed method has 88% accuracy in classifying images into the 3 classes. The classification of images into healthy and unhealthy

  15. Automated Movement Correction for Dynamic PET/CT Images: Evaluation with Phantom and Patient Data

    PubMed Central

    Ye, Hu; Wong, Koon-Pong; Wardak, Mirwais; Dahlbom, Magnus; Kepe, Vladimir; Barrio, Jorge R.; Nelson, Linda D.; Small, Gary W.; Huang, Sung-Cheng

    2014-01-01

    Head movement during a dynamic brain PET/CT imaging results in mismatch between CT and dynamic PET images. It can cause artifacts in CT-based attenuation corrected PET images, thus affecting both the qualitative and quantitative aspects of the dynamic PET images and the derived parametric images. In this study, we developed an automated retrospective image-based movement correction (MC) procedure. The MC method first registered the CT image to each dynamic PET frames, then re-reconstructed the PET frames with CT-based attenuation correction, and finally re-aligned all the PET frames to the same position. We evaluated the MC method's performance on the Hoffman phantom and dynamic FDDNP and FDG PET/CT images of patients with neurodegenerative disease or with poor compliance. Dynamic FDDNP PET/CT images (65 min) were obtained from 12 patients and dynamic FDG PET/CT images (60 min) were obtained from 6 patients. Logan analysis with cerebellum as the reference region was used to generate regional distribution volume ratio (DVR) for FDDNP scan before and after MC. For FDG studies, the image derived input function was used to generate parametric image of FDG uptake constant (Ki) before and after MC. Phantom study showed high accuracy of registration between PET and CT and improved PET images after MC. In patient study, head movement was observed in all subjects, especially in late PET frames with an average displacement of 6.92 mm. The z-direction translation (average maximum = 5.32 mm) and x-axis rotation (average maximum = 5.19 degrees) occurred most frequently. Image artifacts were significantly diminished after MC. There were significant differences (P<0.05) in the FDDNP DVR and FDG Ki values in the parietal and temporal regions after MC. In conclusion, MC applied to dynamic brain FDDNP and FDG PET/CT scans could improve the qualitative and quantitative aspects of images of both tracers. PMID:25111700

  16. A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data

    NASA Astrophysics Data System (ADS)

    Ali, Abder-Rahman A.; Deserno, Thomas M.

    2012-02-01

    Malignant melanoma is the third most frequent type of skin cancer and one of the most malignant tumors, accounting for 79% of skin cancer deaths. Melanoma is highly curable if diagnosed early and treated properly as survival rate varies between 15% and 65% from early to terminal stages, respectively. So far, melanoma diagnosis is depending subjectively on the dermatologist's expertise. Computer-aided diagnosis (CAD) systems based on epiluminescense light microscopy can provide an objective second opinion on pigmented skin lesions (PSL). This work systematically analyzes the evidence of the effectiveness of automated melanoma detection in images from a dermatoscopic device. Automated CAD applications were analyzed to estimate their diagnostic outcome. Searching online databases for publication dates between 1985 and 2011, a total of 182 studies on dermatoscopic CAD were found. With respect to the systematic selection criterions, 9 studies were included, published between 2002 and 2011. Those studies formed databases of 14,421 dermatoscopic images including both malignant "melanoma" and benign "nevus", with 8,110 images being available ranging in resolution from 150 x 150 to 1568 x 1045 pixels. Maximum and minimum of sensitivity and specificity are 100.0% and 80.0% as well as 98.14% and 61.6%, respectively. Area under the receiver operator characteristics (AUC) and pooled sensitivity, specificity and diagnostics odds ratio are respectively 0.87, 0.90, 0.81, and 15.89. So, although that automated melanoma detection showed good accuracy in terms of sensitivity, specificity, and AUC, but diagnostic performance in terms of DOR was found to be poor. This might be due to the lack of dermatoscopic image resources (ground truth) that are needed for comprehensive assessment of diagnostic performance. In future work, we aim at testing this hypothesis by joining dermatoscopic images into a unified database that serves as a standard reference for dermatology related research in

  17. Live single-cell laser tag

    PubMed Central

    Binan, Loïc; Mazzaferri, Javier; Choquet, Karine; Lorenzo, Louis-Etienne; Wang, Yu Chang; Affar, El Bachir; De Koninck, Yves; Ragoussis, Jiannis; Kleinman, Claudia L.; Costantino, Santiago

    2016-01-01

    The ability to conduct image-based, non-invasive cell tagging, independent of genetic engineering, is key to cell biology applications. Here we introduce cell labelling via photobleaching (CLaP), a method that enables instant, specific tagging of individual cells based on a wide array of criteria such as shape, behaviour or positional information. CLaP uses laser illumination to crosslink biotin onto the plasma membrane, coupled with streptavidin conjugates to label individual cells for genomic, cell-tracking, flow cytometry or ultra-microscopy applications. We show that the incorporated mark is stable, non-toxic, retained for several days, and transferred by cell division but not to adjacent cells in culture. To demonstrate the potential of CLaP for genomic applications, we combine CLaP with microfluidics-based single-cell capture followed by transcriptome-wide next-generation sequencing. Finally, we show that CLaP can also be exploited for inducing transient cell adhesion to substrates for microengineering cultures with spatially patterned cell types. PMID:27198043

  18. Single cell correlation fractal dimension of chromatin

    PubMed Central

    Récamier, Vincent; Izeddin, Ignacio; Bosanac, Lana; Dahan, Maxime; Proux, Florence; Darzacq, Xavier

    2014-01-01

    Chromatin is a major nuclear component, and it is an active matter of debate to understand its different levels of spatial organization, as well as its implication in gene regulation. Measurements of nuclear chromatin compaction were recently used to understand how DNA is folded inside the nucleus and to detect cellular dysfunctions such as cancer. Super-resolution imaging opens new possibilities to measure chromatin organization in situ. Here, we performed a direct measure of chromatin compaction at the single cell level. We used histone H2B, one of the 4 core histone proteins forming the nucleosome, as a chromatin density marker. Using photoactivation localization microscopy (PALM) and adaptive optics, we measured the three-dimensional distribution of H2B with nanometric resolution. We computed the distribution of distances between every two points of the chromatin structure, namely the Ripley K(r) distribution. We found that the K(r) distribution of H2B followed a power law, leading to a precise measurement of the correlation fractal dimension of chromatin of 2.7. Moreover, using photoactivable GFP fused to H2B, we observed dynamic evolution of chromatin sub-regions compaction. As a result, the correlation fractal dimension of chromatin reported here can be interpreted as a dynamically maintained non-equilibrium state. PMID:24637833

  19. Live single-cell laser tag.

    PubMed

    Binan, Loïc; Mazzaferri, Javier; Choquet, Karine; Lorenzo, Louis-Etienne; Wang, Yu Chang; Affar, El Bachir; De Koninck, Yves; Ragoussis, Jiannis; Kleinman, Claudia L; Costantino, Santiago

    2016-01-01

    The ability to conduct image-based, non-invasive cell tagging, independent of genetic engineering, is key to cell biology applications. Here we introduce cell labelling via photobleaching (CLaP), a method that enables instant, specific tagging of individual cells based on a wide array of criteria such as shape, behaviour or positional information. CLaP uses laser illumination to crosslink biotin onto the plasma membrane, coupled with streptavidin conjugates to label individual cells for genomic, cell-tracking, flow cytometry or ultra-microscopy applications. We show that the incorporated mark is stable, non-toxic, retained for several days, and transferred by cell division but not to adjacent cells in culture. To demonstrate the potential of CLaP for genomic applications, we combine CLaP with microfluidics-based single-cell capture followed by transcriptome-wide next-generation sequencing. Finally, we show that CLaP can also be exploited for inducing transient cell adhesion to substrates for microengineering cultures with spatially patterned cell types. PMID:27198043

  20. Development of an AutoFlat program for the acquisition of effective flat images in the automated observation system

    NASA Astrophysics Data System (ADS)

    Yoon, Joh-Na; Kim, Yonggi; Kim, Dong-Heun; Yim, Hong-Suh

    2013-12-01

    The purpose of this study is to develop an observation program for obtaining effective flat images that are necessary for photometric observation. The development of the program was achieved by improving the existing method for obtaining twilight flat images. The existing method for obtaining twilight flat images acquires flat images by observing the sky light after sunset or light before sunrise. The decision of when to observe flat images at each night is solely dependent on the judgment of an observer, and thus the obtained flat images for particular nights may not be clean. Especially, in the case of the observatories where an automated observation system is in operation, there is a difficulty that an observer should pay attention during sunrise and sunset in order to obtain flat images. In this study, a computer program is developed to improve this inconvenience and to efficiently perform photometric observation in the observatories where an automated observation system is applied. This program can obtain flat images by calculating the time for obtaining flat images automatically and the exposure time using a numerically calculated function. When obtaining twilight flat images at dusk and at dawn, the developed program performs automated observation and provides effective flat images by acquiring appropriate exposure time considering the sunrise and sunset times that vary depending on the day of observation. The code for performing this task was added to Obs Tool II (Yoon et al. 2006), which is the automated observation system of the Chungbuk National University Observatory, and the usefulness of the developed program was examined by performing an actual automated observation. If this program is applied to other observatories where automated observation is in operation, it is expected that stable and highquality flat images could be obtained, which can be used for the pre-processing of photometric observation data.

