WorldWideScience

Sample records for automated single-cell image

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

    Science.gov (United States)

    Peitz, Ingmar; van Leeuwen, Rien

    2010-11-07

    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.

  2. Automated Single Cell Data Decontamination Pipeline

    Energy Technology Data Exchange (ETDEWEB)

    Tennessen, Kristin [Lawrence Berkeley National Lab. (LBNL), Walnut Creek, CA (United States). Dept. of Energy Joint Genome Inst.; Pati, Amrita [Lawrence Berkeley National Lab. (LBNL), Walnut Creek, CA (United States). Dept. of Energy Joint Genome Inst.

    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

  3. Low-dose DNA damage and replication stress responses quantified by optimized automated single-cell image analysis

    DEFF Research Database (Denmark)

    Mistrik, Martin; Oplustilova, Lenka; Lukas, Jiri

    2009-01-01

    by environmental or metabolic genotoxic insults is critical for contemporary biomedicine. The available physical, flow cytometry and sophisticated scanning approaches to DNA damage estimation each have some drawbacks such as insufficient sensitivity, limitation to analysis of cells in suspension, or high costs...... sensitive, quantitative, rapid and simple fluorescence image analysis in thousands of adherent cells per day. Sensitive DNA breakage estimation through analysis of phosphorylated histone H2AX (gamma-H2AX), and homologous recombination (HR) assessed by a new RPA/Rad51 dual-marker approach illustrate...

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

    Science.gov (United States)

    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.

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

    NARCIS (Netherlands)

    Syvertsson, S.; Vischer, N.O.E.; Gao, Y.; Hamoen, L.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 fluo

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

    Science.gov (United States)

    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.

  7. Electrical impedance tomographic imaging of a single cell electroporation.

    Science.gov (United States)

    Meir, Arie; Rubinsky, Boris

    2014-06-01

    A living cell placed in a high strength electric field, can undergo a process known as electroporation. It is believed that during electroporation nano-scale defects (pores) occur in the membrane of the cell, causing dramatic changes to the permeability of its membrane. Electroporation is an important technique in biotechnology and medicine and numerous methods are being developed to improve the understanding and use of the technology. We propose to extend the toolbox available for studying electroporation by generating impedance distribution images of the cell as it undergoes electroporation using Electrical Impedance Tomography (EIT). To investigate the feasibility of this concept, we develop a mathematical model of the process of electroporation in a single cell and of EIT of the process and show simulation results of a computer-based finite element model (FEM). Our work is an attempt to develop a new imaging tool for visualizing electroporation in a single cell, offering a different temporal and spatial resolution compared to the state of the art, which includes bulk measurements of electrical properties during single cell electroporation, patch clamp and voltage clamp measurement in single cells and optical imaging with colorimetric dyes during single cell electroporation. This paper is a preliminary theoretic feasibility study.

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

    Science.gov (United States)

    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.

  9. RoboSCell: An automated single cell arraying and analysis instrument

    KAUST Repository

    Sakaki, Kelly

    2009-09-09

    Single cell research has the potential to revolutionize experimental methods in biomedical sciences and contribute to clinical practices. Recent studies suggest analysis of single cells reveals novel features of intracellular processes, cell-to-cell interactions and cell structure. The methods of single cell analysis require mechanical resolution and accuracy that is not possible using conventional techniques. Robotic instruments and novel microdevices can achieve higher throughput and repeatability; however, the development of such instrumentation is a formidable task. A void exists in the state-of-the-art for automated analysis of single cells. With the increase in interest in single cell analyses in stem cell and cancer research the ability to facilitate higher throughput and repeatable procedures is necessary. In this paper, a high-throughput, single cell microarray-based robotic instrument, called the RoboSCell, is described. The proposed instrument employs a partially transparent single cell microarray (SCM) integrated with a robotic biomanipulator for in vitro analyses of live single cells trapped at the array sites. Cells, labeled with immunomagnetic particles, are captured at the array sites by channeling magnetic fields through encapsulated permalloy channels in the SCM. The RoboSCell is capable of systematically scanning the captured cells temporarily immobilized at the array sites and using optical methods to repeatedly measure extracellular and intracellular characteristics over time. The instrument\\'s capabilities are demonstrated by arraying human T lymphocytes and measuring the uptake dynamics of calcein acetoxymethylester-all in a fully automated fashion. © 2009 Springer Science+Business Media, LLC.

  10. Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments

    Science.gov (United States)

    Sachs, Christian Carsten; Grünberger, Alexander; Helfrich, Stefan; Probst, Christopher; Wiechert, Wolfgang; Kohlheyer, Dietrich; Nöh, Katharina

    2016-01-01

    Background Microfluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuous cultivation and observation of individual cells over many generations in a highly parallelized manner. To date, the lack of fully automated image analysis software limits the practical applicability of the MM as a phenotypic screening tool. Results We present an image analysis pipeline for the automated processing of MM time lapse image stacks. The pipeline supports all analysis steps, i.e., image registration, orientation correction, channel/cell detection, cell tracking, and result visualization. Tailored algorithms account for the specialized MM layout to enable a robust automated analysis. Image data generated in a two-day growth study (≈ 90 GB) is analyzed in ≈ 30 min with negligible differences in growth rate between automated and manual evaluation quality. The proposed methods are implemented in the software molyso (MOther machine AnaLYsis SOftware) that provides a new profiling tool to analyze unbiasedly hitherto inaccessible large-scale MM image stacks. Conclusion Presented is the software molyso, a ready-to-use open source software (BSD-licensed) for the unsupervised analysis of MM time-lapse image stacks. molyso source code and user manual are available at https://github.com/modsim/molyso. PMID:27661996

  11. High resolution ultrasound and photoacoustic imaging of single cells

    Directory of Open Access Journals (Sweden)

    Eric M. Strohm

    2016-03-01

    Full Text Available 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.

  12. High resolution ultrasound and photoacoustic imaging of single cells.

    Science.gov (United States)

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

    2016-03-01

    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.

  13. Scaling and automation of a high-throughput single-cell-derived tumor sphere assay chip.

    Science.gov (United States)

    Cheng, Yu-Heng; Chen, Yu-Chih; Brien, Riley; Yoon, Euisik

    2016-10-07

    Recent research suggests that cancer stem-like cells (CSCs) are the key subpopulation for tumor relapse and metastasis. Due to cancer plasticity in surface antigen and enzymatic activity markers, functional tumorsphere assays are promising alternatives for CSC identification. To reliably quantify rare CSCs (1-5%), thousands of single-cell suspension cultures are required. While microfluidics is a powerful tool in handling single cells, previous works provide limited throughput and lack automatic data analysis capability required for high-throughput studies. In this study, we present the scaling and automation of high-throughput single-cell-derived tumor sphere assay chips, facilitating the tracking of up to ∼10 000 cells on a chip with ∼76.5% capture rate. The presented cell capture scheme guarantees sampling a representative population from the bulk cells. To analyze thousands of single-cells with a variety of fluorescent intensities, a highly adaptable analysis program was developed for cell/sphere counting and size measurement. Using a Pluronic® F108 (poly(ethylene glycol)-block-poly(propylene glycol)-block-poly(ethylene glycol)) coating on polydimethylsiloxane (PDMS), a suspension culture environment was created to test a controversial hypothesis: whether larger or smaller cells are more stem-like defined by the capability to form single-cell-derived spheres. Different cell lines showed different correlations between sphere formation rate and initial cell size, suggesting heterogeneity in pathway regulation among breast cancer cell lines. More interestingly, by monitoring hundreds of spheres, we identified heterogeneity in sphere growth dynamics, indicating the cellular heterogeneity even within CSCs. These preliminary results highlight the power of unprecedented high-throughput and automation in CSC studies.

  14. Preparation of Single Cells for Imaging Mass Spectrometry

    Energy Technology Data Exchange (ETDEWEB)

    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.

  15. Automated transportation of single cells using robot-tweezer manipulation system.

    Science.gov (United States)

    Hu, Songyu; Sun, Dong

    2011-08-01

    Manipulation of biological cells becomes increasingly important in biomedical engineering to address challenge issues in cell-cell interaction, drug discovery, and tissue engineering. Significant demand for both accuracy and productivity in cell manipulation highlights the need for automated cell transportation with integrated robotics and micro/nano manipulation technologies. Optical tweezers, which use highly focused low-power laser beams to trap and manipulate particles at micro/nanoscale, have emerged as an essential tool for manipulating single cells. In this article, we propose to use a robot-tweezer manipulation system to solve the problem of automatic transportation of biological cells, where optical tweezers function as special robot end effectors. Dynamics equation of the cell in optical tweezers is analyzed. A closed-loop controller is designed for transporting and positioning cells. Experiments are performed on live cells to demonstrate the effectiveness of the proposed approach in effective cell positioning.

  16. Automated three-dimensional single cell phenotyping of spindle dynamics, cell shape, and volume

    CERN Document Server

    Plumb, Kemp; Pelletier, Vincent; Kilfoil, Maria L

    2015-01-01

    We present feature finding and tracking algorithms in 3D in living cells, and demonstrate their utility to measure metrics important in cell biological processes. We developed a computational imaging hybrid approach that combines automated three-dimensional tracking of point-like features with surface determination from which cell (or nuclear) volume, shape, and planes of interest can be extracted. After validation, we applied the technique to real space context-rich dynamics of the mitotic spindle, and cell volume and its relationship to spindle length, in dividing living cells. These methods are additionally useful for automated segregation of pre-anaphase and anaphase spindle populations in budding yeast. We found that genetic deletion of the yeast kinesin-5 mitotic motor cin8 leads to large mother and daughter cells that were indistinguishable based on size, and that in those cells the spindle length becomes uncorrelated with cell size. The technique can be used to visualize and quantify tracked feature c...

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

    Science.gov (United States)

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

    2015-11-01

    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.

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

    OpenAIRE

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

    2012-01-01

    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....

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

    Science.gov (United States)

    Ferreira, Bianca Lima; Orikaza, Cristina Mary; Cordero, Esteban Mauricio

    2016-01-01

    Summary 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

  20. Automating Shallow Seismic Imaging

    Energy Technology Data Exchange (ETDEWEB)

    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

  1. Automated Orientation of Aerial Images

    DEFF Research Database (Denmark)

    Høhle, Joachim

    2002-01-01

    Methods for automated orientation of aerial images are presented. They are based on the use of templates, which are derived from existing databases, and area-based matching. The characteristics of available database information and the accuracy requirements for map compilation and orthoimage...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  6. A single-cell bioluminescence imaging system for monitoring cellular gene expression in a plant body.

    Science.gov (United States)

    Muranaka, Tomoaki; Kubota, Saya; Oyama, Tokitaka

    2013-12-01

    Gene expression is a fundamental cellular process and expression dynamics are of great interest in life science. We succeeded in monitoring cellular gene expression in a duckweed plant, Lemna gibba, using bioluminescent reporters. Using particle bombardment, epidermal and mesophyll cells were transfected with the luciferase gene (luc+) under the control of a constitutive [Cauliflower mosaic virus 35S (CaMV35S)] and a rhythmic [Arabidopsis thaliana CIRCADIAN CLOCK ASSOCIATED 1 (AtCCA1)] promoter. Bioluminescence images were captured using an EM-CCD (electron multiply charged couple device) camera. Luminescent spots of the transfected cells in the plant body were quantitatively measured at the single-cell level. Luminescence intensities varied over a 1,000-fold range among CaMV35S::luc+-transfected cells in the same plant body and showed a log-normal-like frequency distribution. We monitored cellular gene expression under light-dark conditions by capturing bioluminescence images every hour. Luminescence traces of ≥50 individual cells in a frond were successfully obtained in each monitoring procedure. Rhythmic and constitutive luminescence behaviors were observed in cells transfected with AtCCA1::luc+ and CaMV35S::luc+, respectively. Diurnal rhythms were observed in every AtCCA1::luc+-introduced cell with traceable luminescence, and slight differences were detected in their rhythmic waveforms. Thus the single-cell bioluminescence monitoring system was useful for the characterization of cellular gene expression in a plant body.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Jian, E-mail: jian@cfs.biomet.umaryland.edu [Center for Fluorescence Spectroscopy, University of Maryland School of Medicine, Department of Biochemistry and Molecular Biology, 725 West Lombard Street, Baltimore, MD 21201 (United States); Fu, Yi [Center for Fluorescence Spectroscopy, University of Maryland School of Medicine, Department of Biochemistry and Molecular Biology, 725 West Lombard Street, Baltimore, MD 21201 (United States); Li, Ge [Division of Molecular Pathology, Department of Pathology, University of Maryland School of Medicine, 10 South Pine Street, Baltimore, MD 21201 (United States); Lakowicz, Joseph R. [Center for Fluorescence Spectroscopy, University of Maryland School of Medicine, Department of Biochemistry and Molecular Biology, 725 West Lombard Street, Baltimore, MD 21201 (United States); Zhao, Richard Y., E-mail: rzhao@som.umaryland.edu [Division of Molecular Pathology, Department of Pathology, University of Maryland School of Medicine, 10 South Pine Street, Baltimore, MD 21201 (United States); Department of Microbiology-Immunology, University of Maryland School of Medicine, 10 South Pine Street, Baltimore, MD 21201 (United States); Institute of Human Virology, University of Maryland School of Medicine, 10 South Pine Street, Baltimore, MD 21201 (United States)

    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.

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

    Science.gov (United States)

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

    2011-01-07

    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.

  11. Large heterogeneity of mitochondrial DNA transcription and initiation of replication exposed by single-cell imaging.

    Science.gov (United States)

    Chatre, Laurent; Ricchetti, Miria

    2013-02-15

    Mitochondrial DNA (mtDNA) replication and transcription are crucial for cell function, but these processes are poorly understood at the single-cell level. We describe a novel fluorescence in situ hybridization protocol, called mTRIP (mitochondrial transcription and replication imaging protocol), that reveals simultaneously mtDNA and RNA, and that can also be coupled to immunofluorescence for in situ protein examination. mTRIP reveals mitochondrial structures engaged in initiation of DNA replication by identification of a specific sequence in the regulatory D-loop, as well as unique transcription profiles in single human cells. We observe and quantify at least three classes of mitochondrial structures: (i) replication initiation active and transcript-positive (Ia-Tp); (ii) replication initiation silent and transcript-positive (Is-Tp); and (iii) replication initiation silent and transcript-negative (Is-Tn). Thus, individual mitochondria are dramatically heterogeneous within the same cell. Moreover, mTRIP exposes a mosaic of distinct nucleic acid patterns in the D-loop, including H-strand versus L-strand transcripts, and uncoupled rRNA transcription and mtDNA initiation of replication, which might have functional consequences in the regulation of the mtDNA. Finally, mTRIP identifies altered mtDNA processing in cells with unbalanced mtDNA content and function, including in human mitochondrial disorders. Thus, mTRIP reveals qualitative and quantitative alterations that provide additional tools for elucidating the dynamics of mtDNA processing in single cells and mitochondrial dysfunction in diseases.

  12. Micro-PIXE for the quantitative imaging of chemical elements in single cells

    Energy Technology Data Exchange (ETDEWEB)

    Ortega, R. [Univ. Bordeaux, CENBG, Gradignan (France); CNRS, IN2P3, CENBG, Gradignan (France)

    2013-07-01

    Full text: The knowledge of the intracellular distribution of biological relevant metals is important to understand their mechanisms of action in cells, either for physiological, toxicological or pathological processes. However, the direct detection of trace metals in single cells is a challenging task that requires sophisticated analytical developments. The aim of this seminar will be to present the recent achievements in this field using micro-PIXE analysis. The combination of micro-PIXE with RBS (Rutherford Backscattering Spectrometry) and STIM (Scanning Transmission lon Microscopy) allows the quantitative determination of trace metal content within sub-cellular compartments. The application of STlM analysis will be more specifically highlighted as it provides high spatial resolution imaging (<200 nm) and excellent mass sensitivity (<0.1 ng). Application of the STIM-PIXE-RBS methodology is absolutely needed when organic mass loss appears during PIXE-RBS irradiation. This combination of STIM-PIXE-RBS provides fully quantitative determination of trace element content, expressed in μg/g, which is a quite unique capability for micro-PIXE compared to other micro-analytical methods such as the electron and synchrotron X-ray fluorescence or the techniques based on mass spectrometry. Examples of micro-PIXE studies for subcellular imaging of trace elements in the various fields of interest will be presented such as metal-based toxicology, pharmacology, and neuro degeneration [1] R. Ortega, G. Devés, A. Carmona. J. R. Soc. Interface, 6, (2009) S649-S658. (author)

  13. Automated image enhancement using power law transformations

    Indian Academy of Sciences (India)

    S P Vimal; P K Thiruvikraman

    2012-12-01

    We propose a scheme for automating power law transformations which are used for image enhancement. The scheme we propose does not require the user to choose the exponent in the power law transformation. This method works well for images having poor contrast, especially to those images in which the peaks corresponding to the background and the foreground are not widely separated.

  14. Automated imaging system for single molecules

    Science.gov (United States)

    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.

  15. Automated image analysis techniques for cardiovascular magnetic resonance imaging

    NARCIS (Netherlands)

    Geest, Robertus Jacobus van der

    2011-01-01

    The introductory chapter provides an overview of various aspects related to quantitative analysis of cardiovascular MR (CMR) imaging studies. Subsequently, the thesis describes several automated methods for quantitative assessment of left ventricular function from CMR imaging studies. Several novel

  16. Analyzing and mining automated imaging experiments.

    Science.gov (United States)

    Berlage, Thomas

    2007-04-01

    Image mining is the application of computer-based techniques that extract and exploit information from large image sets to support human users in generating knowledge from these sources. This review focuses on biomedical applications of this technique, in particular automated imaging at the cellular level. Due to increasing automation and the availability of integrated instruments, biomedical users are becoming increasingly confronted with the problem of analyzing such data. Image database applications need to combine data management, image analysis and visual data mining. The main point of such a system is a software layer that represents objects within an image and the ability to use a large spectrum of quantitative and symbolic object features. Image analysis needs to be adapted to each particular experiment; therefore, 'end user programming' will be desired to make the technology more widely applicable.

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

    Science.gov (United States)

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

    2011-09-01

    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.

  18. Auto-luminescent genetically-encoded ratiometric indicator for real-time Ca2+ imaging at the single cell level.

    Directory of Open Access Journals (Sweden)

    Kenta Saito

    Full Text Available BACKGROUND: Efficient bioluminescence resonance energy transfer (BRET from a bioluminescent protein to a fluorescent protein with high fluorescent quantum yield has been utilized to enhance luminescence intensity, allowing single-cell imaging in near real time without external light illumination. METHODOLOGY/PRINCIPAL FINDINGS: We applied BRET to develop an autoluminescent Ca(2+ indicator, BRAC, which is composed of Ca(2+-binding protein, calmodulin, and its target peptide, M13, sandwiched between a yellow fluorescent protein variant, Venus, and an enhanced Renilla luciferase, RLuc8. Adjusting the relative dipole orientation of the luminescent protein's chromophores improved the dynamic range of BRET signal change in BRAC up to 60%, which is the largest dynamic range among BRET-based indicators reported so far. Using BRAC, we demonstrated successful visualization of Ca(2+ dynamics at the single-cell level with temporal resolution at 1 Hz. Moreover, BRAC signals were acquired by ratiometric imaging capable of canceling out Ca(2+-independent signal drifts due to change in cell shape, focus shift, etc. CONCLUSIONS/SIGNIFICANCE: The brightness and large dynamic range of BRAC should facilitate high-sensitive Ca(2+ imaging not only in single live cells but also in small living subjects.

  19. Single cell electroporation for longitudinal imaging of synaptic structure and function in the adult mouse neocortex in vivo

    Directory of Open Access Journals (Sweden)

    Stephane ePages

    2015-04-01

    Full Text Available Longitudinal imaging studies of neuronal structures in vivo have revealed rich dynamics in dendritic spines and axonal boutons. Spines and boutons are considered to be proxies for synapses. This implies that synapses display similar dynamics. However, spines and boutons do not always bear synapses, some may contain more than one, and dendritic shaft synapses have no clear structural proxies. In addition, synaptic strength is not always accurately revealed by just the size of these structures. Structural and functional dynamics of synapses could be studied more reliably using fluorescent synaptic proteins as markers for size and function. These proteins are often large and possibly interfere with circuit development, which renders them less suitable for conventional transfection or transgenesis methods such as viral vectors, in utero electroporation and germline transgenesis. Single cell electroporation has been shown to be a potential alternative for transfection of recombinant fluorescent proteins in adult cortical neurons. Here we provide proof of principle for the use of single cell electroporation to express and subsequently image fluorescently tagged synaptic proteins over days to weeks in vivo.

  20. Automated Image Data Exploitation Final Report

    Energy Technology Data Exchange (ETDEWEB)

    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.

  1. HaloJ: an ImageJ program for semiautomatic quantification of DNA damage at single-cell level.

    Science.gov (United States)

    Maurya, Dharmendra Kumar

    2014-01-01

    Although Halo assay is a fast and more economic technique, it is not popular compared to comet assay for the measurement of DNA damage. One of the reasons behind this was nonavailability of suitable user-friendly program. Currently, most of the researchers were analyzing halo images manually using image analysis software (Scion Image or ImageJ). To address this problem, I have developed a semiautomatic halo analysis ImageJ program, HaloJ, and applied in the assessment of DNA damage at the single-cell level. In this article, we have shown that data obtained from the HaloJ program have a very good correlation with the data obtained using comet assay analysis program such as Comet Assay Software Project. To the best of our knowledge, this will be the first program to quantify DNA damage of halo images. This program will be of great use for researchers working on the DNA damage and repair, radiation biology, toxicology, cancer biology, and so on.

  2. Plenoptic Imager for Automated Surface Navigation

    Science.gov (United States)

    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.

  3. Automated spectral imaging for clinical diagnostics

    Science.gov (United States)

    Breneman, John; Heffelfinger, David M.; Pettipiece, Ken; Tsai, Chris; Eden, Peter; Greene, Richard A.; Sorensen, Karen J.; Stubblebine, Will; Witney, Frank

    1998-04-01

    Bio-Rad Laboratories supplies imaging equipment for many applications in the life sciences. As part of our effort to offer more flexibility to the investigator, we are developing a microscope-based imaging spectrometer for the automated detection and analysis of either conventionally or fluorescently labeled samples. Immediate applications will include the use of fluorescence in situ hybridization (FISH) technology. The field of cytogenetics has benefited greatly from the increased sensitivity of FISH producing simplified analysis of complex chromosomal rearrangements. FISH methods for identification lends itself to automation more easily than the current cytogenetics industry standard of G- banding, however, the methods are complementary. Several technologies have been demonstrated successfully for analyzing the signals from labeled samples, including filter exchanging and interferometry. The detection system lends itself to other fluorescent applications including the display of labeled tissue sections, DNA chips, capillary electrophoresis or any other system using color as an event marker. Enhanced displays of conventionally stained specimens will also be possible.

  4. From in vitro to in vivo: imaging from the single cell to the whole organism.

    Science.gov (United States)

    Kang, Jung Julie; Toma, Ildiko; Sipos, Arnold; Peti-Peterdi, Janos

    2008-04-01

    This unit addresses the applications of fluorescence microscopy and quantitative imaging to study multiple physiological variables of living tissue. Protocols are presented for fluorescence-based investigations ranging from in vitro cell and tissue approaches to in vivo imaging of intact organs. These include the measurement of cytosolic parameters both in vitro and in vivo (such as calcium, pH, and nitric oxide), dynamic cellular processes (renin granule exocytosis), FRET-based real-time assays of enzymatic activity (renin), physiological processes (vascular contraction, membrane depolarization), and whole organ functional parameters (blood flow, glomerular filtration). Multi-photon microscopy is ideal for minimally invasive and undisruptive deep optical sectioning of the living tissue, which translates into ultra-sensitive real-time measurement of these parameters with high spatial and temporal resolution. With the combination of cell and tissue cultures, microperfusion techniques, and whole organ or animal models, fluorescence imaging provides unmatched versatility for biological and medical studies of the living organism.

  5. In situ probing of cholesterol in astrocytes at the single-cell level using laser desorption ionization mass spectrometric imaging with colloidal silver.

    Science.gov (United States)

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

    2010-04-30

    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.

  6. Automated landmark-guided deformable image registration

    Science.gov (United States)

    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.

  7. Automated Quality Assurance Applied to Mammographic Imaging

    Directory of Open Access Journals (Sweden)

    Anne Davis

    2002-07-01

    Full Text Available Quality control in mammography is based upon subjective interpretation of the image quality of a test phantom. In order to suppress subjectivity due to the human observer, automated computer analysis of the Leeds TOR(MAM test phantom is investigated. Texture analysis via grey-level co-occurrence matrices is used to detect structures in the test object. Scoring of the substructures in the phantom is based on grey-level differences between regions and information from grey-level co-occurrence matrices. The results from scoring groups of particles within the phantom are presented.

  8. Bacterial growth on surfaces: Automated image analysis for quantification of growth rate-related parameters

    DEFF Research Database (Denmark)

    Møller, S.; Sternberg, Claus; Poulsen, L. K.

    1995-01-01

    species-specific hybridizations with fluorescence-labelled ribosomal probes to estimate the single-cell concentration of RNA. By automated analysis of digitized images of stained cells, we determined four independent growth rate-related parameters: cellular RNA and DNA contents, cell volume......, and the frequency of dividing cells in a cell population. These parameters were used to compare physiological states of liquid-suspended and surfacegrowing Pseudomonas putida KT2442 in chemostat cultures. The major finding is that the correlation between substrate availability and cellular growth rate found...

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

    Science.gov (United States)

    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

    2014-09-02

    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.

  10. Automated vertebra identification in CT images

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Yi-Hsing Hsiao

    2016-07-01

    Full Text Available 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 Ca2+ concentration. However, glucose evoked a rapid elevation of intracellular Ca2+ 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.

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

    Science.gov (United States)

    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

  13. An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

    Science.gov (United States)

    Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

    2017-01-01

    A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

  14. A novel luciferase fusion protein for highly sensitive optical imaging: from single-cell analysis to in vivo whole-body bioluminescence imaging.

    Science.gov (United States)

    Mezzanotte, Laura; Blankevoort, Vicky; Löwik, Clemens W G M; Kaijzel, Eric L

    2014-09-01

    Fluorescence and bioluminescence imaging have different advantages and disadvantages depending on the application. Bioluminescence imaging is now the most sensitive optical technique for tracking cells, promoter activity studies, or for longitudinal in vivo preclinical studies. Far-red and near-infrared fluorescence imaging have the advantage of being suitable for both ex vivo and in vivo analysis and have translational potential, thanks to the availability of very sensitive imaging instrumentation. Here, we report the development and validation of a new luciferase fusion reporter generated by the fusion of the firefly luciferase Luc2 to the far-red fluorescent protein TurboFP635 by a 14-amino acid linker peptide. Expression of the fusion protein, named TurboLuc, was analyzed in human embryonic kidney cells, (HEK)-293 cells, via Western blot analysis, fluorescence microscopy, and in vivo optical imaging. The created fusion protein maintained the characteristics of the original bioluminescent and fluorescent protein and showed no toxicity when expressed in living cells. To assess the sensitivity of the reporter for in vivo imaging, transfected cells were subcutaneously injected in animals. Detection limits of cells were 5 × 10(3) and 5 × 10(4) cells for bioluminescent and fluorescent imaging, respectively. In addition, hydrodynamics-based in vivo gene delivery using a minicircle vector expressing TurboLuc allowed for the analysis of luminescent signals over time in deep tissue. Bioluminescence could be monitored for over 30 days in the liver of animals. In conclusion, TurboLuc combines the advantages of both bioluminescence and fluorescence and allows for highly sensitive optical imaging ranging from single-cell analysis to in vivo whole-body bioluminescence imaging.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-05-15

    Chemical imaging of single cells is important in capturing biological dynamics. Single cell correlative imaging is realized between structured illumination microscopy (SIM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) using System for Analysis at the Liquid Vacuum Interface (SALVI), a multimodal microreactor. SIM characterized cells and guided subsequent ToF-SIMS analysis. Dynamic ToF-SIMS provided time- and space-resolved cell molecular mapping. Lipid fragments were identified in the hydrated cell membrane. Principal component analysis was used to elucidate chemical component differences among mouse lung cells that uptake zinc oxide nanoparticles. Our results provided submicron chemical spatial mapping for investigations of cell dynamics at the molecular level.

  16. 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.

    Science.gov (United States)

    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 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.

  17. OpenComet: An automated tool for comet assay image analysis

    Directory of Open Access Journals (Sweden)

    Benjamin M. Gyori

    2014-01-01

    Full Text Available Reactive species such as free radicals are constantly generated in vivo and DNA is the most important target of oxidative stress. Oxidative DNA damage is used as a predictive biomarker to monitor the risk of development of many diseases. The comet assay is widely used for measuring oxidative DNA damage at a single cell level. The analysis of comet assay output images, however, poses considerable challenges. Commercial software is costly and restrictive, while free software generally requires laborious manual tagging of cells. This paper presents OpenComet, an open-source software tool providing automated analysis of comet assay images. It uses a novel and robust method for finding comets based on geometric shape attributes and segmenting the comet heads through image intensity profile analysis. Due to automation, OpenComet is more accurate, less prone to human bias, and faster than manual analysis. A live analysis functionality also allows users to analyze images captured directly from a microscope. We have validated OpenComet on both alkaline and neutral comet assay images as well as sample images from existing software packages. Our results show that OpenComet achieves high accuracy with significantly reduced analysis time.

  18. OpenComet: an automated tool for comet assay image analysis.

    Science.gov (United States)

    Gyori, Benjamin M; Venkatachalam, Gireedhar; Thiagarajan, P S; Hsu, David; Clement, Marie-Veronique

    2014-01-01

    Reactive species such as free radicals are constantly generated in vivo and DNA is the most important target of oxidative stress. Oxidative DNA damage is used as a predictive biomarker to monitor the risk of development of many diseases. The comet assay is widely used for measuring oxidative DNA damage at a single cell level. The analysis of comet assay output images, however, poses considerable challenges. Commercial software is costly and restrictive, while free software generally requires laborious manual tagging of cells. This paper presents OpenComet, an open-source software tool providing automated analysis of comet assay images. It uses a novel and robust method for finding comets based on geometric shape attributes and segmenting the comet heads through image intensity profile analysis. Due to automation, OpenComet is more accurate, less prone to human bias, and faster than manual analysis. A live analysis functionality also allows users to analyze images captured directly from a microscope. We have validated OpenComet on both alkaline and neutral comet assay images as well as sample images from existing software packages. Our results show that OpenComet achieves high accuracy with significantly reduced analysis time.

  19. Toward Automated Feature Detection in UAVSAR Images

    Science.gov (United States)

    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

  20. Image analysis and platform development for automated phenotyping in cytomics

    NARCIS (Netherlands)

    Yan, Kuan

    2013-01-01

    This thesis is dedicated to the empirical study of image analysis in HT/HC screen study. Often a HT/HC screening produces extensive amounts that cannot be manually analyzed. Thus, an automated image analysis solution is prior to an objective understanding of the raw image data. Compared to general a

  1. Automated Segmentation of Cardiac Magnetic Resonance Images

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Nilsson, Jens Chr.; Grønning, Bjørn A.

    2001-01-01

    is based on determination of the left-ventricular endocardial and epicardial borders. Since manual border detection is laborious, automated segmentation is highly desirable as a fast, objective and reproducible alternative. Automated segmentation will thus enhance comparability between and within cardiac...... studies and increase accuracy by allowing acquisition of thinner MRI-slices. This abstract demonstrates that statistical models of shape and appearance, namely the deformable models: Active Appearance Models, can successfully segment cardiac MRIs....

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

    Directory of Open Access Journals (Sweden)

    Oscar Beijbom

    Full Text Available 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.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  5. Automated Real-Time Conjunctival Microvasculature Image Stabilization.

    Science.gov (United States)

    Felder, Anthony E; Mercurio, Cesare; Wanek, Justin; Ansari, Rashid; Shahidi, Mahnaz

    2016-07-01

    The bulbar conjunctiva is a thin, vascularized membrane covering the sclera of the eye. Non-invasive imaging techniques have been utilized to assess the conjunctival vasculature as a means of studying microcirculatory hemodynamics. However, eye motion often confounds quantification of these hemodynamic properties. In the current study, we present a novel optical imaging system for automated stabilization of conjunctival microvasculature images by real-time eye motion tracking and realignment of the optical path. The ability of the system to stabilize conjunctival images acquired over time by reducing image displacements and maintaining the imaging area was demonstrated.

  6. Automation of Cassini Support Imaging Uplink Command Development

    Science.gov (United States)

    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.

  7. Rapid assessment of different oxygenic phototrophs and single-cell photosynthesis with multicolour variable chlorophyll fluorescence imaging

    DEFF Research Database (Denmark)

    Trampe, Erik Christian Løvbjerg; Kolbowski, J.; Schreiber, U.

    2011-01-01

    We present a new system for microscopic multicolour variable chlorophyll fluorescence imaging of aquatic phototrophs. The system is compact and portable and enables microscopic imaging of photosynthetic performance of individual cells and chloroplasts using different combinations of blue, green, ...

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

    Science.gov (United States)

    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.

  9. Extended -Regular Sequence for Automated Analysis of Microarray Images

    Directory of Open Access Journals (Sweden)

    Jin Hee-Jeong

    2006-01-01

    Full Text Available Microarray study enables us to obtain hundreds of thousands of expressions of genes or genotypes at once, and it is an indispensable technology for genome research. The first step is the analysis of scanned microarray images. This is the most important procedure for obtaining biologically reliable data. Currently most microarray image processing systems require burdensome manual block/spot indexing work. Since the amount of experimental data is increasing very quickly, automated microarray image analysis software becomes important. In this paper, we propose two automated methods for analyzing microarray images. First, we propose the extended -regular sequence to index blocks and spots, which enables a novel automatic gridding procedure. Second, we provide a methodology, hierarchical metagrid alignment, to allow reliable and efficient batch processing for a set of microarray images. Experimental results show that the proposed methods are more reliable and convenient than the commercial tools.

  10. Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

    Directory of Open Access Journals (Sweden)

    Jianfang Cao

    2015-01-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  12. Automated Localization of Optic Disc in Retinal Images

    Directory of Open Access Journals (Sweden)

    Deepali A.Godse

    2013-03-01

    Full Text Available An efficient detection of optic disc (OD in colour retinal images is a significant task in an automated retinal image analysis system. Most of the algorithms developed for OD detection are especially applicable to normal and healthy retinal images. It is a challenging task to detect OD in all types of retinal images, that is, normal, healthy images as well as abnormal, that is, images affected due to disease. This paper presents an automated system to locate an OD and its centre in all types of retinal images. The ensemble of steps based on different criteria produces more accurate results. The proposed algorithm gives excellent results and avoids false OD detection. The technique is developed and tested on standard databases provided for researchers on internet, Diaretdb0 (130 images, Diaretdb1 (89 images, Drive (40 images and local database (194 images. The local database images are collected from ophthalmic clinics. It is able to locate OD and its centre in 98.45% of all tested cases. The results achieved by different algorithms can be compared when algorithms are applied on same standard databases. This comparison is also discussed in this paper which shows that the proposed algorithm is more efficient.

  13. Automated image registration for FDOPA PET studies

    Science.gov (United States)

    Lin, Kang-Ping; Huang, Sung-Cheng; Yu, Dan-Chu; Melega, William; Barrio, Jorge R.; Phelps, Michael E.

    1996-12-01

    In this study, various image registration methods are investigated for their suitability for registration of L-6-[18F]-fluoro-DOPA (FDOPA) PET images. Five different optimization criteria including sum of absolute difference (SAD), mean square difference (MSD), cross-correlation coefficient (CC), standard deviation of pixel ratio (SDPR), and stochastic sign change (SSC) were implemented and Powell's algorithm was used to optimize the criteria. The optimization criteria were calculated either unidirectionally (i.e. only evaluating the criteria for comparing the resliced image 1 with the original image 2) or bidirectionally (i.e. averaging the criteria for comparing the resliced image 1 with the original image 2 and those for the sliced image 2 with the original image 1). Monkey FDOPA images taken at various known orientations were used to evaluate the accuracy of different methods. A set of human FDOPA dynamic images was used to investigate the ability of the methods for correcting subject movement. It was found that a large improvement in performance resulted when bidirectional rather than unidirectional criteria were used. Overall, the SAD, MSD and SDPR methods were found to be comparable in performance and were suitable for registering FDOPA images. The MSD method gave more adequate results for frame-to-frame image registration for correcting subject movement during a dynamic FDOPA study. The utility of the registration method is further demonstrated by registering FDOPA images in monkeys before and after amphetamine injection to reveal more clearly the changes in spatial distribution of FDOPA due to the drug intervention.

  14. Automated model-based calibration of imaging spectrographs

    Science.gov (United States)

    Kosec, Matjaž; Bürmen, Miran; Tomaževič, Dejan; Pernuš, Franjo; Likar, Boštjan

    2012-03-01

    Hyper-spectral imaging has gained recognition as an important non-invasive research tool in the field of biomedicine. Among the variety of available hyperspectral imaging systems, systems comprising an imaging spectrograph, lens, wideband illumination source and a corresponding camera stand out for the short acquisition time and good signal to noise ratio. The individual images acquired by imaging spectrograph-based systems contain full spectral information along one spatial dimension. Due to the imperfections in the camera lens and in particular the optical components of the imaging spectrograph, the acquired images are subjected to spatial and spectral distortions, resulting in scene dependent nonlinear spectral degradations and spatial misalignments which need to be corrected. However, the existing correction methods require complex calibration setups and a tedious manual involvement, therefore, the correction of the distortions is often neglected. Such simplified approach can lead to significant errors in the analysis of the acquired hyperspectral images. In this paper, we present a novel fully automated method for correction of the geometric and spectral distortions in the acquired images. The method is based on automated non-rigid registration of the reference and acquired images corresponding to the proposed calibration object incorporating standardized spatial and spectral information. The obtained transformation was successfully used for sub-pixel correction of various hyperspectral images, resulting in significant improvement of the spectral and spatial alignment. It was found that the proposed calibration is highly accurate and suitable for routine use in applications involving either diffuse reflectance or transmittance measurement setups.

  15. Automated morphometry of transgenic mouse brains in MR images

    NARCIS (Netherlands)

    Scheenstra, Alize Elske Hiltje

    2011-01-01

    Quantitative and local morphometry of mouse brain MRI is a relatively new field of research, where automated methods can be exploited to rapidly provide accurate and repeatable results. In this thesis we reviewed several existing methods and applications of quantitative morphometry to brain MR image

  16. Automated image analysis in the study of collagenous colitis

    DEFF Research Database (Denmark)

    Kanstrup, Anne-Marie Fiehn; Kristensson, Martin; Engel, Ulla

    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...

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

    Science.gov (United States)

    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.

  18. Automated Archiving of Archaeological Aerial Images

    Directory of Open Access Journals (Sweden)

    Michael Doneus

    2016-03-01

    Full Text Available The main purpose of any aerial photo archive is to allow quick access to images based on content and location. Therefore, next to a description of technical parameters and depicted content, georeferencing of every image is of vital importance. This can be done either by identifying the main photographed object (georeferencing of the image content or by mapping the center point and/or the outline of the image footprint. The paper proposes a new image archiving workflow. The new pipeline is based on the parameters that are logged by a commercial, but cost-effective GNSS/IMU solution and processed with in-house-developed software. Together, these components allow one to automatically geolocate and rectify the (oblique aerial images (by a simple planar rectification using the exterior orientation parameters and to retrieve their footprints with reasonable accuracy, which is automatically stored as a vector file. The data of three test flights were used to determine the accuracy of the device, which turned out to be better than 1° for roll and pitch (mean between 0.0 and 0.21 with a standard deviation of 0.17–0.46 and better than 2.5° for yaw angles (mean between 0.0 and −0.14 with a standard deviation of 0.58–0.94. This turned out to be sufficient to enable a fast and almost automatic GIS-based archiving of all of the imagery.

  19. Automated image-based tracking and its application in ecology.

    Science.gov (United States)

    Dell, Anthony I; Bender, John A; Branson, Kristin; Couzin, Iain D; de Polavieja, Gonzalo G; Noldus, Lucas P J J; Pérez-Escudero, Alfonso; Perona, Pietro; Straw, Andrew D; Wikelski, Martin; Brose, Ulrich

    2014-07-01

    The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers.

  20. Automated Pointing of Cardiac Imaging Catheters.

    Science.gov (United States)

    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.

  1. Automated vasculature extraction from placenta images

    Science.gov (United States)

    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 thresholding in radiographic image for welded joints

    Science.gov (United States)

    Yazid, Haniza; Arof, Hamzah; Yazid, Hafizal

    2012-03-01

    Automated detection of welding defects in radiographic images becomes non-trivial when uneven illumination, contrast and noise are present. In this paper, a new surface thresholding method is introduced to detect defects in radiographic images of welding joints. In the first stage, several image processing techniques namely fuzzy c means clustering, region filling, mean filtering, edge detection, Otsu's thresholding and morphological operations method are utilised to locate the area in which defects might exist. This is followed by the implementation of inverse surface thresholding with partial differential equation to locate isolated areas that represent the defects in the second stage. The proposed method obtained a promising result with high precision.

  3. SAND: Automated VLBI imaging and analyzing pipeline

    Science.gov (United States)

    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.

  4. Single Cell Oncogenesis

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  6. Automated delineation of stroke lesions using brain CT images

    Directory of Open Access Journals (Sweden)

    Céline R. Gillebert

    2014-01-01

    Full Text Available 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.

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    Jie-Long He

    2015-09-01

    Full Text Available 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.

  9. Practical approach to apply range image sensors in machine automation

    Science.gov (United States)

    Moring, Ilkka; Paakkari, Jussi

    1993-10-01

    In this paper we propose a practical approach to apply range imaging technology in machine automation. The applications we are especially interested in are industrial heavy-duty machines like paper roll manipulators in harbor terminals, harvesters in forests and drilling machines in mines. Characteristic of these applications is that the sensing system has to be fast, mid-ranging, compact, robust, and relatively cheap. On the other hand the sensing system is not required to be generic with respect to the complexity of scenes and objects or number of object classes. The key in our approach is that just a limited range data set or as we call it, a sparse range image is acquired and analyzed. This makes both the range image sensor and the range image analysis process more feasible and attractive. We believe that this is the way in which range imaging technology will enter the large industrial machine automation market. In the paper we analyze as a case example one of the applications mentioned and, based on that, we try to roughly specify the requirements for a range imaging based sensing system. The possibilities to implement the specified system are analyzed based on our own work on range image acquisition and interpretation.

  10. Automated morphological analysis approach for classifying colorectal microscopic images

    Science.gov (United States)

    Marghani, Khaled A.; Dlay, Satnam S.; Sharif, Bayan S.; Sims, Andrew J.

    2003-10-01

    Automated medical image diagnosis using quantitative measurements is extremely helpful for cancer prognosis to reach a high degree of accuracy and thus make reliable decisions. In this paper, six morphological features based on texture analysis were studied in order to categorize normal and cancer colon mucosa. They were derived after a series of pre-processing steps to generate a set of different shape measurements. Based on the shape and the size, six features known as Euler Number, Equivalent Diamater, Solidity, Extent, Elongation, and Shape Factor AR were extracted. Mathematical morphology is used firstly to remove background noise from segmented images and then to obtain different morphological measures to describe shape, size, and texture of colon glands. The automated system proposed is tested to classifying 102 microscopic samples of colorectal tissues, which consist of 44 normal color mucosa and 58 cancerous. The results were first statistically evaluated, using one-way ANOVA method in order to examine the significance of each feature extracted. Then significant features are selected in order to classify the dataset into two categories. Finally, using two discrimination methods; linear method and k-means clustering, important classification factors were estimated. In brief, this study demonstrates that abnormalities in low-level power tissue morphology can be distinguished using quantitative image analysis. This investigation shows the potential of an automated vision system in histopathology. Furthermore, it has the advantage of being objective, and more importantly a valuable diagnostic decision support tool.

  11. 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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  12. 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

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Ziqiang [Iowa State Univ., Ames, IA (United States)

    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 10-8 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.

  13. Near Infrared Microspectroscopy, Fluorescence Microspectroscopy, Infrared Chemical Imaging and High Resolution Nuclear Magnetic Resonance Analysis of Soybean Seeds, Somatic Embryos and Single Cells

    CERN Document Server

    Baianu, I C; Hofmann, N E; Korban, S S; Lozano, P; You, T; AOCS 94th Meeting, Kansas

    2002-01-01

    Novel methodologies are currently being developed and established for the chemical analysis of soybean seeds, embryos and single cells by Fourier Transform Infrared (FT-IR), Fourier Transform Near Infrared (FT-NIR) Microspectroscopy, Fluorescence and High-Resolution NMR (HR-NMR). The first FT-NIR chemical images of biological systems approaching one micron resolution are presented here. Chemical images obtained by FT-NIR and FT-IR Microspectroscopy are presented for oil in soybean seeds and somatic embryos under physiological conditions. FT-NIR spectra of oil and proteins were obtained for volumes as small as two cubic microns. Related, HR-NMR analyses of oil contents in somatic embryos are also presented here with nanoliter precision. Such 400 MHz 1H NMR analyses allowed the selection of mutagenized embryos with higher oil content (e.g. ~20%) compared to non-mutagenized control embryos. Moreover, developmental changes in single soybean seeds and/or somatic embryos may be monitored by FT-NIR with a precision ...

  14. Image mosaicing for automated pipe scanning

    Science.gov (United States)

    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.

  15. AUTOMATED IMAGE MATCHING WITH CODED POINTS IN STEREOVISION MEASUREMENT

    Institute of Scientific and Technical Information of China (English)

    Dong Mingli; Zhou Xiaogang; Zhu Lianqing; Lü Naiguang; Sun Yunan

    2005-01-01

    A coding-based method to solve the image matching problems in stereovision measurement is presented. The solution is to add and append an identity ID to the retro-reflect point, so it can be identified efficiently under the complicated circumstances and has the characteristics of rotation, zooming, and deformation independence. Its design architecture and implementation process in details based on the theory of stereovision measurement are described. The method is effective on reducing processing data time, improving accuracy of image matching and automation of measuring system through experiments.

  16. GPU Accelerated Automated Feature Extraction From Satellite Images

    Directory of Open Access Journals (Sweden)

    K. Phani Tejaswi

    2013-04-01

    Full Text Available The availability of large volumes of remote sensing data insists on higher degree of automation in featureextraction, making it a need of thehour. Fusingdata from multiple sources, such as panchromatic,hyperspectraland LiDAR sensors, enhances the probability of identifying and extracting features such asbuildings, vegetation or bodies of water by using a combination of spectral and elevation characteristics.Utilizing theaforementioned featuresin remote sensing is impracticable in the absence ofautomation.Whileefforts are underway to reduce human intervention in data processing, this attempt alone may notsuffice. Thehuge quantum of data that needs to be processed entailsaccelerated processing to be enabled.GPUs, which were originally designed to provide efficient visualization,arebeing massively employed forcomputation intensive parallel processing environments. Image processing in general and hence automatedfeatureextraction, is highly computation intensive, where performance improvements have a direct impacton societal needs. In this context, an algorithm has been formulated for automated feature extraction froma panchromatic or multispectral image based on image processing techniques.Two Laplacian of Guassian(LoGmasks were applied on the image individually followed by detection of zero crossing points andextracting the pixels based on their standard deviationwiththe surrounding pixels. The two extractedimages with different LoG masks were combined together which resulted in an image withthe extractedfeatures and edges.Finally the user is at liberty to apply the image smoothing step depending on the noisecontent in the extracted image.The image ispassed through a hybrid median filter toremove the salt andpepper noise from the image.This paper discusses theaforesaidalgorithmforautomated featureextraction, necessity of deployment of GPUs for thesame;system-level challenges and quantifies thebenefits of integrating GPUs in such environment. The

  17. Automated curved planar reformation of 3D spine images

    Energy Technology Data Exchange (ETDEWEB)

    Vrtovec, Tomaz; Likar, Bostjan; Pernus, Franjo [University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, SI-1000 Ljubljana (Slovenia)

    2005-10-07

    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.

  18. Automated 3D renal segmentation based on image partitioning

    Science.gov (United States)

    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.

  19. Automated localization of vertebra landmarks in MRI images

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  1. Automated blood vessel extraction using local features on retinal images

    Science.gov (United States)

    Hatanaka, Yuji; Samo, Kazuki; Tajima, Mikiya; Ogohara, Kazunori; Muramatsu, Chisako; Okumura, Susumu; Fujita, Hiroshi

    2016-03-01

    An automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images is presented. Although many blood vessel extraction methods based on contrast have been proposed, a technique based on the relation of neighbor pixels has not been published. HLAC features are shift-invariant; therefore, we applied HLAC features to retinal images. However, HLAC features are weak to turned image, thus a method was improved by the addition of HLAC features to a polar transformed image. The blood vessels were classified using an artificial neural network (ANN) with HLAC features using 105 mask patterns as input. To improve performance, the second ANN (ANN2) was constructed by using the green component of the color retinal image and the four output values of ANN, Gabor filter, double-ring filter and black-top-hat transformation. The retinal images used in this study were obtained from the "Digital Retinal Images for Vessel Extraction" (DRIVE) database. The ANN using HLAC output apparent white values in the blood vessel regions and could also extract blood vessels with low contrast. The outputs were evaluated using the area under the curve (AUC) based on receiver operating characteristics (ROC) analysis. The AUC of ANN2 was 0.960 as a result of our study. The result can be used for the quantitative analysis of the blood vessels.

  2. Automated monitoring of activated sludge using image analysis

    OpenAIRE

    Motta, Maurício da; M. N. Pons; Roche, N; A.L. Amaral; Ferreira, E. C.; Alves, M.M.; Mota, M.; Vivier, H.

    2000-01-01

    An automated procedure for the characterisation by image analysis of the morphology of activated sludge has been used to monitor in a systematic manner the biomass in wastewater treatment plants. Over a period of one year, variations in terms mainly of the fractal dimension of flocs and of the amount of filamentous bacteria could be related to rain events affecting the plant influent flow rate and composition. Grand Nancy Council. Météo-France. Brasil. Ministério da Ciênc...

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

    Science.gov (United States)

    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

  4. Automated classification of colon polyps in endoscopic image data

    Science.gov (United States)

    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.

  5. Automated Image Processing for the Analysis of DNA Repair Dynamics

    CERN Document Server

    Riess, Thorsten; Tomas, Martin; Ferrando-May, Elisa; Merhof, Dorit

    2011-01-01

    The efficient repair of cellular DNA is essential for the maintenance and inheritance of genomic information. In order to cope with the high frequency of spontaneous and induced DNA damage, a multitude of repair mechanisms have evolved. These are enabled by a wide range of protein factors specifically recognizing different types of lesions and finally restoring the normal DNA sequence. This work focuses on the repair factor XPC (xeroderma pigmentosum complementation group C), which identifies bulky DNA lesions and initiates their removal via the nucleotide excision repair pathway. The binding of XPC to damaged DNA can be visualized in living cells by following the accumulation of a fluorescent XPC fusion at lesions induced by laser microirradiation in a fluorescence microscope. In this work, an automated image processing pipeline is presented which allows to identify and quantify the accumulation reaction without any user interaction. The image processing pipeline comprises a preprocessing stage where the ima...

  6. Automated segmentation of three-dimensional MR brain images

    Science.gov (United States)

    Park, Jonggeun; Baek, Byungjun; Ahn, Choong-Il; Ku, Kyo Bum; Jeong, Dong Kyun; Lee, Chulhee

    2006-03-01

    Brain segmentation is a challenging problem due to the complexity of the brain. In this paper, we propose an automated brain segmentation method for 3D magnetic resonance (MR) brain images which are represented as a sequence of 2D brain images. The proposed method consists of three steps: pre-processing, removal of non-brain regions (e.g., the skull, meninges, other organs, etc), and spinal cord restoration. In pre-processing, we perform adaptive thresholding which takes into account variable intensities of MR brain images corresponding to various image acquisition conditions. In segmentation process, we iteratively apply 2D morphological operations and masking for the sequences of 2D sagittal, coronal, and axial planes in order to remove non-brain tissues. Next, final 3D brain regions are obtained by applying OR operation for segmentation results of three planes. Finally we reconstruct the spinal cord truncated during the previous processes. Experiments are performed with fifteen 3D MR brain image sets with 8-bit gray-scale. Experiment results show the proposed algorithm is fast, and provides robust and satisfactory results.

  7. An automated deformable image registration evaluation of confidence tool

    Science.gov (United States)

    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.

  8. Single Cell Physiology

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Shedden Kerby A

    2009-02-01

    Full Text Available Abstract 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

  10. An investigation of image compression on NIIRS rating degradation through automated image analysis

    Science.gov (United States)

    Chen, Hua-Mei; Blasch, Erik; Pham, Khanh; Wang, Zhonghai; Chen, Genshe

    2016-05-01

    The National Imagery Interpretability Rating Scale (NIIRS) is a subjective quantification of static image widely adopted by the Geographic Information System (GIS) community. Efforts have been made to relate NIIRS image quality to sensor parameters using the general image quality equations (GIQE), which make it possible to automatically predict the NIIRS rating of an image through automated image analysis. In this paper, we present an automated procedure to extract line edge profile based on which the NIIRS rating of a given image can be estimated through the GIQEs if the ground sampling distance (GSD) is known. Steps involved include straight edge detection, edge stripes determination, and edge intensity determination, among others. Next, we show how to employ GIQEs to estimate NIIRS degradation without knowing the ground truth GSD and investigate the effects of image compression on the degradation of an image's NIIRS rating. Specifically, we consider JPEG and JPEG2000 image compression standards. The extensive experimental results demonstrate the effect of image compression on the ground sampling distance and relative edge response, which are the major factors effecting NIIRS rating.

  11. Automated in situ brain imaging for mapping the Drosophila connectome.

    Science.gov (United States)

    Lin, Chi-Wen; Lin, Hsuan-Wen; Chiu, Mei-Tzu; Shih, Yung-Hsin; Wang, Ting-Yuan; Chang, Hsiu-Ming; Chiang, Ann-Shyn

    2015-01-01

    Mapping the connectome, a wiring diagram of the entire brain, requires large-scale imaging of numerous single neurons with diverse morphology. It is a formidable challenge to reassemble these neurons into a virtual brain and correlate their structural networks with neuronal activities, which are measured in different experiments to analyze the informational flow in the brain. Here, we report an in situ brain imaging technique called Fly Head Array Slice Tomography (FHAST), which permits the reconstruction of structural and functional data to generate an integrative connectome in Drosophila. Using FHAST, the head capsules of an array of flies can be opened with a single vibratome sectioning to expose the brains, replacing the painstaking and inconsistent brain dissection process. FHAST can reveal in situ brain neuroanatomy with minimal distortion to neuronal morphology and maintain intact neuronal connections to peripheral sensory organs. Most importantly, it enables the automated 3D imaging of 100 intact fly brains in each experiment. The established head model with in situ brain neuroanatomy allows functional data to be accurately registered and associated with 3D images of single neurons. These integrative data can then be shared, searched, visualized, and analyzed for understanding how brain-wide activities in different neurons within the same circuit function together to control complex behaviors.

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

    Science.gov (United States)

    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.

  13. Precision Relative Positioning for Automated Aerial Refueling from a Stereo Imaging System

    Science.gov (United States)

    2015-03-01

    PRECISION RELATIVE POSITIONING FOR AUTOMATED AERIAL REFUELING FROM A STEREO IMAGING SYSTEM THESIS Kyle P. Werner, 2Lt, USAF AFIT-ENG-MS-15-M-048...Government and is not subject to copyright protection in the United States. AFIT-ENG-MS-15-M-048 PRECISION RELATIVE POSITIONING FOR AUTOMATED AERIAL...RELEASE; DISTRIBUTION UNLIMITED. AFIT-ENG-MS-15-M-048 PRECISION RELATIVE POSITIONING FOR AUTOMATED AERIAL REFUELING FROM A STEREO IMAGING SYSTEM THESIS

  14. Automated image analysis for space debris identification and astrometric measurements

    Science.gov (United States)

    Piattoni, Jacopo; Ceruti, Alessandro; Piergentili, Fabrizio

    2014-10-01

    The space debris is a challenging problem for the human activity in the space. Observation campaigns are conducted around the globe to detect and track uncontrolled space objects. One of the main problems in optical observation is obtaining useful information about the debris dynamical state by the images collected. For orbit determination, the most relevant information embedded in optical observation is the precise angular position, which can be evaluated by astrometry procedures, comparing the stars inside the image with star catalogs. This is typically a time consuming process, if done by a human operator, which makes this task impractical when dealing with large amounts of data, in the order of thousands images per night, generated by routinely conducted observations. An automated procedure is investigated in this paper that is capable to recognize the debris track inside a picture, calculate the celestial coordinates of the image's center and use these information to compute the debris angular position in the sky. This procedure has been implemented in a software code, that does not require human interaction and works without any supplemental information besides the image itself, detecting space objects and solving for their angular position without a priori information. The algorithm for object detection was developed inside the research team. For the star field computation, the software code astrometry.net was used and released under GPL v2 license. The complete procedure was validated by an extensive testing, using the images obtained in the observation campaign performed in a joint project between the Italian Space Agency (ASI) and the University of Bologna at the Broglio Space center, Kenya.

  15. Single cell dynamic phenotyping

    OpenAIRE

    Katherin Patsch; Chi-Li Chiu; Mark Engeln; Agus, David B.; Parag Mallick; Shannon M. Mumenthaler; Daniel Ruderman

    2016-01-01

    Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype track...

  16. Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images

    Directory of Open Access Journals (Sweden)

    Akara Sopharak

    2013-07-01

    Full Text Available Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed.

  17. Image auto-zoom technology for AFM automation

    Institute of Scientific and Technical Information of China (English)

    LIU Wen-liang; QIAN Jian-qiang; LI Yuan

    2009-01-01

    For the case of atomic force microscope (AFM) automation, we extract the most valuable sub-region of a given AFM image automatically for succeeding scanning to get the higher resolution of interesting region. Two objective functions are sum-marized based on the analysis of evaluation of the information of a sub-region, and corresponding algorithm principles based on standard deviation and Discrete Cosine Transform (DCT) compression are determined from math. Algorithm realizations are analyzed and two select patterns of sub-region: fixed grid mode and sub-region walk mode are compared. To speed up the algorithm of DCT compression which is too slow to practical applied, a new algorithm is proposed based on analysis of DCT's block computing feature, and it can perform hundreds times faster than original. Implementation result of the algorithms proves that this technology can be applied to the AFM automatic operation. Finally the difference between the two objective functions is discussed with detail computations.

  18. Automated processing of webcam images for phenological classification.

    Science.gov (United States)

    Bothmann, Ludwig; Menzel, Annette; Menze, Bjoern H; Schunk, Christian; Kauermann, Göran

    2017-01-01

    Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels' time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the statistical software

  19. Automated determination of spinal centerline in CT and MR images

    Science.gov (United States)

    Štern, Darko; Vrtovec, Tomaž; Pernuš, Franjo; Likar, Boštjan

    2009-02-01

    The spinal curvature is one of the most important parameters for the evaluation of spinal deformities. The spinal centerline, represented by the curve that passes through the centers of the vertebral bodies in three-dimensions (3D), allows valid quantitative measurements of the spinal curvature at any location along the spine. We propose a novel automated method for the determination of the spinal centerline in 3D spine images. Our method exploits the anatomical property that the vertebral body walls are cylindrically-shaped and therefore the lines normal to the edges of the vertebral body walls most often intersect in the middle of the vertebral bodies, i.e. at the location of spinal centerline. These points of intersection are first obtained by a novel algorithm that performs a selective search in the directions normal to the edges of the structures and then connected with a parametric curve that represents the spinal centerline in 3D. As the method is based on anatomical properties of the 3D spine anatomy, it is modality-independent, i.e. applicable to images obtained by computed tomography (CT) and magnetic resonance (MR). The proposed method was evaluated on six CT and four MR images (T1- and T2-weighted) of normal spines and on one scoliotic CT spine image. The qualitative and quantitative results for the normal spines show that the spinal centerline can be successfully determined in both CT and MR spine images, while the results for the scoliotic spine indicate that the method may also be used to evaluate pathological curvatures.

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

    Science.gov (United States)

    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.

  1. Automated indexing of Laue images from polycrystalline materials

    Energy Technology Data Exchange (ETDEWEB)

    Chung, J.S.; Ice, G.E. [Oak Ridge National Lab., TN (United States). Metals and Ceramics Div.

    1998-12-31

    Third generation hard x-ray synchrotron sources and new x-ray optics have revolutionized x-ray microbeams. Now intense sub-micron x-ray beams are routinely available for x-ray diffraction measurement. An important application of sub-micron x-ray beams is analyzing polycrystalline material by measuring the diffraction of individual grains. For these measurements, conventional analysis methods will not work. The most suitable method for microdiffraction on polycrystalline samples is taking broad-bandpass or white-beam Laue images. With this method, the crystal orientation and non-isostatic strain can be measured rapidly without rotation of sample or detector. The essential step is indexing the reflections from more than one grain. An algorithm has recently been developed to index broad bandpass Laue images from multi-grain samples. For a single grain, a unique set of indices is found by comparing measured angles between Laue reflections and angles between possible indices derived from the x-ray energy bandpass and the scattering angle 2 theta. This method has been extended to multigrain diffraction by successively indexing points not recognized in preceding indexing iterations. This automated indexing method can be used in a wide range of applications.

  2. 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

    Directory of Open Access Journals (Sweden)

    Waterston Robert H

    2010-02-01

    Full Text Available Abstract 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.

  3. Microfluidics for single cell analysis

    DEFF Research Database (Denmark)

    Jensen, Marie Pødenphant

    Isolation and manipulation of single cells have gained an increasing interest from researchers because of the heterogeneity of cells from the same cell culture. Single cell analysis can ensure a better understanding of differences between individual cells and potentially solve a variety of clinic...

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

    Science.gov (United States)

    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.

  5. Automated processing of webcam images for phenological classification

    Science.gov (United States)

    Bothmann, Ludwig; Menzel, Annette; Menze, Bjoern H.; Schunk, Christian; Kauermann, Göran

    2017-01-01

    Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels’ time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the statistical software

  6. Automated Nanofiber Diameter Measurement in SEM Images Using a Robust Image Analysis Method

    Directory of Open Access Journals (Sweden)

    Ertan Öznergiz

    2014-01-01

    Full Text Available Due to the high surface area, porosity, and rigidity, applications of nanofibers and nanosurfaces have developed in recent years. Nanofibers and nanosurfaces are typically produced by electrospinning method. In the production process, determination of average fiber diameter is crucial for quality assessment. Average fiber diameter is determined by manually measuring the diameters of randomly selected fibers on scanning electron microscopy (SEM images. However, as the number of the images increases, manual fiber diameter determination becomes a tedious and time consuming task as well as being sensitive to human errors. Therefore, an automated fiber diameter measurement system is desired. In the literature, this task is achieved by using image analysis algorithms. Typically, these methods first isolate each fiber in the image and measure the diameter of each isolated fiber. Fiber isolation is an error-prone process. In this study, automated calculation of nanofiber diameter is achieved without fiber isolation using image processing and analysis algorithms. Performance of the proposed method was tested on real data. The effectiveness of the proposed method is shown by comparing automatically and manually measured nanofiber diameter values.

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

    DEFF Research Database (Denmark)

    Carl, Jesper; Nielsen, Henning; Nielsen, Jane

    2006-01-01

    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...

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Michał Grega

    2016-01-01

    Full Text Available 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.

  10. Application of automated image analysis to coal petrography

    Science.gov (United States)

    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

  11. Automated image analysis of atomic force microscopy images of rotavirus particles

    Energy Technology Data Exchange (ETDEWEB)

    Venkataraman, S. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Department of Electrical and Computer Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Allison, D.P. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Department of Biochemistry, Cellular, and Molecular Biology, University of Tennessee, Knoxville, TN 37996 (United States); Molecular Imaging Inc. Tempe, AZ, 85282 (United States); Qi, H. [Department of Electrical and Computer Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Morrell-Falvey, J.L. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Kallewaard, N.L. [Vanderbilt University Medical Center, Nashville, TN 37232-2905 (United States); Crowe, J.E. [Vanderbilt University Medical Center, Nashville, TN 37232-2905 (United States); Doktycz, M.J. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States)]. E-mail: doktyczmj@ornl.gov

    2006-06-15

    A variety of biological samples can be imaged by the atomic force microscope (AFM) under environments that range from vacuum to ambient to liquid. Generally imaging is pursued to evaluate structural features of the sample or perhaps identify some structural changes in the sample that are induced by the investigator. In many cases, AFM images of sample features and induced structural changes are interpreted in general qualitative terms such as markedly smaller or larger, rougher, highly irregular, or smooth. Various manual tools can be used to analyze images and extract more quantitative data, but this is usually a cumbersome process. To facilitate quantitative AFM imaging, automated image analysis routines are being developed. Viral particles imaged in water were used as a test case to develop an algorithm that automatically extracts average dimensional information from a large set of individual particles. The extracted information allows statistical analyses of the dimensional characteristics of the particles and facilitates interpretation related to the binding of the particles to the surface. This algorithm is being extended for analysis of other biological samples and physical objects that are imaged by AFM.

  12. Automated Imaging System for Pigmented Skin Lesion Diagnosis

    Directory of Open Access Journals (Sweden)

    Mariam Ahmed Sheha

    2016-10-01

    Full Text Available Through the study of pigmented skin lesions risk factors, the appearance of malignant melanoma turns the anomalous occurrence of these lesions to annoying sign. The difficulty of differentiation between malignant melanoma and melanocytic naive is the error-bone problem that usually faces the physicians in diagnosis. To think through the hard mission of pigmented skin lesions diagnosis different clinical diagnosis algorithms were proposed such as pattern analysis, ABCD rule of dermoscopy, Menzies method, and 7-points checklist. Computerized monitoring of these algorithms improves the diagnosis of melanoma compared to simple naked-eye of physician during examination. Toward the serious step of melanoma early detection, aiming to reduce melanoma mortality rate, several computerized studies and procedures were proposed. Through this research different approaches with a huge number of features were discussed to point out the best approach or methodology could be followed to accurately diagnose the pigmented skin lesion. This paper proposes automated system for diagnosis of melanoma to provide quantitative and objective evaluation of skin lesion as opposed to visual assessment, which is subjective in nature. Two different data sets were utilized to reduce the effect of qualitative interpretation problem upon accurate diagnosis. Set of clinical images that are acquired from a standard camera while the other set is acquired from a special dermoscopic camera and so named dermoscopic images. System contribution appears in new, complete and different approaches presented for the aim of pigmented skin lesion diagnosis. These approaches result from using large conclusive set of features fed to different classifiers. The three main types of different features extracted from the region of interest are geometric, chromatic, and texture features. Three statistical methods were proposed to select the most significant features that will cause a valuable effect in

  13. Automated Identification of Rivers and Shorelines in Aerial Imagery Using Image Texture

    Science.gov (United States)

    2011-01-01

    defining the criteria for segmenting the image. For these cases certain automated, unsupervised (or minimally supervised), image classification ...banks, image analysis, edge finding, photography, satellite, texture, entropy 16. SECURITY CLASSIFICATION OF: a. REPORT Unclassified b. ABSTRACT...high resolution bank geometry. Much of the globe is covered by various sorts of multi- or hyperspectral imagery and numerous techniques have been

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

    Science.gov (United States)

    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 ( 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 non-target objects that could not be removed

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

    Science.gov (United States)

    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.

  16. Introduction: why analyze single cells?

    Science.gov (United States)

    Di Carlo, Dino; Tse, Henry Tat Kwong; Gossett, Daniel R

    2012-01-01

    Powerful methods in molecular biology are abundant; however, in many fields including hematology, stem cell biology, tissue engineering, and cancer biology, data from tools and assays that analyze the average signals from many cells may not yield the desired result because the cells of interest may be in the minority-their behavior masked by the majority-or because the dynamics of the populations of interest are offset in time. Accurate characterization of samples with high cellular heterogeneity may only be achieved by analyzing single cells. In this chapter, we discuss the rationale for performing analyses on individual cells in more depth, cover the fields of study in which single-cell behavior is yielding new insights into biological and clinical questions, and speculate on how single-cell analysis will be critical in the future.

  17. AMIsurvey, chimenea and other tools: Automated imaging for transient surveys with existing radio-observatories

    CERN Document Server

    Staley, Tim D

    2015-01-01

    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, making 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. These packages...

  18. Automated interpretation of PET/CT images in patients with lung cancer

    DEFF Research Database (Denmark)

    Gutte, Henrik; Jakobsson, David; Olofsson, Fredrik

    2007-01-01

    PURPOSE: To develop a completely automated method based on image processing techniques and artificial neural networks for the interpretation of combined [(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images for the diagnosis and staging of lung...... for localization of lesions in the PET images in the feature extraction process. Eight features from each examination were used as inputs to artificial neural networks trained to classify the images. Thereafter, the performance of the network was evaluated in the test set. RESULTS: The performance of the automated...... method measured as the area under the receiver operating characteristic curve, was 0.97 in the test group, with an accuracy of 92%. The sensitivity was 86% at a specificity of 100%. CONCLUSIONS: A completely automated method using artificial neural networks can be used to detect lung cancer...

  19. Extending and applying active appearance models for automated, high precision segmentation in different image modalities

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Fisker, Rune; Ersbøll, Bjarne Kjær

    2001-01-01

    , an initialization scheme is designed thus making the usage of AAMs fully automated. Using these extensions it is demonstrated that AAMs can segment bone structures in radiographs, pork chops in perspective images and the left ventricle in cardiovascular magnetic resonance images in a robust, fast and accurate...

  20. Single Cell Isolation and Analysis

    Directory of Open Access Journals (Sweden)

    Ping Hu

    2016-10-01

    Full Text Available Increasing evidence shows that the heterogeneity of individual cells within a genetically identical population can be critical to their peculiar function and fate. Conventional cell based assays mainly analysis the average responses from a population cells, while the difference within individual cells may often be masked. The cell size, RNA transcripts and protein expression level are quite different within individual cells and these variations are key point to answer the problems in cancer, neurobiology, stem cell biology, immunology and developmental biology. To better understand the cell-to-cell variations, the single cell analysis can provide much more detailed information which may be helpful for therapeutic decisions in an increasingly personalized medicine. In this review, we will focus on the recent development in single cell analysis, including methods used in single cell isolation, analysis and some application examples. The review provides the historical background to single cell analysis, discusses limitations, and current and future possibilities in this exciting field of research.

  1. Single cell electroporation on chip

    NARCIS (Netherlands)

    Valero, Ana

    2006-01-01

    In this thesis the results of the development of microfluidic cell trap devices for single cell electroporation are described, which are to be used for gene transfection. The performance of two types of Lab-on-a-Chip trapping devices was tested using beads and cells, whereas the functionality for si

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Tözeren Aydın

    2007-09-01

    Full Text Available Abstract 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.

  4. Potentials of single-cell biology in identification and validation of disease biomarkers.

    Science.gov (United States)

    Niu, Furong; Wang, Diane C; Lu, Jiapei; Wu, Wei; Wang, Xiangdong

    2016-09-01

    Single-cell biology is considered a new approach to identify and validate disease-specific biomarkers. However, the concern raised by clinicians is how to apply single-cell measurements for clinical practice, translate the message of single-cell systems biology into clinical phenotype or explain alterations of single-cell gene sequencing and function in patient response to therapies. This study is to address the importance and necessity of single-cell gene sequencing in the identification and development of disease-specific biomarkers, the definition and significance of single-cell biology and single-cell systems biology in the understanding of single-cell full picture, the development and establishment of whole-cell models in the validation of targeted biological function and the figure and meaning of single-molecule imaging in single cell to trace intra-single-cell molecule expression, signal, interaction and location. We headline the important role of single-cell biology in the discovery and development of disease-specific biomarkers with a special emphasis on understanding single-cell biological functions, e.g. mechanical phenotypes, single-cell biology, heterogeneity and organization of genome function. We have reason to believe that such multi-dimensional, multi-layer, multi-crossing and stereoscopic single-cell biology definitely benefits the discovery and development of disease-specific biomarkers.

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

    Directory of Open Access Journals (Sweden)

    Mohendra Roy

    2016-05-01

    Full Text Available 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.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

    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...... 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....

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

    Directory of Open Access Journals (Sweden)

    Hongsheng Bi

    Full Text Available 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 ( 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 non-target objects that

  8. Digital transplantation pathology: combining whole slide imaging, multiplex staining and automated image analysis.

    Science.gov (United States)

    Isse, K; Lesniak, A; Grama, K; Roysam, B; Minervini, M I; Demetris, A J

    2012-01-01

    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.

  9. A fully automated method for quantifying and localizing white matter hyperintensities on MR images.

    Science.gov (United States)

    Wu, Minjie; Rosano, Caterina; Butters, Meryl; Whyte, Ellen; Nable, Megan; Crooks, Ryan; Meltzer, Carolyn C; Reynolds, Charles F; Aizenstein, Howard J

    2006-12-01

    White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy-connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (Pdepression. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 27 (3), 539-544.]), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large NeuroImage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders.

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

    Science.gov (United States)

    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.

  11. Matrix-free UV-laser desorption/ionization (LDI) mass spectrometric imaging at the single-cell level: distribution of secondary metabolites of Arabidopsis thaliana and Hypericum species.

    Science.gov (United States)

    Hölscher, Dirk; Shroff, Rohit; Knop, Katrin; Gottschaldt, Michael; Crecelius, Anna; Schneider, Bernd; Heckel, David G; Schubert, Ulrich S; Svatos, Ales

    2009-12-01

    The present paper describes matrix-free laser desorption/ionisation mass spectrometric imaging (LDI-MSI) of highly localized UV-absorbing secondary metabolites in plant tissues at single-cell resolution. The scope and limitations of the method are discussed with regard to plants of the genus Hypericum. Naphthodianthrones such as hypericin and pseudohypericin are traceable in dark glands on Hypericum leaves, placenta, stamens and styli; biflavonoids are also traceable in the pollen of this important phytomedical plant. The highest spatial resolution achieved, 10 microm, was much higher than that achieved by commonly used matrix-assisted laser desorption/ionization (MALDI) imaging protocols. The data from imaging experiments were supported by independent LDI-TOF/MS analysis of cryo-sectioned, laser-microdissected and freshly cut plant material. The results confirmed the suitability of combining laser microdissection (LMD) and LDI-TOF/MS or LDI-MSI to analyse localized plant secondary metabolites. Furthermore, Arabidopsis thaliana was analysed to demonstrate the feasibility of LDI-MSI for other commonly occurring compounds such as flavonoids. The organ-specific distribution of kaempferol, quercetin and isorhamnetin, and their glycosides, was imaged at the cellular level.

  12. A method for fast automated microscope image stitching.

    Science.gov (United States)

    Yang, Fan; Deng, Zhen-Sheng; Fan, Qiu-Hong

    2013-05-01

    Image stitching is an important technology to produce a panorama or larger image by combining several images with overlapped areas. In many biomedical researches, image stitching is highly desirable to acquire a panoramic image which represents large areas of certain structures or whole sections, while retaining microscopic resolution. In this study, we develop a fast normal light microscope image stitching algorithm based on feature extraction. At first, an algorithm of scale-space reconstruction of speeded-up robust features (SURF) was proposed to extract features from the images to be stitched with a short time and higher repeatability. Then, the histogram equalization (HE) method was employed to preprocess the images to enhance their contrast for extracting more features. Thirdly, the rough overlapping zones of the images preprocessed were calculated by phase correlation, and the improved SURF was used to extract the image features in the rough overlapping areas. Fourthly, the features were corresponded by matching algorithm and the transformation parameters were estimated, then the images were blended seamlessly. Finally, this procedure was applied to stitch normal light microscope images to verify its validity. Our experimental results demonstrate that the improved SURF algorithm is very robust to viewpoint, illumination, blur, rotation and zoom of the images and our method is able to stitch microscope images automatically with high precision and high speed. Also, the method proposed in this paper is applicable to registration and stitching of common images as well as stitching the microscope images in the field of virtual microscope for the purpose of observing, exchanging, saving, and establishing a database of microscope images.

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

    Science.gov (United States)

    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.

  14. Microscopic images dataset for automation of RBCs counting.

    Science.gov (United States)

    Abbas, Sherif

    2015-12-01

    A method for Red Blood Corpuscles (RBCs) counting has been developed using RBCs light microscopic images and Matlab algorithm. The Dataset consists of Red Blood Corpuscles (RBCs) images and there RBCs segmented images. A detailed description using flow chart is given in order to show how to produce RBCs mask. The RBCs mask was used to count the number of RBCs in the blood smear image.

  15. Automated quadrilateral mesh generation for digital image structures

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    With the development of advanced imaging technology, digital images are widely used. This paper proposes an automatic quadrilateral mesh generation algorithm for multi-colour imaged structures. It takes an original arbitrary digital image as an input for automatic quadrilateral mesh generation, this includes removing the noise, extracting and smoothing the boundary geometries between different colours, and automatic all-quad mesh generation with the above boundaries as constraints. An application example is...

  16. Microscopic images dataset for automation of RBCs counting

    Directory of Open Access Journals (Sweden)

    Sherif Abbas

    2015-12-01

    Full Text Available A method for Red Blood Corpuscles (RBCs counting has been developed using RBCs light microscopic images and Matlab algorithm. The Dataset consists of Red Blood Corpuscles (RBCs images and there RBCs segmented images. A detailed description using flow chart is given in order to show how to produce RBCs mask. The RBCs mask was used to count the number of RBCs in the blood smear image.

  17. An automated detection for axonal boutons in vivo two-photon imaging of mouse

    Science.gov (United States)

    Li, Weifu; Zhang, Dandan; Xie, Qiwei; Chen, Xi; Han, Hua

    2017-02-01

    Activity-dependent changes in the synaptic connections of the brain are tightly related to learning and memory. Previous studies have shown that essentially all new synaptic contacts were made by adding new partners to existing synaptic elements. To further explore synaptic dynamics in specific pathways, concurrent imaging of pre and postsynaptic structures in identified connections is required. Consequently, considerable attention has been paid for the automated detection of axonal boutons. Different from most previous methods proposed in vitro data, this paper considers a more practical case in vivo neuron images which can provide real time information and direct observation of the dynamics of a disease process in mouse. Additionally, we present an automated approach for detecting axonal boutons by starting with deconvolving the original images, then thresholding the enhanced images, and reserving the regions fulfilling a series of criteria. Experimental result in vivo two-photon imaging of mouse demonstrates the effectiveness of our proposed method.

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

    Science.gov (United States)

    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.