  1. Computational analysis of signaling patterns in single cells

    PubMed Central

    Davis, Denise M.; Purvis, Jeremy E.

    2014-01-01

    Signaling proteins are flexible in both form and function. They can bind to multiple molecular partners and integrate diverse types of cellular information. When imaged by time-lapse microscopy, many signaling proteins show complex patterns of activity or localization that vary from cell to cell. This heterogeneity is so prevalent that it has spurred the development of new computational strategies to analyze single-cell signaling patterns. A collective observation from these analyses is that cells appear less heterogeneous when their responses are normalized to, or synchronized with, other single-cell measurements. In many cases, these transformed signaling patterns show distinct dynamical trends that correspond with predictable phenotypic outcomes. When signaling mechanisms are unclear, computational models can suggest putative molecular interactions that are experimentally testable. Thus, computational analysis of single-cell signaling has not only provided new ways to quantify the responses of individual cells, but has helped resolve longstanding questions surrounding many well-studied human signaling proteins including NF-κB, p53, ERK1/2, and CDK2. A number of specific challenges lie ahead for single-cell analysis such as quantifying the contribution of non-cell autonomous signaling as well as the characterization of protein signaling dynamics in vivo. PMID:25263011

  2. Automated segmentation of cardiac visceral fat in low-dose non-contrast chest CT images

    NASA Astrophysics Data System (ADS)

    Xie, Yiting; Liang, Mingzhu; Yankelevitz, David F.; Henschke, Claudia I.; Reeves, Anthony P.

    2015-03-01

    Cardiac visceral fat was segmented from low-dose non-contrast chest CT images using a fully automated method. Cardiac visceral fat is defined as the fatty tissues surrounding the heart region, enclosed by the lungs and posterior to the sternum. It is measured by constraining the heart region with an Anatomy Label Map that contains robust segmentations of the lungs and other major organs and estimating the fatty tissue within this region. The algorithm was evaluated on 124 low-dose and 223 standard-dose non-contrast chest CT scans from two public datasets. Based on visual inspection, 343 cases had good cardiac visceral fat segmentation. For quantitative evaluation, manual markings of cardiac visceral fat regions were made in 3 image slices for 45 low-dose scans and the Dice similarity coefficient (DSC) was computed. The automated algorithm achieved an average DSC of 0.93. Cardiac visceral fat volume (CVFV), heart region volume (HRV) and their ratio were computed for each case. The correlation between cardiac visceral fat measurement and coronary artery and aortic calcification was also evaluated. Results indicated the automated algorithm for measuring cardiac visceral fat volume may be an alternative method to the traditional manual assessment of thoracic region fat content in the assessment of cardiovascular disease risk.

  3. Protein expression analyses at the single cell level.

    PubMed

    Ohno, Masae; Karagiannis, Peter; Taniguchi, Yuichi

    2014-01-01

    The central dogma of molecular biology explains how genetic information is converted into its end product, proteins, which are responsible for the phenotypic state of the cell. Along with the protein type, the phenotypic state depends on the protein copy number. Therefore, quantification of the protein expression in a single cell is critical for quantitative characterization of the phenotypic states. Protein expression is typically a dynamic and stochastic phenomenon that cannot be well described by standard experimental methods. As an alternative, fluorescence imaging is being explored for the study of protein expression, because of its high sensitivity and high throughput. Here we review key recent progresses in fluorescence imaging-based methods and discuss their application to proteome analysis at the single cell level. PMID:25197931

  4. Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction.

    PubMed

    Holan, Scott H; Viator, John A

    2008-06-21

    Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples. PMID:18495977

  5. Drop-on-Demand Single Cell Isolation and Total RNA Analysis

    PubMed Central

    Moon, Sangjun; Kim, Yun-Gon; Dong, Lingsheng; Lombardi, Michael; Haeggstrom, Edward; Jensen, Roderick V.; Hsiao, Li-Li; Demirci, Utkan

    2011-01-01

    Technologies that rapidly isolate viable single cells from heterogeneous solutions have significantly contributed to the field of medical genomics. Challenges remain both to enable efficient extraction, isolation and patterning of single cells from heterogeneous solutions as well as to keep them alive during the process due to a limited degree of control over single cell manipulation. Here, we present a microdroplet based method to isolate and pattern single cells from heterogeneous cell suspensions (10% target cell mixture), preserve viability of the extracted cells (97.0±0.8%), and obtain genomic information from isolated cells compared to the non-patterned controls. The cell encapsulation process is both experimentally and theoretically analyzed. Using the isolated cells, we identified 11 stem cell markers among 1000 genes and compare to the controls. This automated platform enabling high-throughput cell manipulation for subsequent genomic analysis employs fewer handling steps compared to existing methods. PMID:21412416

  6. Can Automated Imaging for Optic Disc and Retinal Nerve Fiber Layer Analysis Aid Glaucoma Detection?

    PubMed Central

    Banister, Katie; Boachie, Charles; Bourne, Rupert; Cook, Jonathan; Burr, Jennifer M.; Ramsay, Craig; Garway-Heath, David; Gray, Joanne; McMeekin, Peter; Hernández, Rodolfo; Azuara-Blanco, Augusto

    2016-01-01

    Purpose To compare the diagnostic performance of automated imaging for glaucoma. Design Prospective, direct comparison study. Participants Adults with suspected glaucoma or ocular hypertension referred to hospital eye services in the United Kingdom. Methods We evaluated 4 automated imaging test algorithms: the Heidelberg Retinal Tomography (HRT; Heidelberg Engineering, Heidelberg, Germany) glaucoma probability score (GPS), the HRT Moorfields regression analysis (MRA), scanning laser polarimetry (GDx enhanced corneal compensation; Glaucoma Diagnostics (GDx), Carl Zeiss Meditec, Dublin, CA) nerve fiber indicator (NFI), and Spectralis optical coherence tomography (OCT; Heidelberg Engineering) retinal nerve fiber layer (RNFL) classification. We defined abnormal tests as an automated classification of outside normal limits for HRT and OCT or NFI ≥ 56 (GDx). We conducted a sensitivity analysis, using borderline abnormal image classifications. The reference standard was clinical diagnosis by a masked glaucoma expert including standardized clinical assessment and automated perimetry. We analyzed 1 eye per patient (the one with more advanced disease). We also evaluated the performance according to severity and using a combination of 2 technologies. Main Outcome Measures Sensitivity and specificity, likelihood ratios, diagnostic, odds ratio, and proportion of indeterminate tests. Results We recruited 955 participants, and 943 were included in the analysis. The average age was 60.5 years (standard deviation, 13.8 years); 51.1% were women. Glaucoma was diagnosed in at least 1 eye in 16.8%; 32% of participants had no glaucoma-related findings. The HRT MRA had the highest sensitivity (87.0%; 95% confidence interval [CI], 80.2%–92.1%), but lowest specificity (63.9%; 95% CI, 60.2%–67.4%); GDx had the lowest sensitivity (35.1%; 95% CI, 27.0%–43.8%), but the highest specificity (97.2%; 95% CI, 95.6%–98.3%). The HRT GPS sensitivity was 81.5% (95% CI, 73.9%–87.6%), and

  7. Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue

    PubMed Central

    Bjornsson, Christopher S; Lin, Gang; Al-Kofahi, Yousef; Narayanaswamy, Arunachalam; Smith, Karen L; Shain, William; Roysam, Badrinath

    2009-01-01

    Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic ‘divide and conquer’ methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick (~100 μm) slices of rat brain tissue were labeled using 3 – 5 fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81–92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system. PMID:18294697

  8. Video imaging system for automated shaping and analysis of complex locomotory behavior.

    PubMed

    Publicover, Nelson G; Hayes, Linda J; Fernando Guerrero, L; Hunter, Kenneth W

    2009-08-30

    Although many observational technologies have been developed for the study of behavior, most of these technologies have suffered from the inability to engender highly reproducible behaviors that can be observed and modified. We have developed ACROBAT (Automated Control in Real-Time of Operant Behavior and Training), a video imaging system and associated computer algorithms that allow the fully automated shaping and analysis of complex locomotory behaviors. While this operant conditioning system is particularly useful for measuring the acquisition and maintenance of complex topographies, it also provides a more general and user friendly platform on which to develop novel paradigms for the study of learning and memory in animals. In this paper we describe the instrumentation and software developed, demonstrate the use of ACROBAT to shape a specific topography, and show how the system can be used to facilitate the study of arthritic pain in mice. PMID:19501618

  9. Automated classification of optical coherence tomography images of human atrial tissue.

    PubMed

    Gan, Yu; Tsay, David; Amir, Syed B; Marboe, Charles C; Hendon, Christine P

    2016-10-01

    Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions. PMID:26926869

  10. Automating the Processing Steps for Obtaining Bone Tissue-Engineered Substitutes: From Imaging Tools to Bioreactors

    PubMed Central

    Costa, Pedro F.; Martins, Albino; Neves, Nuno M.; Gomes, Manuela E.