  19. Automated quantification of budding Saccharomyces cerevisiae using a novel image cytometry method.

    Science.gov (United States)

    Laverty, Daniel J; Kury, Alexandria L; Kuksin, Dmitry; Pirani, Alnoor; Flanagan, Kevin; Chan, Leo Li-Ying

    2013-06-01

    The measurements of concentration, viability, and budding percentages of Saccharomyces cerevisiae are performed on a routine basis in the brewing and biofuel industries. Generation of these parameters is of great importance in a manufacturing setting, where they can aid in the estimation of product quality, quantity, and fermentation time of the manufacturing process. Specifically, budding percentages can be used to estimate the reproduction rate of yeast populations, which directly correlates with metabolism of polysaccharides and bioethanol production, and can be monitored to maximize production of bioethanol during fermentation. The traditional method involves manual counting using a hemacytometer, but this is time-consuming and prone to human error. In this study, we developed a novel automated method for the quantification of yeast budding percentages using Cellometer image cytometry. The automated method utilizes a dual-fluorescent nucleic acid dye to specifically stain live cells for imaging analysis of unique morphological characteristics of budding yeast. In addition, cell cycle analysis is performed as an alternative method for budding analysis. We were able to show comparable yeast budding percentages between manual and automated counting, as well as cell cycle analysis. The automated image cytometry method is used to analyze and characterize corn mash samples directly from fermenters during standard fermentation. Since concentration, viability, and budding percentages can be obtained simultaneously, the automated method can be integrated into the fermentation quality assurance protocol, which may improve the quality and efficiency of beer and bioethanol production processes.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  2. Advanced automated gain adjustments for in-vivo ultrasound imaging

    DEFF Research Database (Denmark)

    Moshavegh, Ramin; Hemmsen, Martin Christian; Martins, Bo

    2015-01-01

    Automatic gain adjustments are necessary on the state-of-the-art ultrasound scanners to obtain optimal scan quality, while reducing the unnecessary user interactions with the scanner. However, when large anechoic regions exist in the scan plane, the sudden and drastic variation of attenuations...... in the scanned media complicates the gain compensation. This paper presents an advanced and automated gain adjustment method that precisely compensate for the gains on scans and dynamically adapts to the drastic attenuation variations between different media. The proposed algorithm makes use of several...

  3. High-content single-cell analysis on-chip using a laser microarray scanner.

    Science.gov (United States)

    Zhou, Jing; Wu, Yu; Lee, Sang-Kwon; Fan, Rong

    2012-12-07

    High-content cellomic analysis is a powerful tool for rapid screening of cellular responses to extracellular cues and examination of intracellular signal transduction pathways at the single-cell level. In conjunction with microfluidics technology that provides unique advantages in sample processing and precise control of fluid delivery, it holds great potential to transform lab-on-a-chip systems for high-throughput cellular analysis. However, high-content imaging instruments are expensive, sophisticated, and not readily accessible. Herein, we report on a laser scanning cytometry approach that exploits a bench-top microarray scanner as an end-point reader to perform rapid and automated fluorescence imaging of cells cultured on a chip. Using high-content imaging analysis algorithms, we demonstrated multiplexed measurements of morphometric and proteomic parameters from all single cells. Our approach shows the improvement of both sensitivity and dynamic range by two orders of magnitude as compared to conventional epifluorescence microscopy. We applied this technology to high-throughput analysis of mesenchymal stem cells on an extracellular matrix protein array and characterization of heterotypic cell populations. This work demonstrates the feasibility of a laser microarray scanner for high-content cellomic analysis and opens up new opportunities to conduct informative cellular analysis and cell-based screening in the lab-on-a-chip systems.

  4. Novel automated motion compensation technique for producing cumulative maximum intensity subharmonic images.

    Science.gov (United States)

    Dave, Jaydev K; Forsberg, Flemming

    2009-09-01

    The aim of this study was to develop a novel automated motion compensation algorithm for producing cumulative maximum intensity (CMI) images from subharmonic imaging (SHI) of breast lesions. SHI is a nonlinear contrast-specific ultrasound imaging technique in which pulses are received at half the frequency of the transmitted pulses. A Logiq 9 scanner (GE Healthcare, Milwaukee, WI, USA) was modified to operate in grayscale SHI mode (transmitting/receiving at 4.4/2.2 MHz) and used to scan 14 women with 16 breast lesions. Manual CMI images were reconstructed by temporal maximum-intensity projection of pixels traced from the first frame to the last. In the new automated technique, the user selects a kernel in the first frame and the algorithm then uses the sum of absolute difference (SAD) technique to identify motion-induced displacements in the remaining frames. A reliability parameter was used to estimate the accuracy of the motion tracking based on the ratio of the minimum SAD to the average SAD. Two thresholds (the mean and 85% of the mean reliability parameter) were used to eliminate images plagued by excessive motion and/or noise. The automated algorithm was compared with the manual technique for computational time, correction of motion artifacts, removal of noisy frames and quality of the final image. The automated algorithm compensated for motion artifacts and noisy frames. The computational time was 2 min compared with 60-90 minutes for the manual method. The quality of the motion-compensated CMI-SHI images generated by the automated technique was comparable to the manual method and provided a snapshot of the microvasculature showing interconnections between vessels, which was less evident in the original data. In conclusion, an automated algorithm for producing CMI-SHI images has been developed. It eliminates the need for manual processing and yields reproducible images, thereby increasing the throughput and efficiency of reconstructing CMI-SHI images. The

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  8. Infrared thermal imaging for automated detection of diabetic foot complications

    NARCIS (Netherlands)

    Netten, van Jaap J.; Baal, van Jeff G.; Liu, Chanjuan; Heijden, van der 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 ap

  9. An Automated Method for Semantic Classification of Regions in Coastal Images

    NARCIS (Netherlands)

    Hoonhout, B.M.; Radermacher, M.; Baart, F.; Van der Maaten, L.J.P.

    2015-01-01

    Large, long-term coastal imagery datasets are nowadays a low-cost source of information for various coastal research disciplines. However, the applicability of many existing algorithms for coastal image analysis is limited for these large datasets due to a lack of automation and robustness. Therefor

  10. Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images

    NARCIS (Netherlands)

    Lee, K.; Buitendijk, G.H.; Bogunovic, H.; Springelkamp, H.; Hofman, A.; Wahle, A.; Sonka, M.; Vingerling, J.R.; Klaver, C.C.W.; Abramoff, M.D.

    2016-01-01

    PURPOSE: To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies. METHODS: Six hundred ninety macular SD-OCT image volumes (6.0 x 6.0 x 2.3 mm3) we

  11. Automated Selection of Uniform Regions for CT Image Quality Detection

    CERN Document Server

    Naeemi, Maitham D; Roychodhury, Sohini

    2016-01-01

    CT images are widely used in pathology detection and follow-up treatment procedures. Accurate identification of pathological features requires diagnostic quality CT images with minimal noise and artifact variation. In this work, a novel Fourier-transform based metric for image quality (IQ) estimation is presented that correlates to additive CT image noise. In the proposed method, two windowed CT image subset regions are analyzed together to identify the extent of variation in the corresponding Fourier-domain spectrum. The two square windows are chosen such that their center pixels coincide and one window is a subset of the other. The Fourier-domain spectral difference between these two sub-sampled windows is then used to isolate spatial regions-of-interest (ROI) with low signal variation (ROI-LV) and high signal variation (ROI-HV), respectively. Finally, the spatial variance ($var$), standard deviation ($std$), coefficient of variance ($cov$) and the fraction of abdominal ROI pixels in ROI-LV ($\

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

    Energy Technology Data Exchange (ETDEWEB)

    Orphan, Victoria Jeanne [California Inst. of Technology (CalTech), Pasadena, CA (United States). Division of Geological and Planetary Sciences

    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

  13. Efficient parallel Levenberg-Marquardt model fitting towards real-time automated parametric imaging microscopy.

    Science.gov (United States)

    Zhu, Xiang; Zhang, Dianwen

    2013-01-01

    We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetime imaging microscopy.

  14. Fully Automated Prostate Magnetic Resonance Imaging and Transrectal Ultrasound Fusion via a Probabilistic Registration Metric

    OpenAIRE

    Sparks, Rachel; Bloch, B. Nicolas; Feleppa, Ernest; Barratt, Dean; Madabhushi, Anant

    2013-01-01

    In this work, we present a novel, automated, registration method to fuse magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) images of the prostate. Our methodology consists of: (1) delineating the prostate on MRI, (2) building a probabilistic model of prostate location on TRUS, and (3) aligning the MRI prostate segmentation to the TRUS probabilistic model. TRUS-guided needle biopsy is the current gold standard for prostate cancer (CaP) diagnosis. Up to 40% of CaP lesions appea...

  15. Automated interpretation of optic nerve images: a data mining framework for glaucoma diagnostic support.

    Science.gov (United States)

    Abidi, Syed S R; Artes, Paul H; Yun, Sanjan; Yu, Jin

    2007-01-01

    Confocal Scanning Laser Tomography (CSLT) techniques capture high-quality images of the optic disc (the retinal region where the optic nerve exits the eye) that are used in the diagnosis and monitoring of glaucoma. We present a hybrid framework, combining image processing and data mining methods, to support the interpretation of CSLT optic nerve images. Our framework features (a) Zernike moment methods to derive shape information from optic disc images; (b) classification of optic disc images, based on shape information, to distinguish between healthy and glaucomatous optic discs. We apply Multi Layer Perceptrons, Support Vector Machines and Bayesian Networks for feature sub-set selection and image classification; and (c) clustering of optic disc images, based on shape information, using Self-Organizing Maps to visualize sub-types of glaucomatous optic disc damage. Our framework offers an automated and objective analysis of optic nerve images that can potentially support both diagnosis and monitoring of glaucoma.

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

    Energy Technology Data Exchange (ETDEWEB)

    Klooster, R. van ' t; Staring, M.; Reiber, J. H. C.; Lelieveldt, B. P. F.; Geest, R. J. van der, E-mail: rvdgeest@lumc.nl [Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC Leiden (Netherlands); Klein, S. [Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 GE (Netherlands); Kwee, R. M.; Kooi, M. E. [Department of Radiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht 6202 AZ (Netherlands)

    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

  17. Automated wavelet denoising of photoacoustic signals for burn-depth image reconstruction

    Science.gov (United States)

    Holan, Scott H.; Viator, John A.

    2007-02-01

    Photoacoustic image reconstruction involves dozens or perhaps 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 sample with laser light are used to produce an image of the acoustic source. Each of these point measurements must undergo some signal processing, such as denoising and system deconvolution. In order to efficiently process the numerous signals acquired for photoacoustic imaging, we have developed an automated wavelet algorithm for processing signals generated in a burn injury phantom. We used the discrete wavelet transform to denoise photoacoustic signals generated in an optically turbid phantom containing whole blood. The denoising used universal level independent thresholding, as developed by Donoho and Johnstone. The entire signal processing technique was automated so that no user intervention was needed to reconstruct the images. The signals were backprojected using the automated wavelet processing software and showed reconstruction using denoised signals improved image quality by 21%, using a relative 2-norm difference scheme.

  18. An automated image analysis system to measure and count organisms in laboratory microcosms.

    Directory of Open Access Journals (Sweden)

    François Mallard

    Full Text Available 1. Because of recent technological improvements in the way computer and digital camera perform, the potential use of imaging for contributing to the study of communities, populations or individuals in laboratory microcosms has risen enormously. However its limited use is due to difficulties in the automation of image analysis. 2. We present an accurate and flexible method of image analysis for detecting, counting and measuring moving particles on a fixed but heterogeneous substrate. This method has been specifically designed to follow individuals, or entire populations, in experimental laboratory microcosms. It can be used in other applications. 3. The method consists in comparing multiple pictures of the same experimental microcosm in order to generate an image of the fixed background. This background is then used to extract, measure and count the moving organisms, leaving out the fixed background and the motionless or dead individuals. 4. We provide different examples (springtails, ants, nematodes, daphnia to show that this non intrusive method is efficient at detecting organisms under a wide variety of conditions even on faintly contrasted and heterogeneous substrates. 5. The repeatability and reliability of this method has been assessed using experimental populations of the Collembola Folsomia candida. 6. We present an ImageJ plugin to automate the analysis of digital pictures of laboratory microcosms. The plugin automates the successive steps of the analysis and recursively analyses multiple sets of images, rapidly producing measurements from a large number of replicated microcosms.

  19. A performance analysis system for MEMS using automated imaging methods

    Energy Technology Data Exchange (ETDEWEB)

    LaVigne, G.F.; Miller, S.L.

    1998-08-01

    The ability to make in-situ performance measurements of MEMS operating at high speeds has been demonstrated using a new image analysis system. Significant improvements in performance and reliability have directly resulted from the use of this system.

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

    Science.gov (United States)

    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.

  1. Multispectral Image Road Extraction Based Upon Automated Map Conflation

    Science.gov (United States)

    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

  2. Automated Contour Detection for Intravascular Ultrasound Image Sequences Based on Fast Active Contour Algorithm

    Institute of Scientific and Technical Information of China (English)

    DONG Hai-yan; WANG Hui-nan

    2006-01-01

    Intravascular ultrasound can provide high-resolution real-time crosssectional images about lumen, plaque and tissue. Traditionally, the luminal border and medial-adventitial border are traced manually. This process is extremely timeconsuming and the subjective difference would be large. In this paper, a new automated contour detection method is introduced based on fast active contour model.Experimental results found that lumen and vessel area measurements after automated detection showed good agreement with manual tracings with high correlation coefficients (0.94 and 0.95, respectively) and small system difference ( -0.32 and 0.56, respectively). So it can be a reliable and accurate diagnostic tool.

  3. Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method

    Science.gov (United States)

    Irshad, Humayun; Oh, Eun-Yeong; Schmolze, Daniel; Quintana, Liza M.; Collins, Laura; Tamimi, Rulla M.; Beck, Andrew H.

    2017-01-01

    The assessment of protein expression in immunohistochemistry (IHC) images provides important diagnostic, prognostic and predictive information for guiding cancer diagnosis and therapy. Manual scoring of IHC images represents a logistical challenge, as the process is labor intensive and time consuming. Since the last decade, computational methods have been developed to enable the application of quantitative methods for the analysis and interpretation of protein expression in IHC images. These methods have not yet replaced manual scoring for the assessment of IHC in the majority of diagnostic laboratories and in many large-scale research studies. An alternative approach is crowdsourcing the quantification of IHC images to an undefined crowd. The aim of this study is to quantify IHC images for labeling of ER status with two different crowdsourcing approaches, image-labeling and nuclei-labeling, and compare their performance with automated methods. Crowdsourcing- derived scores obtained greater concordance with the pathologist interpretations for both image-labeling and nuclei-labeling tasks (83% and 87%), as compared to the pathologist concordance achieved by the automated method (81%) on 5,338 TMA images from 1,853 breast cancer patients. This analysis shows that crowdsourcing the scoring of protein expression in IHC images is a promising new approach for large scale cancer molecular pathology studies. PMID:28230179

  4. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Ricardo Andres Pizarro

    2016-12-01

    Full Text Available High-resolution three-dimensional magnetic resonance imaging (3D-MRI is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM algorithm in the quality assessment of structural brain images, using global and region of interest (ROI automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  5. Automated analysis of image mammogram for breast cancer diagnosis

    Science.gov (United States)

    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.

  6. Automated Classification of Glaucoma Images by Wavelet Energy Features

    Directory of Open Access Journals (Sweden)

    N.Annu

    2013-04-01

    Full Text Available Glaucoma is the second leading cause of blindness worldwide. As glaucoma progresses, more optic nerve tissue is lost and the optic cup grows which leads to vision loss. This paper compiles a systemthat could be used by non-experts to filtrate cases of patients not affected by the disease. This work proposes glaucomatous image classification using texture features within images and efficient glaucoma classification based on Probabilistic Neural Network (PNN. Energy distribution over wavelet sub bands is applied to compute these texture features. Wavelet features were obtained from the daubechies (db3, symlets (sym3, and biorthogonal (bio3.3, bio3.5, and bio3.7 wavelet filters. It uses a technique to extract energy signatures obtained using 2-D discrete wavelet transform and the energy obtained from the detailed coefficients can be used to distinguish between normal and glaucomatous images. We observedan accuracy of around 95%, this demonstrates the effectiveness of these methods.

  7. System and method for automated object detection in an image

    Energy Technology Data Exchange (ETDEWEB)

    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.

  8. Automated Structure Detection in HRTEM Images: An Example with Graphene

    DEFF Research Database (Denmark)

    Kling, Jens; Vestergaard, Jacob Schack; Dahl, Anders Bjorholm

    of time making it difficult to resolve dynamic processes or unstable structures. Tools that assist to get the maximum of information out of recorded images are therefore greatly appreciated. In order to get the most accurate results out of the structure detection, we have optimized the imaging conditions...... used for the FEI Titan ETEM with a monochromator and an objective-lens Cs-corrector. To reduce the knock-on damage of the carbon atoms in the graphene structure, the microscope was operated at 80kV. As this strongly increases the influence of the chromatic aberration of the lenses, the energy spread...

  9. An Imaging System for Automated Characteristic Length Measurement of Debrisat Fragments

    Science.gov (United States)

    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.

  10. Computer-assisted tree taxonomy by automated image recognition

    NARCIS (Netherlands)

    Pauwels, E.J.; Zeeuw, P.M.de; Ranguelova, E.B.

    2009-01-01

    We present an algorithm that performs image-based queries within the domain of tree taxonomy. As such, it serves as an example relevant to many other potential applications within the field of biodiversity and photo-identification. Unsupervised matching results are produced through a chain of comput

  11. Automated identification of retained surgical items in radiological images

    Science.gov (United States)

    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.

  12. Automated Coronal Loop Identification Using Digital Image Processing Techniques

    Science.gov (United States)

    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.

  13. AUTOMATED VIDEO IMAGE MORPHOMETRY OF THE CORNEAL ENDOTHELIUM

    NARCIS (Netherlands)

    SIERTSEMA, JV; LANDESZ, M; VANDENBROM, H; VANRIJ, G

    1993-01-01

    The central corneal endothelium of 13 eyes in 13 subjects was visualized with a non-contact specular microscope. This report describes the computer-assisted morphometric analysis of enhanced digitized images, using a direct input by means of a frame grabber. The output consisted of mean cell area, c

  14. Automated marker tracking using noisy X-ray images degraded by the treatment beam

    Energy Technology Data Exchange (ETDEWEB)

    Wisotzky, E. [Fraunhofer Institute for Production Systems and Design Technology (IPK), Berlin (Germany); German Cancer Research Center (DKFZ), Heidelberg (Germany); Fast, M.F.; Nill, S. [The Royal Marsden NHS Foundation Trust, London (United Kingdom). Joint Dept. of Physics; Oelfke, U. [The Royal Marsden NHS Foundation Trust, London (United Kingdom). Joint Dept. of Physics; German Cancer Research Center (DKFZ), Heidelberg (Germany)

    2015-09-01

    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 {sup registered} 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.

  15. Single-cell force spectroscopy.

    Science.gov (United States)

    Helenius, Jonne; Heisenberg, Carl-Philipp; Gaub, Hermann E; Muller, Daniel J

    2008-06-01

    The controlled adhesion of cells to each other and to the extracellular matrix is crucial for tissue development and maintenance. Numerous assays have been developed to quantify cell adhesion. Among these, the use of atomic force microscopy (AFM) for single-cell force spectroscopy (SCFS) has recently been established. This assay permits the adhesion of living cells to be studied in near-physiological conditions. This implementation of AFM allows unrivaled spatial and temporal control of cells, as well as highly quantitative force actuation and force measurement that is sufficiently sensitive to characterize the interaction of single molecules. Therefore, not only overall cell adhesion but also the properties of single adhesion-receptor-ligand interactions can be studied. Here we describe current implementations and applications of SCFS, as well as potential pitfalls, and outline how developments will provide insight into the forces, energetics and kinetics of cell-adhesion processes.

  16. Automated Detection of Contaminated Radar Image Pixels in Mountain Areas

    Institute of Scientific and Technical Information of China (English)

    LIU Liping; Qin XU; Pengfei ZHANG; Shun LIU

    2008-01-01

    In mountain areas,radar observations are often contaminated(1)by echoes from high-speed moving vehicles and(2)by point-wise ground clutter under either normal propagation(NP)or anomalous propa-gation(AP)conditions.Level II data are collected from KMTX(Salt Lake City,Utah)radar to analyze these two types of contamination in the mountain area around the Great Salt Lake.Human experts provide the"ground truth"for possible contamination of either type on each individual pixel.Common features are then extracted for contaminated pixels of each type.For example,pixels contaminated by echoes from high-speed moving vehicles are characterized by large radial velocity and spectrum width.Echoes from a moving train tend to have larger velocity and reflectivity but smaller spectrum width than those from moving vehicles on highways.These contaminated pixels are only seen in areas of large terrain gradient(in the radial direction along the radar beam).The same is true for the second type of contamination-point-wise ground clutters.Six quality control(QC)parameters are selected to quantify the extracted features.Histograms are computed for each QC parameter and grouped for contaminated pixels of each type and also for non-contaminated pixels.Based on the computed histograms,a fuzzy logical algorithm is developed for automated detection of contaminated pixels.The algorithm is tested with KMTX radar data under different(clear and rainy)weather conditions.

  17. Automated segmentation of regions of interest in whole slide skin histopathological images.

    Science.gov (United States)

    Xu, Hongming; Lu, Cheng; Mandal, Mrinal

    2015-01-01

    In the diagnosis of skin melanoma by analyzing histopathological images, the epidermis and epidermis-dermis junctional areas are regions of interest as they provide the most important histologic diagnosis features. This paper presents an automated technique for segmenting epidermis and dermis regions from whole slide skin histopathological images. The proposed technique first performs epidermis segmentation using a thresholding and thickness measurement based method. The dermis area is then segmented based on a predefined depth of segmentation from the epidermis outer boundary. Experimental results on 66 different skin images show that the proposed technique can robustly segment regions of interest as desired.

  18. Automated 3D ultrasound image segmentation for assistant diagnosis of breast cancer

    Science.gov (United States)

    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.

  19. Inkjet-like printing of single-cells.

    Science.gov (United States)

    Yusof, Azmi; Keegan, Helen; Spillane, Cathy D; Sheils, Orla M; Martin, Cara M; O'Leary, John J; Zengerle, Roland; Koltay, Peter

    2011-07-21

    Cell sorting and separation techniques are essential tools for cell biology research and for many diagnostic and therapeutic applications. For many of these applications, it is imperative that heterogeneous populations of cells are segregated according to their cell type and that individual cells can be isolated and analysed. We present a novel technique to isolate single cells encapsulated in a picolitre sized droplet that are then deposited by inkjet-like printing at defined locations for downstream genomic analysis. The single-cell-manipulator (SCM) developed for this purpose consists of a dispenser chip to print cells contained in a free flying droplet, a computer vision system to detect single-cells inside the dispenser chip prior to printing, and appropriate automation equipment to print single-cells onto defined locations on a substrate. This technique is spatially dynamic, enabling cell printing on a wide range of commonly used substrates such as microscope slides, membranes and microtiter plates. Demonstration experiments performed using the SCM resulted in a printing efficiency of 87% for polystyrene microbeads of 10 μm size. When the SCM was applied to a cervical cancer cell line (HeLa), a printing efficiency of 87% was observed and a post-SCM cell viability rate of 75% was achieved.

  20. Automated Dermoscopy Image Analysis of Pigmented Skin Lesions

    Directory of Open Access Journals (Sweden)

    Alfonso Baldi

    2010-03-01

    Full Text Available 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.

  1. Automated Detection and Removal of Cloud Shadows on HICO Images

    Science.gov (United States)

    2011-01-01

    Gross, F. Moshary and S. Ahmed, "Impacts of atmospheric corrections on algal bloom detection techniques," 89th AMS Annual Meeting, Phoenix, Arizona... Remote Sens. 36, 880-897, (1998). 4] R. Amin, J. Zhou, A. Gilerson, B. Gross, F. Moshary and S. Ahmed, "Novel optical techniques for detecting and...32157 (1998). 11]J. Cihlar, J. Howarth, " Detection and removal of cloud contamination from AVHRR images," IEEE Trans. Geos. Remote Sens., 32, 583

  2. Extended Field Laser Confocal Microscopy (EFLCM: Combining automated Gigapixel image capture with in silico virtual microscopy

    Directory of Open Access Journals (Sweden)

    Strandh Christer

    2008-07-01

    Full Text Available Abstract 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.

  3. A semi-automated single day image differencing technique to identify animals in aerial imagery.

    Directory of Open Access Journals (Sweden)

    Pat Terletzky

    Full Text Available 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.

  4. A semi-automated single day image differencing technique to identify animals in aerial imagery.

    Science.gov (United States)

    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.

  5. Automated analysis of craniofacial morphology using magnetic resonance images.

    Directory of Open Access Journals (Sweden)

    M Mallar Chakravarty

    Full Text Available Quantitative analysis of craniofacial morphology is of interest to scholars working in a wide variety of disciplines, such as anthropology, developmental biology, and medicine. T1-weighted (anatomical magnetic resonance images (MRI provide excellent contrast between soft tissues. Given its three-dimensional nature, MRI represents an ideal imaging modality for the analysis of craniofacial structure in living individuals. Here we describe how T1-weighted MR images, acquired to examine brain anatomy, can also be used to analyze facial features. Using a sample of typically developing adolescents from the Saguenay Youth Study (N = 597; 292 male, 305 female, ages: 12 to 18 years, we quantified inter-individual variations in craniofacial structure in two ways. First, we adapted existing nonlinear registration-based morphological techniques to generate iteratively a group-wise population average of craniofacial features. The nonlinear transformations were used to map the craniofacial structure of each individual to the population average. Using voxel-wise measures of expansion and contraction, we then examined the effects of sex and age on inter-individual variations in facial features. Second, we employed a landmark-based approach to quantify variations in face surfaces. This approach involves: (a placing 56 landmarks (forehead, nose, lips, jaw-line, cheekbones, and eyes on a surface representation of the MRI-based group average; (b warping the landmarks to the individual faces using the inverse nonlinear transformation estimated for each person; and (3 using a principal components analysis (PCA of the warped landmarks to identify facial features (i.e. clusters of landmarks that vary in our sample in a correlated fashion. As with the voxel-wise analysis of the deformation fields, we examined the effects of sex and age on the PCA-derived spatial relationships between facial features. Both methods demonstrated significant sexual dimorphism in

  6. Image cytometer method for automated assessment of human spermatozoa concentration

    DEFF Research Database (Denmark)

    Egeberg, D L; Kjaerulff, S; Hansen, C

    2013-01-01

    to investigator bias. Here we show that image cytometry can be used to accurately measure the sperm concentration of human semen samples with great ease and reproducibility. The impact of several factors (pipetting, mixing, round cell content, sperm concentration), which can influence the read-out as well......In the basic clinical work-up of infertile couples, a semen analysis is mandatory and the sperm concentration is one of the most essential variables to be determined. Sperm concentration is usually assessed by manual counting using a haemocytometer and is hence labour intensive and may be subjected...... and easy measurement of human sperm concentration....

  7. Automated Hierarchical Time Gain Compensation for In Vivo Ultrasound Imaging

    DEFF Research Database (Denmark)

    Moshavegh, Ramin; Hemmsen, Martin Christian; Martins, Bo

    2015-01-01

    Time gain compensation (TGC) is essential to ensure the optimal image quality of the clinical ultrasound scans. When large fluid collections are present within the scan plane, the attenuation distribution is changed drastically and TGC compensation becomes challenging. This paper presents...... tissue and the ultrasound signal strength. The proposed algorithm was applied to a set of 44 in vivo abdominal movie sequences each containing 15 frames. Matching pairs of in vivo sequences, unprocessed and processed with the proposed AHTGC were visualized side by side and evaluated by two radiologists...

  8. Automated image analysis for quantification of filamentous bacteria

    DEFF Research Database (Denmark)

    Fredborg, M.; Rosenvinge, F. S.; Spillum, E.

    2015-01-01

    Background: Antibiotics of the beta-lactam group are able to alter the shape of the bacterial cell wall, e.g. filamentation or a spheroplast formation. Early determination of antimicrobial susceptibility may be complicated by filamentation of bacteria as this can be falsely interpreted as growth...... displaying different resistant profiles and differences in filamentation kinetics were used to study a novel image analysis algorithm to quantify length of bacteria and bacterial filamentation. A total of 12 beta-lactam antibiotics or beta-lactam-beta-lactamase inhibitor combinations were analyzed...

  9. Automated Image-Based Procedures for Adaptive Radiotherapy

    DEFF Research Database (Denmark)

    Bjerre, Troels

    -tissue complication probability (NTCP), margins used to account for interfraction and intrafraction anatomical changes and motion need to be reduced. This can only be achieved through proper treatment plan adaptations and intrafraction motion management. This thesis describes methods in support of image...... to encourage bone rigidity and local tissue volume change only in the gross tumour volume and the lungs. This is highly relevant in adaptive radiotherapy when modelling significant tumour volume changes. - It is described how cone beam CT reconstruction can be modelled as a deformation of a planning CT scan...

  10. Automated Hierarchical Time Gain Compensation for In Vivo Ultrasound Imaging

    DEFF Research Database (Denmark)

    Moshavegh, Ramin; Hemmsen, Martin Christian; Martins, Bo;

    2015-01-01

    in terms of image quality. Wilcoxon signed-rank test was used to evaluate whether radiologists preferred the processed sequences or the unprocessed data. The results indicate that the average visual analogue scale (VAS) is positive ( p-value: 2.34 × 10−13) and estimated to be 1.01 (95% CI: 0.85; 1...... tissue and the ultrasound signal strength. The proposed algorithm was applied to a set of 44 in vivo abdominal movie sequences each containing 15 frames. Matching pairs of in vivo sequences, unprocessed and processed with the proposed AHTGC were visualized side by side and evaluated by two radiologists...

  11. Automated detection of diabetic retinopathy in retinal images

    Directory of Open Access Journals (Sweden)

    Carmen Valverde

    2016-01-01

    Full Text Available 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.

  12. Automated classification of female facial beauty by image analysis and supervised learning

    Science.gov (United States)

    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.

  13. A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image

    Directory of Open Access Journals (Sweden)

    Phlypo Ronald

    2010-01-01

    Full Text Available We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.

  14. Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

    Science.gov (United States)

    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.

  15. Real time assays for quantifying cytotoxicity with single cell resolution.

    Directory of Open Access Journals (Sweden)

    Sonny C Hsiao

    Full Text Available A new live cell-based assay platform has been developed for the determination of complement dependent cytotoxicity (CDC, antibody dependent cellular cytotoxicity (ADCC, and overall cytotoxicity in human whole blood. In these assays, the targeted tumor cell populations are first labeled with fluorescent Cell Tracker dyes and immobilized using a DNA-based adhesion technique. This allows the facile generation of live cell arrays that are arranged arbitrarily or in ordered rectilinear patterns. Following the addition of antibodies in combination with serum, PBMCs, or whole blood, cell death within the targeted population can be assessed by the addition of propidium iodide (PI as a viability probe. The array is then analyzed with an automated microscopic imager. The extent of cytotoxicity can be quantified accurately by comparing the number of surviving target cells to the number of dead cells labeled with both Cell Tracker and PI. Excellent batch-to-batch reproducibility has been achieved using this method. In addition to allowing cytotoxicity analysis to be conducted in real time on a single cell basis, this new assay overcomes the need for hazardous radiochemicals. Fluorescently-labeled antibodies can be used to identify individual cells that bear the targeted receptors, but yet resist the CDC and ADCC mechanisms. This new approach also allows the use of whole blood in cytotoxicity assays, providing an assessment of antibody efficacy in a highly relevant biological mixture. Given the rapid development of new antibody-based therapeutic agents, this convenient assay platform is well-poised to streamline the drug discovery process significantly.

  16. Automated grading of renal cell carcinoma using whole slide imaging

    Directory of Open Access Journals (Sweden)

    Fang-Cheng Yeh

    2014-01-01

    Full Text Available Introduction: Recent technology developments have demonstrated the benefit of using whole slide imaging (WSI in computer-aided diagnosis. In this paper, we explore the feasibility of using automatic WSI analysis to assist grading of clear cell renal cell carcinoma (RCC, which is a manual task traditionally performed by pathologists. Materials and Methods: Automatic WSI analysis was applied to 39 hematoxylin and eosin-stained digitized slides of clear cell RCC with varying grades. Kernel regression was used to estimate the spatial distribution of nuclear size across the entire slides. The analysis results were correlated with Fuhrman nuclear grades determined by pathologists. Results: The spatial distribution of nuclear size provided a panoramic view of the tissue sections. The distribution images facilitated locating regions of interest, such as high-grade regions and areas with necrosis. The statistical analysis showed that the maximum nuclear size was significantly different (P < 0.001 between low-grade (Grades I and II and high-grade tumors (Grades III and IV. The receiver operating characteristics analysis showed that the maximum nuclear size distinguished high-grade and low-grade tumors with a false positive rate of 0.2 and a true positive rate of 1.0. The area under the curve is 0.97. Conclusion: The automatic WSI analysis allows pathologists to see the spatial distribution of nuclei size inside the tumors. The maximum nuclear size can also be used to differentiate low-grade and high-grade clear cell RCC with good sensitivity and specificity. These data suggest that automatic WSI analysis may facilitate pathologic grading of renal tumors and reduce variability encountered with manual grading.

  17. Automated semantic indexing of imaging reports to support retrieval of medical images in the multimedia electronic medical record.

    Science.gov (United States)

    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.

  18. Efficient Parallel Levenberg-Marquardt Model Fitting towards Real-Time Automated Parametric Imaging Microscopy

    OpenAIRE

    Xiang Zhu; Dianwen Zhang

    2013-01-01

    We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetim...

  19. Automated static image analysis as a novel tool in describing the physical properties of dietary fiber

    OpenAIRE

    Kurek,Marcin Andrzej; Piwińska, Monika; Wyrwisz, Jarosław; Wierzbicka, Agnieszka

    2015-01-01

    Abstract The growing interest in the usage of dietary fiber in food has caused the need to provide precise tools for describing its physical properties. This research examined two dietary fibers from oats and beets, respectively, in variable particle sizes. The application of automated static image analysis for describing the hydration properties and particle size distribution of dietary fiber was analyzed. Conventional tests for water holding capacity (WHC) were conducted. The particles were...

  20. Automated Formosat Image Processing System for Rapid Response to International Disasters

    Science.gov (United States)

    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.

  1. Use of an Automated Image Processing Program to Quantify Recombinant Adenovirus Particles

    Science.gov (United States)

    Obenauer-Kutner, Linda J.; Halperin, Rebecca; Ihnat, Peter M.; Tully, Christopher P.; Bordens, Ronald W.; Grace, Michael J.

    2005-02-01

    Electron microscopy has a pivotal role as an analytical tool in pharmaceutical research. However, digital image data have proven to be too large for efficient quantitative analysis. We describe here the development and application of an automated image processing (AIP) program that rapidly quantifies shape measurements of recombinant adenovirus (rAd) obtained from digitized field emission scanning electron microscope (FESEM) images. The program was written using the macro-recording features within Image-Pro® Plus software. The macro program, which is linked to a Microsoft Excel spreadsheet, consists of a series of subroutines designed to automatically measure rAd vector objects from the FESEM images. The application and utility of this macro program has enabled us to rapidly and efficiently analyze very large data sets of rAd samples while minimizing operator time.

  2. Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles.

    Science.gov (United States)

    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.

  3. RootGraph: a graphic optimization tool for automated image analysis of plant roots.

    Science.gov (United States)

    Cai, Jinhai; Zeng, Zhanghui; Connor, Jason N; Huang, Chun Yuan; Melino, Vanessa; Kumar, Pankaj; Miklavcic, Stanley J

    2015-11-01

    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.

  4. Automated measurement of parameters related to the deformities of lower limbs based on x-rays images.

    Science.gov (United States)

    Wojciechowski, Wadim; Molka, Adrian; Tabor, Zbisław

    2016-03-01

    Measurement of the deformation of the lower limbs in the current standard full-limb X-rays images presents significant challenges to radiologists and orthopedists. The precision of these measurements is deteriorated because of inexact positioning of the leg during image acquisition, problems with selecting reliable anatomical landmarks in projective X-ray images, and inevitable errors of manual measurements. The influence of the random errors resulting from the last two factors on the precision of the measurement can be reduced if an automated measurement method is used instead of a manual one. In the paper a framework for an automated measurement of various metric and angular quantities used in the description of the lower extremity deformation in full-limb frontal X-ray images is described. The results of automated measurements are compared with manual measurements. These results demonstrate that an automated method can be a valuable alternative to the manual measurements.

  5. Automated 3D-Objectdocumentation on the Base of an Image Set

    Directory of Open Access Journals (Sweden)

    Sebastian Vetter

    2011-12-01

    Full Text Available Digital stereo-photogrammetry allows users an automatic evaluation of the spatial dimension and the surface texture of objects. The integration of image analysis techniques simplifies the automation of evaluation of large image sets and offers a high accuracy [1]. Due to the substantial similarities of stereoscopic image pairs, correlation techniques provide measurements of subpixel precision for corresponding image points. With the help of an automated point search algorithm in image sets identical points are used to associate pairs of images to stereo models and group them. The found identical points in all images are basis for calculation of the relative orientation of each stereo model as well as defining the relation of neighboured stereo models. By using proper filter strategies incorrect points are removed and the relative orientation of the stereo model can be made automatically. With the help of 3D-reference points or distances at the object or a defined distance of camera basis the stereo model is orientated absolute. An adapted expansion- and matching algorithm offers the possibility to scan the object surface automatically. The result is a three dimensional point cloud; the scan resolution depends on image quality. With the integration of the iterative closest point- algorithm (ICP these partial point clouds are fitted to a total point cloud. In this way, 3D-reference points are not necessary. With the help of the implemented triangulation algorithm a digital surface models (DSM can be created. The texturing can be made automatically by the usage of the images that were used for scanning the object surface. It is possible to texture the surface model directly or to generate orthophotos automatically. By using of calibrated digital SLR cameras with full frame sensor a high accuracy can be reached. A big advantage is the possibility to control the accuracy and quality of the 3d-objectdocumentation with the resolution of the images. The

  6. Automated construction of arterial and venous trees in retinal images.

    Science.gov (United States)

    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.

  7. Scanner-based image quality measurement system for automated analysis of EP output

    Science.gov (United States)

    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

  8. Semi-automated Digital Imaging and Processing System for Measuring Lake Ice Thickness

    Science.gov (United States)

    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

  9. Automated volume of interest delineation and rendering of cone beam CT images in interventional cardiology

    Science.gov (United States)

    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.

  10. Quality Control in Automated Manufacturing Processes – Combined Features for Image Processing

    Directory of Open Access Journals (Sweden)

    B. Kuhlenkötter

    2006-01-01

    Full Text Available In production processes the use of image processing systems is widespread. Hardware solutions and cameras respectively are available for nearly every application. One important challenge of image processing systems is the development and selection of appropriate algorithms and software solutions in order to realise ambitious quality control for production processes. This article characterises the development of innovative software by combining features for an automatic defect classification on product surfaces. The artificial intelligent method Support Vector Machine (SVM is used to execute the classification task according to the combined features. This software is one crucial element for the automation of a manually operated production process. 

  11. Automated Registration of Images from Multiple Bands of Resourcesat-2 Liss-4 camera

    OpenAIRE

    2014-01-01

    Continuous and automated co-registration and geo-tagging of images from multiple bands of Liss-4 camera is one of the interesting challenges of Resourcesat-2 data processing. Three arrays of the Liss-4 camera are physically separated in the focal plane in alongtrack direction. Thus, same line on the ground will be imaged by extreme bands with a time interval of as much as 2.1 seconds. During this time, the satellite would have covered a distance of about 14 km on the ground and the e...

  12. Automated reconstruction of standing posture panoramas from multi-sector long limb x-ray images

    Science.gov (United States)

    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.

  13. Sfm_georef: Automating image measurement of ground control points for SfM-based projects

    Science.gov (United States)

    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

  14. Automated classification of atherosclerotic plaque from magnetic resonance images using predictive models.

    Science.gov (United States)

    Anderson, Russell W; Stomberg, Christopher; Hahm, Charles W; Mani, Venkatesh; Samber, Daniel D; Itskovich, Vitalii V; Valera-Guallar, Laura; Fallon, John T; Nedanov, Pavel B; Huizenga, Joel; Fayad, Zahi A

    2007-01-01

    The information contained within multicontrast magnetic resonance images (MRI) promises to improve tissue classification accuracy, once appropriately analyzed. Predictive models capture relationships empirically, from known outcomes thereby combining pattern classification with experience. In this study, we examine the applicability of predictive modeling for atherosclerotic plaque component classification of multicontrast ex vivo MR images using stained, histopathological sections as ground truth. Ten multicontrast images from seven human coronary artery specimens were obtained on a 9.4 T imaging system using multicontrast-weighted fast spin-echo (T1-, proton density-, and T2-weighted) imaging with 39-mum isotropic voxel size. Following initial data transformations, predictive modeling focused on automating the identification of specimen's plaque, lipid, and media. The outputs of these three models were used to calculate statistics such as total plaque burden and the ratio of hard plaque (fibrous tissue) to lipid. Both logistic regression and an artificial neural network model (Relevant Input Processor Network-RIPNet) were used for predictive modeling. When compared against segmentation resulting from cluster analysis, the RIPNet models performed between 25 and 30% better in absolute terms. This translates to a 50% higher true positive rate over given levels of false positives. This work indicates that it is feasible to build an automated system of plaque detection using MRI and data mining.

  15. Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets.

    Science.gov (United States)

    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.

  16. Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets

    Directory of Open Access Journals (Sweden)

    Charita Bhikha

    2015-01-01

    Full Text Available 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.

  17. NeuriteTracer: a novel ImageJ plugin for automated quantification of neurite outgrowth.

    Science.gov (United States)

    Pool, Madeline; Thiemann, Joachim; Bar-Or, Amit; Fournier, Alyson E

    2008-02-15

    In vitro assays to measure neuronal growth are a fundamental tool used by many neurobiologists studying neuronal development and regeneration. The quantification of these assays requires accurate measurements of neurite length and neuronal cell numbers in neuronal cultures. Generally, these measurements are obtained through labor-intensive manual or semi-manual tracing of images. To automate these measurements, we have written NeuriteTracer, a neurite tracing plugin for the freely available image-processing program ImageJ. The plugin analyzes fluorescence microscopy images of neurites and nuclei of dissociated cultured neurons. Given user-defined thresholds, the plugin counts neuronal nuclei, and traces and measures neurite length. We find that NeuriteTracer accurately measures neurite outgrowth from cerebellar, DRG and hippocampal neurons. Values obtained by NeuriteTracer correlate strongly with those obtained by semi-manual tracing with NeuronJ and by using a sophisticated analysis package, MetaXpress. We reveal the utility of NeuriteTracer by demonstrating its ability to detect the neurite outgrowth promoting capacity of the rho kinase inhibitor Y-27632. Our plugin is an attractive alternative to existing tracing tools because it is fully automated and ready for use within a freely accessible imaging program.

  18. Bright field microscopy as an alternative to whole cell fluorescence in automated analysis of macrophage images.

    Directory of Open Access Journals (Sweden)

    Jyrki Selinummi

    Full Text Available BACKGROUND: Fluorescence microscopy is the standard tool for detection and analysis of cellular phenomena. This technique, however, has a number of drawbacks such as the limited number of available fluorescent channels in microscopes, overlapping excitation and emission spectra of the stains, and phototoxicity. METHODOLOGY: We here present and validate a method to automatically detect cell population outlines directly from bright field images. By imaging samples with several focus levels forming a bright field -stack, and by measuring the intensity variations of this stack over the -dimension, we construct a new two dimensional projection image of increased contrast. With additional information for locations of each cell, such as stained nuclei, this bright field projection image can be used instead of whole cell fluorescence to locate borders of individual cells, separating touching cells, and enabling single cell analysis. Using the popular CellProfiler freeware cell image analysis software mainly targeted for fluorescence microscopy, we validate our method by automatically segmenting low contrast and rather complex shaped murine macrophage cells. SIGNIFICANCE: The proposed approach frees up a fluorescence channel, which can be used for subcellular studies. It also facilitates cell shape measurement in experiments where whole cell fluorescent staining is either not available, or is dependent on a particular experimental condition. We show that whole cell area detection results using our projected bright field images match closely to the standard approach where cell areas are localized using fluorescence, and conclude that the high contrast bright field projection image can directly replace one fluorescent channel in whole cell quantification. Matlab code for calculating the projections can be downloaded from the supplementary site: http://sites.google.com/site/brightfieldorstaining.

  19. An automated method for comparing motion artifacts in cine four-dimensional computed tomography images.

    Science.gov (United States)

    Cui, Guoqiang; Jew, Brian; Hong, Julian C; Johnston, Eric W; Loo, Billy W; Maxim, Peter G

    2012-11-08

    The aim of this study is to develop an automated method to objectively compare motion artifacts in two four-dimensional computed tomography (4D CT) image sets, and identify the one that would appear to human observers with fewer or smaller artifacts. Our proposed method is based on the difference of the normalized correlation coefficients between edge slices at couch transitions, which we hypothesize may be a suitable metric to identify motion artifacts. We evaluated our method using ten pairs of 4D CT image sets that showed subtle differences in artifacts between images in a pair, which were identifiable by human observers. One set of 4D CT images was sorted using breathing traces in which our clinically implemented 4D CT sorting software miscalculated the respiratory phase, which expectedly led to artifacts in the images. The other set of images consisted of the same images; however, these were sorted using the same breathing traces but with corrected phases. Next we calculated the normalized correlation coefficients between edge slices at all couch transitions for all respiratory phases in both image sets to evaluate for motion artifacts. For nine image set pairs, our method identified the 4D CT sets sorted using the breathing traces with the corrected respiratory phase to result in images with fewer or smaller artifacts, whereas for one image pair, no difference was noted. Two observers independently assessed the accuracy of our method. Both observers identified 9 image sets that were sorted using the breathing traces with corrected respiratory phase as having fewer or smaller artifacts. In summary, using the 4D CT data of ten pairs of 4D CT image sets, we have demonstrated proof of principle that our method is able to replicate the results of two human observers in identifying the image set with fewer or smaller artifacts.

  20. Automated segmentation of cardiac visceral fat in low-dose non-contrast chest CT images

    Science.gov (United States)

    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.

  1. NOTE: Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction

    Science.gov (United States)

    Holan, Scott H.; Viator, John A.

    2008-06-01

    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.

  2. Single-cell analysis - Methods and protocols

    OpenAIRE

    Carlo Alberto Redi

    2013-01-01

    This is certainly a timely volume in the Methods in molecular biology series: we already entered the synthetic biology era and thus we need to be aware of the new methodological advances able to fulfill the new and necessary needs for biologists, biotechnologists and nano-biotechnologists. Notably, among these, the possibility to perform single cell analysis allow researchers to capture single cell responses....

  3. Single cell enzyme diagnosis on the chip

    DEFF Research Database (Denmark)

    Jensen, Sissel Juul; Harmsen, Charlotte; Nielsen, Mette Juul

    2013-01-01

    detection of enzymatic activities down to the single cell level with small quantities of biological samples, which outcompetes existing techniques. Such a system, capable of resolving single cell activities, will ultimately have clinical applications in diagnosis, prediction of drug response and treatment...... evaluation, as well as fundamental impact on the understanding of disease mechanisms...

  4. Automated Line Tracking of lambda-DNA for Single-Molecule Imaging

    CERN Document Server

    Guan, Juan; Granick, Steve

    2011-01-01

    We describe a straightforward, automated line tracking method to visualize within optical resolution the contour of linear macromolecules as they rearrange shape as a function of time by Brownian diffusion and under external fields such as electrophoresis. Three sequential stages of analysis underpin this method: first, "feature finding" to discriminate signal from noise; second, "line tracking" to approximate those shapes as lines; third, "temporal consistency check" to discriminate reasonable from unreasonable fitted conformations in the time domain. The automated nature of this data analysis makes it straightforward to accumulate vast quantities of data while excluding the unreliable parts of it. We implement the analysis on fluorescence images of lambda-DNA molecules in agarose gel to demonstrate its capability to produce large datasets for subsequent statistical analysis.

  5. Estimation of urinary stone composition by automated processing of CT images

    CERN Document Server

    Chevreau, Grégoire; Conort, Pierre; Renard-Penna, Raphaëlle; Mallet, Alain; Daudon, Michel; Mozer, Pierre; 10.1007/s00240-009-0195-3

    2009-01-01

    The objective of this article was developing an automated tool for routine clinical practice to estimate urinary stone composition from CT images based on the density of all constituent voxels. A total of 118 stones for which the composition had been determined by infrared spectroscopy were placed in a helical CT scanner. A standard acquisition, low-dose and high-dose acquisitions were performed. All voxels constituting each stone were automatically selected. A dissimilarity index evaluating variations of density around each voxel was created in order to minimize partial volume effects: stone composition was established on the basis of voxel density of homogeneous zones. Stone composition was determined in 52% of cases. Sensitivities for each compound were: uric acid: 65%, struvite: 19%, cystine: 78%, carbapatite: 33.5%, calcium oxalate dihydrate: 57%, calcium oxalate monohydrate: 66.5%, brushite: 75%. Low-dose acquisition did not lower the performances (P < 0.05). This entirely automated approach eliminat...

  6. Fully automated segmentation of left ventricle using dual dynamic programming in cardiac cine MR images

    Science.gov (United States)

    Jiang, Luan; Ling, Shan; Li, Qiang

    2016-03-01

    Cardiovascular diseases are becoming a leading cause of death all over the world. The cardiac function could be evaluated by global and regional parameters of left ventricle (LV) of the heart. The purpose of this study is to develop and evaluate a fully automated scheme for segmentation of LV in short axis cardiac cine MR images. Our fully automated method consists of three major steps, i.e., LV localization, LV segmentation at end-diastolic phase, and LV segmentation propagation to the other phases. First, the maximum intensity projection image along the time phases of the midventricular slice, located at the center of the image, was calculated to locate the region of interest of LV. Based on the mean intensity of the roughly segmented blood pool in the midventricular slice at each phase, end-diastolic (ED) and end-systolic (ES) phases were determined. Second, the endocardial and epicardial boundaries of LV of each slice at ED phase were synchronously delineated by use of a dual dynamic programming technique. The external costs of the endocardial and epicardial boundaries were defined with the gradient values obtained from the original and enhanced images, respectively. Finally, with the advantages of the continuity of the boundaries of LV across adjacent phases, we propagated the LV segmentation from the ED phase to the other phases by use of dual dynamic programming technique. The preliminary results on 9 clinical cardiac cine MR cases show that the proposed method can obtain accurate segmentation of LV based on subjective evaluation.

  7. A method for the automated detection phishing websites through both site characteristics and image analysis

    Science.gov (United States)

    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.

  8. Detailed interrogation of trypanosome cell biology via differential organelle staining and automated image analysis

    Directory of Open Access Journals (Sweden)

    Wheeler Richard J

    2012-01-01

    Full Text Available Abstract Background Many trypanosomatid protozoa are important human or animal pathogens. The well defined morphology and precisely choreographed division of trypanosomatid cells makes morphological analysis a powerful tool for analyzing the effect of mutations, chemical insults and changes between lifecycle stages. High-throughput image analysis of micrographs has the potential to accelerate collection of quantitative morphological data. Trypanosomatid cells have two large DNA-containing organelles, the kinetoplast (mitochondrial DNA and nucleus, which provide useful markers for morphometric analysis; however they need to be accurately identified and often lie in close proximity. This presents a technical challenge. Accurate identification and quantitation of the DNA content of these organelles is a central requirement of any automated analysis method. Results We have developed a technique based on double staining of the DNA with a minor groove binding (4'', 6-diamidino-2-phenylindole (DAPI and a base pair intercalating (propidium iodide (PI or SYBR green fluorescent stain and color deconvolution. This allows the identification of kinetoplast and nuclear DNA in the micrograph based on whether the organelle has DNA with a more A-T or G-C rich composition. Following unambiguous identification of the kinetoplasts and nuclei the resulting images are amenable to quantitative automated analysis of kinetoplast and nucleus number and DNA content. On this foundation we have developed a demonstrative analysis tool capable of measuring kinetoplast and nucleus DNA content, size and position and cell body shape, length and width automatically. Conclusions Our approach to DNA staining and automated quantitative analysis of trypanosomatid morphology accelerated analysis of trypanosomatid protozoa. We have validated this approach using Leishmania mexicana, Crithidia fasciculata and wild-type and mutant Trypanosoma brucei. Automated analysis of T. brucei

  9. Benchmarking, Research, Development, and Support for ORNL Automated Image and Signature Retrieval (AIR/ASR) Technologies

    Energy Technology Data Exchange (ETDEWEB)

    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.

  10. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging

    Directory of Open Access Journals (Sweden)

    Ani eEloyan

    2012-08-01

    Full Text Available Successful automated diagnoses of attention deficit hyperactive disorder (ADHD using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions, CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD.

  11. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging.

    Science.gov (United States)

    Eloyan, Ani; Muschelli, John; Nebel, Mary Beth; Liu, Han; Han, Fang; Zhao, Tuo; Barber, Anita D; Joel, Suresh; Pekar, James J; Mostofsky, Stewart H; Caffo, Brian

    2012-01-01

    Successful automated diagnoses of attention deficit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions (SVDs), CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry, and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD.

  12. Single-cell transcriptome analysis of endometrial tissue

    Science.gov (United States)

    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

  13. Automated choroid segmentation based on gradual intensity distance in HD-OCT images.

    Science.gov (United States)

    Chen, Qiang; Fan, Wen; Niu, Sijie; Shi, Jiajia; Shen, Honglie; Yuan, Songtao

    2015-04-06

    The choroid is an important structure of the eye and plays a vital role in the pathology of retinal diseases. This paper presents an automated choroid segmentation method for high-definition optical coherence tomography (HD-OCT) images, including Bruch's membrane (BM) segmentation and choroidal-scleral interface (CSI) segmentation. An improved retinal nerve fiber layer (RNFL) complex removal algorithm is presented to segment BM by considering the structure characteristics of retinal layers. By analyzing the characteristics of CSI boundaries, we present a novel algorithm to generate a gradual intensity distance image. Then an improved 2-D graph search method with curve smooth constraints is used to obtain the CSI segmentation. Experimental results with 212 HD-OCT images from 110 eyes in 66 patients demonstrate that the proposed method can achieve high segmentation accuracy. The mean choroid thickness difference and overlap ratio between our proposed method and outlines drawn by experts was 6.72µm and 85.04%, respectively.

  14. Technique for Automated Recognition of Sunspots on Full-Disk Solar Images

    Directory of Open Access Journals (Sweden)

    Zharkov S

    2005-01-01

    Full Text Available A new robust technique is presented for automated identification of sunspots on full-disk white-light (WL solar images obtained from SOHO/MDI instrument and Ca II K1 line images from the Meudon Observatory. Edge-detection methods are applied to find sunspot candidates followed by local thresholding using statistical properties of the region around sunspots. Possible initial oversegmentation of images is remedied with a median filter. The features are smoothed by using morphological closing operations and filled by applying watershed, followed by dilation operator to define regions of interest containing sunspots. A number of physical and geometrical parameters of detected sunspot features are extracted and stored in a relational database along with umbra-penumbra information in the form of pixel run-length data within a bounding rectangle. The detection results reveal very good agreement with the manual synoptic maps and a very high correlation with those produced manually by NOAA Observatory, USA.

  15. Automated system for acquisition and image processing for the control and monitoring boned nopal

    Science.gov (United States)

    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

  16. Automated characterization of blood vessels as arteries and veins in retinal images.

    Science.gov (United States)

    Mirsharif, Qazaleh; Tajeripour, Farshad; Pourreza, Hamidreza

    2013-01-01

    In recent years researchers have found that alternations in arterial or venular tree of the retinal vasculature are associated with several public health problems such as diabetic retinopathy which is also the leading cause of blindness in the world. A prerequisite for automated assessment of subtle changes in arteries and veins, is to accurately separate those vessels from each other. This is a difficult task due to high similarity between arteries and veins in addition to variation of color and non-uniform illumination inter and intra retinal images. In this paper a novel structural and automated method is presented for artery/vein classification of blood vessels in retinal images. The proposed method consists of three main steps. In the first step, several image enhancement techniques are employed to improve the images. Then a specific feature extraction process is applied to separate major arteries from veins. Indeed, vessels are divided to smaller segments and feature extraction and vessel classification are applied to each small vessel segment instead of each vessel point. Finally, a post processing step is added to improve the results obtained from the previous step using structural characteristics of the retinal vascular network. In the last stage, vessel features at intersection and bifurcation points are processed for detection of arterial and venular sub trees. Ultimately vessel labels are revised by publishing the dominant label through each identified connected tree of arteries or veins. Evaluation of the proposed approach against two different datasets of retinal images including DRIVE database demonstrates the good performance and robustness of the method. The proposed method may be used for determination of arteriolar to venular diameter ratio in retinal images. Also the proposed method potentially allows for further investigation of labels of thinner arteries and veins which might be found by tracing them back to the major vessels.

  17. Difference Tracker: ImageJ plugins for fully automated analysis of multiple axonal transport parameters.

    Science.gov (United States)

    Andrews, Simon; Gilley, Jonathan; Coleman, Michael P

    2010-11-30

    Studies of axonal transport are critical, not only to understand its normal regulation, but also to determine the roles of transport impairment in disease. Exciting new resources have recently become available allowing live imaging of axonal transport in physiologically relevant settings, such as mammalian nerves. Thus the effects of disease, ageing and therapies can now be assessed directly in nervous system tissue. However, these imaging studies present new challenges. Manual or semi-automated analysis of the range of transport parameters required for a suitably complete evaluation is very time-consuming and can be subjective due to the complexity of the particle movements in axons in ex vivo explants or in vivo. We have developed Difference Tracker, a program combining two new plugins for the ImageJ image-analysis freeware, to provide fast, fully automated and objective analysis of a number of relevant measures of trafficking of fluorescently labeled particles so that axonal transport in different situations can be easily compared. We confirm that Difference Tracker can accurately track moving particles in highly simplified, artificial simulations. It can also identify and track multiple motile fluorescently labeled mitochondria simultaneously in time-lapse image stacks from live imaging of tibial nerve axons, reporting values for a number of parameters that are comparable to those obtained through manual analysis of the same axons. Difference Tracker therefore represents a useful free resource for the comparative analysis of axonal transport under different conditions, and could potentially be used and developed further in many other studies requiring quantification of particle movements.

  18. The use of the Kalman filter in the automated segmentation of EIT lung images.

    Science.gov (United States)

    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.

  19. Automated generation of curved planar reformations from MR images of the spine

    Energy Technology Data Exchange (ETDEWEB)

    Vrtovec, Tomaz [Faculty of Electrical Engineering, University of Ljubljana, Trzaska 25, SI-1000 Ljubljana (Slovenia); Ourselin, Sebastien [CSIRO ICT Centre, Autonomous Systems Laboratory, BioMedIA Lab, Locked Bag 17, North Ryde, NSW 2113 (Australia); Gomes, Lavier [Department of Radiology, Westmead Hospital, University of Sydney, Hawkesbury Road, Westmead NSW 2145 (Australia); Likar, Bostjan [Faculty of Electrical Engineering, University of Ljubljana, Trzaska 25, SI-1000 Ljubljana (Slovenia); Pernus, Franjo [Faculty of Electrical Engineering, University of Ljubljana, Trzaska 25, SI-1000 Ljubljana (Slovenia)

    2007-05-21

    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{sup 0} 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.

  20. Automated generation of curved planar reformations from MR images of the spine

    Science.gov (United States)

    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.

  1. Fully automated quantitative analysis of breast cancer risk in DCE-MR images

    Science.gov (United States)

    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.

  2. Automating quality assurance of digital linear accelerators using a radioluminescent phosphor coated phantom and optical imaging

    Science.gov (United States)

    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

  3. Chest-wall segmentation in automated 3D breast ultrasound images using thoracic volume classification

    Science.gov (United States)

    Tan, Tao; van Zelst, Jan; Zhang, Wei; Mann, Ritse M.; Platel, Bram; Karssemeijer, Nico

    2014-03-01

    Computer-aided detection (CAD) systems are expected to improve effectiveness and efficiency of radiologists in reading automated 3D breast ultrasound (ABUS) images. One challenging task on developing CAD is to reduce a large number of false positives. A large amount of false positives originate from acoustic shadowing caused by ribs. Therefore determining the location of the chestwall in ABUS is necessary in CAD systems to remove these false positives. Additionally it can be used as an anatomical landmark for inter- and intra-modal image registration. In this work, we extended our previous developed chestwall segmentation method that fits a cylinder to automated detected rib-surface points and we fit the cylinder model by minimizing a cost function which adopted a term of region cost computed from a thoracic volume classifier to improve segmentation accuracy. We examined the performance on a dataset of 52 images where our previous developed method fails. Using region-based cost, the average mean distance of the annotated points to the segmented chest wall decreased from 7.57±2.76 mm to 6.22±2.86 mm.art.

  4. Automatic nipple detection on 3D images of an automated breast ultrasound system (ABUS)

    Science.gov (United States)

    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.

  5. Comparison of manually produced and automated cross country movement maps using digital image processing techniques

    Science.gov (United States)

    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.

  6. Automated segmentation of oral mucosa from wide-field OCT images (Conference Presentation)

    Science.gov (United States)

    Goldan, Ryan N.; Lee, Anthony M. D.; Cahill, Lucas; Liu, Kelly; MacAulay, Calum; Poh, Catherine F.; Lane, Pierre

    2016-03-01

    Optical Coherence Tomography (OCT) can discriminate morphological tissue features important for oral cancer detection such as the presence or absence of basement membrane and epithelial thickness. We previously reported an OCT system employing a rotary-pullback catheter capable of in vivo, rapid, wide-field (up to 90 x 2.5mm2) imaging in the oral cavity. Due to the size and complexity of these OCT data sets, rapid automated image processing software that immediately displays important tissue features is required to facilitate prompt bed-side clinical decisions. We present an automated segmentation algorithm capable of detecting the epithelial surface and basement membrane in 3D OCT images of the oral cavity. The algorithm was trained using volumetric OCT data acquired in vivo from a variety of tissue types and histology-confirmed pathologies spanning normal through cancer (8 sites, 21 patients). The algorithm was validated using a second dataset of similar size and tissue diversity. We demonstrate application of the algorithm to an entire OCT volume to map epithelial thickness, and detection of the basement membrane, over the tissue surface. These maps may be clinically useful for delineating pre-surgical tumor margins, or for biopsy site guidance.

  7. Automated detection of regions of interest for tissue microarray experiments: an image texture analysis

    Directory of Open Access Journals (Sweden)

    Tözeren Aydin

    2007-03-01

    Full Text Available Abstract Background Recent research with tissue microarrays led to a rapid progress toward quantifying the expressions of large sets of biomarkers in normal and diseased tissue. However, standard procedures for sampling tissue for molecular profiling have not yet been established. Methods This study presents a high throughput analysis of texture heterogeneity on breast tissue images for the purpose of identifying regions of interest in the tissue for molecular profiling via tissue microarray technology. Image texture of breast histology slides was described in terms of three parameters: the percentage of area occupied in an image block by chromatin (B, percentage occupied by stroma-like regions (P, and a statistical heterogeneity index H commonly used in image analysis. Texture parameters were defined and computed for each of the thousands of image blocks in our dataset using both the gray scale and color segmentation. The image blocks were then classified into three categories using the texture feature parameters in a novel statistical learning algorithm. These categories are as follows: image blocks specific to normal breast tissue, blocks specific to cancerous tissue, and those image blocks that are non-specific to normal and disease states. Results Gray scale and color segmentation techniques led to identification of same regions in histology slides as cancer-specific. Moreover the image blocks identified as cancer-specific belonged to those cell crowded regions in whole section image slides that were marked by two pathologists as regions of interest for further histological studies. Conclusion These results indicate the high efficiency of our automated method for identifying pathologic regions of interest on histology slides. Automation of critical region identification will help minimize the inter-rater variability among different raters (pathologists as hundreds of tumors that are used to develop an array have typically been evaluated

  8. Hybrid Segmentation of Vessels and Automated Flow Measures in In-Vivo Ultrasound Imaging

    DEFF Research Database (Denmark)

    Moshavegh, Ramin; Martins, Bo; Hansen, Kristoffer Lindskov

    2016-01-01

    Vector Flow Imaging (VFI) has received an increasing attention in the scientific field of ultrasound, as it enables angle independent visualization of blood flow. VFI can be used in volume flow estimation, but a vessel segmentation is needed to make it fully automatic. A novel vessel segmentation...... procedure is crucial for wall-to-wall visualization, automation of adjustments, and quantification of flow in state-of-the-art ultrasound scanners. We propose and discuss a method for accurate vessel segmentation that fuses VFI data and B-mode for robustly detecting and delineating vessels. The proposed...

  9. Automated Image Segmentation And Characterization Technique For Effective Isolation And Representation Of Human Face

    Directory of Open Access Journals (Sweden)

    Rajesh Reddy N

    2014-01-01

    Full Text Available In areas such as defense and forensics, it is necessary to identify the face of the criminals from the already available database. Automated face recognition system involves face isolation, feature extraction and classification technique. Challenges in face recognition system are isolating the face effectively as it may be affected by illumination, posture and variation in skin color. Hence it is necessary to develop an effective algorithm that isolates face from the image. In this paper, advanced face isolation technique and feature extraction technique has been proposed.

  10. Single-cell technologies in environmental omics

    KAUST Repository

    Kodzius, Rimantas

    2015-10-22

    Environmental studies are primarily done by culturing isolated microorganisms or by amplifying and sequencing conserved genes. Difficulties understanding the complexity of large numbers of various microorganisms in an environment led to the development of techniques to enrich specific microorganisms for upstream analysis, ultimately leading to single-cell isolation and analyses. We discuss the significance of single-cell technologies in omics studies with focus on metagenomics and metatranscriptomics. We propose that by reducing sample heterogeneity using single-cell genomics, metaomic studies can be simplified.

  11. Semi-automated discrimination of retinal pigmented epithelial cells in two-photon fluorescence images of mouse retinas

    Science.gov (United States)

    Alexander, Nathan S.; Palczewska, Grazyna; Palczewski, Krzysztof

    2015-01-01

    Automated image segmentation is a critical step toward achieving a quantitative evaluation of disease states with imaging techniques. Two-photon fluorescence microscopy (TPM) has been employed to visualize the retinal pigmented epithelium (RPE) and provide images indicating the health of the retina. However, segmentation of RPE cells within TPM images is difficult due to small differences in fluorescence intensity between cell borders and cell bodies. Here we present a semi-automated method for segmenting RPE cells that relies upon multiple weak features that differentiate cell borders from the remaining image. These features were scored by a search optimization procedure that built up the cell border in segments around a nucleus of interest. With six images used as a test, our method correctly identified cell borders for 69% of nuclei on average. Performance was strongly dependent upon increasing retinosome content in the RPE. TPM image analysis has the potential of providing improved early quantitative assessments of diseases affecting the RPE. PMID:26309765

  12. Automating the Analysis of Spatial Grids A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications

    CERN Document Server

    Lakshmanan, Valliappa

    2012-01-01

    The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets. The aim is for readers to be able to devise and implement automated techniques to extract information from spatial grids such as radar, satellite or high-resolution survey imagery.

  13. AI (artificial intelligence in histopathology--from image analysis to automated diagnosis.

    Directory of Open Access Journals (Sweden)

    Aleksandar Bogovac

    2010-02-01

    Full Text Available The technological progress in digitalization of complete histological glass slides has opened a new door in tissue--based diagnosis. The presentation of microscopic images as a whole in a digital matrix is called virtual slide. A virtual slide allows calculation and related presentation of image information that otherwise can only be seen by individual human performance. The digital world permits attachments of several (if not all fields of view and the contemporary visualization on a screen. The presentation of all microscopic magnifications is possible if the basic pixel resolution is less than 0.25 microns. To introduce digital tissue--based diagnosis into the daily routine work of a surgical pathologist requires a new setup of workflow arrangement and procedures. The quality of digitized images is sufficient for diagnostic purposes; however, the time needed for viewing virtual slides exceeds that of viewing original glass slides by far. The reason lies in a slower and more difficult sampling procedure, which is the selection of information containing fields of view. By application of artificial intelligence, tissue--based diagnosis in routine work can be managed automatically in steps as follows: 1. The individual image quality has to be measured, and corrected, if necessary. 2. A diagnostic algorithm has to be applied. An algorithm has be developed, that includes both object based (object features, structures and pixel based (texture measures. 3. These measures serve for diagnosis classification and feedback to order additional information, for example in virtual immunohistochemical slides. 4. The measures can serve for automated image classification and detection of relevant image information by themselves without any labeling. 5. The pathologists' duty will not be released by such a system; to the contrary, it will manage and supervise the system, i.e., just working at a "higher level". Virtual slides are already in use for teaching and

  14. AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis.

    Science.gov (United States)

    Kayser, Klaus; Görtler, Jürgen; Bogovac, Milica; Bogovac, Aleksandar; Goldmann, Torsten; Vollmer, Ekkehard; Kayser, Gian

    2009-01-01

    The technological progress in digitalization of complete histological glass slides has opened a new door in tissue--based diagnosis. The presentation of microscopic images as a whole in a digital matrix is called virtual slide. A virtual slide allows calculation and related presentation of image information that otherwise can only be seen by individual human performance. The digital world permits attachments of several (if not all) fields of view and the contemporary visualization on a screen. The presentation of all microscopic magnifications is possible if the basic pixel resolution is less than 0.25 microns. To introduce digital tissue--based diagnosis into the daily routine work of a surgical pathologist requires a new setup of workflow arrangement and procedures. The quality of digitized images is sufficient for diagnostic purposes; however, the time needed for viewing virtual slides exceeds that of viewing original glass slides by far. The reason lies in a slower and more difficult sampling procedure, which is the selection of information containing fields of view. By application of artificial intelligence, tissue--based diagnosis in routine work can be managed automatically in steps as follows: 1. The individual image quality has to be measured, and corrected, if necessary. 2. A diagnostic algorithm has to be applied. An algorithm has be developed, that includes both object based (object features, structures) and pixel based (texture) measures. 3. These measures serve for diagnosis classification and feedback to order additional information, for example in virtual immunohistochemical slides. 4. The measures can serve for automated image classification and detection of relevant image information by themselves without any labeling. 5. The pathologists' duty will not be released by such a system; to the contrary, it will manage and supervise the system, i.e., just working at a "higher level". Virtual slides are already in use for teaching and continuous

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  16. Automation of PCXMC and ImPACT for NASA Astronaut Medical Imaging Dose and Risk Tracking

    Science.gov (United States)

    Bahadori, Amir; Picco, Charles; Flores-McLaughlin, John; Shavers, Mark; Semones, Edward

    2011-01-01

    To automate astronaut organ and effective dose calculations from occupational X-ray and computed tomography (CT) examinations incorporating PCXMC and ImPACT tools and to estimate the associated lifetime cancer risk per the National Council on Radiation Protection & Measurements (NCRP) using MATLAB(R). Methods: NASA follows guidance from the NCRP on its operational radiation safety program for astronauts. NCRP Report 142 recommends that astronauts be informed of the cancer risks from reported exposures to ionizing radiation from medical imaging. MATLAB(R) code was written to retrieve exam parameters for medical imaging procedures from a NASA database, calculate associated dose and risk, and return results to the database, using the Microsoft .NET Framework. This code interfaces with the PCXMC executable and emulates the ImPACT Excel spreadsheet to calculate organ doses from X-rays and CTs, respectively, eliminating the need to utilize the PCXMC graphical user interface (except for a few special cases) and the ImPACT spreadsheet. Results: Using MATLAB(R) code to interface with PCXMC and replicate ImPACT dose calculation allowed for rapid evaluation of multiple medical imaging exams. The user inputs the exam parameter data into the database and runs the code. Based on the imaging modality and input parameters, the organ doses are calculated. Output files are created for record, and organ doses, effective dose, and cancer risks associated with each exam are written to the database. Annual and post-flight exposure reports, which are used by the flight surgeon to brief the astronaut, are generated from the database. Conclusions: Automating PCXMC and ImPACT for evaluation of NASA astronaut medical imaging radiation procedures allowed for a traceable and rapid method for tracking projected cancer risks associated with over 12,000 exposures. This code will be used to evaluate future medical radiation exposures, and can easily be modified to accommodate changes to the risk

  17. Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

    NARCIS (Netherlands)

    Jong, Imke G. de; Beilharz, Katrin; Kuipers, Oscar P.; Veening, Jan-Willem

    2011-01-01

    During the last few years scientists became increasingly aware that average data obtained from microbial population based experiments are not representative of the behavior, status or phenotype of single cells. Due to this new insight the number of single cell studies rises continuously. However, ma

  18. Automated segmentation and geometrical modeling of the tricuspid aortic valve in 3D echocardiographic images.

    Science.gov (United States)

    Pouch, Alison M; Wang, Hongzhi; Takabe, Manabu; Jackson, Benjamin M; Sehgal, Chandra M; Gorman, Joseph H; Gorman, Robert C; Yushkevich, Paul A

    2013-01-01

    The aortic valve has been described with variable anatomical definitions, and the consistency of 2D manual measurement of valve dimensions in medical image data has been questionable. Given the importance of image-based morphological assessment in the diagnosis and surgical treatment of aortic valve disease, there is considerable need to develop a standardized framework for 3D valve segmentation and shape representation. Towards this goal, this work integrates template-based medial modeling and multi-atlas label fusion techniques to automatically delineate and quantitatively describe aortic leaflet geometry in 3D echocardiographic (3DE) images, a challenging task that has been explored only to a limited extent. The method makes use of expert knowledge of aortic leaflet image appearance, generates segmentations with consistent topology, and establishes a shape-based coordinate system on the aortic leaflets that enables standardized automated measurements. In this study, the algorithm is evaluated on 11 3DE images of normal human aortic leaflets acquired at mid systole. The clinical relevance of the method is its ability to capture leaflet geometry in 3DE image data with minimal user interaction while producing consistent measurements of 3D aortic leaflet geometry.

  19. Epigenetics reloaded: the single-cell revolution.

    Science.gov (United States)

    Bheda, Poonam; Schneider, Robert

    2014-11-01

    Mechanistically, how epigenetic states are inherited through cellular divisions remains an important open question in the chromatin field and beyond. Defining the heritability of epigenetic states and the underlying chromatin-based mechanisms within a population of cells is complicated due to cell heterogeneity combined with varying levels of stability of these states; thus, efforts must be focused toward single-cell analyses. The approaches presented here constitute the forefront of epigenetics research at the single-cell level using classic and innovative methods to dissect epigenetics mechanisms from the limited material available in a single cell. This review further outlines exciting future avenues of research to address the significance of epigenetic heterogeneity and the contributions of microfluidics technologies to single-cell isolation and analysis.

  20. Single-cell analysis - Methods and protocols

    Directory of Open Access Journals (Sweden)

    Carlo Alberto Redi

    2013-06-01

    Full Text Available This is certainly a timely volume in the Methods in molecular biology series: we already entered the synthetic biology era and thus we need to be aware of the new methodological advances able to fulfill the new and necessary needs for biologists, biotechnologists and nano-biotechnologists. Notably, among these, the possibility to perform single cell analysis allow researchers to capture single cell responses....

  1. Pseudotime estimation: deconfounding single cell time series

    OpenAIRE

    John E Reid; Wernisch, Lorenz

    2016-01-01

    Motivation: Repeated cross-sectional time series single cell data confound several sources of variation, with contributions from measurement noise, stochastic cell-to-cell variation and cell progression at different rates. Time series from single cell assays are particularly susceptible to confounding as the measurements are not averaged over populations of cells. When several genes are assayed in parallel these effects can be estimated and corrected for under certain smoothness assumptions o...

  2. Single-cell analysis in cancer genomics

    Science.gov (United States)

    Saadatpour, Assieh; Lai, Shujing; Guo, Guoji; Yuan, Guo-Cheng

    2017-01-01

    Genetic changes and environmental differences result in cellular heterogeneity among cancer cells within the same tumor, thereby complicating treatment outcomes. Recent advances in single-cell technologies have opened new avenues to characterize the intra-tumor cellular heterogeneity, identify rare cell types, measure mutation rates, and, ultimately, guide diagnosis and treatment. In this paper, we review the recent single-cell technological and computational advances at the genomic, transcriptomic, and proteomic levels, and discuss their applications in cancer research. PMID:26450340

  3. Automated Abnormal Mass Detection in the Mammogram Images Using Chebyshev Moments

    Directory of Open Access Journals (Sweden)

    Alireza Talebpour

    2013-01-01

    Full Text Available Breast cancer is the second leading cause of cancer mortality among women after lung cancer. Early diagnosis of this disease has a major role in its treatment. Thus the use of computer systems as a detection tool could be viewed as essential to helping with this disease. In this study a new system for automated mass detection in mammography images is presented as being more accurate and valid. After optimization of the image and extracting a better picture of the breast tissue from the image and applying log-polar transformation, Chebyshev moments can be calculated in all areas of breast tissue. Then after extracting effective features in the diagnosis of mammography images, abnormal masses, which are important for the physician and specialists, can be determined with applying the appropriate threshold. To check the system performance, images in the MIAS (Mammographic Image Analysis Society mammogram database have been used and the results allowed us to draw a FROC (Free Response Receiver Operating Characteristic curve. When compared the FROC curve with similar systems experts, the high ability of our system was confirmed. In this system, images of different thresholds, specifically 445, 450, 455 are processed and then put through a sensitivity analysis. The process garnered good results 100, 92 and 84%, respectively and a false positive rate per image 2.56, 0.86, 0.26, respectively have been calculated. Comparing other automatic mass detection systems, the proposed method has a few advantages over prior systems: Our process allows us to determine the amount of false positives and/or sensitivity parameters within the system. This can be determined by the importance of the detection work being done. The proposed system achieves 100% sensitivity and 2.56 false positive for every image.

  4. Image patch-based method for automated classification and detection of focal liver lesions on CT

    Science.gov (United States)

    Safdari, Mustafa; Pasari, Raghav; Rubin, Daniel; Greenspan, Hayit

    2013-03-01

    We developed a method for automated classification and detection of liver lesions in CT images based on image patch representation and bag-of-visual-words (BoVW). BoVW analysis has been extensively used in the computer vision domain to analyze scenery images. In the current work we discuss how it can be used for liver lesion classification and detection. The methodology includes building a dictionary for a training set using local descriptors and representing a region in the image using a visual word histogram. Two tasks are described: a classification task, for lesion characterization, and a detection task in which a scan window moves across the image and is determined to be normal liver tissue or a lesion. Data: In the classification task 73 CT images of liver lesions were used, 25 images having cysts, 24 having metastasis and 24 having hemangiomas. A radiologist circumscribed the lesions, creating a region of interest (ROI), in each of the images. He then provided the diagnosis, which was established either by biopsy or clinical follow-up. Thus our data set comprises 73 images and 73 ROIs. In the detection task, a radiologist drew ROIs around each liver lesion and two regions of normal liver, for a total of 159 liver lesion ROIs and 146 normal liver ROIs. The radiologist also demarcated the liver boundary. Results: Classification results of more than 95% were obtained. In the detection task, F1 results obtained is 0.76. Recall is 84%, with precision of 73%. Results show the ability to detect lesions, regardless of shape.