    2014-01-01

    Bone diseases and injuries are highly incapacitating and result in a high demand for tissue substitutes with specific biomechanical and structural features. Tissue engineering has already proven to be effective in regenerating bone tissue, but has not yet been able to become an economically viable solution due to the complexity of the tissue, which is very difficult to be replicated, eventually requiring the utilization of highly labor-intensive processes. Process automation is seen as the solution for mass production of cellularized bone tissue substitutes at an affordable cost by being able to reduce human intervention as well as reducing product variability. The combination of tools such as medical imaging, computer-aided fabrication, and bioreactor technologies, which are currently used in tissue engineering, shows the potential to generate automated production ecosystems, which will, in turn, enable the generation of commercially available products with widespread clinical application. PMID:24673688

  11. Automated Detection of Oil Depots from High Resolution Images: a New Perspective

    NASA Astrophysics Data System (ADS)

    Ok, A. O.; Başeski, E.

    2015-03-01

    This paper presents an original approach to identify oil depots from single high resolution aerial/satellite images in an automated manner. The new approach considers the symmetric nature of circular oil depots, and it computes the radial symmetry in a unique way. An automated thresholding method to focus on circular regions and a new measure to verify circles are proposed. Experiments are performed on six GeoEye-1 test images. Besides, we perform tests on 16 Google Earth images of an industrial test site acquired in a time series manner (between the years 1995 and 2012). The results reveal that our approach is capable of detecting circle objects in very different/difficult images. We computed an overall performance of 95.8% for the GeoEye-1 dataset. The time series investigation reveals that our approach is robust enough to locate oil depots in industrial environments under varying illumination and environmental conditions. The overall performance is computed as 89.4% for the Google Earth dataset, and this result secures the success of our approach compared to a state-of-the-art approach.

  12. An automated images-to-graphs framework for high resolution connectomics.

    PubMed

    Gray Roncal, William R; Kleissas, Dean M; Vogelstein, Joshua T; Manavalan, Priya; Lillaney, Kunal; Pekala, Michael; Burns, Randal; Vogelstein, R Jacob; Priebe, Carey E; Chevillet, Mark A; Hager, Gregory D

    2015-01-01

    Reconstructing a map of neuronal connectivity is a critical challenge in contemporary neuroscience. Recent advances in high-throughput serial section electron microscopy (EM) have produced massive 3D image volumes of nanoscale brain tissue for the first time. The resolution of EM allows for individual neurons and their synaptic connections to be directly observed. Recovering neuronal networks by manually tracing each neuronal process at this scale is unmanageable, and therefore researchers are developing automated image processing modules. Thus far, state-of-the-art algorithms focus only on the solution to a particular task (e.g., neuron segmentation or synapse identification). In this manuscript we present the first fully-automated images-to-graphs pipeline (i.e., a pipeline that begins with an imaged volume of neural tissue and produces a brain graph without any human interaction). To evaluate overall performance and select the best parameters and methods, we also develop a metric to assess the quality of the output graphs. We evaluate a set of algorithms and parameters, searching possible operating points to identify the best available brain graph for our assessment metric. Finally, we deploy a reference end-to-end version of the pipeline on a large, publicly available data set. This provides a baseline result and framework for community analysis and future algorithm development and testing. All code and data derivatives have been made publicly available in support of eventually unlocking new biofidelic computational primitives and understanding of neuropathologies. PMID:26321942

  13. A method for the automated detection phishing websites through both site characteristics and image analysis

    NASA Astrophysics Data System (ADS)

    White, Joshua S.; Matthews, Jeanna N.; Stacy, John L.

    2012-06-01

    Phishing website analysis is largely still a time-consuming manual process of discovering potential phishing sites, verifying if suspicious sites truly are malicious spoofs and if so, distributing their URLs to the appropriate blacklisting services. Attackers increasingly use sophisticated systems for bringing phishing sites up and down rapidly at new locations, making automated response essential. In this paper, we present a method for rapid, automated detection and analysis of phishing websites. Our method relies on near real-time gathering and analysis of URLs posted on social media sites. We fetch the pages pointed to by each URL and characterize each page with a set of easily computed values such as number of images and links. We also capture a screen-shot of the rendered page image, compute a hash of the image and use the Hamming distance between these image hashes as a form of visual comparison. We provide initial results demonstrate the feasibility of our techniques by comparing legitimate sites to known fraudulent versions from Phishtank.com, by actively introducing a series of minor changes to a phishing toolkit captured in a local honeypot and by performing some initial analysis on a set of over 2.8 million URLs posted to Twitter over a 4 days in August 2011. We discuss the issues encountered during our testing such as resolvability and legitimacy of URL's posted on Twitter, the data sets used, the characteristics of the phishing sites we discovered, and our plans for future work.

  14. Fully automated muscle quality assessment by Gabor filtering of second harmonic generation images

    NASA Astrophysics Data System (ADS)

    Paesen, Rik; Smolders, Sophie; Vega, José Manolo de Hoyos; Eijnde, Bert O.; Hansen, Dominique; Ameloot, Marcel

    2016-02-01

    Although structural changes on the sarcomere level of skeletal muscle are known to occur due to various pathologies, rigorous studies of the reduced sarcomere quality remain scarce. This can possibly be explained by the lack of an objective tool for analyzing and comparing sarcomere images across biological conditions. Recent developments in second harmonic generation (SHG) microscopy and increasing insight into the interpretation of sarcomere SHG intensity profiles have made SHG microscopy a valuable tool to study microstructural properties of sarcomeres. Typically, sarcomere integrity is analyzed by fitting a set of manually selected, one-dimensional SHG intensity profiles with a supramolecular SHG model. To circumvent this tedious manual selection step, we developed a fully automated image analysis procedure to map the sarcomere disorder for the entire image at once. The algorithm relies on a single-frequency wavelet-based Gabor approach and includes a newly developed normalization procedure allowing for unambiguous data interpretation. The method was validated by showing the correlation between the sarcomere disorder, quantified by the M-band size obtained from manually selected profiles, and the normalized Gabor value ranging from 0 to 1 for decreasing disorder. Finally, to elucidate the applicability of our newly developed protocol, Gabor analysis was used to study the effect of experimental autoimmune encephalomyelitis on the sarcomere regularity. We believe that the technique developed in this work holds great promise for high-throughput, unbiased, and automated image analysis to study sarcomere integrity by SHG microscopy.

  15. An automated images-to-graphs framework for high resolution connectomics

    PubMed Central

    Gray Roncal, William R.; Kleissas, Dean M.; Vogelstein, Joshua T.; Manavalan, Priya; Lillaney, Kunal; Pekala, Michael; Burns, Randal; Vogelstein, R. Jacob; Priebe, Carey E.; Chevillet, Mark A.; Hager, Gregory D.