  5. Automated segmentation of murine lung tumors in x-ray micro-CT images

    Science.gov (United States)

    Swee, Joshua K. Y.; Sheridan, Clare; de Bruin, Elza; Downward, Julian; Lassailly, Francois; Pizarro, Luis

    2014-03-01

    Recent years have seen micro-CT emerge as a means of providing imaging analysis in pre-clinical study, with in-vivo micro-CT having been shown to be particularly applicable to the examination of murine lung tumors. Despite this, existing studies have involved substantial human intervention during the image analysis process, with the use of fully-automated aids found to be almost non-existent. We present a new approach to automate the segmentation of murine lung tumors designed specifically for in-vivo micro-CT-based pre-clinical lung cancer studies that addresses the specific requirements of such study, as well as the limitations human-centric segmentation approaches experience when applied to such micro-CT data. Our approach consists of three distinct stages, and begins by utilizing edge enhancing and vessel enhancing non-linear anisotropic diffusion filters to extract anatomy masks (lung/vessel structure) in a pre-processing stage. Initial candidate detection is then performed through ROI reduction utilizing obtained masks and a two-step automated segmentation approach that aims to extract all disconnected objects within the ROI, and consists of Otsu thresholding, mathematical morphology and marker-driven watershed. False positive reduction is finally performed on initial candidates through random-forest-driven classification using the shape, intensity, and spatial features of candidates. We provide validation of our approach using data from an associated lung cancer study, showing favorable results both in terms of detection (sensitivity=86%, specificity=89%) and structural recovery (Dice Similarity=0.88) when compared against manual specialist annotation.

  6. Deep learning for automated skeletal bone age assessment in X-ray images.

    Science.gov (United States)

    Spampinato, C; Palazzo, S; Giordano, D; Aldinucci, M; Leonardi, R

    2017-02-01

    Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich and Pyle (G&P) method or the Tanner-Whitehouse (TW) one. However, both clinical procedures show several limitations, from the examination effort of radiologists to (most importantly) significant intra- and inter-operator variability. To address these problems, several automated approaches (especially relying on the TW method) have been proposed; nevertheless, none of them has been proved able to generalize to different races, age ranges and genders. In this paper, we propose and test several deep learning approaches to assess skeletal bone age automatically; the results showed an average discrepancy between manual and automatic evaluation of about 0.8 years, which is state-of-the-art performance. Furthermore, this is the first automated skeletal bone age assessment work tested on a public dataset and for all age ranges, races and genders, for which the source code is available, thus representing an exhaustive baseline for future research in the field. Beside the specific application scenario, this paper aims at providing answers to more general questions about deep learning on medical images: from the comparison between deep-learned features and manually-crafted ones, to the usage of deep-learning methods trained on general imagery for medical problems, to how to train a CNN with few images.

  7. Fully automated image-guided needle insertion: application to small animal biopsies.

    Science.gov (United States)

    Ayadi, A; Bour, G; Aprahamian, M; Bayle, B; Graebling, P; Gangloff, J; Soler, L; Egly, J M; Marescaux, J

    2007-01-01

    The study of biological process evolution in small animals requires time-consuming and expansive analyses of a large population of animals. Serial analyses of the same animal is potentially a great alternative. However non-invasive procedures must be set up, to retrieve valuable tissue samples from precisely defined areas in living animals. Taking advantage of the high resolution level of in vivo molecular imaging, we defined a procedure to perform image-guided needle insertion and automated biopsy using a micro CT-scan, a robot and a vision system. Workspace limitations in the scanner require the animal to be removed and laid in front of the robot. A vision system composed of a grid projector and a camera is used to register the designed animal-bed with to respect to the robot and to calibrate automatically the needle position and orientation. Automated biopsy is then synchronised with respiration and performed with a pneumatic translation device, at high velocity, to minimize organ deformation. We have experimentally tested our biopsy system with different needles.

  8. GAUSSIAN MIXTURE MODEL BASED LEVEL SET TECHNIQUE FOR AUTOMATED SEGMENTATION OF CARDIAC MR IMAGES

    Directory of Open Access Journals (Sweden)

    G. Dharanibai,

    2011-04-01

    Full Text Available In this paper we propose a Gaussian Mixture Model (GMM integrated level set method for automated segmentation of left ventricle (LV, right ventricle (RV and myocardium from short axis views of cardiacmagnetic resonance image. By fitting GMM to the image histogram, global pixel intensity characteristics of the blood pool, myocardium and background are estimated. GMM provides initial segmentation andthe segmentation solution is regularized using level set. Parameters for controlling the level set evolution are automatically estimated from the Bayesian inference classification of pixels. We propose a new speed function that combines edge and region information that stops the evolving level set at the myocardial boundary. Segmentation efficacy is analyzed qualitatively via visual inspection. Results show the improved performance of our of proposed speed function over the conventional Bayesian driven adaptive speed function in automatic segmentation of myocardium

  9. Quantitative Assessment of Mouse Mammary Gland Morphology Using Automated Digital Image Processing and TEB Detection.

    Science.gov (United States)

    Blacher, Silvia; Gérard, Céline; Gallez, Anne; Foidart, Jean-Michel; Noël, Agnès; Péqueux, Christel

    2016-04-01

    The assessment of rodent mammary gland morphology is largely used to study the molecular mechanisms driving breast development and to analyze the impact of various endocrine disruptors with putative pathological implications. In this work, we propose a methodology relying on fully automated digital image analysis methods including image processing and quantification of the whole ductal tree and of the terminal end buds as well. It allows to accurately and objectively measure both growth parameters and fine morphological glandular structures. Mammary gland elongation was characterized by 2 parameters: the length and the epithelial area of the ductal tree. Ductal tree fine structures were characterized by: 1) branch end-point density, 2) branching density, and 3) branch length distribution. The proposed methodology was compared with quantification methods classically used in the literature. This procedure can be transposed to several software and thus largely used by scientists studying rodent mammary gland morphology.

  10. Automated analysis of heterogeneous carbon nanostructures by high-resolution electron microscopy and on-line image processing

    Energy Technology Data Exchange (ETDEWEB)

    Toth, P., E-mail: toth.pal@uni-miskolc.hu [Department of Chemical Engineering, University of Utah, 50 S. Central Campus Drive, Salt Lake City, UT 84112-9203 (United States); Farrer, J.K. [Department of Physics and Astronomy, Brigham Young University, N283 ESC, Provo, UT 84602 (United States); Palotas, A.B. [Department of Combustion Technology and Thermal Energy, University of Miskolc, H3515, Miskolc-Egyetemvaros (Hungary); Lighty, J.S.; Eddings, E.G. [Department of Chemical Engineering, University of Utah, 50 S. Central Campus Drive, Salt Lake City, UT 84112-9203 (United States)

    2013-06-15

    High-resolution electron microscopy is an efficient tool for characterizing heterogeneous nanostructures; however, currently the analysis is a laborious and time-consuming manual process. In order to be able to accurately and robustly quantify heterostructures, one must obtain a statistically high number of micrographs showing images of the appropriate sub-structures. The second step of analysis is usually the application of digital image processing techniques in order to extract meaningful structural descriptors from the acquired images. In this paper it will be shown that by applying on-line image processing and basic machine vision algorithms, it is possible to fully automate the image acquisition step; therefore, the number of acquired images in a given time can be increased drastically without the need for additional human labor. The proposed automation technique works by computing fields of structural descriptors in situ and thus outputs sets of the desired structural descriptors in real-time. The merits of the method are demonstrated by using combustion-generated black carbon samples. - Highlights: ► The HRTEM analysis of heterogeneous nanostructures is a tedious manual process. ► Automatic HRTEM image acquisition and analysis can improve data quantity and quality. ► We propose a method based on on-line image analysis for the automation of HRTEM image acquisition. ► The proposed method is demonstrated using HRTEM images of soot particles.

  11. Automated image analysis of the host-pathogen interaction between phagocytes and Aspergillus fumigatus.

    Directory of Open Access Journals (Sweden)

    Franziska Mech

    Full Text Available Aspergillus fumigatus is a ubiquitous airborne fungus and opportunistic human pathogen. In immunocompromised hosts, the fungus can cause life-threatening diseases like invasive pulmonary aspergillosis. Since the incidence of fungal systemic infections drastically increased over the last years, it is a major goal to investigate the pathobiology of A. fumigatus and in particular the interactions of A. fumigatus conidia with immune cells. Many of these studies include the activity of immune effector cells, in particular of macrophages, when they are confronted with conidia of A. fumigus wild-type and mutant strains. Here, we report the development of an automated analysis of confocal laser scanning microscopy images from macrophages coincubated with different A. fumigatus strains. At present, microscopy images are often analysed manually, including cell counting and determination of interrelations between cells, which is very time consuming and error-prone. Automation of this process overcomes these disadvantages and standardises the analysis, which is a prerequisite for further systems biological studies including mathematical modeling of the infection process. For this purpose, the cells in our experimental setup were differentially stained and monitored by confocal laser scanning microscopy. To perform the image analysis in an automatic fashion, we developed a ruleset that is generally applicable to phagocytosis assays and in the present case was processed by the software Definiens Developer XD. As a result of a complete image analysis we obtained features such as size, shape, number of cells and cell-cell contacts. The analysis reported here, reveals that different mutants of A. fumigatus have a major influence on the ability of macrophages to adhere and to phagocytose the respective conidia. In particular, we observe that the phagocytosis ratio and the aggregation behaviour of pksP mutant compared to wild-type conidia are both significantly

  12. Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification.

    Science.gov (United States)

    Hu, Zhihong; Medioni, Gerard G; Hernandez, Matthias; Sadda, Srinivas R

    2015-01-01

    Geographic atrophy (GA) is a manifestation of the advanced or late stage of age-related macular degeneration (AMD). AMD is the leading cause of blindness in people over the age of 65 in the western world. The purpose of this study is to develop a fully automated supervised pixel classification approach for segmenting GA, including uni- and multifocal patches in fundus autofluorescene (FAF) images. The image features include region-wise intensity measures, gray-level co-occurrence matrix measures, and Gaussian filter banks. A [Formula: see text]-nearest-neighbor pixel classifier is applied to obtain a GA probability map, representing the likelihood that the image pixel belongs to GA. Sixteen randomly chosen FAF images were obtained from 16 subjects with GA. The algorithm-defined GA regions are compared with manual delineation performed by a certified image reading center grader. Eight-fold cross-validation is applied to evaluate the algorithm performance. The mean overlap ratio (OR), area correlation (Pearson's [Formula: see text]), accuracy (ACC), true positive rate (TPR), specificity (SPC), positive predictive value (PPV), and false discovery rate (FDR) between the algorithm- and manually defined GA regions are [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively.

  13. Long-term live cell imaging and automated 4D analysis of drosophila neuroblast lineages.

    Directory of Open Access Journals (Sweden)

    Catarina C F Homem

    Full Text Available The developing Drosophila brain is a well-studied model system for neurogenesis and stem cell biology. In the Drosophila central brain, around 200 neural stem cells called neuroblasts undergo repeated rounds of asymmetric cell division. These divisions typically generate a larger self-renewing neuroblast and a smaller ganglion mother cell that undergoes one terminal division to create two differentiating neurons. Although single mitotic divisions of neuroblasts can easily be imaged in real time, the lack of long term imaging procedures has limited the use of neuroblast live imaging for lineage analysis. Here we describe a method that allows live imaging of cultured Drosophila neuroblasts over multiple cell cycles for up to 24 hours. We describe a 4D image analysis protocol that can be used to extract cell cycle times and growth rates from the resulting movies in an automated manner. We use it to perform lineage analysis in type II neuroblasts where clonal analysis has indicated the presence of a transit-amplifying population that potentiates the number of neurons. Indeed, our experiments verify type II lineages and provide quantitative parameters for all cell types in those lineages. As defects in type II neuroblast lineages can result in brain tumor formation, our lineage analysis method will allow more detailed and quantitative analysis of tumorigenesis and asymmetric cell division in the Drosophila brain.

  14. Automated Brain Image classification using Neural Network Approach and Abnormality Analysis

    Directory of Open Access Journals (Sweden)

    P.Muthu Krishnammal

    2015-06-01

    Full Text Available Image segmentation of surgical images plays an important role in diagnosis and analysis the anatomical structure of human body. Magnetic Resonance Imaging (MRI helps in obtaining a structural image of internal parts of the body. This paper aims at developing an automatic support system for stage classification using learning machine and to detect brain Tumor by fuzzy clustering methods to detect the brain Tumor in its early stages and to analyze anatomical structures. The three stages involved are: feature extraction using GLCM and the tumor classification using PNN-RBF network and segmentation using SFCM. Here fast discrete curvelet transformation is used to analyze texture of an image which be used as a base for a Computer Aided Diagnosis (CAD system .The Probabilistic Neural Network with radial basis function is employed to implement an automated Brain Tumor classification. It classifies the stage of Brain Tumor that is benign, malignant or normal automatically. Then the segmentation of the brain abnormality using Spatial FCM and the severity of the tumor is analysed using the number of tumor cells in the detected abnormal region.The proposed method reports promising results in terms of training performance and classification accuracies.

  15. Automated detection of synapses in serial section transmission electron microscopy image stacks.

    Directory of Open Access Journals (Sweden)

    Anna Kreshuk

    Full Text Available We describe a method for fully automated detection of chemical synapses in serial electron microscopy images with highly anisotropic axial and lateral resolution, such as images taken on transmission electron microscopes. Our pipeline starts from classification of the pixels based on 3D pixel features, which is followed by segmentation with an Ising model MRF and another classification step, based on object-level features. Classifiers are learned on sparse user labels; a fully annotated data subvolume is not required for training. The algorithm was validated on a set of 238 synapses in 20 serial 7197×7351 pixel images (4.5×4.5×45 nm resolution of mouse visual cortex, manually labeled by three independent human annotators and additionally re-verified by an expert neuroscientist. The error rate of the algorithm (12% false negative, 7% false positive detections is better than state-of-the-art, even though, unlike the state-of-the-art method, our algorithm does not require a prior segmentation of the image volume into cells. The software is based on the ilastik learning and segmentation toolkit and the vigra image processing library and is freely available on our website, along with the test data and gold standard annotations (http://www.ilastik.org/synapse-detection/sstem.

  16. Whole-slide imaging and automated image analysis: considerations and opportunities in the practice of pathology.

    Science.gov (United States)

    Webster, J D; Dunstan, R W

    2014-01-01

    Digital pathology, the practice of pathology using digitized images of pathologic specimens, has been transformed in recent years by the development of whole-slide imaging systems, which allow for the evaluation and interpretation of digital images of entire histologic sections. Applications of whole-slide imaging include rapid transmission of pathologic data for consultations and collaborations, standardization and distribution of pathologic materials for education, tissue specimen archiving, and image analysis of histologic specimens. Histologic image analysis allows for the acquisition of objective measurements of histomorphologic, histochemical, and immunohistochemical properties of tissue sections, increasing both the quantity and quality of data obtained from histologic assessments. Currently, numerous histologic image analysis software solutions are commercially available. Choosing the appropriate solution is dependent on considerations of the investigative question, computer programming and image analysis expertise, and cost. However, all studies using histologic image analysis require careful consideration of preanalytical variables, such as tissue collection, fixation, and processing, and experimental design, including sample selection, controls, reference standards, and the variables being measured. The fields of digital pathology and histologic image analysis are continuing to evolve, and their potential impact on pathology is still growing. These methodologies will increasingly transform the practice of pathology, allowing it to mature toward a quantitative science. However, this maturation requires pathologists to be at the forefront of the process, ensuring their appropriate application and the validity of their results. Therefore, histologic image analysis and the field of pathology should co-evolve, creating a symbiotic relationship that results in high-quality reproducible, objective data.

  17. Automated Astrometric Analysis of Satellite Observations using Wide-field Imaging

    Science.gov (United States)

    Skuljan, J.; Kay, J.

    2016-09-01

    An observational trial was conducted in the South Island of New Zealand from 24 to 28 February 2015, as a collaborative effort between the United Kingdom and New Zealand in the area of space situational awareness. The aim of the trial was to observe a number of satellites in low Earth orbit using wide-field imaging from two separate locations, in order to determine the space trajectory and compare the measurements with the predictions based on the standard two-line elements. This activity was an initial step in building a space situational awareness capability at the Defence Technology Agency of the New Zealand Defence Force. New Zealand has an important strategic position as the last land mass that many satellites selected for deorbiting pass before entering the Earth's atmosphere over the dedicated disposal area in the South Pacific. A preliminary analysis of the trial data has demonstrated that relatively inexpensive equipment can be used to successfully detect satellites at moderate altitudes. A total of 60 satellite passes were observed over the five nights of observation and about 2600 images were collected. A combination of cooled CCD and standard DSLR cameras were used, with a selection of lenses between 17 mm and 50 mm in focal length, covering a relatively wide field of view of 25 to 60 degrees. The CCD cameras were equipped with custom-made GPS modules to record the time of exposure with a high accuracy of one millisecond, or better. Specialised software has been developed for automated astrometric analysis of the trial data. The astrometric solution is obtained as a two-dimensional least-squares polynomial fit to the measured pixel positions of a large number of stars (typically 1000) detected across the image. The star identification is fully automated and works well for all camera-lens combinations used in the trial. A moderate polynomial degree of 3 to 5 is selected to take into account any image distortions introduced by the lens. A typical RMS

  18. Automated detection and labeling of high-density EEG electrodes from structural MR images

    Science.gov (United States)

    Marino, Marco; Liu, Quanying; Brem, Silvia; Wenderoth, Nicole; Mantini, Dante

    2016-10-01

    Objective. Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. Approach. Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. Main results. Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. Significance. We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work

  19. Automated Analysis of {sup 123}I-beta-CIT SPECT Images with Statistical Probabilistic Anatomical Mapping

    Energy Technology Data Exchange (ETDEWEB)

    Eo, Jae Seon; Lee, Hoyoung; Lee, Jae Sung; Kim, Yu Kyung; Jeon, Bumseok; Lee, Dong Soo [Seoul National Univ., Seoul (Korea, Republic of)

    2014-03-15

    Population-based statistical probabilistic anatomical maps have been used to generate probabilistic volumes of interest for analyzing perfusion and metabolic brain imaging. We investigated the feasibility of automated analysis for dopamine transporter images using this technique and evaluated striatal binding potentials in Parkinson's disease and Wilson's disease. We analyzed 2β-Carbomethoxy-3β-(4-{sup 123}I-iodophenyl)tropane ({sup 123}I-beta-CIT) SPECT images acquired from 26 people with Parkinson's disease (M:F=11:15,mean age=49±12 years), 9 people with Wilson's disease (M: F=6:3, mean age=26±11 years) and 17 normal controls (M:F=5:12, mean age=39±16 years). A SPECT template was created using striatal statistical probabilistic map images. All images were spatially normalized onto the template, and probability-weighted regional counts in striatal structures were estimated. The binding potential was calculated using the ratio of specific and nonspecific binding activities at equilibrium. Voxel-based comparisons between groups were also performed using statistical parametric mapping. Qualitative assessment showed that spatial normalizations of the SPECT images were successful for all images. The striatal binding potentials of participants with Parkinson's disease and Wilson's disease were significantly lower than those of normal controls. Statistical parametric mapping analysis found statistically significant differences only in striatal regions in both disease groups compared to controls. We successfully evaluated the regional {sup 123}I-beta-CIT distribution using the SPECT template and probabilistic map data automatically. This procedure allows an objective and quantitative comparison of the binding potential, which in this case showed a significantly decreased binding potential in the striata of patients with Parkinson's disease or Wilson's disease.

  20. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Chen, Ken Chung [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Stomatology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan 70403 (China); Shen, Steve G. F.; Yan, Jin [Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Lee, Philip K. M.; Chow, Ben [Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077 (China); Liu, Nancy X. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China 100050 (China); Xia, James J. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 (United States); Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065 (United States); Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul, 136701 (Korea, Republic of)

    2014-04-15

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  1. Breast Density Analysis with Automated Whole-Breast Ultrasound: Comparison with 3-D Magnetic Resonance Imaging.

    Science.gov (United States)

    Chen, Jeon-Hor; Lee, Yan-Wei; Chan, Si-Wa; Yeh, Dah-Cherng; Chang, Ruey-Feng

    2016-05-01

    In this study, a semi-automatic breast segmentation method was proposed on the basis of the rib shadow to extract breast regions from 3-D automated whole-breast ultrasound (ABUS) images. The density results were correlated with breast density values acquired with 3-D magnetic resonance imaging (MRI). MRI images of 46 breasts were collected from 23 women without a history of breast disease. Each subject also underwent ABUS. We used Otsu's thresholding method on ABUS images to obtain local rib shadow information, which was combined with the global rib shadow information (extracted from all slice projections) and integrated with the anatomy's breast tissue structure to determine the chest wall line. The fuzzy C-means classifier was used to extract the fibroglandular tissues from the acquired images. Whole-breast volume (WBV) and breast percentage density (BPD) were calculated in both modalities. Linear regression was used to compute the correlation of density results between the two modalities. The consistency of density measurement was also analyzed on the basis of intra- and inter-operator variation. There was a high correlation of density results between MRI and ABUS (R(2) = 0.798 for WBV, R(2) = 0.825 for PBD). The mean WBV from ABUS images was slightly smaller than the mean WBV from MR images (MRI: 342.24 ± 128.08 cm(3), ABUS: 325.47 ± 136.16 cm(3), p MRI: 24.71 ± 15.16%, ABUS: 28.90 ± 17.73%, p breast density measurement variation between the two modalities. Our results revealed a high correlation in WBV and BPD between MRI and ABUS. Our study suggests that ABUS provides breast density information useful in the assessment of breast health.

  2. Automated segmentation of lung airway wall area measurements from bronchoscopic optical coherence tomography imaging

    Science.gov (United States)

    Heydarian, Mohammadreza; Choy, Stephen; Wheatley, Andrew; McCormack, David; Coxson, Harvey O.; Lam, Stephen; Parraga, Grace

    2011-03-01

    Chronic Obstructive Pulmonary Disease (COPD) affects almost 600 million people and is currently the fourth leading cause of death worldwide. COPD is an umbrella term for respiratory symptoms that accompany destruction of the lung parenchyma and/or remodeling of the airway wall, the sum of which result in decreased expiratory flow, dyspnea and gas trapping. Currently, x-ray computed tomography (CT) is the main clinical method used for COPD imaging, providing excellent spatial resolution for quantitative tissue measurements although dose limitations and the fundamental spatial resolution of CT limit the measurement of airway dimensions beyond the 5th generation. To address this limitation, we are piloting the use of bronchoscopic Optical Coherence Tomography (OCT), by exploiting its superior spatial resolution of 5-15 micrometers for in vivo airway imaging. Currently, only manual segmentation of OCT airway lumen and wall have been reported but manual methods are time consuming and prone to observer variability. To expand the utility of bronchoscopic OCT, automatic and robust measurement methods are required. Therefore, our objective was to develop a fully automated method for segmenting OCT airway wall dimensions and here we explore several different methods of image-regeneration, voxel clustering and post-processing. Our resultant automated method used K-means or Fuzzy c-means to cluster pixel intensity and then a series of algorithms (i.e. cluster selection, artifact removal, de-noising) was applied to process the clustering results and segment airway wall dimensions. This approach provides a way to automatically and rapidly segment and reproducibly measure airway lumen and wall area.

  3. Development of an automated imaging pipeline for the analysis of the zebrafish larval kidney.

    Directory of Open Access Journals (Sweden)

    Jens H Westhoff

    Full Text Available The analysis of kidney malformation caused by environmental influences during nephrogenesis or by hereditary nephropathies requires animal models allowing the in vivo observation of developmental processes. The zebrafish has emerged as a useful model system for the analysis of vertebrate organ development and function, and it is suitable for the identification of organotoxic or disease-modulating compounds on a larger scale. However, to fully exploit its potential in high content screening applications, dedicated protocols are required allowing the consistent visualization of inner organs such as the embryonic kidney. To this end, we developed a high content screening compatible pipeline for the automated imaging of standardized views of the developing pronephros in zebrafish larvae. Using a custom designed tool, cavities were generated in agarose coated microtiter plates allowing for accurate positioning and orientation of zebrafish larvae. This enabled the subsequent automated acquisition of stable and consistent dorsal views of pronephric kidneys. The established pipeline was applied in a pilot screen for the analysis of the impact of potentially nephrotoxic drugs on zebrafish pronephros development in the Tg(wt1b:EGFP transgenic line in which the developing pronephros is highlighted by GFP expression. The consistent image data that was acquired allowed for quantification of gross morphological pronephric phenotypes, revealing concentration dependent effects of several compounds on nephrogenesis. In addition, applicability of the imaging pipeline was further confirmed in a morpholino based model for cilia-associated human genetic disorders associated with different intraflagellar transport genes. The developed tools and pipeline can be used to study various aspects in zebrafish kidney research, and can be readily adapted for the analysis of other organ systems.

  4. Vision 20/20: perspectives on automated image segmentation for radiotherapy.

    Science.gov (United States)

    Sharp, Gregory; Fritscher, Karl D; Pekar, Vladimir; Peroni, Marta; Shusharina, Nadya; Veeraraghavan, Harini; Yang, Jinzhong

    2014-05-01

    Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods' strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.

  5. Vision 20/20: Perspectives on automated image segmentation for radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Sharp, Gregory, E-mail: gcsharp@partners.org; Fritscher, Karl D.; Shusharina, Nadya [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114 (United States); Pekar, Vladimir [Philips Healthcare, Markham, Ontario 6LC 2S3 (Canada); Peroni, Marta [Center for Proton Therapy, Paul Scherrer Institut, 5232 Villigen-PSI (Switzerland); Veeraraghavan, Harini [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065 (United States); Yang, Jinzhong [Department of Radiation Physics, MD Anderson Cancer Center, Houston, Texas 77030 (United States)

    2014-05-15

    Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods’ strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.

  6. Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images.

    Science.gov (United States)

    Acharya, U Rajendra; Sree, S Vinitha; Muthu Rama Krishnan, M; Krishnananda, N; Ranjan, Shetty; Umesh, Pai; Suri, Jasjit S

    2013-12-01

    Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics.

  7. Automated tracking of lava lake level using thermal images at Kīlauea Volcano, Hawai’i

    Science.gov (United States)

    Patrick, Matthew R.; Swanson, Don; Orr, Tim

    2016-01-01

    Tracking the level of the lava lake in Halema‘uma‘u Crater, at the summit of Kīlauea Volcano, Hawai’i, is an essential part of monitoring the ongoing eruption and forecasting potentially hazardous changes in activity. We describe a simple automated image processing routine that analyzes continuously-acquired thermal images of the lava lake and measures lava level. The method uses three image segmentation approaches, based on edge detection, short-term change analysis, and composite temperature thresholding, to identify and track the lake margin in the images. These relative measurements from the images are periodically calibrated with laser rangefinder measurements to produce real-time estimates of lake elevation. Continuous, automated tracking of the lava level has been an important tool used by the U.S. Geological Survey’s Hawaiian Volcano Observatory since 2012 in real-time operational monitoring of the volcano and its hazard potential.

  8. Single cell induced optical confinement in biological lasers

    Science.gov (United States)

    Karl, M.; Dietrich, C. P.; Schubert, M.; Samuel, I. D. W.; Turnbull, G. A.; Gather, M. C.

    2017-03-01

    Biological single cell lasers have shown great potential for fundamental research and next generation sensing applications. In this study, the potential of fluorescent biological cells as refractive index landscapes and active optical elements is investigated using a combined Fourier- and hyperspectral imaging technique. We show that the refractive index contrast between cell and surrounding leads to 3D confinement of photons inside living cells. The Fourier- and real-space emission characteristics of these biological lasers are closely related and can be predicted from one another. Investigations of the lasing threshold for different energy and momentum position in Fourier-space give insight into the fundamental creation of longitudinal and transverse lasing modes within the cell. These findings corroborate the potential of living biological materials for precision engineering of photonic structures and may pave the way towards low threshold polariton lasing from single cells.

  9. Technologies for Single-Cell Isolation

    Directory of Open Access Journals (Sweden)

    Andre Gross

    2015-07-01

    Full Text Available The handling of single cells is of great importance in applications such as cell line development or single-cell analysis, e.g., for cancer research or for emerging diagnostic methods. This review provides an overview of technologies that are currently used or in development to isolate single cells for subsequent single-cell analysis. Data from a dedicated online market survey conducted to identify the most relevant technologies, presented here for the first time, shows that FACS (fluorescence activated cell sorting respectively Flow cytometry (33% usage, laser microdissection (17%, manual cell picking (17%, random seeding/dilution (15%, and microfluidics/lab-on-a-chip devices (12% are currently the most frequently used technologies. These most prominent technologies are described in detail and key performance factors are discussed. The survey data indicates a further increasing interest in single-cell isolation tools for the coming years. Additionally, a worldwide patent search was performed to screen for emerging technologies that might become relevant in the future. In total 179 patents were found, out of which 25 were evaluated by screening the title and abstract to be relevant to the field.

  10. Dislocation tomography made easy: a reconstruction from ADF STEM images obtained using automated image shift correction

    Energy Technology Data Exchange (ETDEWEB)

    Sharp, J H; Barnard, J S; Midgley, P A [Department of Materials Science, University of Cambridge, Pembroke Street, Cambridge, CB2 3QZ (United Kingdom); Kaneko, K; Higashida, K [Department of Materials Science and Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395 (Japan)], E-mail: jhd28@cam.ac.uk

    2008-08-15

    After previous work producing a successful 3D tomographic reconstruction of dislocations in GaN from conventional weak-beam dark-field (WBDF) images, we have reconstructed a cascade of dislocations in deformed and annealed silicon to a comparable standard using the more experimentally straightforward technique of STEM annular dark-field imaging (STEM ADF). In this mode, image contrast was much more consistent over the specimen tilt range than in conventional weak-beam dark-field imaging. Automatic acquisition software could thus restore the correct dislocation array to the field of view at each tilt angle, though manual focusing was still required. Reconstruction was carried out by sequential iterative reconstruction technique using FEI's Inspect3D software. Dislocations were distributed non-uniformly along cascades, with sparse areas between denser clumps in which individual dislocations of in-plane image width 24 nm could be distinguished in images and reconstruction. Denser areas showed more complicated stacking-fault contrast, hampering tomographic reconstruction. The general three-dimensional form of the denser areas was reproduced well, showing the dislocation array to be planar and not parallel to the foil surfaces.

  11. Automated multidimensional image analysis reveals a role for Abl in embryonic wound repair.

    Science.gov (United States)

    Zulueta-Coarasa, Teresa; Tamada, Masako; Lee, Eun J; Fernandez-Gonzalez, Rodrigo

    2014-07-01

    The embryonic epidermis displays a remarkable ability to repair wounds rapidly. Embryonic wound repair is driven by the evolutionary conserved redistribution of cytoskeletal and junctional proteins around the wound. Drosophila has emerged as a model to screen for factors implicated in wound closure. However, genetic screens have been limited by the use of manual analysis methods. We introduce MEDUSA, a novel image-analysis tool for the automated quantification of multicellular and molecular dynamics from time-lapse confocal microscopy data. We validate MEDUSA by quantifying wound closure in Drosophila embryos, and we show that the results of our automated analysis are comparable to analysis by manual delineation and tracking of the wounds, while significantly reducing the processing time. We demonstrate that MEDUSA can also be applied to the investigation of cellular behaviors in three and four dimensions. Using MEDUSA, we find that the conserved nonreceptor tyrosine kinase Abelson (Abl) contributes to rapid embryonic wound closure. We demonstrate that Abl plays a role in the organization of filamentous actin and the redistribution of the junctional protein β-catenin at the wound margin during embryonic wound repair. Finally, we discuss different models for the role of Abl in the regulation of actin architecture and adhesion dynamics at the wound margin.

  12. Automated detection of optic disk in retinal fundus images using intuitionistic fuzzy histon segmentation.

    Science.gov (United States)

    Mookiah, Muthu Rama Krishnan; Acharya, U Rajendra; Chua, Chua Kuang; Min, Lim Choo; Ng, E Y K; Mushrif, Milind M; Laude, Augustinus

    2013-01-01

    The human eye is one of the most sophisticated organs, with perfectly interrelated retina, pupil, iris cornea, lens, and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetic retinopathy (DR) and glaucoma may lead to blindness. The identification of retinal anatomical regions is a prerequisite for the computer-aided diagnosis of several retinal diseases. The manual examination of optic disk (OD) is a standard procedure used for detecting different stages of DR and glaucoma. In this article, a novel automated, reliable, and efficient OD localization and segmentation method using digital fundus images is proposed. General-purpose edge detection algorithms often fail to segment the OD due to fuzzy boundaries, inconsistent image contrast, or missing edge features. This article proposes a novel and probably the first method using the Attanassov intuitionistic fuzzy histon (A-IFSH)-based segmentation to detect OD in retinal fundus images. OD pixel intensity and column-wise neighborhood operation are employed to locate and isolate the OD. The method has been evaluated on 100 images comprising 30 normal, 39 glaucomatous, and 31 DR images. Our proposed method has yielded precision of 0.93, recall of 0.91, F-score of 0.92, and mean segmentation accuracy of 93.4%. We have also compared the performance of our proposed method with the Otsu and gradient vector flow (GVF) snake methods. Overall, our result shows the superiority of proposed fuzzy segmentation technique over other two segmentation methods.

  13. SparkMaster: automated calcium spark analysis with ImageJ.

    Science.gov (United States)

    Picht, Eckard; Zima, Aleksey V; Blatter, Lothar A; Bers, Donald M

    2007-09-01

    Ca sparks are elementary Ca-release events from intracellular Ca stores that are observed in virtually all types of muscle. Typically, Ca sparks are measured in the line-scan mode with confocal laser-scanning microscopes, yielding two-dimensional images (distance vs. time). The manual analysis of these images is time consuming and prone to errors as well as investigator bias. Therefore, we developed SparkMaster, an automated analysis program that allows rapid and reliable spark analysis. The underlying analysis algorithm is adapted from the threshold-based standard method of spark analysis developed by Cheng et al. (Biophys J 76: 606-617, 1999) and is implemented here in the freely available image-processing software ImageJ. SparkMaster offers a graphical user interface through which all analysis parameters and output options are selected. The analysis includes general image parameters (number of detected sparks, spark frequency) and individual spark parameters (amplitude, full width at half-maximum amplitude, full duration at half-maximum amplitude, full width, full duration, time to peak, maximum steepness of spark upstroke, time constant of spark decay). We validated the algorithm using images with synthetic sparks embedded into backgrounds with different signal-to-noise ratios to determine an analysis criteria at which a high sensitivity is combined with a low frequency of false-positive detections. Finally, we applied SparkMaster to analyze experimental data of sparks measured in intact and permeabilized ventricular cardiomyocytes, permeabilized mammalian skeletal muscle, and intact smooth muscle cells. We found that SparkMaster provides a reliable, easy to use, and fast way of analyzing Ca sparks in a wide variety of experimental conditions.

  14. Studies of the Ecophysiology of Single Cells in Microbial Communities by (Quantitative) Microautoradiography and Fluorescence In Situ Hybridization (MAR-FISH)

    DEFF Research Database (Denmark)

    Nierychlo, Marta; Nielsen, Jeppe Lund; Nielsen, Per Halkjær

    2015-01-01

    Microautoradiography (MAR) in combination with fluorescence in situ hybridization (FISH) is a powerful method of obtaining information about the ecophysiology of probe-defined single cells in mixed microbial communities. The incorporation of radiolabelled substrates can be quantified by automated...

  15. Single cell adhesion assay using computer controlled micropipette.

    Directory of Open Access Journals (Sweden)

    Rita Salánki

    Full Text Available Cell adhesion is a fundamental phenomenon vital for all multicellular organisms. Recognition of and adhesion to specific macromolecules is a crucial task of leukocytes to initiate the immune response. To gain statistically reliable information of cell adhesion, large numbers of cells should be measured. However, direct measurement of the adhesion force of single cells is still challenging and today's techniques typically have an extremely low throughput (5-10 cells per day. Here, we introduce a computer controlled micropipette mounted onto a normal inverted microscope for probing single cell interactions with specific macromolecules. We calculated the estimated hydrodynamic lifting force acting on target cells by the numerical simulation of the flow at the micropipette tip. The adhesion force of surface attached cells could be accurately probed by repeating the pick-up process with increasing vacuum applied in the pipette positioned above the cell under investigation. Using the introduced methodology hundreds of cells adhered to specific macromolecules were measured one by one in a relatively short period of time (∼30 min. We blocked nonspecific cell adhesion by the protein non-adhesive PLL-g-PEG polymer. We found that human primary monocytes are less adherent to fibrinogen than their in vitro differentiated descendants: macrophages and dendritic cells, the latter producing the highest average adhesion force. Validation of the here introduced method was achieved by the hydrostatic step-pressure micropipette manipulation technique. Additionally the result was reinforced in standard microfluidic shear stress channels. Nevertheless, automated micropipette gave higher sensitivity and less side-effect than the shear stress channel. Using our technique, the probed single cells can be easily picked up and further investigated by other techniques; a definite advantage of the computer controlled micropipette. Our experiments revealed the existence of a

  16. An automated image processing method to quantify collagen fibre organization within cutaneous scar tissue.

    Science.gov (United States)

    Quinn, Kyle P; Golberg, Alexander; Broelsch, G Felix; Khan, Saiqa; Villiger, Martin; Bouma, Brett; Austen, William G; Sheridan, Robert L; Mihm, Martin C; Yarmush, Martin L; Georgakoudi, Irene

    2015-01-01

    Standard approaches to evaluate scar formation within histological sections rely on qualitative evaluations and scoring, which limits our understanding of the remodelling process. We have recently developed an image analysis technique for the rapid quantification of fibre alignment at each pixel location. The goal of this study was to evaluate its application for quantitatively mapping scar formation in histological sections of cutaneous burns. To this end, we utilized directional statistics to define maps of fibre density and directional variance from Masson's trichrome-stained sections for quantifying changes in collagen organization during scar remodelling. Significant increases in collagen fibre density are detectable soon after burn injury in a rat model. Decreased fibre directional variance in the scar was also detectable between 3 weeks and 6 months after injury, indicating increasing fibre alignment. This automated analysis of fibre organization can provide objective surrogate endpoints for evaluating cutaneous wound repair and regeneration.

  17. Analysis of irradiated U-7wt%Mo dispersion fuel microstructures using automated image processing

    Science.gov (United States)

    Collette, R.; King, J.; Buesch, C.; Keiser, D. D.; Williams, W.; Miller, B. D.; Schulthess, J.

    2016-07-01

    The High Performance Research Reactor Fuel Development (HPPRFD) program is responsible for developing low enriched uranium (LEU) fuel substitutes for high performance reactors fueled with highly enriched uranium (HEU) that have not yet been converted to LEU. The uranium-molybdenum (U-Mo) fuel system was selected for this effort. In this study, fission gas pore segmentation was performed on U-7wt%Mo dispersion fuel samples at three separate fission densities using an automated image processing interface developed in MATLAB. Pore size distributions were attained that showed both expected and unexpected fission gas behavior. In general, it proved challenging to identify any dominant trends when comparing fission bubble data across samples from different fuel plates due to varying compositions and fabrication techniques. The results exhibited fair agreement with the fission density vs. porosity correlation developed by the Russian reactor conversion program.

  18. Automated Detection of Coronal Mass Ejections in STEREO Heliospheric Imager data

    CERN Document Server

    Pant, V; Rodriguez, L; Mierla, M; Banerjee, D; Davies, J A

    2016-01-01

    We have performed, for the first time, the successful automated detection of Coronal Mass Ejections (CMEs) in data from the inner heliospheric imager (HI-1) cameras on the STEREO A spacecraft. Detection of CMEs is done in time-height maps based on the application of the Hough transform, using a modified version of the CACTus software package, conventionally applied to coronagraph data. In this paper we describe the method of detection. We present the result of the application of the technique to a few CMEs that are well detected in the HI-1 imagery, and compare these results with those based on manual cataloging methodologies. We discuss in detail the advantages and disadvantages of this method.

  19. Automated Detection of Coronal Mass Ejections in STEREO Heliospheric Imager Data

    Science.gov (United States)

    Pant, V.; Willems, S.; Rodriguez, L.; Mierla, M.; Banerjee, D.; Davies, J. A.

    2016-12-01

    We have performed, for the first time, the successful automated detection of coronal mass ejections (CMEs) in data from the inner heliospheric imager (HI-1) cameras on the STEREO-A spacecraft. Detection of CMEs is done in time-height maps based on the application of the Hough transform, using a modified version of the CACTus software package, conventionally applied to coronagraph data. In this paper, we describe the method of detection. We present the results of the application of the technique to a few CMEs, which are well detected in the HI-1 imagery, and compare these results with those based on manual-cataloging methodologies. We discuss, in detail, the advantages and disadvantages of this method.

  20. Automated analysis of images acquired with electronic portal imaging device during delivery of quality assurance plans for inversely optimized arc therapy

    DEFF Research Database (Denmark)

    Fredh, Anna; Korreman, Stine; Rosenschöld, Per Munck af

    2010-01-01

    This work presents an automated method for comprehensively analyzing EPID images acquired for quality assurance of RapidArc treatment delivery. In-house-developed software has been used for the analysis and long-term results from measurements on three linacs are presented.......This work presents an automated method for comprehensively analyzing EPID images acquired for quality assurance of RapidArc treatment delivery. In-house-developed software has been used for the analysis and long-term results from measurements on three linacs are presented....

  1. Improving cervical region of interest by eliminating vaginal walls and cotton-swabs for automated image analysis

    Science.gov (United States)

    Venkataraman, Sankar; Li, Wenjing

    2008-03-01

    Image analysis for automated diagnosis of cervical cancer has attained high prominence in the last decade. Automated image analysis at all levels requires a basic segmentation of the region of interest (ROI) within a given image. The precision of the diagnosis is often reflected by the precision in detecting the initial region of interest, especially when some features outside the ROI mimic the ones within the same. Work described here discusses algorithms that are used to improve the cervical region of interest as a part of automated cervical image diagnosis. A vital visual aid in diagnosing cervical cancer is the aceto-whitening of the cervix after the application of acetic acid. Color and texture are used to segment acetowhite regions within the cervical ROI. Vaginal walls along with cottonswabs sometimes mimic these essential features leading to several false positives. Work presented here is focused towards detecting in-focus vaginal wall boundaries and then extrapolating them to exclude vaginal walls from the cervical ROI. In addition, discussed here is a marker-controlled watershed segmentation that is used to detect cottonswabs from the cervical ROI. A dataset comprising 50 high resolution images of the cervix acquired after 60 seconds of acetic acid application were used to test the algorithm. Out of the 50 images, 27 benefited from a new cervical ROI. Significant improvement in overall diagnosis was observed in these images as false positives caused by features outside the actual ROI mimicking acetowhite region were eliminated.

  2. A linear programming approach to reconstructing subcellular structures from confocal images for automated generation of representative 3D cellular models.

    Science.gov (United States)

    Wood, Scott T; Dean, Brian C; Dean, Delphine

    2013-04-01

    This paper presents a novel computer vision algorithm to analyze 3D stacks of confocal images of fluorescently stained single cells. The goal of the algorithm is to create representative in silico model structures that can be imported into finite element analysis software for mechanical characterization. Segmentation of cell and nucleus boundaries is accomplished via standard thresholding methods. Using novel linear programming methods, a representative actin stress fiber network is generated by computing a linear superposition of fibers having minimum discrepancy compared with an experimental 3D confocal image. Qualitative validation is performed through analysis of seven 3D confocal image stacks of adherent vascular smooth muscle cells (VSMCs) grown in 2D culture. The presented method is able to automatically generate 3D geometries of the cell's boundary, nucleus, and representative F-actin network based on standard cell microscopy data. These geometries can be used for direct importation and implementation in structural finite element models for analysis of the mechanics of a single cell to potentially speed discoveries in the fields of regenerative medicine, mechanobiology, and drug discovery.

  3. Automated Transient Recovery Algorithm using Discrete Zernike Polynomials on Image-Subtracted Data

    Science.gov (United States)

    Ackley, Kendall; Eikenberry, Stephen S.; Klimenko, Sergey

    2016-01-01

    We present an unsupervised algorithm for the automated identification of astrophysical transients recovered through image subtraction techniques. We use a set of discrete Zernike polynomials to decompose and characterize residual energy discovered in the final subtracted image, identifying candidate sources which appear point-like in nature. This work is motivated for use in collaboration with Advanced gravitational wave (GW) interferometers, such as Advanced LIGO and Virgo, where multiwavelength electromagnetic (EM) emission is expected in parallel with gravitational radiation from compact binary object mergers of neutron stars (NS-NS) and stellar-mass black holes (NS-BH). Imaging an EM counterpart coincident with a GW trigger will help to constrain the multi-dimensional GW parameter space as well as aid in the resolution of long-standing astrophysical mysteries, such as the true nature of the progenitor relationship between short-duration GRBs and massive compact binary mergers. We are working on making our method an open-source package optimized for low-latency response for community use during the upcoming era of GW astronomy.

  4. Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images.

    Science.gov (United States)

    Athanasiou, Lambros S; Bourantas, Christos V; Rigas, George; Sakellarios, Antonis I; Exarchos, Themis P; Siogkas, Panagiotis K; Ricciardi, Andrea; Naka, Katerina K; Papafaklis, Michail I; Michalis, Lampros K; Prati, Francesco; Fotiadis, Dimitrios I

    2014-02-01

    Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by expert observers. However, this process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, we present a methodology that is able to process the OCT data in a fully automated fashion. The proposed methodology is able to detect the lumen borders in the OCT frames, identify the plaque region, and detect four tissue types: calcium (CA), lipid tissue (LT), fibrous tissue (FT), and mixed tissue (MT). The efficiency of the developed methodology was evaluated using annotations from 27 OCT pullbacks acquired from 22 patients. High Pearson's correlation coefficients were obtained between the output of the developed methodology and the manual annotations (from 0.96 to 0.99), while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping areas ratio between experts' annotations and methodology in detecting CA, LT, FT, and MT was 0.81, 0.71, 0.87, and 0.81, respectively.

  5. A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging

    Science.gov (United States)

    Vickers, H.; Eckerstorfer, M.; Malnes, E.; Larsen, Y.; Hindberg, H.

    2016-11-01

    Avalanches are a natural hazard that occur in mountainous regions of Troms County in northern Norway during winter and can cause loss of human life and damage to infrastructure. Knowledge of when and where they occur especially in remote, high mountain areas is often lacking due to difficult access. However, complete, spatiotemporal avalanche activity data sets are important for accurate avalanche forecasting, as well as for deeper understanding of the link between avalanche occurrences and the triggering snowpack and meteorological factors. It is therefore desirable to develop a technique that enables active mapping and monitoring of avalanches over an entire winter. Avalanche debris can be observed remotely over large spatial areas, under all weather and light conditions by synthetic aperture radar (SAR) satellites. The recently launched Sentinel-1A satellite acquires SAR images covering the entire Troms County with frequent updates. By focusing on a case study from New Year 2015 we use Sentinel-1A images to develop an automated avalanche debris detection algorithm that utilizes change detection and unsupervised object classification methods. We compare our results with manually identified avalanche debris and field-based images to quantify the algorithm accuracy. Our results indicate that a correct detection rate of over 60% can be achieved, which is sensitive to several algorithm parameters that may need revising. With further development and refinement of the algorithm, we believe that this method could play an effective role in future operational monitoring of avalanches within Troms and has potential application in avalanche forecasting areas worldwide.

  6. Automated centreline extraction of neuronal dendrite from optical microscopy image stacks

    Science.gov (United States)

    Xiao, Liang; Zhang, Fanbiao

    2010-11-01

    In this work we present a novel vision-based pipeline for automated skeleton detection and centreline extraction of neuronal dendrite from optical microscopy image stacks. The proposed pipeline is an integrated solution that merges image stacks pre-processing, the seed points detection, ridge traversal procedure, minimum spanning tree optimization and tree trimming into to a unified framework to deal with the challenge problem. In image stacks preprocessing, we first apply a curvelet transform based shrinkage and cycle spinning technique to remove the noise. This is followed by the adaptive threshold method to compute the result of neuronal object segmentation, and the 3D distance transformation is performed to get the distance map. According to the eigenvalues and eigenvectors of the Hessian matrix, the skeleton seed points are detected. Staring from the seed points, the initial centrelines are obtained using ridge traversal procedure. After that, we use minimum spanning tree to organize the geometrical structure of the skeleton points, and then we use graph trimming post-processing to compute the final centreline. Experimental results on different datasets demonstrate that our approach has high reliability, good robustness and requires less user interaction.

  7. Cell Image Velocimetry (CIV): boosting the automated quantification of cell migration in wound healing assays.

    Science.gov (United States)

    Milde, Florian; Franco, Davide; Ferrari, Aldo; Kurtcuoglu, Vartan; Poulikakos, Dimos; Koumoutsakos, Petros

    2012-11-01

    Cell migration is commonly quantified by tracking the speed of the cell layer interface in wound healing assays. This quantification is often hampered by low signal to noise ratio, in particular when complex substrates are employed to emulate in vivo cell migration in geometrically complex environments. Moreover, information about the cell motion, readily available inside the migrating cell layers, is not usually harvested. We introduce Cell Image Velocimetry (CIV), a combination of cell layer segmentation and image velocimetry algorithms, to drastically enhance the quantification of cell migration by wound healing assays. The resulting software analyses the speed of the interface as well as the detailed velocity field inside the cell layers in an automated fashion. CIV is shown to be highly robust for images with low signal to noise ratio, low contrast and frame shifting and it is portable across various experimental settings. The modular design and parametrization of CIV is not restricted to wound healing assays and allows for the exploration and quantification of flow phenomena in any optical microscopy dataset. Here, we demonstrate the capabilities of CIV in wound healing assays over topographically engineered surfaces and quantify the relative merits of differently aligned gratings on cell migration.

  8. Automated Segmentation of in Vivo and Ex Vivo Mouse Brain Magnetic Resonance Images

    Directory of Open Access Journals (Sweden)

    Alize E.H. Scheenstra

    2009-01-01

    Full Text Available Segmentation of magnetic resonance imaging (MRI data is required for many applications, such as the comparison of different structures or time points, and for annotation purposes. Currently, the gold standard for automated image segmentation is nonlinear atlas-based segmentation. However, these methods are either not sufficient or highly time consuming for mouse brains, owing to the low signal to noise ratio and low contrast between structures compared with other applications. We present a novel generic approach to reduce processing time for segmentation of various structures of mouse brains, in vivo and ex vivo. The segmentation consists of a rough affine registration to a template followed by a clustering approach to refine the rough segmentation near the edges. Compared with manual segmentations, the presented segmentation method has an average kappa index of 0.7 for 7 of 12 structures in in vivo MRI and 11 of 12 structures in ex vivo MRI. Furthermore, we found that these results were equal to the performance of a nonlinear segmentation method, but with the advantage of being 8 times faster. The presented automatic segmentation method is quick and intuitive and can be used for image registration, volume quantification of structures, and annotation.

  9. Semi-automated porosity identification from thin section images using image analysis and intelligent discriminant classifiers

    Science.gov (United States)

    Ghiasi-Freez, Javad; Soleimanpour, Iman; Kadkhodaie-Ilkhchi, Ali; Ziaii, Mansur; Sedighi, Mahdi; Hatampour, Amir

    2012-08-01

    Identification of different types of porosity within a reservoir rock is a functional parameter for reservoir characterization since various pore types play different roles in fluid transport and also, the pore spaces determine the fluid storage capacity of the reservoir. The present paper introduces a model for semi-automatic identification of porosity types within thin section images. To get this goal, a pattern recognition algorithm is followed. Firstly, six geometrical shape parameters of sixteen largest pores of each image are extracted using image analysis techniques. The extracted parameters and their corresponding pore types of 294 pores are used for training two intelligent discriminant classifiers, namely linear and quadratic discriminant analysis. The trained classifiers take the geometrical features of the pores to identify the type and percentage of five types of porosity, including interparticle, intraparticle, oomoldic, biomoldic, and vuggy in each image. The accuracy of classifiers is determined from two standpoints. Firstly, the predicted and measured percentages of each type of porosity are compared with each other. The results indicate reliable performance for predicting percentage of each type of porosity. In the second step, the precisions of classifiers for categorizing the pore spaces are analyzed. The classifiers also took a high acceptance score when used for individual recognition of pore spaces. The proposed methodology is a further promising application for petroleum geologists allowing statistical study of pore types in a rapid and accurate way.

  10. A new automated method for analysis of gated-SPECT images based on a three-dimensional heart shaped model

    DEFF Research Database (Denmark)

    Lomsky, Milan; Richter, Jens; Johansson, Lena

    2005-01-01

    A new automated method for quantification of left ventricular function from gated-single photon emission computed tomography (SPECT) images has been developed. The method for quantification of cardiac function (CAFU) is based on a heart shaped model and the active shape algorithm. The model...

  11. Automated high-throughput assessment of prostate biopsy tissue using infrared spectroscopic chemical imaging

    Science.gov (United States)

    Bassan, Paul; Sachdeva, Ashwin; Shanks, Jonathan H.; Brown, Mick D.; Clarke, Noel W.; Gardner, Peter

    2014-03-01

    Fourier transform infrared (FT-IR) chemical imaging has been demonstrated as a promising technique to complement histopathological assessment of biomedical tissue samples. Current histopathology practice involves preparing thin tissue sections and staining them using hematoxylin and eosin (H&E) after which a histopathologist manually assess the tissue architecture under a visible microscope. Studies have shown that there is disagreement between operators viewing the same tissue suggesting that a complementary technique for verification could improve the robustness of the evaluation, and improve patient care. FT-IR chemical imaging allows the spatial distribution of chemistry to be rapidly imaged at a high (diffraction-limited) spatial resolution where each pixel represents an area of 5.5 × 5.5 μm2 and contains a full infrared spectrum providing a chemical fingerprint which studies have shown contains the diagnostic potential to discriminate between different cell-types, and even the benign or malignant state of prostatic epithelial cells. We report a label-free (i.e. no chemical de-waxing, or staining) method of imaging large pieces of prostate tissue (typically 1 cm × 2 cm) in tens of minutes (at a rate of 0.704 × 0.704 mm2 every 14.5 s) yielding images containing millions of spectra. Due to refractive index matching between sample and surrounding paraffin, minimal signal processing is required to recover spectra with their natural profile as opposed to harsh baseline correction methods, paving the way for future quantitative analysis of biochemical signatures. The quality of the spectral information is demonstrated by building and testing an automated cell-type classifier based upon spectral features.

  12. Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: Automated measurement development for full field digital mammography

    OpenAIRE

    Fowler, E. E.; Sellers, T.A.; Lu, B.; Heine, J.J.

    2013-01-01

    Purpose: The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data.

  13. Rapid and Semi-Automated Extraction of Neuronal Cell Bodies and Nuclei from Electron Microscopy Image Stacks

    Science.gov (United States)

    Holcomb, Paul S.; Morehead, Michael; Doretto, Gianfranco; Chen, Peter; Berg, Stuart; Plaza, Stephen; Spirou, George

    2016-01-01

    Connectomics—the study of how neurons wire together in the brain—is at the forefront of modern neuroscience research. However, many connectomics studies are limited by the time and precision needed to correctly segment large volumes of electron microscopy (EM) image data. We present here a semi-automated segmentation pipeline using freely available software that can significantly decrease segmentation time for extracting both nuclei and cell bodies from EM image volumes. PMID:27259933

  14. LOCALIZATION OF PALM DORSAL VEIN PATTERN USING IMAGE PROCESSING FOR AUTOMATED INTRA-VENOUS DRUG NEEDLE INSERTION

    OpenAIRE

    Mrs. Kavitha. R,; Tripty Singh

    2011-01-01

    Vein pattern in palms is a random mesh of interconnected and inter- wining blood vessels. This project is the application of vein detection concept to automate the drug delivery process. It dealswith extracting palm dorsal vein structures, which is a key procedure for selecting the optimal drug needle insertion point. Gray scale images obtained from a low cost IR-webcam are poor in contrast, and usually noisy which make an effective vein segmentation a great challenge. Here a new vein image s...

  15. Hyper-Cam automated calibration method for continuous hyperspectral imaging measurements

    Science.gov (United States)

    Gagnon, Jean-Philippe; Habte, Zewdu; George, Jacks; Farley, Vincent; Tremblay, Pierre; Chamberland, Martin; Romano, Joao; Rosario, Dalton

    2010-04-01

    The midwave and longwave infrared regions of the electromagnetic spectrum contain rich information which can be captured by hyperspectral sensors thus enabling enhanced detection of targets of interest. A continuous hyperspectral imaging measurement capability operated 24/7 over varying seasons and weather conditions permits the evaluation of hyperspectral imaging for detection of different types of targets in real world environments. Such a measurement site was built at Picatinny Arsenal under the Spectral and Polarimetric Imagery Collection Experiment (SPICE), where two Hyper-Cam hyperspectral imagers are installed at the Precision Armament Laboratory (PAL) and are operated autonomously since Fall of 2009. The Hyper-Cam are currently collecting a complete hyperspectral database that contains the MWIR and LWIR hyperspectral measurements of several targets under day, night, sunny, cloudy, foggy, rainy and snowy conditions. The Telops Hyper-Cam sensor is an imaging spectrometer that enables the spatial and spectral analysis capabilities using a single sensor. It is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides datacubes of up to 320x256 pixels at spectral resolutions of up to 0.25 cm-1. The MWIR version covers the 3 to 5 μm spectral range and the LWIR version covers the 8 to 12 μm spectral range. This paper describes the automated operation of the two Hyper-Cam sensors being used in the SPICE data collection. The Reveal Automation Control Software (RACS) developed collaboratively between Telops, ARDEC, and ARL enables flexible operating parameters and autonomous calibration. Under the RACS software, the Hyper-Cam sensors can autonomously calibrate itself using their internal blackbody targets, and the calibration events are initiated by user defined time intervals and on internal beamsplitter temperature monitoring. The RACS software is the first software developed for

  16. Single-cell measurements of IgE-mediated FcεRI signaling using an integrated microfluidic platform.

    Directory of Open Access Journals (Sweden)

    Yanli Liu

    Full Text Available Heterogeneity in responses of cells to a stimulus, such as a pathogen or allergen, can potentially play an important role in deciding the fate of the responding cell population and the overall systemic response. Measuring heterogeneous responses requires tools capable of interrogating individual cells. Cell signaling studies commonly do not have single-cell resolution because of the limitations of techniques used such as Westerns, ELISAs, mass spectrometry, and DNA microarrays. Microfluidics devices are increasingly being used to overcome these limitations. Here, we report on a microfluidic platform for cell signaling analysis that combines two orthogonal single-cell measurement technologies: on-chip flow cytometry and optical imaging. The device seamlessly integrates cell culture, stimulation, and preparation with downstream measurements permitting hands-free, automated analysis to minimize experimental variability. The platform was used to interrogate IgE receptor (FcεRI signaling, which is responsible for triggering allergic reactions, in RBL-2H3 cells. Following on-chip crosslinking of IgE-FcεRI complexes by multivalent antigen, we monitored signaling events including protein phosphorylation, calcium mobilization and the release of inflammatory mediators. The results demonstrate the ability of our platform to produce quantitative measurements on a cell-by-cell basis from just a few hundred cells. Model-based analysis of the Syk phosphorylation data suggests that heterogeneity in Syk phosphorylation can be attributed to protein copy number variations, with the level of Syk phosphorylation being particularly sensitive to the copy number of Lyn.

  17. Exploring symbioses by single-cell genomics.

    Science.gov (United States)

    Kamke, Janine; Bayer, Kristina; Woyke, Tanja; Hentschel, Ute

    2012-08-01

    Single-cell genomics has advanced the field of microbiology from the analysis of microbial metagenomes where information is "drowning in a sea of sequences," to recognizing each microbial cell as a separate and unique entity. Single-cell genomics employs Phi29 polymerase-mediated whole-genome amplification to yield microgram-range genomic DNA from single microbial cells. This method has now been applied to a handful of symbiotic systems, including bacterial symbionts of marine sponges, insects (grasshoppers, termites), and vertebrates (mouse, human). In each case, novel insights were obtained into the functional genomic repertoire of the bacterial partner, which, in turn, led to an improved understanding of the corresponding host. Single-cell genomics is particularly valuable when dealing with uncultivated microorganisms, as is still the case for many bacterial symbionts. In this review, we explore the power of single-cell genomics for symbiosis research and highlight recent insights into the symbiotic systems that were obtained by this approach.

  18. Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

    Directory of Open Access Journals (Sweden)

    Huang Kai

    2004-06-01

    accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes was improved by 5–15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images. Conclusions The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.

  19. Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells

    Science.gov (United States)

    Park, Han Sang; Rinehart, Matthew T.; Walzer, Katelyn A.; Chi, Jen-Tsan Ashley; Wax, Adam

    2016-01-01

    Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite Plasmodium falciparum at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and k-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection

  20. Automated local bright feature image analysis of nuclear proteindistribution identifies changes in tissue phenotype

    Energy Technology Data Exchange (ETDEWEB)

    Knowles, David; Sudar, Damir; Bator, Carol; Bissell, Mina

    2006-02-01

    The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, the distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is an increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype, and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently-stained nuclear protein NuMA in different mammary phenotypes obtained using three-dimensional cell culture. Automated image segmentation of DAPI-stained nuclei was generated to isolate thousands of nuclei from three-dimensional confocal images. Prominent features of fluorescently-stained NuMA were detected using a novel local bright feature analysis technique, and their normalized spatial density calculated as a function of the distance from the nuclear perimeter to its center. The results revealed marked changes in the distribution of the density of NuMA bright features as non-neoplastic cells underwent phenotypically normal acinar morphogenesis. In contrast, we did not detect any reorganization of NuMA during the formation of tumor nodules by malignant cells. Importantly, the analysis also discriminated proliferating non-neoplastic cells from proliferating malignant cells, suggesting that these imaging methods are capable of identifying alterations linked not only to the proliferation status but also to the malignant character of cells. We believe that this quantitative analysis will have additional applications for classifying normal and pathological tissues.

  1. Primary histologic diagnosis using automated whole slide imaging: a validation study

    Directory of Open Access Journals (Sweden)

    Jukic Drazen M

    2006-04-01

    Full Text Available Abstract Background Only prototypes 5 years ago, high-speed, automated whole slide imaging (WSI systems (also called digital slide systems, virtual microscopes or wide field imagers are becoming increasingly capable and robust. Modern devices can capture a slide in 5 minutes at spatial sampling periods of less than 0.5 micron/pixel. The capacity to rapidly digitize large numbers of slides should eventually have a profound, positive impact on pathology. It is important, however, that pathologists validate these systems during development, not only to identify their limitations but to guide their evolution. Methods Three pathologists fully signed out 25 cases representing 31 parts. The laboratory information system was used to simulate real-world sign-out conditions including entering a full diagnostic field and comment (when appropriate and ordering special stains and recuts. For each case, discrepancies between diagnoses were documented by committee and a "consensus" report was formed and then compared with the microscope-based, sign-out report from the clinical archive. Results In 17 of 25 cases there were no discrepancies between the individual study pathologist reports. In 8 of the remaining cases, there were 12 discrepancies, including 3 in which image quality could be at least partially implicated. When the WSI consensus diagnoses were compared with the original sign-out diagnoses, no significant discrepancies were found. Full text of the pathologist reports, the WSI consensus diagnoses, and the original sign-out diagnoses are available as an attachment to this publication. Conclusion The results indicated that the image information contained in current whole slide images is sufficient for pathologists to make reliable diagnostic decisions and compose complex diagnostic reports. This is a very positive result; however, this does not mean that WSI is as good as a microscope. Virtually every slide had focal areas in which image quality (focus

  2. Fully automated prostate magnetic resonance imaging and transrectal ultrasound fusion via a probabilistic registration metric

    Science.gov (United States)

    Sparks, Rachel; Bloch, B. Nicholas; Feleppa, Ernest; Barratt, Dean; Madabhushi, Anant

    2013-03-01

    In this work, we present a novel, automated, registration method to fuse magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) images of the prostate. Our methodology consists of: (1) delineating the prostate on MRI, (2) building a probabilistic model of prostate location on TRUS, and (3) aligning the MRI prostate segmentation to the TRUS probabilistic model. TRUS-guided needle biopsy is the current gold standard for prostate cancer (CaP) diagnosis. Up to 40% of CaP lesions appear isoechoic on TRUS, hence TRUS-guided biopsy cannot reliably target CaP lesions and is associated with a high false negative rate. MRI is better able to distinguish CaP from benign prostatic tissue, but requires special equipment and training. MRI-TRUS fusion, whereby MRI is acquired pre-operatively and aligned to TRUS during the biopsy procedure, allows for information from both modalities to be used to help guide the biopsy. The use of MRI and TRUS in combination to guide biopsy at least doubles the yield of positive biopsies. Previous work on MRI-TRUS fusion has involved aligning manually determined fiducials or prostate surfaces to achieve image registration. The accuracy of these methods is dependent on the reader's ability to determine fiducials or prostate surfaces with minimal error, which is a difficult and time-consuming task. Our novel, fully automated MRI-TRUS fusion method represents a significant advance over the current state-of-the-art because it does not require manual intervention after TRUS acquisition. All necessary preprocessing steps (i.e. delineation of the prostate on MRI) can be performed offline prior to the biopsy procedure. We evaluated our method on seven patient studies, with B-mode TRUS and a 1.5 T surface coil MRI. Our method has a root mean square error (RMSE) for expertly selected fiducials (consisting of the urethra, calcifications, and the centroids of CaP nodules) of 3.39 +/- 0.85 mm.

  3. Automated image analysis reveals the dynamic 3-dimensional organization of multi-ciliary arrays.

    Science.gov (United States)

    Galati, Domenico F; Abuin, David S; Tauber, Gabriel A; Pham, Andrew T; Pearson, Chad G

    2015-12-23

    Multi-ciliated cells (MCCs) use polarized fields of undulating cilia (ciliary array) to produce fluid flow that is essential for many biological processes. Cilia are positioned by microtubule scaffolds called basal bodies (BBs) that are arranged within a spatially complex 3-dimensional geometry (3D). Here, we develop a robust and automated computational image analysis routine to quantify 3D BB organization in the ciliate, Tetrahymena thermophila. Using this routine, we generate the first morphologically constrained 3D reconstructions of Tetrahymena cells and elucidate rules that govern the kinetics of MCC organization. We demonstrate the interplay between BB duplication and cell size expansion through the cell cycle. In mutant cells, we identify a potential BB surveillance mechanism that balances large gaps in BB spacing by increasing the frequency of closely spaced BBs in other regions of the cell. Finally, by taking advantage of a mutant predisposed to BB disorganization, we locate the spatial domains that are most prone to disorganization by environmental stimuli. Collectively, our analyses reveal the importance of quantitative image analysis to understand the principles that guide the 3D organization of MCCs.

  4. Automated hierarchical time gain compensation for in-vivo ultrasound imaging

    Science.gov (United States)

    Moshavegh, Ramin; Hemmsen, Martin C.; Martins, Bo; Brandt, Andreas H.; Hansen, Kristoffer L.; Nielsen, Michael B.; Jensen, Jørgen A.

    2015-03-01

    Time gain compensation (TGC) is essential to ensure the optimal image quality of the clinical ultrasound scans. When large fluid collections are present within the scan plane, the attenuation distribution is changed drastically and TGC compensation becomes challenging. This paper presents an automated hierarchical TGC (AHTGC) algorithm that accurately adapts to the large attenuation variation between different types of tissues and structures. The algorithm relies on estimates of tissue attenuation, scattering strength, and noise level to gain a more quantitative understanding of the underlying tissue and the ultrasound signal strength. The proposed algorithm was applied to a set of 44 in vivo abdominal movie sequences each containing 15 frames. Matching pairs of in vivo sequences, unprocessed and processed with the proposed AHTGC were visualized side by side and evaluated by two radiologists in terms of image quality. Wilcoxon signed-rank test was used to evaluate whether radiologists preferred the processed sequences or the unprocessed data. The results indicate that the average visual analogue scale (VAS) is positive ( p-value: 2.34 × 10-13) and estimated to be 1.01 (95% CI: 0.85; 1.16) favoring the processed data with the proposed AHTGC algorithm.

  5. Automated Waterline Detection in the Wadden Sea Using High-Resolution TerraSAR-X Images

    Directory of Open Access Journals (Sweden)

    Stefan Wiehle

    2015-01-01

    Full Text Available We present an algorithm for automatic detection of the land-water-line from TerraSAR-X images acquired over the Wadden Sea. In this coastal region of the southeastern North Sea, a strip of up to 20 km of seabed falls dry during low tide, revealing mudflats and tidal creeks. The tidal currents transport sediments and can change the coastal shape with erosion rates of several meters per month. This rate can be strongly increased by storm surges which also cause flooding of usually dry areas. Due to the high number of ships traveling through the Wadden Sea to the largest ports of Germany, frequent monitoring of the bathymetry is also an important task for maritime security. For such an extended area and the required short intervals of a few months, only remote sensing methods can perform this task efficiently. Automating the waterline detection in weather-independent radar images provides a fast and reliable way to spot changes in the coastal topography. The presented algorithm first performs smoothing, brightness thresholding, and edge detection. In the second step, edge drawing and flood filling are iteratively performed to determine optimal thresholds for the edge drawing. In the last step, small misdetections are removed.

  6. Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis

    Science.gov (United States)

    Chung, Howard; Cobzas, Dana; Birdsell, Laura; Lieffers, Jessica; Baracos, Vickie

    2009-02-01

    The ability to compute body composition in cancer patients lends itself to determining the specific clinical outcomes associated with fat and lean tissue stores. For example, a wasting syndrome of advanced disease associates with shortened survival. Moreover, certain tissue compartments represent sites for drug distribution and are likely determinants of chemotherapy efficacy and toxicity. CT images are abundant, but these cannot be fully exploited unless there exist practical and fast approaches for tissue quantification. Here we propose a fully automated method for segmenting muscle, visceral and subcutaneous adipose tissues, taking the approach of shape modeling for the analysis of skeletal muscle. Muscle shape is represented using PCA encoded Free Form Deformations with respect to a mean shape. The shape model is learned from manually segmented images and used in conjunction with a tissue appearance prior. VAT and SAT are segmented based on the final deformed muscle shape. In comparing the automatic and manual methods, coefficients of variation (COV) (1 - 2%), were similar to or smaller than inter- and intra-observer COVs reported for manual segmentation.

  7. Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images.

    Science.gov (United States)

    Kreshuk, Anna; Straehle, Christoph N; Sommer, Christoph; Koethe, Ullrich; Cantoni, Marco; Knott, Graham; Hamprecht, Fred A

    2011-01-01

    We describe a protocol for fully automated detection and segmentation of asymmetric, presumed excitatory, synapses in serial electron microscopy images of the adult mammalian cerebral cortex, taken with the focused ion beam, scanning electron microscope (FIB/SEM). The procedure is based on interactive machine learning and only requires a few labeled synapses for training. The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data. On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision). Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D. The source code, the test dataset and the ground truth annotation are freely available on the website http://www.ilastik.org/synapse-detection.

  8. Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images.

    Directory of Open Access Journals (Sweden)

    Anna Kreshuk

    Full Text Available We describe a protocol for fully automated detection and segmentation of asymmetric, presumed excitatory, synapses in serial electron microscopy images of the adult mammalian cerebral cortex, taken with the focused ion beam, scanning electron microscope (FIB/SEM. The procedure is based on interactive machine learning and only requires a few labeled synapses for training. The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data. On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision. Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D. The source code, the test dataset and the ground truth annotation are freely available on the website http://www.ilastik.org/synapse-detection.

  9. Automated initial guess in digital image correlation aided by Fourier-Mellin transform

    Science.gov (United States)

    Pan, Bing; Wang, Yuejiao; Tian, Long

    2017-01-01

    The state-of-the-art digital image correlation (DIC) method using iterative spatial-domain cross correlation, e.g., the inverse-compositional Gauss-Newton algorithm, for full-field displacement mapping requires an initial guess of deformation, which should be sufficiently close to the true value to ensure a rapid and accurate convergence. Although various initial guess approaches have been proposed, automated, robust, and fast initial guess remains to be a challenging task, especially when large rotation occurs to the deformed images. An integrated scheme, which combines the Fourier-Mellin transform-based cross correlation (FMT-CC) for seed point initiation with a reliability-guided displacement tracking (RGDT) strategy for the remaining points, is proposed to provide accurate initial guess for DIC calculation, even in the presence of large rotations. By using FMT-CC algorithm, the initial guess of the seed point can be automatically and accurately determined between pairs of interrogation subsets with up to ±180 deg of rotation even in the presence of large translation. Then the initial guess of the rest of the calculation points can be accurately predicted by the robust RGDT scheme. The robustness and effectiveness of the present initial guess approach are verified by numerical simulation tests and real experiment.

  10. Development of an MRI fiducial marker prototype for automated MR-US fusion of abdominal images

    Science.gov (United States)

    Favazza, C. P.; Gorny, K. R.; Washburn, M. J.; Hangiandreou, N. J.

    2014-03-01

    External MRI fiducial marker devices are expected to facilitate robust, accurate, and efficient image fusion between MRI and other modalities. Automating of this process requires careful selection of a suitable marker size and material visible across a variety of pulse sequences, design of an appropriate fiducial device, and a robust segmentation algorithm. A set of routine clinical abdominal MRI pulse sequences was used to image a variety of marker materials and range of marker sizes. The most successfully detected marker was 12.7 mm diameter cylindrical reservoir filled with 1 g/L copper sulfate solution. A fiducial device was designed and fabricated from four such markers arranged in a tetrahedral orientation. MRI examinations were performed with the device attached to phantom and a volunteer, and custom developed algorithm was used to detect and segment the individual markers. The individual markers were accurately segmented in all sequences for both the phantom and volunteer. The measured intra-marker spacings matched well with the dimensions of the fiducial device. The average deviations from the actual physical spacings were 0.45+/- 0.40 mm and 0.52 +/- 0.36 mm for the phantom and the volunteer data, respectively. These preliminary results suggest that this general fiducial design and detection algorithm could be used for MRI multimodality fusion applications.

  11. Automated tissue classification of intracardiac optical coherence tomography images (Conference Presentation)

    Science.gov (United States)

    Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

    2016-03-01

    Remodeling of the myocardium is associated with increased risk of arrhythmia and heart failure. Our objective is to automatically identify regions of fibrotic myocardium, dense collagen, and adipose tissue, which can serve as a way to guide radiofrequency ablation therapy or endomyocardial biopsies. Using computer vision and machine learning, we present an automated algorithm to classify tissue compositions from cardiac optical coherence tomography (OCT) images. Three dimensional OCT volumes were obtained from 15 human hearts ex vivo within 48 hours of donor death (source, NDRI). We first segmented B-scans using a graph searching method. We estimated the boundary of each region by minimizing a cost function, which consisted of intensity, gradient, and contour smoothness. Then, features, including texture analysis, optical properties, and statistics of high moments, were extracted. We used a statistical model, relevance vector machine, and trained this model with abovementioned features to classify tissue compositions. To validate our method, we applied our algorithm to 77 volumes. The datasets for validation were manually segmented and classified by two investigators who were blind to our algorithm results and identified the tissues based on trichrome histology and pathology. The difference between automated and manual segmentation was 51.78 +/- 50.96 μm. Experiments showed that the attenuation coefficients of dense collagen were significantly different from other tissue types (P < 0.05, ANOVA). Importantly, myocardial fibrosis tissues were different from normal myocardium in entropy and kurtosis. The tissue types were classified with an accuracy of 84%. The results show good agreements with histology.

  12. Automated coronary artery calcification detection on low-dose chest CT images

    Science.gov (United States)

    Xie, Yiting; Cham, Matthew D.; Henschke, Claudia; Yankelevitz, David; Reeves, Anthony P.

    2014-03-01

    Coronary artery calcification (CAC) measurement from low-dose CT images can be used to assess the risk of coronary artery disease. A fully automatic algorithm to detect and measure CAC from low-dose non-contrast, non-ECG-gated chest CT scans is presented. Based on the automatically detected CAC, the Agatston score (AS), mass score and volume score were computed. These were compared with scores obtained manually from standard-dose ECG-gated scans and low-dose un-gated scans of the same patient. The automatic algorithm segments the heart region based on other pre-segmented organs to provide a coronary region mask. The mitral valve and aortic valve calcification is identified and excluded. All remaining voxels greater than 180HU within the mask region are considered as CAC candidates. The heart segmentation algorithm was evaluated on 400 non-contrast cases with both low-dose and regular dose CT scans. By visual inspection, 371 (92.8%) of the segmentations were acceptable. The automated CAC detection algorithm was evaluated on 41 low-dose non-contrast CT scans. Manual markings were performed on both low-dose and standard-dose scans for these cases. Using linear regression, the correlation of the automatic AS with the standard-dose manual scores was 0.86; with the low-dose manual scores the correlation was 0.91. Standard risk categories were also computed. The automated method risk category agreed with manual markings of gated scans for 24 cases while 15 cases were 1 category off. For low-dose scans, the automatic method agreed with 33 cases while 7 cases were 1 category off.

  13. An automated classification system for the differentiation of obstructive lung diseases based on the textural analysis of HRCT images

    Energy Technology Data Exchange (ETDEWEB)

    Park, Seong Hoon; Seo, Joon Beom; Kim, Nam Kug; Lee, Young Kyung; Kim, Song Soo; Chae, Eun Jin [University of Ulsan, College of Medicine, Asan Medical Center, Seoul (Korea, Republic of); Lee, June Goo [Seoul National University College of Medicine, Seoul (Korea, Republic of)

    2007-07-15

    To develop an automated classification system for the differentiation of obstructive lung diseases based on the textural analysis of HRCT images, and to evaluate the accuracy and usefulness of the system. For textural analysis, histogram features, gradient features, run length encoding, and a co-occurrence matrix were employed. A Bayesian classifier was used for automated classification. The images (image number n = 256) were selected from the HRCT images obtained from 17 healthy subjects (n = 67), 26 patients with bronchiolitis obliterans (n = 70), 28 patients with mild centrilobular emphysema (n = 65), and 21 patients with panlobular emphysema or severe centrilobular emphysema (n = 63). An five-fold cross-validation method was used to assess the performance of the system. Class-specific sensitivities were analyzed and the overall accuracy of the system was assessed with kappa statistics. The sensitivity of the system for each class was as follows: normal lung 84.9%, bronchiolitis obliterans 83.8%, mild centrilobular emphysema 77.0%, and panlobular emphysema or severe centrilobular emphysema 95.8%. The overall performance for differentiating each disease and the normal lung was satisfactory with a kappa value of 0.779. An automated classification system for the differentiation between obstructive lung diseases based on the textural analysis of HRCT images was developed. The proposed system discriminates well between the various obstructive lung diseases and the normal lung.