    2015-01-01

    Reconstructing a map of neuronal connectivity is a critical challenge in contemporary neuroscience. Recent advances in high-throughput serial section electron microscopy (EM) have produced massive 3D image volumes of nanoscale brain tissue for the first time. The resolution of EM allows for individual neurons and their synaptic connections to be directly observed. Recovering neuronal networks by manually tracing each neuronal process at this scale is unmanageable, and therefore researchers are developing automated image processing modules. Thus far, state-of-the-art algorithms focus only on the solution to a particular task (e.g., neuron segmentation or synapse identification). In this manuscript we present the first fully-automated images-to-graphs pipeline (i.e., a pipeline that begins with an imaged volume of neural tissue and produces a brain graph without any human interaction). To evaluate overall performance and select the best parameters and methods, we also develop a metric to assess the quality of the output graphs. We evaluate a set of algorithms and parameters, searching possible operating points to identify the best available brain graph for our assessment metric. Finally, we deploy a reference end-to-end version of the pipeline on a large, publicly available data set. This provides a baseline result and framework for community analysis and future algorithm development and testing. All code and data derivatives have been made publicly available in support of eventually unlocking new biofidelic computational primitives and understanding of neuropathologies. PMID:26321942

  16. MAGNETIC RESONANCE IMAGING COMPATIBLE ROBOTIC SYSTEM FOR FULLY AUTOMATED BRACHYTHERAPY SEED PLACEMENT

    PubMed Central

    Muntener, Michael; Patriciu, Alexandru; Petrisor, Doru; Mazilu, Dumitru; Bagga, Herman; Kavoussi, Louis; Cleary, Kevin; Stoianovici, Dan

    2011-01-01

    Objectives To introduce the development of the first magnetic resonance imaging (MRI)-compatible robotic system capable of automated brachytherapy seed placement. Methods An MRI-compatible robotic system was conceptualized and manufactured. The entire robot was built of nonmagnetic and dielectric materials. The key technology of the system is a unique pneumatic motor that was specifically developed for this application. Various preclinical experiments were performed to test the robot for precision and imager compatibility. Results The robot was fully operational within all closed-bore MRI scanners. Compatibility tests in scanners of up to 7 Tesla field intensity showed no interference of the robot with the imager. Precision tests in tissue mockups yielded a mean seed placement error of 0.72 ± 0.36 mm. Conclusions The robotic system is fully MRI compatible. The new technology allows for automated and highly accurate operation within MRI scanners and does not deteriorate the MRI quality. We believe that this robot may become a useful instrument for image-guided prostate interventions. PMID:17169653

  17. Automated multilayer segmentation and characterization in 3D spectral-domain optical coherence tomography images

    NASA Astrophysics Data System (ADS)

    Hu, Zhihong; Wu, Xiaodong; Hariri, Amirhossein; Sadda, SriniVas R.

    2013-03-01

    Spectral-domain optical coherence tomography (SD-OCT) is a 3-D imaging technique, allowing direct visualization of retinal morphology and architecture. The various layers of the retina may be affected differentially by various diseases. In this study, an automated graph-based multilayer approach was developed to sequentially segment eleven retinal surfaces including the inner retinal bands to the outer retinal bands in normal SD-OCT volume scans at three different stages. For stage 1, the four most detectable and/or distinct surfaces were identified in the four-times-downsampled images and were used as a priori positional information to limit the graph search for other surfaces at stage 2. Eleven surfaces were then detected in the two-times-downsampled images at stage 2, and refined in the original image space at stage 3 using the graph search integrating the estimated morphological shape models. Twenty macular SD-OCT (Heidelberg Spectralis) volume scans from 20 normal subjects (one eye per subject) were used in this study. The overall mean and absolute mean differences in border positions between the automated and manual segmentation for all 11 segmented surfaces were -0.20 +/- 0.53 voxels (-0.76 +/- 2.06 μm) and 0.82 +/- 0.64 voxels (3.19 +/- 2.46 μm). Intensity and thickness properties in the resultant retinal layers were investigated. This investigation in normal subjects may provide a comparative reference for subsequent investigations in eyes with disease.

  18. Efficient Synergistic Single-Cell Genome Assembly

    PubMed Central

    Movahedi, Narjes S.; Embree, Mallory; Nagarajan, Harish; Zengler, Karsten; Chitsaz, Hamidreza

    2016-01-01

    As the vast majority of all microbes are unculturable, single-cell sequencing has become a significant method to gain insight into microbial physiology. Single-cell sequencing methods, currently powered by multiple displacement genome amplification (MDA), have passed important milestones such as finishing and closing the genome of a prokaryote. However, the quality and reliability of genome assemblies from single cells are still unsatisfactory due to uneven coverage depth and the absence of scattered chunks of the genome in the final collection of reads caused by MDA bias. In this work, our new algorithm Hybrid De novo Assembler (HyDA) demonstrates the power of coassembly of multiple single-cell genomic data sets through significant improvement of the assembly quality in terms of predicted functional elements and length statistics. Coassemblies contain significantly more base pairs and protein coding genes, cover more subsystems, and consist of longer contigs compared to individual assemblies by the same algorithm as well as state-of-the-art single-cell assemblers SPAdes and IDBA-UD. Hybrid De novo Assembler (HyDA) is also able to avoid chimeric assemblies by detecting and separating shared and exclusive pieces of sequence for input data sets. By replacing one deep single-cell sequencing experiment with a few single-cell sequencing experiments of lower depth, the coassembly method can hedge against the risk of failure and loss of the sample, without significantly increasing sequencing cost. Application of the single-cell coassembler HyDA to the study of three uncultured members of an alkane-degrading methanogenic community validated the usefulness of the coassembly concept. HyDA is open source and publicly available at http://chitsazlab.org/software.html, and the raw reads are available at http://chitsazlab.org/research.html. PMID:27243002

  19. Efficient Synergistic Single-Cell Genome Assembly.

    PubMed

    Movahedi, Narjes S; Embree, Mallory; Nagarajan, Harish; Zengler, Karsten; Chitsaz, Hamidreza

    2016-01-01

    As the vast majority of all microbes are unculturable, single-cell sequencing has become a significant method to gain insight into microbial physiology. Single-cell sequencing methods, currently powered by multiple displacement genome amplification (MDA), have passed important milestones such as finishing and closing the genome of a prokaryote. However, the quality and reliability of genome assemblies from single cells are still unsatisfactory due to uneven coverage depth and the absence of scattered chunks of the genome in the final collection of reads caused by MDA bias. In this work, our new algorithm Hybrid De novo Assembler (HyDA) demonstrates the power of coassembly of multiple single-cell genomic data sets through significant improvement of the assembly quality in terms of predicted functional elements and length statistics. Coassemblies contain significantly more base pairs and protein coding genes, cover more subsystems, and consist of longer contigs compared to individual assemblies by the same algorithm as well as state-of-the-art single-cell assemblers SPAdes and IDBA-UD. Hybrid De novo Assembler (HyDA) is also able to avoid chimeric assemblies by detecting and separating shared and exclusive pieces of sequence for input data sets. By replacing one deep single-cell sequencing experiment with a few single-cell sequencing experiments of lower depth, the coassembly method can hedge against the risk of failure and loss of the sample, without significantly increasing sequencing cost. Application of the single-cell coassembler HyDA to the study of three uncultured members of an alkane-degrading methanogenic community validated the usefulness of the coassembly concept. HyDA is open source and publicly available at http://chitsazlab.org/software.html, and the raw reads are available at http://chitsazlab.org/research.html. PMID:27243002

  20. Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function.

    PubMed

    Shrestha, Ravi; Mohammed, Shahed K; Hasan, Md Mehedi; Zhang, Xuechao; Wahid, Khan A

    2016-08-01

    Wireless capsule endoscopy (WCE) plays an important role in the diagnosis of gastrointestinal (GI) diseases by capturing images of human small intestine. Accurate diagnosis of endoscopic images depends heavily on the quality of captured images. Along with image and frame rate, brightness of the image is an important parameter that influences the image quality which leads to the design of an efficient illumination system. Such design involves the choice and placement of proper light source and its ability to illuminate GI surface with proper brightness. Light emitting diodes (LEDs) are normally used as sources where modulated pulses are used to control LED's brightness. In practice, instances like under- and over-illumination are very common in WCE, where the former provides dark images and the later provides bright images with high power consumption. In this paper, we propose a low-power and efficient illumination system that is based on an automated brightness algorithm. The scheme is adaptive in nature, i.e., the brightness level is controlled automatically in real-time while the images are being captured. The captured images are segmented into four equal regions and the brightness level of each region is calculated. Then an adaptive sigmoid function is used to find the optimized brightness level and accordingly a new value of duty cycle of the modulated pulse is generated to capture future images. The algorithm is fully implemented in a capsule prototype and tested with endoscopic images. Commercial capsules like Pillcam and Mirocam were also used in the experiment. The results show that the proposed algorithm works well in controlling the brightness level accordingly to the environmental condition, and as a result, good quality images are captured with an average of 40% brightness level that saves power consumption of the capsule. PMID:27333609

  1. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks.