  14. Comparison of Automated Image-Based Grain Sizing to Standard Pebble Count Methods

    Science.gov (United States)

    Strom, K. B.

    2009-12-01

    This study explores the use of an automated, image-based method for characterizing grain-size distributions (GSDs) of exposed, open-framework gravel beds. This was done by comparing the GSDs measured with an image-based method to distributions obtained with two pebble-count methods. Selection of grains for the two pebble-count methods was carried out using a gridded sampling frame and the heel-to-toe Wolman walk method at six field sites. At each site, 500-partcle pebble-count samples were collected with each of the two pebble-count methods and digital images were systematically collected over the same sampling area. For the methods used, the pebble counts collected with the gridded sampling frame were assumed to be the most accurate representations of the true grain-size population, and results from the image-based method were compared to the grid derived GSDs for accuracy estimates; comparisons between the grid and Wolman walk methods were conducted to give an indication of possible variation between commonly used methods for each particular field site. Comparison of grain sizes were made at two spatial scales. At the larger scale, results from the image-based method were integrated over the sampling area required to collect the 500-particle pebble-count samples. At the smaller sampling scale, the image derived GSDs were compared to those from 100-particle, pebble-count samples obtained with the gridded sampling frame. The comparisons show that the image-based method performed reasonably well on five of the six study sites. For those five sites, the image-based method slightly underestimate all grain-size percentiles relative to the pebble counts collected with the gridded sampling frame. The average bias for Ψ5, Ψ50, and Ψ95 between the image and grid count methods at the larger sampling scale was 0.07Ψ, 0.04Ψ, and 0.19Ψ respectively; at the smaller sampling scale the average bias was 0.004Ψ, 0.03Ψ, and 0.18Ψ respectively. The average bias between the

  15. Optic disc boundary segmentation from diffeomorphic demons registration of monocular fundus image sequences versus 3D visualization of stereo fundus image pairs for automated early stage glaucoma assessment

    Science.gov (United States)

    Gatti, Vijay; Hill, Jason; Mitra, Sunanda; Nutter, Brian

    2014-03-01

    Despite the current availability in resource-rich regions of advanced technologies in scanning and 3-D imaging in current ophthalmology practice, world-wide screening tests for early detection and progression of glaucoma still consist of a variety of simple tools, including fundus image-based parameters such as CDR (cup to disc diameter ratio) and CAR (cup to disc area ratio), especially in resource -poor regions. Reliable automated computation of the relevant parameters from fundus image sequences requires robust non-rigid registration and segmentation techniques. Recent research work demonstrated that proper non-rigid registration of multi-view monocular fundus image sequences could result in acceptable segmentation of cup boundaries for automated computation of CAR and CDR. This research work introduces a composite diffeomorphic demons registration algorithm for segmentation of cup boundaries from a sequence of monocular images and compares the resulting CAR and CDR values with those computed manually by experts and from 3-D visualization of stereo pairs. Our preliminary results show that the automated computation of CDR and CAR from composite diffeomorphic segmentation of monocular image sequences yield values comparable with those from the other two techniques and thus may provide global healthcare with a cost-effective yet accurate tool for management of glaucoma in its early stage.

  16. Evaluation of a software package for automated quality assessment of contrast detail images--comparison with subjective visual assessment.

    Science.gov (United States)

    Pascoal, A; Lawinski, C P; Honey, I; Blake, P

    2005-12-07

    Contrast detail analysis is commonly used to assess image quality (IQ) associated with diagnostic imaging systems. Applications include routine assessment of equipment performance and optimization studies. Most frequently, the evaluation of contrast detail images involves human observers visually detecting the threshold contrast detail combinations in the image. However, the subjective nature of human perception and the variations in the decision threshold pose limits to the minimum image quality variations detectable with reliability. Objective methods of assessment of image quality such as automated scoring have the potential to overcome the above limitations. A software package (CDRAD analyser) developed for automated scoring of images produced with the CDRAD test object was evaluated. Its performance to assess absolute and relative IQ was compared with that of an average observer. Results show that the software does not mimic the absolute performance of the average observer. The software proved more sensitive and was able to detect smaller low-contrast variations. The observer's performance was superior to the software's in the detection of smaller details. Both scoring methods showed frequent agreement in the detection of image quality variations resulting from changes in kVp and KERMA(detector), which indicates the potential to use the software CDRAD analyser for assessment of relative IQ.

  17. Evaluation of a software package for automated quality assessment of contrast detail images-comparison with subjective visual assessment

    Energy Technology Data Exchange (ETDEWEB)

    Pascoal, A [Medical Engineering and Physics, King' s College London, Faraday Building Denmark Hill, London SE5 8RX (Denmark); Lawinski, C P [KCARE - King' s Centre for Assessment of Radiological Equipment, King' s College Hospital, Faraday Building Denmark Hill, London SE5 8RX (Denmark); Honey, I [KCARE - King' s Centre for Assessment of Radiological Equipment, King' s College Hospital, Faraday Building Denmark Hill, London SE5 8RX (Denmark); Blake, P [KCARE - King' s Centre for Assessment of Radiological Equipment, King' s College Hospital, Faraday Building Denmark Hill, London SE5 8RX (Denmark)

    2005-12-07

    Contrast detail analysis is commonly used to assess image quality (IQ) associated with diagnostic imaging systems. Applications include routine assessment of equipment performance and optimization studies. Most frequently, the evaluation of contrast detail images involves human observers visually detecting the threshold contrast detail combinations in the image. However, the subjective nature of human perception and the variations in the decision threshold pose limits to the minimum image quality variations detectable with reliability. Objective methods of assessment of image quality such as automated scoring have the potential to overcome the above limitations. A software package (CDRAD analyser) developed for automated scoring of images produced with the CDRAD test object was evaluated. Its performance to assess absolute and relative IQ was compared with that of an average observer. Results show that the software does not mimic the absolute performance of the average observer. The software proved more sensitive and was able to detect smaller low-contrast variations. The observer's performance was superior to the software's in the detection of smaller details. Both scoring methods showed frequent agreement in the detection of image quality variations resulting from changes in kVp and KERMA{sub detector}, which indicates the potential to use the software CDRAD analyser for assessment of relative IQ.

  18. Investigation into Cloud Computing for More Robust Automated Bulk Image Geoprocessing

    Science.gov (United States)

    Brown, Richard B.; Smoot, James C.; Underwood, Lauren; Armstrong, C. Duane

    2012-01-01

    Geospatial resource assessments frequently require timely geospatial data processing that involves large multivariate remote sensing data sets. In particular, for disasters, response requires rapid access to large data volumes, substantial storage space and high performance processing capability. The processing and distribution of this data into usable information products requires a processing pipeline that can efficiently manage the required storage, computing utilities, and data handling requirements. In recent years, with the availability of cloud computing technology, cloud processing platforms have made available a powerful new computing infrastructure resource that can meet this need. To assess the utility of this resource, this project investigates cloud computing platforms for bulk, automated geoprocessing capabilities with respect to data handling and application development requirements. This presentation is of work being conducted by Applied Sciences Program Office at NASA-Stennis Space Center. A prototypical set of image manipulation and transformation processes that incorporate sample Unmanned Airborne System data were developed to create value-added products and tested for implementation on the "cloud". This project outlines the steps involved in creating and testing of open source software developed process code on a local prototype platform, and then transitioning this code with associated environment requirements into an analogous, but memory and processor enhanced cloud platform. A data processing cloud was used to store both standard digital camera panchromatic and multi-band image data, which were subsequently subjected to standard image processing functions such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), band stacking, reprojection, and other similar type data processes. Cloud infrastructure service providers were evaluated by taking these locally tested processing functions, and then

  19. Single cell-resolution western blotting.

    Science.gov (United States)

    Kang, Chi-Chih; Yamauchi, Kevin A; Vlassakis, Julea; Sinkala, Elly; Duncombe, Todd A; Herr, Amy E

    2016-08-01

    This protocol describes how to perform western blotting on individual cells to measure cell-to-cell variation in protein expression levels and protein state. Like conventional western blotting, single-cell western blotting (scWB) is particularly useful for protein targets that lack selective antibodies (e.g., isoforms) and in cases in which background signal from intact cells is confounding. scWB is performed on a microdevice that comprises an array of microwells molded in a thin layer of a polyacrylamide gel (PAG). The gel layer functions as both a molecular sieving matrix during PAGE and a blotting scaffold during immunoprobing. scWB involves five main stages: (i) gravity settling of cells into microwells; (ii) chemical lysis of cells in each microwell; (iii) PAGE of each single-cell lysate; (iv) exposure of the gel to UV light to blot (immobilize) proteins to the gel matrix; and (v) in-gel immunoprobing of immobilized proteins. Multiplexing can be achieved by probing with antibody cocktails and using antibody stripping/reprobing techniques, enabling detection of 10+ proteins in each cell. We also describe microdevice fabrication for both uniform and pore-gradient microgels. To extend in-gel immunoprobing to gels of small pore size, we describe an optional gel de-cross-linking protocol for more effective introduction of antibodies into the gel layer. Once the microdevice has been fabricated, the assay can be completed in 4-6 h by microfluidic novices and it generates high-selectivity, multiplexed data from single cells. The technique is relevant when direct measurement of proteins in single cells is needed, with applications spanning the fundamental biosciences to applied biomedicine.

  20. Emerging single-cell technologies in immunology.

    Science.gov (United States)

    Herderschee, Jacobus; Fenwick, Craig; Pantaleo, Giuseppe; Roger, Thierry; Calandra, Thierry

    2015-07-01

    During evolution, the immune system has diversified to protect the host from the extremely wide array of possible pathogens. Until recently, immune responses were dissected by use of global approaches and bulk tools, averaging responses across samples and potentially missing particular contributions of individual cells. This is a strongly limiting factor, considering that initial immune responses are likely to be triggered by a restricted number of cells at the vanguard of host defenses. The development of novel, single-cell technologies is a major innovation offering great promise for basic and translational immunology with the potential to overcome some of the limitations of traditional research tools, such as polychromatic flow cytometry or microscopy-based methods. At the transcriptional level, much progress has been made in the fields of microfluidics and single-cell RNA sequencing. At the protein level, mass cytometry already allows the analysis of twice as many parameters as flow cytometry. In this review, we explore the basis and outcome of immune-cell diversity, how genetically identical cells become functionally different, and the consequences for the exploration of host-immune defense responses. We will highlight the advantages, trade-offs, and potential pitfalls of emerging, single-cell-based technologies and how they provide unprecedented detail of immune responses.

  1. Optimization of genetic analysis for single cell

    Directory of Open Access Journals (Sweden)

    hussein mouawia

    2012-03-01

    Full Text Available The molecular genetic analysis of microdissected cells by laser, a method for selecting a starting material of pure DNA or RNA uncontaminated. Our study focuses on technical pre-PCR (polymerase chain reaction for the amplification of DNA from a single cell (leukocyte isolated from human blood after laser microdissection and aims to optimize the yield of DNA extracted of this cell to be amplified without errors and provide reliable genetic analyzes. This study has allowed us to reduce the duration of cell lysis in order to perform the step of expanding genomic PEP (primer extension preamplification directly after lysis the same day and the quality of genomic amplification and eliminate purification step of the product PEP, step with a risk of contamination and risk of loss of genetic material related to manipulation. This approach has shown that the combination of at least 3 STR (short tandem repeat markers for genetic analysis of single cell improves the efficiency and accuracy of PCR and minimizes the loss of allele (allele drop out; ADO. This protocol can be applied to large scale and an effective means suitable for genetic testing for molecular diagnostic from isolated single cell (cancerous - fetal.

  2. Analysis of mitochondria isolated from single cells.

    Science.gov (United States)

    Johnson, Ryan D; Navratil, Marian; Poe, Bobby G; Xiong, Guohua; Olson, Karen J; Ahmadzadeh, Hossein; Andreyev, Dmitry; Duffy, Ciarán F; Arriaga, Edgar A

    2007-01-01

    Bulk studies are not suitable to describe and study cell-to-cell variation, which is of high importance in biological processes such as embryogenesis, tissue differentiation, and disease. Previously, capillary electrophoresis with laser-induced fluorescence detection (CE-LIF) was used to measure the properties of organelles isolated from millions of cells. As such, these bulk measurements reported average properties for the organelles of cell populations. Similar measurements for organelles released from single cells would be highly relevant to describe the subcellular variations among cells. Toward this goal, here we introduce an approach to analyze the mitochondria released from single mammalian cells. Osteosarcoma 143B cells are labeled with either the fluorescent mitochondrion-specific 10-N-nonyl acridine orange (NAO) or via expression of the fluorescent protein DsRed2. Subsequently, a single cell is introduced into the CE-LIF capillary where the organelles are released by a combined treatment of digitonin and trypsin. After this treatment, an electric field is applied and the released organelles electromigrate toward the LIF detector. From an electropherogram, the number of detected events per cell, their individual electrophoretic mobilities, and their individual fluorescence intensities are calculated. The results obtained from DsRed2 labeling, which is retained in intact mitochondria, and NAO labeling, which labels all mitochondria, are the basis for discussion of the strengths and limitations of this single-cell approach.

  3. Automated sub-5 nm image registration in integrated correlative fluorescence and electron microscopy using cathodoluminescence pointers

    Science.gov (United States)

    Haring, Martijn T.; Liv, Nalan; Zonnevylle, A. Christiaan; Narvaez, Angela C.; Voortman, Lenard M.; Kruit, Pieter; Hoogenboom, Jacob P.

    2017-01-01

    In the biological sciences, data from fluorescence and electron microscopy is correlated to allow fluorescence biomolecule identification within the cellular ultrastructure and/or ultrastructural analysis following live-cell imaging. High-accuracy (sub-100 nm) image overlay requires the addition of fiducial markers, which makes overlay accuracy dependent on the number of fiducials present in the region of interest. Here, we report an automated method for light-electron image overlay at high accuracy, i.e. below 5 nm. Our method relies on direct visualization of the electron beam position in the fluorescence detection channel using cathodoluminescence pointers. We show that image overlay using cathodoluminescence pointers corrects for image distortions, is independent of user interpretation, and does not require fiducials, allowing image correlation with molecular precision anywhere on a sample. PMID:28252673

  4. Automated sub-5 nm image registration in integrated correlative fluorescence and electron microscopy using cathodoluminescence pointers

    Science.gov (United States)

    Haring, Martijn T.; Liv, Nalan; Zonnevylle, A. Christiaan; Narvaez, Angela C.; Voortman, Lenard M.; Kruit, Pieter; Hoogenboom, Jacob P.

    2017-03-01

    In the biological sciences, data from fluorescence and electron microscopy is correlated to allow fluorescence biomolecule identification within the cellular ultrastructure and/or ultrastructural analysis following live-cell imaging. High-accuracy (sub-100 nm) image overlay requires the addition of fiducial markers, which makes overlay accuracy dependent on the number of fiducials present in the region of interest. Here, we report an automated method for light-electron image overlay at high accuracy, i.e. below 5 nm. Our method relies on direct visualization of the electron beam position in the fluorescence detection channel using cathodoluminescence pointers. We show that image overlay using cathodoluminescence pointers corrects for image distortions, is independent of user interpretation, and does not require fiducials, allowing image correlation with molecular precision anywhere on a sample.

  5. Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images.

    Science.gov (United States)

    Jaccard, Nicolas; Griffin, Lewis D; Keser, Ana; Macown, Rhys J; Super, Alexandre; Veraitch, Farlan S; Szita, Nicolas

    2014-03-01

    The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non-invasive determination of these characteristics. We present an image-processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (Source-code for MATLAB and ImageJ is freely available under a permissive open-source license.

  6. Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images

    Directory of Open Access Journals (Sweden)

    Hardy Craig Hall

    2016-02-01

    Full Text Available While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to 1 segment radial plant organs into individual cells, 2 classify cells into cell type categories based upon random forest classification, 3 divide each cell into sub-regions, and 4 quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types.

  7. Automated gas bubble imaging at sea floor – a new method of in situ gas flux quantification

    Directory of Open Access Journals (Sweden)

    K. Thomanek

    2010-02-01

    Full Text Available Photo-optical systems are common in marine sciences and have been extensively used in coastal and deep-sea research. However, due to technical limitations in the past photo images had to be processed manually or semi-automatically. Recent advances in technology have rapidly improved image recording, storage and processing capabilities which are used in a new concept of automated in situ gas quantification by photo-optical detection. The design for an in situ high-speed image acquisition and automated data processing system is reported ("Bubblemeter". New strategies have been followed with regards to back-light illumination, bubble extraction, automated image processing and data management. This paper presents the design of the novel method, its validation procedures and calibration experiments. The system will be positioned and recovered from the sea floor using a remotely operated vehicle (ROV. It is able to measure bubble flux rates up to 10 L/min with a maximum error of 33% for worst case conditions. The Bubblemeter has been successfully deployed at a water depth of 1023 m at the Makran accretionary prism offshore Pakistan during a research expedition with R/V Meteor in November 2007.

  8. Automated MALDI matrix deposition method with inkjet printing for imaging mass spectrometry.

    Science.gov (United States)

    Baluya, Dodge L; Garrett, Timothy J; Yost, Richard A

    2007-09-01

    Careful matrix deposition on tissue samples for matrix-assisted laser desorption/ionization (MALDI) is critical for producing reproducible analyte ion signals. Traditional methods for matrix deposition are often considered an art rather than a science, with significant sample-to-sample variability. Here we report an automated method for matrix deposition, employing a desktop inkjet printer (printer tray, designed to hold CDs and DVDs, was modified to hold microscope slides. Empty ink cartridges were filled with MALDI matrix solutions, including DHB in methanol/water (70:30) at concentrations up to 40 mg/mL. Various samples (including rat brain tissue sections and standards of small drug molecules) were prepared using three deposition methods (electrospray, airbrush, inkjet). A linear ion trap equipped with an intermediate-pressure MALDI source was used for analyses. Optical microscopic examination showed that matrix crystals were formed evenly across the sample. There was minimal background signal after storing the matrix in the cartridges over a 6-month period. Overall, the mass spectral images gathered from inkjet-printed tissue specimens were of better quality and more reproducible than from specimens prepared by the electrospray and airbrush methods.

  9. Automated MALDI Matrix Coating System for Multiple Tissue Samples for Imaging Mass Spectrometry

    Science.gov (United States)

    Mounfield, William P.; Garrett, Timothy J.

    2012-03-01

    Uniform matrix deposition on tissue samples for matrix-assisted laser desorption/ionization (MALDI) is key for reproducible analyte ion signals. Current methods often result in nonhomogenous matrix deposition, and take time and effort to produce acceptable ion signals. Here we describe a fully-automated method for matrix deposition using an enclosed spray chamber and spray nozzle for matrix solution delivery. A commercial air-atomizing spray nozzle was modified and combined with solenoid controlled valves and a Programmable Logic Controller (PLC) to control and deliver the matrix solution. A spray chamber was employed to contain the nozzle, sample, and atomized matrix solution stream, and to prevent any interference from outside conditions as well as allow complete control of the sample environment. A gravity cup was filled with MALDI matrix solutions, including DHB in chloroform/methanol (50:50) at concentrations up to 60 mg/mL. Various samples (including rat brain tissue sections) were prepared using two deposition methods (spray chamber, inkjet). A linear ion trap equipped with an intermediate-pressure MALDI source was used for analyses. Optical microscopic examination showed a uniform coating of matrix crystals across the sample. Overall, the mass spectral images gathered from tissues coated using the spray chamber system were of better quality and more reproducible than from tissue specimens prepared by the inkjet deposition method.

  10. Automated midline shift and intracranial pressure estimation based on brain CT images.

    Science.gov (United States)

    Chen, Wenan; Belle, Ashwin; Cockrell, Charles; Ward, Kevin R; Najarian, Kayvan

    2013-04-13

    In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.

  11. Automated foveola localization in retinal 3D-OCT images using structural support vector machine prediction.

    Science.gov (United States)

    Liu, Yu-Ying; Ishikawa, Hiroshi; Chen, Mei; Wollstein, Gadi; Schuman, Joel S; Rehg, James M

    2012-01-01

    We develop an automated method to determine the foveola location in macular 3D-OCT images in either healthy or pathological conditions. Structural Support Vector Machine (S-SVM) is trained to directly predict the location of the foveola, such that the score at the ground truth position is higher than that at any other position by a margin scaling with the associated localization loss. This S-SVM formulation directly minimizes the empirical risk of localization error, and makes efficient use of all available training data. It deals with the localization problem in a more principled way compared to the conventional binary classifier learning that uses zero-one loss and random sampling of negative examples. A total of 170 scans were collected for the experiment. Our method localized 95.1% of testing scans within the anatomical area of the foveola. Our experimental results show that the proposed method can effectively identify the location of the foveola, facilitating diagnosis around this important landmark.

  12. Experimental saltwater intrusion in coastal aquifers using automated image analysis: Applications to homogeneous aquifers

    Science.gov (United States)

    Robinson, G.; Ahmed, Ashraf A.; Hamill, G. A.

    2016-07-01

    This paper presents the applications of a novel methodology to quantify saltwater intrusion parameters in laboratory-scale experiments. The methodology uses an automated image analysis procedure, minimising manual inputs and the subsequent systematic errors that can be introduced. This allowed the quantification of the width of the mixing zone which is difficult to measure in experimental methods that are based on visual observations. Glass beads of different grain sizes were tested for both steady-state and transient conditions. The transient results showed good correlation between experimental and numerical intrusion rates. The experimental intrusion rates revealed that the saltwater wedge reached a steady state condition sooner while receding than advancing. The hydrodynamics of the experimental mixing zone exhibited similar traits; a greater increase in the width of the mixing zone was observed in the receding saltwater wedge, which indicates faster fluid velocities and higher dispersion. The angle of intrusion analysis revealed the formation of a volume of diluted saltwater at the toe position when the saltwater wedge is prompted to recede. In addition, results of different physical repeats of the experiment produced an average coefficient of variation less than 0.18 of the measured toe length and width of the mixing zone.

  13. Automated collection of imaging and phenotypic data to centralized and distributed data repositories

    Directory of Open Access Journals (Sweden)

    Margaret D King

    2014-06-01

    Full Text Available Accurate data collection at the ground level is vital to the integrity of neuroimaging research. Similarly important is the ability to connect and curate data in order to make it meaningful and sharable with other investigators. Collecting data, especially with several different modalities, can be time consuming and expensive. These issues have driven the development of automated collection of neuroimaging and clinical assessment data within COINS (Collaborative Informatics and Neuroimaging Suite. COINS is an end-to-end data management system. It provides a comprehensive platform for data collection, management, secure storage, and flexible data retrieval (Bockholt et al., 2010, Scott et al., 2011. Self Assessment (SAis an application embedded in the Assessment Manager tool in the COINS. It is an innovative tool that allows participants to fill out assessments via the web-based Participant Portal. It eliminates the need for paper collection and data entry by allowing participants to submit their assessments directly to COINS. After a queue has been created for the participant, they can access the Participant Portal via the internet to fill out their assessments. This allows them the flexibility to participate from home, a library, on site, etc. The collected data is stored in a PostgresSQL database at the Mind Research Network behind a firewall to protect sensitive data. An added benefit to using COINS is the ability to collect, store and share imaging data and assessment data with no interaction with outside tools or programs. All study data collected (imaging and assessment are stored and exported with a participant's unique subject identifier so there is no need to keep extra spreadsheets or databases to link and keep track of the data. There is a great need for data collection tools that limit human intervention and error. COINS aims to be a leader in database solutions for research studies collecting data from several different modalities

  14. Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing.

    Science.gov (United States)

    Li, Yanxin; Knoll, Joan H; Wilkins, Ruth C; Flegal, Farrah N; Rogan, Peter K

    2016-05-01

    Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automated DC detection by extracting features in Giemsa-stained metaphase chromosome images and classifying objects by machine learning (ML). DC detection involves (i) intensity thresholded segmentation of metaphase objects, (ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, (iii) determination of chromosome width and centreline, (iv) derivation of centromere candidates, and (v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). Sixteen features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2-4 Gy) radiation dose. Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, σ. At larger σ, PPV decreases and TPR increases. At high dose, for σ = 1.3, TPR = 0.52 and PPV = 0.83, while at σ = 1.6, the TPR = 0.65 and PPV = 0.72. At low dose and σ = 1.3, TPR = 0.67 and PPV = 0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures.

  15. Quantification of Eosinophilic Granule Protein Deposition in Biopsies of Inflammatory Skin Diseases by Automated Image Analysis of Highly Sensitive Immunostaining

    Directory of Open Access Journals (Sweden)

    Peter Kiehl

    1999-01-01

    Full Text Available Eosinophilic granulocytes are major effector cells in inflammation. Extracellular deposition of toxic eosinophilic granule proteins (EGPs, but not the presence of intact eosinophils, is crucial for their functional effect in situ. As even recent morphometric approaches to quantify the involvement of eosinophils in inflammation have been only based on cell counting, we developed a new method for the cell‐independent quantification of EGPs by image analysis of immunostaining. Highly sensitive, automated immunohistochemistry was done on paraffin sections of inflammatory skin diseases with 4 different primary antibodies against EGPs. Image analysis of immunostaining was performed by colour translation, linear combination and automated thresholding. Using strictly standardized protocols, the assay was proven to be specific and accurate concerning segmentation in 8916 fields of 520 sections, well reproducible in repeated measurements and reliable over 16 weeks observation time. The method may be valuable for the cell‐independent segmentation of immunostaining in other applications as well.

  16. Preparation of cell lines for single-cell analysis of transcriptional activation dynamics.

    Science.gov (United States)

    Rafalska-Metcalf, Ilona U; Janicki, Susan M

    2013-01-01

    Imaging molecularly defined regions of chromatin in single living cells during transcriptional activation has the potential to provide new insight into gene regulatory mechanisms. Here, we describe a method for isolating cell lines with multi-copy arrays of reporter transgenes, which can be used for real-time high-resolution imaging of transcriptional activation dynamics in single cells.

  17. Immunohistochemical Ki-67/KL1 double stains increase accuracy of Ki-67 indices in breast cancer and simplify automated image analysis

    DEFF Research Database (Denmark)

    Nielsen, Patricia S; Bentzer, Nina K; Jensen, Vibeke

    2014-01-01

    observers and automated image analysis. RESULTS: Indices were predominantly higher for single stains than double stains (P≤0.002), yet the difference between observers was statistically significant (PPearson correlation coefficient for manual and automated indices ranged from 0.......69 to 0.85 (Pcorrelating automated indices with tumor characteristics, for example, tumor size (P... stains, Ki-67 should be quantified on double stains to reach a higher accuracy. Automated indices correlated well with manual estimates and tumor characteristics, and they are thus possibly valuable tools in future exploration of Ki-67 in breast cancer....

  18. Automated quantification and sizing of unbranched filamentous cyanobacteria by model-based object-oriented image analysis.

    Science.gov (United States)

    Zeder, Michael; Van den Wyngaert, Silke; Köster, Oliver; Felder, Kathrin M; Pernthaler, Jakob

    2010-03-01

    Quantification and sizing of filamentous cyanobacteria in environmental samples or cultures are time-consuming and are often performed by using manual or semiautomated microscopic analysis. Automation of conventional image analysis is difficult because filaments may exhibit great variations in length and patchy autofluorescence. Moreover, individual filaments frequently cross each other in microscopic preparations, as deduced by modeling. This paper describes a novel approach based on object-oriented image analysis to simultaneously determine (i) filament number, (ii) individual filament lengths, and (iii) the cumulative filament length of unbranched cyanobacterial morphotypes in fluorescent microscope images in a fully automated high-throughput manner. Special emphasis was placed on correct detection of overlapping objects by image analysis and on appropriate coverage of filament length distribution by using large composite images. The method was validated with a data set for Planktothrix rubescens from field samples and was compared with manual filament tracing, the line intercept method, and the Utermöhl counting approach. The computer program described allows batch processing of large images from any appropriate source and annotation of detected filaments. It requires no user interaction, is available free, and thus might be a useful tool for basic research and drinking water quality control.

  19. Development and application of an automated analysis method for individual cerebral perfusion single photon emission tomography images

    CERN Document Server

    Cluckie, A J

    2001-01-01

    Neurological images may be analysed by performing voxel by voxel comparisons with a group of control subject images. An automated, 3D, voxel-based method has been developed for the analysis of individual single photon emission tomography (SPET) scans. Clusters of voxels are identified that represent regions of abnormal radiopharmaceutical uptake. Morphological operators are applied to reduce noise in the clusters, then quantitative estimates of the size and degree of the radiopharmaceutical uptake abnormalities are derived. Statistical inference has been performed using a Monte Carlo method that has not previously been applied to SPET scans, or for the analysis of individual images. This has been validated for group comparisons of SPET scans and for the analysis of an individual image using comparison with a group. Accurate statistical inference was obtained independent of experimental factors such as degrees of freedom, image smoothing and voxel significance level threshold. The analysis method has been eval...

  20. LOCALIZATION OF PALM DORSAL VEIN PATTERN USING IMAGE PROCESSING FOR AUTOMATED INTRA-VENOUS DRUG NEEDLE INSERTION

    Directory of Open Access Journals (Sweden)

    Mrs. Kavitha. R,

    2011-06-01

    Full Text Available Vein pattern in palms is a random mesh of interconnected and inter- wining blood vessels. This project is the application of vein detection concept to automate the drug delivery process. It dealswith extracting palm dorsal vein structures, which is a key procedure for selecting the optimal drug needle insertion point. Gray scale images obtained from a low cost IR-webcam are poor in contrast, and usually noisy which make an effective vein segmentation a great challenge. Here a new vein image segmentation method is introduced, based on enhancement techniques resolves the conflict between poor contrast vein image and good quality image segmentation. Gaussian filter is used to remove the high frequency noise in the image. The ultimate goal is to identify venous bifurcations and determine the insertion point for the needle in between their branches.

  1. Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models

    Science.gov (United States)

    Neubert, A.; Fripp, J.; Engstrom, C.; Schwarz, R.; Lauer, L.; Salvado, O.; Crozier, S.

    2012-12-01

    Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

  2. Using dual-energy x-ray imaging to enhance automated lung tumor tracking during real-time adaptive radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Menten, Martin J., E-mail: martin.menten@icr.ac.uk; Fast, Martin F.; Nill, Simeon; Oelfke, Uwe, E-mail: uwe.oelfke@icr.ac.uk [Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG (United Kingdom)

    2015-12-15

    Purpose: Real-time, markerless localization of lung tumors with kV imaging is often inhibited by ribs obscuring the tumor and poor soft-tissue contrast. This study investigates the use of dual-energy imaging, which can generate radiographs with reduced bone visibility, to enhance automated lung tumor tracking for real-time adaptive radiotherapy. Methods: kV images of an anthropomorphic breathing chest phantom were experimentally acquired and radiographs of actual lung cancer patients were Monte-Carlo-simulated at three imaging settings: low-energy (70 kVp, 1.5 mAs), high-energy (140 kVp, 2.5 mAs, 1 mm additional tin filtration), and clinical (120 kVp, 0.25 mAs). Regular dual-energy images were calculated by weighted logarithmic subtraction of high- and low-energy images and filter-free dual-energy images were generated from clinical and low-energy radiographs. The weighting factor to calculate the dual-energy images was determined by means of a novel objective score. The usefulness of dual-energy imaging for real-time tracking with an automated template matching algorithm was investigated. Results: Regular dual-energy imaging was able to increase tracking accuracy in left–right images of the anthropomorphic phantom as well as in 7 out of 24 investigated patient cases. Tracking accuracy remained comparable in three cases and decreased in five cases. Filter-free dual-energy imaging was only able to increase accuracy in 2 out of 24 cases. In four cases no change in accuracy was observed and tracking accuracy worsened in nine cases. In 9 out of 24 cases, it was not possible to define a tracking template due to poor soft-tissue contrast regardless of input images. The mean localization errors using clinical, regular dual-energy, and filter-free dual-energy radiographs were 3.85, 3.32, and 5.24 mm, respectively. Tracking success was dependent on tumor position, tumor size, imaging beam angle, and patient size. Conclusions: This study has highlighted the influence of

  3. Automated registration of freehand B-mode ultrasound and magnetic resonance imaging of the carotid arteries based on geometric features

    DEFF Research Database (Denmark)

    Carvalho, Diego D. B.; Arias Lorza, Andres Mauricio; Niessen, Wiro J.;

    2017-01-01

    An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual...... annotation of three seed points in US and MRI. The registration procedure starts with alignment of the lumen centerlines using a point-based registration algorithm. The resulting rigid transformation is used to initialize a rigid and subsequent non-rigid registration procedure that jointly aligns centerlines...

  4. Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network

    Directory of Open Access Journals (Sweden)

    Jacopo Aguzzi

    2011-11-01

    Full Text Available The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina, as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp., using a camera deployed in Saanich Inlet (103 m depth. For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters with Euclidean Distances (ED on Red-Green-Blue (RGB channels. The Scale-Invariant Feature Transform (SIFT features and Fourier Descriptors (FD of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA on Mean RGB (RGBv value for each object and Fourier Descriptors (RGBv+FD matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent

  5. Automated image analysis for the detection of benthic crustaceans and bacterial mat coverage using the VENUS undersea cabled network.

    Science.gov (United States)

    Aguzzi, Jacopo; Costa, Corrado; Robert, Katleen; Matabos, Marjolaine; Antonucci, Francesca; Juniper, S Kim; Menesatti, Paolo

    2011-01-01

    The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage

  6. Application of automated methodologies based on digital images for phenological behaviour analysis in Mediterranean species

    Science.gov (United States)

    Cesaraccio, Carla; Piga, Alessandra; Ventura, Andrea; Arca, Angelo; Duce, Pierpaolo; Granados, Joel

    2015-04-01

    The importance of phenological research for understanding the consequences of global environmental change on vegetation is highlighted in the most recent IPCC reports. Collecting time series of phenological events appears to be of crucial importance to better understand how vegetation systems respond to climatic regime fluctuations, and, consequently, to develop effective management and adaptation strategies. Vegetation monitoring based on "near-surface" remote sensing techniques have been proposed in recent researches. In particular, the use of digital cameras has become more common for phenological monitoring. Digital images provide spectral information in the red, green, and blue (RGB) wavelengths. Inflection points in seasonal variations of intensities of each color channel can be used to identify phenological events. In this research, an Automated Phenological Observation System (APOS), based on digital image sensors, was used for monitoring the phenological behavior of shrubland species in a Mediterranean site. Major species of the shrubland ecosystem that were analyzed were: Cistus monspeliensis L., Cistus incanus L., Rosmarinus officinalis L., Pistacia lentiscus L., and Pinus halepensis Mill. The system was developed under the INCREASE (an Integrated Network on Climate Change Research) EU-funded research infrastructure project, which is based upon large scale field experiments with non-intrusive climatic manipulations. Monitoring of phenological behavior was conducted during 2012-2014 years. To the end of retrieve phenological information from digital images, a routine of commands to process the digital image file using the program MATLAB (R2013b, The MathWorks, Natick, Mass.) was specifically created. The images of the dataset have been re-classified and renamed files according to the date and time of acquisition. The analysis was focused on regions of interest (ROIs) of the panoramas acquired, defined by the presence of the most representative species of

  7. A Novel Automated High-Content Analysis Workflow Capturing Cell Population Dynamics from Induced Pluripotent Stem Cell Live Imaging Data

    Science.gov (United States)

    Kerz, Maximilian; Folarin, Amos; Meleckyte, Ruta; Watt, Fiona M.; Dobson, Richard J.; Danovi, Davide

    2016-01-01

    Most image analysis pipelines rely on multiple channels per image with subcellular reference points for cell segmentation. Single-channel phase-contrast images are often problematic, especially for cells with unfavorable morphology, such as induced pluripotent stem cells (iPSCs). Live imaging poses a further challenge, because of the introduction of the dimension of time. Evaluations cannot be easily integrated with other biological data sets including analysis of endpoint images. Here, we present a workflow that incorporates a novel CellProfiler-based image analysis pipeline enabling segmentation of single-channel images with a robust R-based software solution to reduce the dimension of time to a single data point. These two packages combined allow robust segmentation of iPSCs solely on phase-contrast single-channel images and enable live imaging data to be easily integrated to endpoint data sets while retaining the dynamics of cellular responses. The described workflow facilitates characterization of the response of live-imaged iPSCs to external stimuli and definition of cell line–specific, phenotypic signatures. We present an efficient tool set for automated high-content analysis suitable for cells with challenging morphology. This approach has potentially widespread applications for human pluripotent stem cells and other cell types. PMID:27256155

  8. An Improved Method for Measuring Quantitative Resistance to the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis.

    Science.gov (United States)

    Stewart, Ethan L; Hagerty, Christina H; Mikaberidze, Alexey; Mundt, Christopher C; Zhong, Ziming; McDonald, Bruce A

    2016-07-01

    Zymoseptoria tritici causes Septoria tritici blotch (STB) on wheat. An improved method of quantifying STB symptoms was developed based on automated analysis of diseased leaf images made using a flatbed scanner. Naturally infected leaves (n = 949) sampled from fungicide-treated field plots comprising 39 wheat cultivars grown in Switzerland and 9 recombinant inbred lines (RIL) grown in Oregon were included in these analyses. Measures of quantitative resistance were percent leaf area covered by lesions, pycnidia size and gray value, and pycnidia density per leaf and lesion. These measures were obtained automatically with a batch-processing macro utilizing the image-processing software ImageJ. All phenotypes in both locations showed a continuous distribution, as expected for a quantitative trait. The trait distributions at both sites were largely overlapping even though the field and host environments were quite different. Cultivars and RILs could be assigned to two or more statistically different groups for each measured phenotype. Traditional visual assessments of field resistance were highly correlated with quantitative resistance measures based on image analysis for the Oregon RILs. These results show that automated image analysis provides a promising tool for assessing quantitative resistance to Z. tritici under field conditions.