    PubMed

    Reddick, W E; Glass, J O; Cook, E N; Elkin, T D; Deaton, R J

    1997-12-01

    We present a fully automated process for segmentation and classification of multispectral magnetic resonance (MR) images. This hybrid neural network method uses a Kohonen self-organizing neural network for segmentation and a multilayer backpropagation neural network for classification. To separate different tissue types, this process uses the standard T1-, T2-, and PD-weighted MR images acquired in clinical examinations. Volumetric measurements of brain structures, relative to intracranial volume, were calculated for an index transverse section in 14 normal subjects (median age 25 years; seven male, seven female). This index slice was at the level of the basal ganglia, included both genu and splenium of the corpus callosum, and generally, showed the putamen and lateral ventricle. An intraclass correlation of this automated segmentation and classification of tissues with the accepted standard of radiologist identification for the index slice in the 14 volunteers demonstrated coefficients (ri) of 0.91, 0.95, and 0.98 for white matter, gray matter, and ventricular cerebrospinal fluid (CSF), respectively. An analysis of variance for estimates of brain parenchyma volumes in five volunteers imaged five times each demonstrated high intrasubject reproducibility with a significance of at least p < 0.05 for white matter, gray matter, and white/gray partial volumes. The population variation, across 14 volunteers, demonstrated little deviation from the averages for gray and white matter, while partial volume classes exhibited a slightly higher degree of variability. This fully automated technique produces reliable and reproducible MR image segmentation and classification while eliminating intra- and interobserver variability. PMID:9533591

  2. Automated identification of copepods using digital image processing and artificial neural network

    PubMed Central

    2015-01-01

    Background Copepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique. Results We developed an automated technique to extract morphological features of copepods' specimen from captured images using digital image processing techniques. An Artificial Neural Network (ANN) was used to classify the copepod specimens from species Acartia spinicauda, Bestiolina similis, Oithona aruensis, Oithona dissimilis, Oithona simplex, Parvocalanus crassirostris, Tortanus barbatus and Tortanus forcipatus based on the extracted features. 60% of the dataset was used for a two-layer feed-forward network training and the remaining 40% was used as testing dataset for system evaluation. Our approach demonstrated an overall classification accuracy of 93.13% (100% for A. spinicauda, B. similis and O. aruensis, 95% for T. barbatus, 90% for O. dissimilis and P. crassirostris, 85% for O. similis and T. forcipatus). Conclusions The methods presented in this study enable fast classification of copepods to the species level. Future studies should include more classes in the model, improving the selection of features, and reducing the time to capture the copepod images. PMID:26678287

  3. Benchmarking, Research, Development, and Support for ORNL Automated Image and Signature Retrieval (AIR/ASR) Technologies

    SciTech Connect

    Tobin, K.W.

    2004-06-01

    This report describes the results of a Cooperative Research and Development Agreement (CRADA) with Applied Materials, Inc. (AMAT) of Santa Clara, California. This project encompassed the continued development and integration of the ORNL Automated Image Retrieval (AIR) technology, and an extension of the technology denoted Automated Signature Retrieval (ASR), and other related technologies with the Defect Source Identification (DSI) software system that was under development by AMAT at the time this work was performed. In the semiconductor manufacturing environment, defect imagery is used to diagnose problems in the manufacturing line, train yield management engineers, and examine historical data for trends. Image management in semiconductor data systems is a growing cause of concern in the industry as fabricators are now collecting up to 20,000 images each week. In response to this concern, researchers at the Oak Ridge National Laboratory (ORNL) developed a semiconductor-specific content-based image retrieval method and system, also known as AIR. The system uses an image-based query-by-example method to locate and retrieve similar imagery from a database of digital imagery using visual image characteristics. The query method is based on a unique architecture that takes advantage of the statistical, morphological, and structural characteristics of image data, generated by inspection equipment in industrial applications. The system improves the manufacturing process by allowing rapid access to historical records of similar events so that errant process equipment can be isolated and corrective actions can be quickly taken to improve yield. The combined ORNL and AMAT technology is referred to hereafter as DSI-AIR and DSI-ASR.

  4. Experiences and achievements in automated image sequence orientation for close-range photogrammetric projects

    NASA Astrophysics Data System (ADS)

    Barazzetti, Luigi; Forlani, Gianfranco; Remondino, Fabio; Roncella, Riccardo; Scaioni, Marco

    2011-07-01

    Automatic image orientation of close-range image blocks is becoming a task of increasing importance in the practice of photogrammetry. Although image orientation procedures based on interactive tie point measurements do not require any preferential block structure, the use of structured sequences can help to accomplish this task in an automated way. Automatic orientation of image sequences has been widely investigated in the Computer Vision community. Here the method is generally named "Structure from Motion" (SfM), or "Structure and Motion". These refer to the simultaneous estimation of the image orientation parameters and 3D object points of a scene from a set of image correspondences. Such approaches, that generally disregard camera calibration data, do not ensure an accurate 3D reconstruction, which is a requirement for photogrammetric projects. The major contribution of SfM is therefore viewed in the photogrammetric community as a powerful tool to automatically provide a dense set of tie points as well as initial parameters for a final rigorous bundle adjustment. The paper, after a brief overview of automatic procedures for close-range image sequence orientation, will show some characteristic examples. Although powerful and reliable image orientation solutions are nowadays available at research level, there are certain questions that are still open. Thus the paper will also report some open issues, like the geometric characteristics of the sequences, scene's texture and shape, ground constraints (control points and/or free-network adjustment), feature matching techniques, outlier rejection and bundle adjustment models.

  5. Automation of tie-pointing procedure for the geocoding of satellite images

    NASA Astrophysics Data System (ADS)

    Strunz, G.; Knoepfle, W.; Roth, A.

    1994-08-01

    This paper describes two recent developments at the German Remote Sensing Data Centre, which aim at the automatic generation of tie points for optical sensors and radar images, and the operational utilization of automatic tiepointing for ERS-1SAR data. In the first paper, a concept for the multitemporal registration of satellite images from optical sensors is described. This approach aims at the automated extraction of corresponding points in the images. It compresses the detection of distinct points by a so-called interest operator in one image. By cross correlation the corresponding points in the other images are traces. Subpixel accuracy is achieved by a least squares technique for digital image matching. Practical results are shown. where multitemporal image data from the Landsat TM sensor are used. The results are promising; improvements of the procedure are discussed. The second part of the paper describes the operation utilization of an automatic tie pointing, which has been realized for the geocoding of the ERS-1SAR data at the German Processing and Archiving Facility. significant features are extracted from reference data sets. The corresponding SAR image chips are stored in a database. These chips are correlated with the actual SAR data set. The procedure is describes as well as the accuracy achieved. For the time being, the determination of the reference chips is performed interactively. an outlook is given concerning several automatic approaches actually being investigated.

  6. Single-cell biological lasers

    NASA Astrophysics Data System (ADS)

    Gather, Malte C.; Yun, Seok Hyun

    2011-07-01

    Since their invention some 50 years ago, lasers have made a tremendous impact on modern science and technology. Nevertheless, lasing has so far relied on artificial or engineered optical gain materials, such as doped crystals, semiconductors, synthetic dyes and purified gases. Here, we show that fluorescent proteins in cells are a viable gain medium for optical amplification, and report the first successful realization of biological cell lasers based on green fluorescent protein (GFP). We demonstrate in vitro protein lasers using recombinant GFP solutions and introduce a laser based on single live cells expressing GFP. On optical pumping with nanojoule/nanosecond pulses, individual cells in a high-Q microcavity produce bright, directional and narrowband laser emission, with characteristic longitudinal and transverse modes. Lasing cells remained alive even after prolonged lasing action. Light amplification and lasing from and within biological systems pave the way to new forms of intracellular sensing, cytometry and imaging.

  7. Automated Processing of LASCO Coronal Images: Spurious Point-Source-Filtering and Missing-Blocks Correction

    NASA Astrophysics Data System (ADS)

    Pagot, E.; Lamy, P.; Llebaria, A.; Boclet, B.

    2014-04-01

    We report on automated procedures for correcting the images of the LASCO coronagraph for i) spurious quasi-point-sources such as the impacts of cosmic rays, stars, and planets, and ii) the absence of signal due to transmission errors or dropouts, which results in blocks of missing information in the images. Correcting for these undesirable artifacts is mandatory for all quantitative works on the solar corona that require data inversion and/or long series of images, for instance. The nonlinear filtering of spike noise or point-like objects is based on mathematical morphology and implements the procedure opening by morphological reconstruction. However, a simple opening filter is applied whenever the fractional area of corrupted pixels exceeds 50 % of the original image. We describe different strategies for reconstructing the missing information blocks. In general, it is possible to implement the method of averaged neighbors using the two images obtained immediately before and after the corrupted image. For the other cases, and in particular when missing blocks overlapped in three images, we developed an original procedure of weighted interpolation along radial profiles from the center of the Sun that intercept the missing block(s). This procedure is also adequate for the saturated images of bright planets (such as Venus) that bleed along the neighboring pixels. Missing blocks in polarized images may generally be reconstructed using the associated unpolarized image of the same format. But in the case of overlapping missing blocks, we implemented our procedure of weighted interpolation. All tests performed on numerous LASCO-C2 images at various periods of solar activity ( i.e. varying complexity of the structure of the corona) demonstrate the excellent performance of these new procedures, with results vastly superior to the methods implemented so far in the pipeline-processing of the LASCO images.

  8. Intelligent speckle reducing anisotropic diffusion algorithm for automated 3-D ultrasound images.

    PubMed

    Wu, Jun; Wang, Yuanyuan; Yu, Jinhua; Shi, Xinling; Zhang, Junhua; Chen, Yue; Pang, Yun

    2015-02-01

    A novel 3-D filtering method is presented for speckle reduction and detail preservation in automated 3-D ultrasound images. First, texture features of an image are analyzed by using the improved quadtree (QT) decomposition. Then, the optimal homogeneous and the obvious heterogeneous regions are selected from QT decomposition results. Finally, diffusion parameters and diffusion process are automatically decided based on the properties of these two selected regions. The computing time needed for 2-D speckle reduction is very short. However, the computing time required for 3-D speckle reduction is often hundreds of times longer than 2-D speckle reduction. This may limit its potential application in practice. Because this new filter can adaptively adjust the time step of iteration, the computation time is reduced effectively. Both synthetic and real 3-D ultrasound images are used to evaluate the proposed filter. It is shown that this filter is superior to other methods in both practicality and efficiency. PMID:26366596

  9. Automated system for acquisition and image processing for the control and monitoring boned nopal

    NASA Astrophysics Data System (ADS)

    Luevano, E.; de Posada, E.; Arronte, M.; Ponce, L.; Flores, T.

    2013-11-01

    This paper describes the design and fabrication of a system for acquisition and image processing to control the removal of thorns nopal vegetable (Opuntia ficus indica) in an automated machine that uses pulses of a laser of Nd: YAG. The areolas, areas where thorns grow on the bark of the Nopal, are located applying segmentation algorithms to the images obtained by a CCD. Once the position of the areolas is known, coordinates are sent to a motors system that controls the laser to interact with all areolas and remove the thorns of the nopal. The electronic system comprises a video decoder, memory for image and software storage, and digital signal processor for system control. The firmware programmed tasks on acquisition, preprocessing, segmentation, recognition and interpretation of the areolas. This system achievement identifying areolas and generating table of coordinates of them, which will be send the motor galvo system that controls the laser for removal

  10. Automated analysis of infarct heterogeneity on delayed enhancement magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Lu, YingLi; Paul, Gideon A.; Connelly, Kim A.; Wright, Graham A.; Radau, Perry E.

    2011-03-01

    In this work, we propose an automated infarct heterogeneity analysis method for cardiac delayed enhancement magnetic resonance images (DE-MRI). Advantages of this method include that it eliminates manual contouring of the left ventricle and automatically distinguishes infarct, "gray zone" (heterogeneous mixture of healthy and infarct tissue), and healthy tissue pixels despite variability in intensity and noise across images. Quantitative evaluation was performed on 12 patients. The automatically determined infarct core size and gray zone size showed high correlation with that derived from manual delineation (R2 = 0.91 for infarct core size and R2 = 0.87 for gray zone size). The automatic method shortens the evaluation to 5.6 +/-2.2 s per image, compared with 3 min for the manual method. These results indicate a promising method for automatic analysis of infarct heterogeneity with DE-MRI that should be beneficial for reducing variability in quantitative analysis and improving workflow.

  11. PAPNET TM: an automated cytology screener using image processing and neural networks

    NASA Astrophysics Data System (ADS)

    Luck, Randall L.; Tjon-Fo-Sang, Robert; Mango, Laurie; Recht, Joel R.; Lin, Eunice; Knapp, James

    1992-04-01

    The Pap smear is the universally accepted test used for cervical cancer screening. In the United States alone, about 50 to 70 million of these test are done annually. Every one of the tests is done manually be a cytotechnologist looking at cells on a glass slide under a microscope. This paper describes PAPNET, an automated microscope system that combines a high speed image processor and a neural network processor. The image processor performs an algorithmic primary screen of each image. The neural network performs a non-algorithmic secondary classification of candidate cells. The final output of the system is not a diagnosis. Rather it is a display screen of suspicious cells from which a decision about the status of the case can be made.

  12. Automated otolith image classification with multiple views: an evaluation on Sciaenidae.

    PubMed

    Wong, J Y; Chu, C; Chong, V C; Dhillon, S K; Loh, K H

    2016-08-01

    Combined multiple 2D views (proximal, anterior and ventral aspects) of the sagittal otolith are proposed here as a method to capture shape information for fish classification. Classification performance of single view compared with combined 2D views show improved classification accuracy of the latter, for nine species of Sciaenidae. The effects of shape description methods (shape indices, Procrustes analysis and elliptical Fourier analysis) on classification performance were evaluated. Procrustes analysis and elliptical Fourier analysis perform better than shape indices when single view is considered, but all perform equally well with combined views. A generic content-based image retrieval (CBIR) system that ranks dissimilarity (Procrustes distance) of otolith images was built to search query images without the need for detailed information of side (left or right), aspect (proximal or distal) and direction (positive or negative) of the otolith. Methods for the development of this automated classification system are discussed. PMID:27364089

  13. Automating PACS Quality Control with the Vanderbilt Image Processing Enterprise Resource.

    PubMed

    Esparza, Michael L; Welch, E Brian; Landman, Bennett A

    2012-02-12

    Precise image acquisition is an integral part of modern patient care and medical imaging research. Periodic quality control using standardized protocols and phantoms ensures that scanners are operating according to specifications, yet such procedures do not ensure that individual datasets are free from corruption-for example due to patient motion, transient interference, or physiological variability. If unacceptable artifacts are noticed during scanning, a technologist can repeat a procedure. Yet, substantial delays may be incurred if a problematic scan is not noticed until a radiologist reads the scans or an automated algorithm fails. Given scores of slices in typical three-dimensional scans and wide-variety of potential use cases, a technologist cannot practically be expected inspect all images. In large-scale research, automated pipeline systems have had great success in achieving high throughput. However, clinical and institutional workflows are largely based on DICOM and PACS technologies; these systems are not readily compatible with research systems due to security and privacy restrictions. Hence, quantitative quality control has been relegated to individual investigators and too often neglected. Herein, we propose a scalable system, the Vanderbilt Image Processing Enterprise Resource-VIPER, to integrate modular quality control and image analysis routines with a standard PACS configuration. This server unifies image processing routines across an institutional level and provides a simple interface so that investigators can collaborate to deploy new analysis technologies. VIPER integrates with high performance computing environments has successfully analyzed all standard scans from our institutional research center over the course of the last 18 months. PMID:24357910

  14. The use of the Kalman filter in the automated segmentation of EIT lung images.

    PubMed

    Zifan, A; Liatsis, P; Chapman, B E

    2013-06-01

    In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging. PMID:23719169

  15. Automated model-based calibration of short-wavelength infrared (SWIR) imaging spectrographs.

    PubMed

    Kosec, Matjaž; Bürmen, Miran; Tomaževič, Dejan; Pernuš, Franjo; Likar, Boštjan

    2012-10-01

    Among the variety of available hyperspectral imaging systems, the line-scan technique stands out for its short acquisition time and good signal-to-noise ratio. However, due to imperfections in the camera lens and, in particular, optical components of the imaging spectrograph, the acquired images are spatially and spectrally distorted, which can significantly degrade the accuracy of the subsequent hyperspectral image analysis. In this work, we propose and evaluate an automated method for correction of spatial and spectral distortions introduced by a line-scan hyperspectral imaging system operating in the short wavelength infrared (SWIR) spectral range from 1000 nm to 2500 nm. The proposed method is based on non-rigid registration of the distorted and reference images corresponding to two passive calibration objects. The results of the validation show that the proposed method is accurate, efficient, and applicable for calibration of line-scan hyperspectral imaging systems. Moreover, the design of the method and of the calibration objects allows integration with systems operating in diffuse reflectance or transmittance modes. PMID:23031695

  16. An integrated and interactive decision support system for automated melanoma recognition of dermoscopic images.

    PubMed

    Rahman, M M; Bhattacharya, P

    2010-09-01

    This paper presents an integrated and interactive decision support system for the automated melanoma recognition of the dermoscopic images based on image retrieval by content and multiple expert fusion. In this context, the ultimate aim is to support the decision making by retrieving and displaying the relevant past cases as well as predicting the image categories (e.g., melanoma, benign and dysplastic nevi) by combining outputs from different classifiers. However, the most challenging aspect in this domain is to detect the lesion from the healthy background skin and extract the lesion-specific local image features. A thresholding-based segmentation method is applied on the intensity images generated from two different schemes to detect the lesion. For the fusion-based image retrieval and classification, the lesion-specific local color and texture features are extracted and represented in the form of the mean and variance-covariance of color channels and in a combined feature space. The performance is evaluated by using both the precision-recall and classification accuracies. Experimental results on a dermoscopic image collection demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application. PMID:19942406

  17. Automated generation of curved planar reformations from MR images of the spine

    NASA Astrophysics Data System (ADS)

    Vrtovec, Tomaz; Ourselin, Sébastien; Gomes, Lavier; Likar, Boštjan; Pernuš, Franjo

    2007-05-01

    A novel method for automated curved planar reformation (CPR) of magnetic resonance (MR) images of the spine is presented. The CPR images, generated by a transformation from image-based to spine-based coordinate system, follow the structural shape of the spine and allow the whole course of the curved anatomy to be viewed in individual cross-sections. The three-dimensional (3D) spine curve and the axial vertebral rotation, which determine the transformation, are described by polynomial functions. The 3D spine curve passes through the centres of vertebral bodies, while the axial vertebral rotation determines the rotation of vertebrae around the axis of the spinal column. The optimal polynomial parameters are obtained by a robust refinement of the initial estimates of the centres of vertebral bodies and axial vertebral rotation. The optimization framework is based on the automatic image analysis of MR spine images that exploits some basic anatomical properties of the spine. The method was evaluated on 21 MR images from 12 patients and the results provided a good description of spine anatomy, with mean errors of 2.5 mm and 1.7° for the position of the 3D spine curve and axial rotation of vertebrae, respectively. The generated CPR images are independent of the position of the patient in the scanner while comprising both anatomical and geometrical properties of the spine.

  18. Automating quality assurance of digital linear accelerators using a radioluminescent phosphor coated phantom and optical imaging

    NASA Astrophysics Data System (ADS)

    Jenkins, Cesare H.; Naczynski, Dominik J.; Yu, Shu-Jung S.; Yang, Yong; Xing, Lei

    2016-09-01

    Performing mechanical and geometric quality assurance (QA) tests for medical linear accelerators (LINAC) is a predominantly manual process that consumes significant time and resources. In order to alleviate this burden this study proposes a novel strategy to automate the process of performing these tests. The autonomous QA system consists of three parts: (1) a customized phantom coated with radioluminescent material; (2) an optical imaging system capable of visualizing the incidence of the radiation beam, light field or lasers on the phantom; and (3) software to process the captured signals. The radioluminescent phantom, which enables visualization of the radiation beam on the same surface as the light field and lasers, is placed on the couch and imaged while a predefined treatment plan is delivered from the LINAC. The captured images are then processed to self-calibrate the system and perform measurements for evaluating light field/radiation coincidence, jaw position indicators, cross-hair centering, treatment couch position indicators and localizing laser alignment. System accuracy is probed by intentionally introducing errors and by comparing with current clinical methods. The accuracy of self-calibration is evaluated by examining measurement repeatability under fixed and variable phantom setups. The integrated system was able to automatically collect, analyze and report the results for the mechanical alignment tests specified by TG-142. The average difference between introduced and measured errors was 0.13 mm. The system was shown to be consistent with current techniques. Measurement variability increased slightly from 0.1 mm to 0.2 mm when the phantom setup was varied, but no significant difference in the mean measurement value was detected. Total measurement time was less than 10 minutes for all tests as a result of automation. The system’s unique features of a phosphor-coated phantom and fully automated, operator independent self-calibration offer the

  19. Microbleed Detection Using Automated Segmentation (MIDAS): A New Method Applicable to Standard Clinical MR Images

    PubMed Central

    Seghier, Mohamed L.; Kolanko, Magdalena A.; Leff, Alexander P.; Jäger, Hans R.; Gregoire, Simone M.; Werring, David J.

    2011-01-01

    Background Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction. Methodology/Principal Findings Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an “extra” tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (≥2) lobar microbleeds. Conclusions/Significance MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds. PMID:21448456

  20. Automating quality assurance of digital linear accelerators using a radioluminescent phosphor coated phantom and optical imaging.

    PubMed

    Jenkins, Cesare H; Naczynski, Dominik J; Yu, Shu-Jung S; Yang, Yong; Xing, Lei

    2016-09-01

    Performing mechanical and geometric quality assurance (QA) tests for medical linear accelerators (LINAC) is a predominantly manual process that consumes significant time and resources. In order to alleviate this burden this study proposes a novel strategy to automate the process of performing these tests. The autonomous QA system consists of three parts: (1) a customized phantom coated with radioluminescent material; (2) an optical imaging system capable of visualizing the incidence of the radiation beam, light field or lasers on the phantom; and (3) software to process the captured signals. The radioluminescent phantom, which enables visualization of the radiation beam on the same surface as the light field and lasers, is placed on the couch and imaged while a predefined treatment plan is delivered from the LINAC. The captured images are then processed to self-calibrate the system and perform measurements for evaluating light field/radiation coincidence, jaw position indicators, cross-hair centering, treatment couch position indicators and localizing laser alignment. System accuracy is probed by intentionally introducing errors and by comparing with current clinical methods. The accuracy of self-calibration is evaluated by examining measurement repeatability under fixed and variable phantom setups. The integrated system was able to automatically collect, analyze and report the results for the mechanical alignment tests specified by TG-142. The average difference between introduced and measured errors was 0.13 mm. The system was shown to be consistent with current techniques. Measurement variability increased slightly from 0.1 mm to 0.2 mm when the phantom setup was varied, but no significant difference in the mean measurement value was detected. Total measurement time was less than 10 minutes for all tests as a result of automation. The system's unique features of a phosphor-coated phantom and fully automated, operator independent self-calibration offer the

  1. Automated anatomical interpretation of ion distributions in tissue: linking imaging mass spectrometry to curated atlases.

    PubMed

    Verbeeck, Nico; Yang, Junhai; De Moor, Bart; Caprioli, Richard M; Waelkens, Etienne; Van de Plas, Raf

    2014-09-16

    Imaging mass spectrometry (IMS) has become a prime tool for studying the distribution of biomolecules in tissue. Although IMS data sets can become very large, computational methods have made it practically feasible to search these experiments for relevant findings. However, these methods lack access to an important source of information that many human interpretations rely upon: anatomical insight. In this work, we address this need by (1) integrating a curated anatomical data source with an empirically acquired IMS data source, establishing an algorithm-accessible link between them and (2) demonstrating the potential of such an IMS-anatomical atlas link by applying it toward automated anatomical interpretation of ion distributions in tissue. The concept is demonstrated in mouse brain tissue, using the Allen Mouse Brain Atlas as the curated anatomical data source that is linked to MALDI-based IMS experiments. We first develop a method to spatially map the anatomical atlas to the IMS data sets using nonrigid registration techniques. Once a mapping is established, a second computational method, called correlation-based querying, gives an elementary demonstration of the link by delivering basic insight into relationships between ion images and anatomical structures. Finally, a third algorithm moves further beyond both registration and correlation by providing automated anatomical interpretation of ion images. This task is approached as an optimization problem that deconstructs ion distributions as combinations of known anatomical structures. We demonstrate that establishing a link between an IMS experiment and an anatomical atlas enables automated anatomical annotation, which can serve as an important accelerator both for human and machine-guided exploration of IMS experiments. PMID:25153352

  2. Fully automated quantitative analysis of breast cancer risk in DCE-MR images

    NASA Astrophysics Data System (ADS)

    Jiang, Luan; Hu, Xiaoxin; Gu, Yajia; Li, Qiang

    2015-03-01

    Amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) in dynamic contrast enhanced magnetic resonance (DCE-MR) images are two important indices for breast cancer risk assessment in the clinical practice. The purpose of this study is to develop and evaluate a fully automated scheme for quantitative analysis of FGT and BPE in DCE-MR images. Our fully automated method consists of three steps, i.e., segmentation of whole breast, fibroglandular tissues, and enhanced fibroglandular tissues. Based on the volume of interest extracted automatically, dynamic programming method was applied in each 2-D slice of a 3-D MR scan to delineate the chest wall and breast skin line for segmenting the whole breast. This step took advantages of the continuity of chest wall and breast skin line across adjacent slices. We then further used fuzzy c-means clustering method with automatic selection of cluster number for segmenting the fibroglandular tissues within the segmented whole breast area. Finally, a statistical method was used to set a threshold based on the estimated noise level for segmenting the enhanced fibroglandular tissues in the subtraction images of pre- and post-contrast MR scans. Based on the segmented whole breast, fibroglandular tissues, and enhanced fibroglandular tissues, FGT and BPE were automatically computed. Preliminary results of technical evaluation and clinical validation showed that our fully automated scheme could obtain good segmentation of the whole breast, fibroglandular tissues, and enhanced fibroglandular tissues to achieve accurate assessment of FGT and BPE for quantitative analysis of breast cancer risk.

  3. Airborne chemistry single cell level

    NASA Astrophysics Data System (ADS)

    Nilsson, Staffan; Viberg, Peter; Spegel, Peter; Santesson, Sabina; Cedergren, Eila; Degerman, Eva; Johansson, Tomas; Nilsson, Johan

    2002-11-01

    A miniaturized analysis system for the studying of living cells and biochemical reactions in microdrops was developed. Cell studies were performed using single adipocytes in 250-nL drops. Continuous flow-through droplet dispensers, developed in-house, were used for additions to the levitated droplet. Addition of b-adrenergic agonists stimulates the lipolysis in the adipocytes, leading to free fatty acid release and a consequent pH decrease of the surrounding buffer, a change that can be easily followed using a pH-dependent fluorophore continuously monitored by fluorescence imaging detection. An analytical method using capillary electrophoresis and nanospray mass spectrometry for measurement of the cAMP level in activated single adipocytes are now being developed for future use in combination with the levitation technique. The levitation approach was also employed for the screening of nucleation conditions for macromolecules. Here, the acoustic levitator offers a simplified way to determine the main features of the phase diagram (i.e., precipitation diagram). Using the droplet dispensers, different types and amounts of precipitation agents are injected into the levitated drop, allowing a systematic search for nucleation conditions that is not possible using standard crystallization methods. Once the precipitation diagram has been obtained, optimization using standard methods is employed to grow the crystals.

  4. Advances in hardware, software, and automation for 193nm aerial image measurement systems

    NASA Astrophysics Data System (ADS)

    Zibold, Axel M.; Schmid, R.; Seyfarth, A.; Waechter, M.; Harnisch, W.; Doornmalen, H. v.

    2005-05-01

    A new, second generation AIMS fab 193 system has been developed which is capable of emulating lithographic imaging of any type of reticles such as binary and phase shift masks (PSM) including resolution enhancement technologies (RET) such as optical proximity correction (OPC) or scatter bars. The system emulates the imaging process by adjustment of the lithography equivalent illumination and imaging conditions of 193nm wafer steppers including circular, annular, dipole and quadrupole type illumination modes. The AIMS fab 193 allows a rapid prediction of wafer printability of critical mask features, including dense patterns and contacts, defects or repairs by acquiring through-focus image stacks by means of a CCD camera followed by quantitative image analysis. Moreover the technology can be readily applied to directly determine the process window of a given mask under stepper imaging conditions. Since data acquisition is performed electronically, AIMS in many applications replaces the need for costly and time consuming wafer prints using a wafer stepper/ scanner followed by CD SEM resist or wafer analysis. The AIMS fab 193 second generation system is designed for 193nm lithography mask printing predictability down to the 65nm node. In addition to hardware improvements a new modular AIMS software is introduced allowing for a fully automated operation mode. Multiple pre-defined points can be visited and through-focus AIMS measurements can be executed automatically in a recipe based mode. To increase the effectiveness of the automated operation mode, the throughput of the system to locate the area of interest, and to acquire the through-focus images is increased by almost a factor of two in comparison with the first generation AIMS systems. In addition a new software plug-in concept is realised for the tools. One new feature has been successfully introduced as "Global CD Map", enabling automated investigation of global mask quality based on the local determination of

  5. Automated measurement of CT noise in patient images with a novel structure coherence feature

    NASA Astrophysics Data System (ADS)

    Chun, Minsoo; Choi, Young Hun; Hyo Kim, Jong

    2015-12-01

    While the assessment of CT noise constitutes an important task for the optimization of scan protocols in clinical routine, the majority of noise measurements in practice still rely on manual operation, hence limiting their efficiency and reliability. This study presents an algorithm for the automated measurement of CT noise in patient images with a novel structure coherence feature. The proposed algorithm consists of a four-step procedure including subcutaneous fat tissue selection, the calculation of structure coherence feature, the determination of homogeneous ROIs, and the estimation of the average noise level. In an evaluation with 94 CT scans (16 517 images) of pediatric and adult patients along with the participation of two radiologists, ROIs were placed on a homogeneous fat region at 99.46% accuracy, and the agreement of the automated noise measurements with the radiologists’ reference noise measurements (PCC  =  0.86) was substantially higher than the within and between-rater agreements of noise measurements (PCCwithin  =  0.75, PCCbetween  =  0.70). In addition, the absolute noise level measurements matched closely the theoretical noise levels generated by a reduced-dose simulation technique. Our proposed algorithm has the potential to be used for examining the appropriateness of radiation dose and the image quality of CT protocols for research purposes as well as clinical routine.

  6. Automatic nipple detection on 3D images of an automated breast ultrasound system (ABUS)

    NASA Astrophysics Data System (ADS)

    Javanshir Moghaddam, Mandana; Tan, Tao; Karssemeijer, Nico; Platel, Bram

    2014-03-01

    Recent studies have demonstrated that applying Automated Breast Ultrasound in addition to mammography in women with dense breasts can lead to additional detection of small, early stage breast cancers which are occult in corresponding mammograms. In this paper, we proposed a fully automatic method for detecting the nipple location in 3D ultrasound breast images acquired from Automated Breast Ultrasound Systems. The nipple location is a valuable landmark to report the position of possible abnormalities in a breast or to guide image registration. To detect the nipple location, all images were normalized. Subsequently, features have been extracted in a multi scale approach and classification experiments were performed using a gentle boost classifier to identify the nipple location. The method was applied on a dataset of 100 patients with 294 different 3D ultrasound views from Siemens and U-systems acquisition systems. Our database is a representative sample of cases obtained in clinical practice by four medical centers. The automatic method could accurately locate the nipple in 90% of AP (Anterior-Posterior) views and in 79% of the other views.

  7. Automated measurement of CT noise in patient images with a novel structure coherence feature.

    PubMed

    Chun, Minsoo; Choi, Young Hun; Kim, Jong Hyo

    2015-12-01

    While the assessment of CT noise constitutes an important task for the optimization of scan protocols in clinical routine, the majority of noise measurements in practice still rely on manual operation, hence limiting their efficiency and reliability. This study presents an algorithm for the automated measurement of CT noise in patient images with a novel structure coherence feature. The proposed algorithm consists of a four-step procedure including subcutaneous fat tissue selection, the calculation of structure coherence feature, the determination of homogeneous ROIs, and the estimation of the average noise level. In an evaluation with 94 CT scans (16 517 images) of pediatric and adult patients along with the participation of two radiologists, ROIs were placed on a homogeneous fat region at 99.46% accuracy, and the agreement of the automated noise measurements with the radiologists' reference noise measurements (PCC  =  0.86) was substantially higher than the within and between-rater agreements of noise measurements (PCCwithin  =  0.75, PCCbetween  =  0.70). In addition, the absolute noise level measurements matched closely the theoretical noise levels generated by a reduced-dose simulation technique. Our proposed algorithm has the potential to be used for examining the appropriateness of radiation dose and the image quality of CT protocols for research purposes as well as clinical routine. PMID:26561914

  8. Comparison of manually produced and automated cross country movement maps using digital image processing techniques

    NASA Technical Reports Server (NTRS)

    Wynn, L. K.

    1985-01-01

    The Image-Based Information System (IBIS) was used to automate the cross country movement (CCM) mapping model developed by the Defense Mapping Agency (DMA). Existing terrain factor overlays and a CCM map, produced by DMA for the Fort Lewis, Washington area, were digitized and reformatted into geometrically registered images. Terrain factor data from Slope, Soils, and Vegetation overlays were entered into IBIS, and were then combined utilizing IBIS-programmed equations to implement the DMA CCM model. The resulting IBIS-generated CCM map was then compared with the digitized manually produced map to test similarity. The numbers of pixels comprising each CCM region were compared between the two map images, and percent agreement between each two regional counts was computed. The mean percent agreement equalled 86.21%, with an areally weighted standard deviation of 11.11%. Calculation of Pearson's correlation coefficient yielded +9.997. In some cases, the IBIS-calculated map code differed from the DMA codes: analysis revealed that IBIS had calculated the codes correctly. These highly positive results demonstrate the power and accuracy of IBIS in automating models which synthesize a variety of thematic geographic data.

  9. Development of DNA Damage Response Signaling Biomarkers using Automated, Quantitative Image Analysis

    PubMed Central

    Nikolaishvilli-Feinberg, Nana; Cohen, Stephanie M.; Midkiff, Bentley; Zhou, Yingchun; Olorvida, Mark; Ibrahim, Joseph G.; Omolo, Bernard; Shields, Janiel M.; Thomas, Nancy E.; Groben, Pamela A.; Kaufmann, William K.

    2014-01-01

    The DNA damage response (DDR) coordinates DNA repair with cell cycle checkpoints to ameliorate or mitigate the pathological effects of DNA damage. Automated quantitative analysis (AQUA) and Tissue Studio are commercial technologies that use digitized immunofluorescence microscopy images to quantify antigen expression in defined tissue compartments. Because DDR is commonly activated in cancer and may reflect genetic instability within the lesion, a method to quantify DDR in cancer offers potential diagnostic and/or prognostic value. In this study, both AQUA