  9. Towards an automated analysis of video-microscopy images of fungal morphogenesis

    Directory of Open Access Journals (Sweden)

    Diéguez-Uribeondo, Javier

    2005-06-01

    Full Text Available Fungal morphogenesis is an exciting field of cell biology and several mathematical models have been developed to describe it. These models require experimental evidences to be corroborated and, therefore, there is a continuous search for new microscopy and image analysis techniques. In this work, we have used a Canny-edge-detector based technique to automate the generation of hyphal profiles and calculation of morphogenetic parameters such as diameter, elongation rates and hyphoid fitness. The results show that the data obtained with this technique are similar to published data generated with manualbased tracing techniques and that have been carried out on the same species or genus. Thus, we show that application of edge detector-based technique to hyphal growth represents an efficient and accurate method to study hyphal morphogenesis. This represents the first step towards an automated analysis of videomicroscopy images of fungal morphogenesis.La morfogénesis de los hongos es un área de estudio de gran relevancia en la biología celular y en la que se han desarrollado varios modelos matemáticos. Los modelos matemáticos de procesos biológicos precisan de pruebas experimentales que apoyen y corroboren las predicciones teóricas y, por este motivo, existe una búsqueda continua de nuevas técnicas de microscopía y análisis de imágenes para su aplicación en el estudio del crecimiento celular. En este trabajo hemos utilizado una técnica basada en un detector de contornos llamado “Canny-edge-detectorâ€� con el objetivo de automatizar la generación de perfiles de hifas y el cálculo de parámetros morfogenéticos, tales como: el diámetro, la velocidad de elongación y el ajuste con el perfil hifoide, es decir, el perfil teórico de las hifas de los hongos. Los resultados obtenidos son similares a los datos publicados a partir de técnicas manuales de trazado de contornos, generados en la misma especie y género. De esta manera

  10. Application of Reflectance Transformation Imaging Technique to Improve Automated Edge Detection in a Fossilized Oyster Reef

    Science.gov (United States)

    Djuricic, Ana; Puttonen, Eetu; Harzhauser, Mathias; Dorninger, Peter; Székely, Balázs; Mandic, Oleg; Nothegger, Clemens; Molnár, Gábor; Pfeifer, Norbert

    2016-04-01

    The world's largest fossilized oyster reef is located in Stetten, Lower Austria excavated during field campaigns of the Natural History Museum Vienna between 2005 and 2008. It is studied in paleontology to learn about change in climate from past events. In order to support this study, a laser scanning and photogrammetric campaign was organized in 2014 for 3D documentation of the large and complex site. The 3D point clouds and high resolution images from this field campaign are visualized by photogrammetric methods in form of digital surface models (DSM, 1 mm resolution) and orthophoto (0.5 mm resolution) to help paleontological interpretation of data. Due to size of the reef, automated analysis techniques are needed to interpret all digital data obtained from the field. One of the key components in successful automation is detection of oyster shell edges. We have tested Reflectance Transformation Imaging (RTI) to visualize the reef data sets for end-users through a cultural heritage viewing interface (RTIViewer). The implementation includes a Lambert shading method to visualize DSMs derived from terrestrial laser scanning using scientific software OPALS. In contrast to shaded RTI no devices consisting of a hardware system with LED lights, or a body to rotate the light source around the object are needed. The gray value for a given shaded pixel is related to the angle between light source and the normal at that position. Brighter values correspond to the slope surfaces facing the light source. Increasing of zenith angle results in internal shading all over the reef surface. In total, oyster reef surface contains 81 DSMs with 3 m x 2 m each. Their surface was illuminated by moving the virtual sun every 30 degrees (12 azimuth angles from 20-350) and every 20 degrees (4 zenith angles from 20-80). This technique provides paleontologists an interactive approach to virtually inspect the oyster reef, and to interpret the shell surface by changing the light source direction

  11. Microfabricated devices for single cell analysis

    Science.gov (United States)

    Gao, Yuanfang

    BioMEMS or lab-on-a-chip technology is promising technology and enables the possibility of microchip devices with higher throughput or better performance for single cell analysis. We have designed and fabricated microdevices for single cell analysis, with impedance based device for fast cell screening and microchannel based flow systems for high throughput, high time resolution quantal exocytosis measurement with automatic cell positioning and reusability. The automatic cell positioning is realized by differential forces of fluidic dynamics. Microelectrodes are patterned at automatic trap positions for electrochemical detection quantal release of hormones like catecholamines secreted by cells. We also developed diamond-like carbon (DLC) microelectrodes onto chip device for low noise exocytosis measurement. The DLC microelectrodes were deposited by magnetron sputtering process with nitrogen doping and a bottom ITO conductive layer. Test results show the developed DLC can detect exocytosis with low noise and a stable background current which are comparable to that of carbon-fiber electrodes. They are batch producible at low cost and can realize high-throughput on-chip measurement of quantal exocytosis. The technology developed in this research can have wide ranging applications in fields such as electrophysiology, cell based sensors, high throughput screening of new drug development.

  12. Detecting Antigen-Specific T Cell Responses: From Bulk Populations to Single Cells

    Directory of Open Access Journals (Sweden)

    Chansavath Phetsouphanh

    2015-08-01

    Full Text Available A new generation of sensitive T cell-based assays facilitates the direct quantitation and characterization of antigen-specific T cell responses. Single-cell analyses have focused on measuring the quality and breadth of a response. Accumulating data from these studies demonstrate that there is considerable, previously-unrecognized, heterogeneity. Standard assays, such as the ICS, are often insufficient for characterization of rare subsets of cells. Enhanced flow cytometry with imaging capabilities enables the determination of cell morphology, as well as the spatial localization of the protein molecules within a single cell. Advances in both microfluidics and digital PCR have improved the efficiency of single-cell sorting and allowed multiplexed gene detection at the single-cell level. Delving further into the transcriptome of single-cells using RNA-seq is likely to reveal the fine-specificity of cellular events such as alternative splicing (i.e., splice variants and allele-specific expression, and will also define the roles of new genes. Finally, detailed analysis of clonally related antigen-specific T cells using single-cell TCR RNA-seq will provide information on pathways of differentiation of memory T cells. With these state of the art technologies the transcriptomics and genomics of Ag-specific T cells can be more definitively elucidated.

  13. A rapid and automated relocation method of an AFM probe for high-resolution imaging

    Science.gov (United States)

    Zhou, Peilin; Yu, Haibo; Shi, Jialin; Jiao, Niandong; Wang, Zhidong; Wang, Yuechao; Liu, Lianqing

    2016-09-01

    The atomic force microscope (AFM) is one of the most powerful tools for high-resolution imaging and high-precision positioning for nanomanipulation. The selection of the scanning area of the AFM depends on the use of the optical microscope. However, the resolution of an optical microscope is generally no larger than 200 nm owing to wavelength limitations of visible light. Taking into consideration the two determinants of relocation—relative angular rotation and positional offset between the AFM probe and nano target—it is therefore extremely challenging to precisely relocate the AFM probe to the initial scan/manipulation area for the same nano target after the AFM probe has been replaced, or after the sample has been moved. In this paper, we investigate a rapid automated relocation method for the nano target of an AFM using a coordinate transformation. The relocation process is both simple and rapid; moreover, multiple nano targets can be relocated by only identifying a pair of reference points. It possesses a centimeter-scale location range and nano-scale precision. The main advantages of this method are that it overcomes the limitations associated with the resolution of optical microscopes, and that it is label-free on the target areas, which means that it does not require the use of special artificial markers on the target sample areas. Relocation experiments using nanospheres, DNA, SWCNTs, and nano patterns amply demonstrate the practicality and efficiency of the proposed method, which provides technical support for mass nanomanipulation and detection based on AFM for multiple nano targets that are widely distributed in a large area.

  14. Automated integer programming based separation of arteries and veins from thoracic CT images.

    Science.gov (United States)

    Payer, Christian; Pienn, Michael; Bálint, Zoltán; Shekhovtsov, Alexander; Talakic, Emina; Nagy, Eszter; Olschewski, Andrea; Olschewski, Horst; Urschler, Martin

    2016-12-01

    Automated computer-aided analysis of lung vessels has shown to yield promising results for non-invasive diagnosis of lung diseases. To detect vascular changes which affect pulmonary arteries and veins differently, both compartments need to be identified. We present a novel, fully automatic method that separates arteries and veins in thoracic computed tomography images, by combining local as well as global properties of pulmonary vessels. We split the problem into two parts: the extraction of multiple distinct vessel subtrees, and their subsequent labeling into arteries and veins. Subtree extraction is performed with an integer program (IP), based on local vessel geometry. As naively solving this IP is time-consuming, we show how to drastically reduce computational effort by reformulating it as a Markov Random Field. Afterwards, each subtree is labeled as either arterial or venous by a second IP, using two anatomical properties of pulmonary vessels: the uniform distribution of arteries and veins, and the parallel configuration and close proximity of arteries and bronchi. We evaluate algorithm performance by comparing the results with 25 voxel-based manual reference segmentations. On this dataset, we show good performance of the subtree extraction, consisting of very few non-vascular structures (median value: 0.9%) and merged subtrees (median value: 0.6%). The resulting separation of arteries and veins achieves a median voxel-based overlap of 96.3% with the manual reference segmentations, outperforming a state-of-the-art interactive method. In conclusion, our novel approach provides an opportunity to become an integral part of computer aided pulmonary diagnosis, where artery/vein separation is important.

  15. Three-Dimensional Reconstruction of the Bony Nasolacrimal Canal by Automated Segmentation of Computed Tomography Images.

    Directory of Open Access Journals (Sweden)

    Lucia Jañez-Garcia

    Full Text Available To apply a fully automated method to quantify the 3D structure of the bony nasolacrimal canal (NLC from CT scans whereby the size and main morphometric characteristics of the canal can be determined.Cross-sectional study.36 eyes of 18 healthy individuals.Using software designed to detect the boundaries of the NLC on CT images, 36 NLC reconstructions were prepared. These reconstructions were then used to calculate NLC volume. The NLC axis in each case was determined according to a polygonal model and to 2nd, 3rd and 4th degree polynomials. From these models, NLC sectional areas and length were determined. For each variable, descriptive statistics and normality tests (Kolmogorov-Smirnov and Shapiro-Wilk were established.Time for segmentation, NLC volume, axis, sectional areas and length.Mean processing time was around 30 seconds for segmenting each canal. All the variables generated were normally distributed. Measurements obtained using the four models polygonal, 2nd, 3rd and 4th degree polynomial, respectively, were: mean canal length 14.74, 14.3, 14.80, and 15.03 mm; mean sectional area 15.15, 11.77, 11.43, and 11.56 mm2; minimum sectional area 8.69, 7.62, 7.40, and 7.19 mm2; and mean depth of minimum sectional area (craniocaudal 7.85, 7.71, 8.19, and 8.08 mm.The method proposed automatically reconstructs the NLC on CT scans. Using these reconstructions, morphometric measurements can be calculated from NLC axis estimates based on polygonal and 2nd, 3rd and 4th polynomial models.

  16. A rapid and automated relocation method of an AFM probe for high-resolution imaging.

    Science.gov (United States)

    Zhou, Peilin; Yu, Haibo; Shi, Jialin; Jiao, Niandong; Wang, Zhidong; Wang, Yuechao; Liu, Lianqing

    2016-09-30

    The atomic force microscope (AFM) is one of the most powerful tools for high-resolution imaging and high-precision positioning for nanomanipulation. The selection of the scanning area of the AFM depends on the use of the optical microscope. However, the resolution of an optical microscope is generally no larger than 200 nm owing to wavelength limitations of visible light. Taking into consideration the two determinants of relocation-relative angular rotation and positional offset between the AFM probe and nano target-it is therefore extremely challenging to precisely relocate the AFM probe to the initial scan/manipulation area for the same nano target after the AFM probe has been replaced, or after the sample has been moved. In this paper, we investigate a rapid automated relocation method for the nano target of an AFM using a coordinate transformation. The relocation process is both simple and rapid; moreover, multiple nano targets can be relocated by only identifying a pair of reference points. It possesses a centimeter-scale location range and nano-scale precision. The main advantages of this method are that it overcomes the limitations associated with the resolution of optical microscopes, and that it is label-free on the target areas, which means that it does not require the use of special artificial markers on the target sample areas. Relocation experiments using nanospheres, DNA, SWCNTs, and nano patterns amply demonstrate the practicality and efficiency of the proposed method, which provides technical support for mass nanomanipulation and detection based on AFM for multiple nano targets that are widely distributed in a large area.

  17. Automated Thermal Image Processing for Detection and Classification of Birds and Bats - FY2012 Annual Report

    Energy Technology Data Exchange (ETDEWEB)

    Duberstein, Corey A.; Matzner, Shari; Cullinan, Valerie I.; Virden, Daniel J.; Myers, Joshua R.; Maxwell, Adam R.

    2012-09-01

    Surveying wildlife at risk from offshore wind energy development is difficult and expensive. Infrared video can be used to record birds and bats that pass through the camera view, but it is also time consuming and expensive to review video and determine what was recorded. We proposed to conduct algorithm and software development to identify and to differentiate thermally detected targets of interest that would allow automated processing of thermal image data to enumerate birds, bats, and insects. During FY2012 we developed computer code within MATLAB to identify objects recorded in video and extract attribute information that describes the objects recorded. We tested the efficiency of track identification using observer-based counts of tracks within segments of sample video. We examined object attributes, modeled the effects of random variability on attributes, and produced data smoothing techniques to limit random variation within attribute data. We also began drafting and testing methodology to identify objects recorded on video. We also recorded approximately 10 hours of infrared video of various marine birds, passerine birds, and bats near the Pacific Northwest National Laboratory (PNNL) Marine Sciences Laboratory (MSL) at Sequim, Washington. A total of 6 hours of bird video was captured overlooking Sequim Bay over a series of weeks. An additional 2 hours of video of birds was also captured during two weeks overlooking Dungeness Bay within the Strait of Juan de Fuca. Bats and passerine birds (swallows) were also recorded at dusk on the MSL campus during nine evenings. An observer noted the identity of objects viewed through the camera concurrently with recording. These video files will provide the information necessary to produce and test software developed during FY2013. The annotation will also form the basis for creation of a method to reliably identify recorded objects.

  18. LeafJ: an ImageJ plugin for semi-automated leaf shape measurement.

    Science.gov (United States)

    Maloof, Julin N; Nozue, Kazunari; Mumbach, Maxwell R; Palmer, Christine M

    2013-01-21

    High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review and recent examples). Leaves are the primary photosynthetic organ, and their size and shape vary developmentally and environmentally within a plant. For these reasons studies on leaf morphology require measurement of multiple parameters from numerous leaves, which is best done by semi-automated phenomics tools. Canopy shade is an important environmental cue that affects plant architecture and life history; the suite of responses is collectively called the shade avoidance syndrome (SAS). Among SAS responses, shade induced leaf petiole elongation and changes in blade area are particularly useful as indices. To date, leaf shape programs (e.g. SHAPE, LAMINA, LeafAnalyzer, LEAFPROCESSOR) can measure leaf outlines and categorize leaf shapes, but can not output petiole length. Lack of large-scale measurement systems of leaf petioles has inhibited phenomics approaches to SAS research. In this paper, we describe a newly developed ImageJ plugin, called LeafJ, which can rapidly measure petiole length and leaf blade parameters of the model plant Arabidopsis thaliana. For the occasional leaf that required manual correction of the petiole/leaf blade boundary we used a touch-screen tablet. Further, leaf cell shape and leaf cell numbers are important determinants of leaf size. Separate from LeafJ we also present a protocol for using a touch-screen tablet for measuring cell shape, area, and size. Our leaf trait measurement system is not limited to shade-avoidance research and will accelerate leaf phenotyping of many mutants and screening plants by leaf phenotyping.

  19. SU-C-304-04: A Compact Modular Computational Platform for Automated On-Board Imager Quality Assurance

    Energy Technology Data Exchange (ETDEWEB)

    Dolly, S [Washington University School of Medicine, Saint Louis, MO (United States); University of Missouri, Columbia, MO (United States); Cai, B; Chen, H; Anastasio, M; Sun, B; Yaddanapudi, S; Noel, C; Goddu, S; Mutic, S; Li, H [Washington University School of Medicine, Saint Louis, MO (United States); Tan, J [UTSouthwestern Medical Center, Dallas, TX (United States)

    2015-06-15

    Purpose: Traditionally, the assessment of X-ray tube output and detector positioning accuracy of on-board imagers (OBI) has been performed manually and subjectively with rulers and dosimeters, and typically takes hours to complete. In this study, we have designed a compact modular computational platform to automatically analyze OBI images acquired with in-house designed phantoms as an efficient and robust surrogate. Methods: The platform was developed as an integrated and automated image analysis-based platform using MATLAB for easy modification and maintenance. Given a set of images acquired with the in-house designed phantoms, the X-ray output accuracy was examined via cross-validation of the uniqueness and integration minimization of important image quality assessment metrics, while machine geometric and positioning accuracy were validated by utilizing pattern-recognition based image analysis techniques. Results: The platform input was a set of images of an in-house designed phantom. The total processing time is about 1–2 minutes. Based on the data acquired from three Varian Truebeam machines over the course of 3 months, the designed test validation strategy achieved higher accuracy than traditional methods. The kVp output accuracy can be verified within +/−2 kVp, the exposure accuracy within 2%, and exposure linearity with a coefficient of variation (CV) of 0.1. Sub-millimeter position accuracy was achieved for the lateral and longitudinal positioning tests, while vertical positioning accuracy within +/−2 mm was achieved. Conclusion: This new platform delivers to the radiotherapy field an automated, efficient, and stable image analysis-based procedure, for the first time, acting as a surrogate for traditional tests for LINAC OBI systems. It has great potential to facilitate OBI quality assurance (QA) with the assistance of advanced image processing techniques. In addition, it provides flexible integration of additional tests for expediting other OBI

  20. Automation of a high-speed imaging setup for differential viscosity measurements

    Science.gov (United States)

    Hurth, C.; Duane, B.; Whitfield, D.; Smith, S.; Nordquist, A.; Zenhausern, F.

    2013-12-01

    We present the automation of a setup previously used to assess the viscosity of pleural effusion samples and discriminate between transudates and exudates, an important first step in clinical diagnostics. The presented automation includes the design, testing, and characterization of a vacuum-actuated loading station that handles the 2 mm glass spheres used as sensors, as well as the engineering of electronic Printed Circuit Board (PCB) incorporating a microcontroller and their synchronization with a commercial high-speed camera operating at 10 000 fps. The hereby work therefore focuses on the instrumentation-related automation efforts as the general method and clinical application have been reported earlier [Hurth et al., J. Appl. Phys. 110, 034701 (2011)]. In addition, we validate the performance of the automated setup with the calibration for viscosity measurements using water/glycerol standard solutions and the determination of the viscosity of an "unknown" solution of hydroxyethyl cellulose.

  1. Automation of a high-speed imaging setup for differential viscosity measurements

    Energy Technology Data Exchange (ETDEWEB)

    Hurth, C.; Duane, B.; Whitfield, D.; Smith, S.; Nordquist, A.; Zenhausern, F. [Center for Applied Nanobioscience and Medicine, The University of Arizona College of Medicine, 425 N 5th Street, Phoenix, Arizona 85004 (United States)

    2013-12-28

    We present the automation of a setup previously used to assess the viscosity of pleural effusion samples and discriminate between transudates and exudates, an important first step in clinical diagnostics. The presented automation includes the design, testing, and characterization of a vacuum-actuated loading station that handles the 2 mm glass spheres used as sensors, as well as the engineering of electronic Printed Circuit Board (PCB) incorporating a microcontroller and their synchronization with a commercial high-speed camera operating at 10 000 fps. The hereby work therefore focuses on the instrumentation-related automation efforts as the general method and clinical application have been reported earlier [Hurth et al., J. Appl. Phys. 110, 034701 (2011)]. In addition, we validate the performance of the automated setup with the calibration for viscosity measurements using water/glycerol standard solutions and the determination of the viscosity of an “unknown” solution of hydroxyethyl cellulose.

  2. Optimized and Automated Radiosynthesis of [18F]DHMT for Translational Imaging of Reactive Oxygen Species with Positron Emission Tomography

    Directory of Open Access Journals (Sweden)

    Wenjie Zhang

    2016-12-01

    Full Text Available Reactive oxygen species (ROS play important roles in cell signaling and homeostasis. However, an abnormally high level of ROS is toxic, and is implicated in a number of diseases. Positron emission tomography (PET imaging of ROS can assist in the detection of these diseases. For the purpose of clinical translation of [18F]6-(4-((1-(2-fluoroethyl-1H-1,2,3-triazol-4-ylmethoxyphenyl-5-methyl-5,6-dihydrophenanthridine-3,8-diamine ([18F]DHMT, a promising ROS PET radiotracer, we first manually optimized the large-scale radiosynthesis conditions and then implemented them in an automated synthesis module. Our manual synthesis procedure afforded [18F]DHMT in 120 min with overall radiochemical yield (RCY of 31.6% ± 9.3% (n = 2, decay-uncorrected and specific activity of 426 ± 272 GBq/µmol (n = 2. Fully automated radiosynthesis of [18F]DHMT was achieved within 77 min with overall isolated RCY of 6.9% ± 2.8% (n = 7, decay-uncorrected and specific activity of 155 ± 153 GBq/µmol (n = 7 at the end of synthesis. This study is the first demonstration of producing 2-[18F]fluoroethyl azide by an automated module, which can be used for a variety of PET tracers through click chemistry. It is also the first time that [18F]DHMT was successfully tested for PET imaging in a healthy beagle dog.

  3. Simplified automated image analysis for detection and phenotyping of Mycobacterium tuberculosis on porous supports by monitoring growing microcolonies.

    Directory of Open Access Journals (Sweden)

    Alice L den Hertog

    Full Text Available BACKGROUND: Even with the advent of nucleic acid (NA amplification technologies the culture of mycobacteria for diagnostic and other applications remains of critical importance. Notably microscopic observed drug susceptibility testing (MODS, as opposed to traditional culture on solid media or automated liquid culture, has shown potential to both speed up and increase the provision of mycobacterial culture in high burden settings. METHODS: Here we explore the growth of Mycobacterial tuberculosis microcolonies, imaged by automated digital microscopy, cultured on a porous aluminium oxide (PAO supports. Repeated imaging during colony growth greatly simplifies "computer vision" and presumptive identification of microcolonies was achieved here using existing publically available algorithms. Our system thus allows the growth of individual microcolonies to be monitored and critically, also to change the media during the growth phase without disrupting the microcolonies. Transfer of identified microcolonies onto selective media allowed us, within 1-2 bacterial generations, to rapidly detect the drug susceptibility of individual microcolonies, eliminating the need for time consuming subculturing or the inoculation of multiple parallel cultures. SIGNIFICANCE: Monitoring the phenotype of individual microcolonies as they grow has immense potential for research, screening, and ultimately M. tuberculosis diagnostic applications. The method described is particularly appealing with respect to speed and automation.

  4. Nitrogen assimilation by single cells in hot springs

    Science.gov (United States)

    Poret-peterson, A. T.; Romaniello, S. J.; Bose, M.; Williams, P.; Elser, J. J.; Shock, E.; Anbar, A. D.; Hartnett, H. E.

    2012-12-01

    Microorganisms drive biogeochemical cycles and require nutrients, such as ammonium and nitrate, to function. As a result, following nutrient flows provides opportunities to study how microbial activity influences ecosystem-level processes. Most past measurements of microbial nutrient uptake rely on bulk measurements, which are informative but provide little information about heterogeneity among community members involved in elemental transformations, nor about possible effects of physiological state or taxonomic identity. Since microbial communities tend to be phylogenetically and physiologically diverse, it is reasonable to expect that community members will respond differently to nutrient addition. Here, we examine nitrogen assimilation (via addition of 15N-labeled ammonium or nitrate) in Yellowstone hot spring microbial communities. Using the NanoSIMS, we imaged cells at a very high spatial resolution (nanometer scale) necessary to determine 15N enrichments in single micron-sized cells. We compare the N isotopic enrichments observed in single cells to that determined in bulk sediments by standard isotope ratio mass spectrometry. NanoSIMS imaging of 56 individual cells from sediments of an acidic hot spring (pH 4.7, T=67oC) incubated with 15N-ammonium shows that about two-thirds of the cells (38) exhibited 15N-enrichment. Most cells had 15N enrichments from 0.39 to 0.91 atom %, while some cells were much more significantly enriched. Bulk analyses of sediments show that ammonium assimilation and nitrate assimilation readily occurred at this spring. These findings show that microbes in this hot spring may differentially take up ammonium, which may arise from a number of factors including differences in cellular N requirements, growth rates, and the ability to transport ammonium. This work represents some of the first single-cell isotopic measurements from an extreme environment. Efforts are underway to image sediment samples from other hot springs and to pair Nano

  5. Single-Cell and Single-Molecule Analysis of Gene Expression Regulation

    Science.gov (United States)

    Vera, Maria; Biswas, Jeetayu; Senecal, Adrien

    2016-01-01

    Recent advancements in single-cell and single-molecule imaging technologies have resolved biological processes in time and space that are fundamental to understanding the regulation of gene expression. Observations of single-molecule events in their cellular context have revealed highly dynamic aspects of transcriptional and post-transcriptional control in eukaryotic cells. This approach can relate transcription with mRNA abundance and lifetimes. Another key aspect of single-cell analysis is the cell-to-cell variability among populations of cells. Definition of heterogeneity has revealed stochastic processes, determined characteristics of under-represented cell types or transitional states, and integrated cellular behaviors in the context of multicellular organisms. In this review, we discuss novel aspects of gene expression of eukaryotic cells and multicellular organisms revealed by the latest advances in single-cell and single-molecule imaging technology. PMID:27893965

  6. Automated image analysis to quantify the subnuclear organization of transcriptional coregulatory protein complexes in living cell populations

    Science.gov (United States)

    Voss, Ty C.; Demarco, Ignacio A.; Booker, Cynthia F.; Day, Richard N.

    2004-06-01

    Regulated gene transcription is dependent on the steady-state concentration of DNA-binding and coregulatory proteins assembled in distinct regions of the cell nucleus. For example, several different transcriptional coactivator proteins, such as the Glucocorticoid Receptor Interacting Protein (GRIP), localize to distinct spherical intranuclear bodies that vary from approximately 0.2-1 micron in diameter. We are using multi-spectral wide-field microscopy of cells expressing coregulatory proteins labeled with the fluorescent proteins (FP) to study the mechanisms that control the assembly and distribution of these structures in living cells. However, variability between cells in the population makes an unbiased and consistent approach to this image analysis absolutely critical. To address this challenge, we developed a protocol for rigorous quantification of subnuclear organization in cell populations. Cells transiently co-expressing a green FP (GFP)-GRIP and the monomeric red FP (mRFP) are selected for imaging based only on the signal in the red channel, eliminating bias due to knowledge of coregulator organization. The impartially selected images of the GFP-coregulatory protein are then analyzed using an automated algorithm to objectively identify and measure the intranuclear bodies. By integrating all these features, this combination of unbiased image acquisition and automated analysis facilitates the precise and consistent measurement of thousands of protein bodies from hundreds of individual living cells that represent the population.

  7. Fast-FISH Detection and Semi-Automated Image Analysis of Numerical Chromosome Aberrations in Hematological Malignancies

    Directory of Open Access Journals (Sweden)

    Arif Esa

    1998-01-01

    Full Text Available A new fluorescence in situ hybridization (FISH technique called Fast-FISH in combination with semi-automated image analysis was applied to detect numerical aberrations of chromosomes 8 and 12 in interphase nuclei of peripheral blood lymphocytes and bone marrow cells from patients with acute myelogenous leukemia (AML and chronic lymphocytic leukemia (CLL. Commercially available α-satellite DNA probes specific for the centromere regions of chromosome 8 and chromosome 12, respectively, were used. After application of the Fast-FISH protocol, the microscopic images of the fluorescence-labelled cell nuclei were recorded by the true color CCD camera Kappa CF 15 MC and evaluated quantitatively by computer analysis on a PC. These results were compared to results obtained from the same type of specimens using the same analysis system but with a standard FISH protocol. In addition, automated spot counting after both FISH techniques was compared to visual spot counting after standard FISH. A total number of about 3,000 cell nuclei was evaluated. For quantitative brightness parameters, a good correlation between standard FISH labelling and Fast-FISH was found. Automated spot counting after Fast-FISH coincided within a few percent to automated and visual spot counting after standard FISH. The examples shown indicate the reliability and reproducibility of Fast-FISH and its potential for automatized interphase cell diagnostics of numerical chromosome aberrations. Since the Fast-FISH technique requires a hybridization time as low as 1/20 of established standard FISH techniques, omitting most of the time consuming working steps in the protocol, it may contribute considerably to clinical diagnostics. This may especially be interesting in cases where an accurate result is required within a few hours.

  8. A new automated assessment method for contrast-detail images by applying support vector machine and its robustness to nonlinear image processing.

    Science.gov (United States)

    Takei, Takaaki; Ikeda, Mitsuru; Imai, Kuniharu; Yamauchi-Kawaura, Chiyo; Kato, Katsuhiko; Isoda, Haruo

    2013-09-01

    The automated contrast-detail (C-D) analysis methods developed so-far cannot be expected to work well on images processed with nonlinear methods, such as noise reduction methods. Therefore, we have devised a new automated C-D analysis method by applying support vector machine (SVM), and tested for its robustness to nonlinear image processing. We acquired the CDRAD (a commercially available C-D test object) images at a tube voltage of 120 kV and a milliampere-second product (mAs) of 0.5-5.0. A partial diffusion equation based technique was used as noise reduction method. Three radiologists and three university students participated in the observer performance study. The training data for our SVM method was the classification data scored by the one radiologist for the CDRAD images acquired at 1.6 and 3.2 mAs and their noise-reduced images. We also compared the performance of our SVM method with the CDRAD Analyser algorithm. The mean C-D diagrams (that is a plot of the mean of the smallest visible hole diameter vs. hole depth) obtained from our devised SVM method agreed well with the ones averaged across the six human observers for both original and noise-reduced CDRAD images, whereas the mean C-D diagrams from the CDRAD Analyser algorithm disagreed with the ones from the human observers for both original and noise-reduced CDRAD images. In conclusion, our proposed SVM method for C-D analysis will work well for the images processed with the non-linear noise reduction method as well as for the original radiographic images.

  9. Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN.

    Directory of Open Access Journals (Sweden)

    Timm Schoening

    Full Text Available Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences, for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%, some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS.

  10. Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN.

    Science.gov (United States)

    Schoening, Timm; Bergmann, Melanie; Ontrup, Jörg; Taylor, James; Dannheim, Jennifer; Gutt, Julian; Purser, Autun; Nattkemper, Tim W

    2012-01-01

    Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS.

  11. Kinetics of virus production from single cells.

    Science.gov (United States)

    Timm, Andrea; Yin, John

    2012-03-01

    The production of virus by infected cells is an essential process for the spread and persistence of viral diseases, the effectiveness of live-viral vaccines, and the manufacture of viruses for diverse applications. Yet despite its importance, methods to precisely measure virus production from cells are lacking. Most methods test infected-cell populations, masking how individual cells behave. Here we measured the kinetics of virus production from single cells. We combined simple steps of liquid-phase infection, serial dilution, centrifugation, and harvesting, without specialized equipment, to track the production of virus particles from BHK cells infected with vesicular stomatitis virus. Remarkably, cell-to-cell differences in latent times to virus release were within a factor of two, while production rates and virus yields spanned over 300-fold, highlighting an extreme diversity in virus production for cells from the same population. These findings have fundamental and technological implications for health and disease.

  12. Electrochemical nanoprobes for single-cell analysis.

    Science.gov (United States)

    Actis, Paolo; Tokar, Sergiy; Clausmeyer, Jan; Babakinejad, Babak; Mikhaleva, Sofya; Cornut, Renaud; Takahashi, Yasufumi; López Córdoba, Ainara; Novak, Pavel; Shevchuck, Andrew I; Dougan, Jennifer A; Kazarian, Sergei G; Gorelkin, Petr V; Erofeev, Alexander S; Yaminsky, Igor V; Unwin, Patrick R; Schuhmann, Wolfgang; Klenerman, David; Rusakov, Dmitri A; Sviderskaya, Elena V; Korchev, Yuri E

    2014-01-28

    The measurement of key molecules in individual cells with minimal disruption to the biological milieu is the next frontier in single-cell analyses. Nanoscale devices are ideal analytical tools because of their small size and their potential for high spatial and temporal resolution recordings. Here, we report the fabrication of disk-shaped carbon nanoelectrodes whose radius can be precisely tuned within the range 5-200 nm. The functionalization of the nanoelectrode with platinum allowed the monitoring of oxygen consumption outside and inside a brain slice. Furthermore, we show that nanoelectrodes of this type can be used to impale individual cells to perform electrochemical measurements within the cell with minimal disruption to cell function. These nanoelectrodes can be fabricated combined with scanning ion conductance microscopy probes, which should allow high resolution electrochemical mapping of species on or in living cells.

  13. Polyelectrolyte Multilayers: Towards Single Cell Studies

    Directory of Open Access Journals (Sweden)

    Dmitry Volodkin

    2014-05-01

    Full Text Available Single cell analysis (SCA is nowadays recognized as one of the key tools for diagnostics and fundamental cell biology studies. The Layer-by-layer (LbL polyelectrolyte assembly is a rather new but powerful technique to produce multilayers. It allows to model the extracellular matrix in terms of its chemical and physical properties. Utilization of the multilayers for SCA may open new avenues in SCA because of the triple role of the multilayer film: (i high capacity for various biomolecules; (ii natural mimics of signal molecule diffusion to a cell and (iii cell patterning opportunities. Besides, light-triggered release from multilayer films offers a way to deliver biomolecules with high spatio-temporal resolution. Here we review recent works showing strong potential to use multilayers for SCA and address accordingly the following issues: biomolecule loading, cell patterning, and light-triggered release.

  14. Development of an autonomous biological cell manipulator with single-cell electroporation and visual servoing capabilities.

    Science.gov (United States)

    Sakaki, Kelly; Dechev, Nikolai; Burke, Robert D; Park, Edward J

    2009-08-01

    Studies of single cells via microscopy and microinjection are a key component in research on gene functions, cancer, stem cells, and reproductive technology. As biomedical experiments become more complex, there is an urgent need to use robotic systems to improve cell manipulation and microinjection processes. Automation of these tasks using machine vision and visual servoing creates significant benefits for biomedical laboratories, including repeatability of experiments, higher throughput, and improved cell viability. This paper presents the development of a new 5-DOF robotic manipulator, designed for manipulating and microinjecting single cells. This biological cell manipulator (BCM) is capable of autonomous scanning of a cell culture followed by autonomous injection of cells using single-cell electroporation (SCE). SCE does not require piercing the cell membrane, thereby keeping the cell membrane fully intact. The BCM features high-precision 3-DOF translational and 2-DOF rotational motion, and a second z-axis allowing top-down placement of a micropipette tip onto the cell membrane for SCE. As a technical demonstration, the autonomous visual servoing and microinjection capabilities of the single-cell manipulator are experimentally shown using sea urchin eggs.

  15. A quality assurance framework for the fully automated and objective evaluation of image quality in cone-beam computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Steiding, Christian; Kolditz, Daniel; Kalender, Willi A., E-mail: willi.kalender@imp.uni-erlangen.de [Institute of Medical Physics, University of Erlangen-Nürnberg, Henkestraße 91, 91052 Erlangen, Germany and CT Imaging GmbH, 91052 Erlangen (Germany)

    2014-03-15

    Purpose: Thousands of cone-beam computed tomography (CBCT) scanners for vascular, maxillofacial, neurological, and body imaging are in clinical use today, but there is no consensus on uniform acceptance and constancy testing for image quality (IQ) and dose yet. The authors developed a quality assurance (QA) framework for fully automated and time-efficient performance evaluation of these systems. In addition, the dependence of objective Fourier-based IQ metrics on direction and position in 3D volumes was investigated for CBCT. Methods: The authors designed a dedicated QA phantom 10 cm in length consisting of five compartments, each with a diameter of 10 cm, and an optional extension ring 16 cm in diameter. A homogeneous section of water-equivalent material allows measuring CT value accuracy, image noise and uniformity, and multidimensional global and local noise power spectra (NPS). For the quantitative determination of 3D high-contrast spatial resolution, the modulation transfer function (MTF) of centrally and peripherally positioned aluminum spheres was computed from edge profiles. Additional in-plane and axial resolution patterns were used to assess resolution qualitatively. The characterization of low-contrast detectability as well as CT value linearity and artifact behavior was tested by utilizing sections with soft-tissue-equivalent and metallic inserts. For an automated QA procedure, a phantom detection algorithm was implemented. All tests used in the dedicated QA program were initially verified in simulation studies and experimentally confirmed on a clinical dental CBCT system. Results: The automated IQ evaluation of volume data sets of the dental CBCT system was achieved with the proposed phantom requiring only one scan for the determination of all desired parameters. Typically, less than 5 min were needed for phantom set-up, scanning, and data analysis. Quantitative evaluation of system performance over time by comparison to previous examinations was also

  16. Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images

    Directory of Open Access Journals (Sweden)

    L.O. Murta Jr.

    2006-01-01

    Full Text Available The present study describes an auxiliary tool in the diagnosis of left ventricular (LV segmental wall motion (WM abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN was developed and validated for grading LV segmental WM using data from color kinesis (CK images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1 normal, 2 mild hypokinesia, 3 moderate hypokinesia, 4 severe hypokinesia, 5 akinesia, and 6 dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R² = 0.99. In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.

  17. Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images.

    Science.gov (United States)

    Murta, L O; Ruiz, E E S; Pazin-Filho, A; Schmidt, A; Almeida-Filho, O C; Simões, M V; Marin-Neto, J A; Maciel, B C

    2006-01-01

    The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R2 = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.

  18. Use of solid film highlighter in automation of D sight image interpretation

    Science.gov (United States)

    Forsyth, David S.; Komorowski, Jerzy P.; Gould, Ronald W.

    1998-03-01

    Many studies have shown inspector variability to be a crucial parameter in nondestructive evaluation (NDE) reliability. Therefore it is desirable to automate the decision making process in NDE as much as possible. The automation of inspection data handling and interpretation will also enable use of data fusion algorithms currently being researched at IAR for increasing inspection reliability by combination of different NDE modes. Enhanced visual inspection techniques such as D Sight have the capability to rapidly inspect lap splice joints using D Sight and other optical methods. IARs NDI analysis software has been sued to perform analysis and feature extraction on D Sight inspections. Different metrics suitable for automated interpretation have been developed and tested on inspections of actual service-retired aircraft specimens using D Sight with solid film highlighter.

  19. Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

    Science.gov (United States)

    Rey-Villamizar, Nicolas; Somasundar, Vinay; Megjhani, Murad; Xu, Yan; Lu, Yanbin; Padmanabhan, Raghav; Trett, Kristen; Shain, William; Roysam, Badri

    2014-01-01

    In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

  20. Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python

    Directory of Open Access Journals (Sweden)

    Nicolas eRey-Villamizar

    2014-04-01

    Full Text Available In this article, we describe use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis task, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral brain tissue images surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels, 6,000$times$10,000$times$500 voxels with 16 bits/voxel, implying image sizes exceeding 250GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analytics for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment consisting. Our Python script enables efficient data storage and movement between compute and storage servers, logging all processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

  1. Automated Detection of Malarial Retinopathy in Digital Fundus Images for Improved Diagnosis in Malawian Children with Clinically Defined Cerebral Malaria

    Science.gov (United States)

    Joshi, Vinayak; Agurto, Carla; Barriga, Simon; Nemeth, Sheila; Soliz, Peter; MacCormick, Ian J.; Lewallen, Susan; Taylor, Terrie E.; Harding, Simon P.

    2017-02-01

    Cerebral malaria (CM), a complication of malaria infection, is the cause of the majority of malaria-associated deaths in African children. The standard clinical case definition for CM misclassifies ~25% of patients, but when malarial retinopathy (MR) is added to the clinical case definition, the specificity improves from 61% to 95%. Ocular fundoscopy requires expensive equipment and technical expertise not often available in malaria endemic settings, so we developed an automated software system to analyze retinal color images for MR lesions: retinal whitening, vessel discoloration, and white-centered hemorrhages. The individual lesion detection algorithms were combined using a partial least square classifier to determine the presence or absence of MR. We used a retrospective retinal image dataset of 86 pediatric patients with clinically defined CM (70 with MR and 16 without) to evaluate the algorithm performance. Our goal was to reduce the false positive rate of CM diagnosis, and so the algorithms were tuned at high specificity. This yielded sensitivity/specificity of 95%/100% for the detection of MR overall, and 65%/94% for retinal whitening, 62%/100% for vessel discoloration, and 73%/96% for hemorrhages. This automated system for detecting MR using retinal color images has the potential to improve the accuracy of CM diagnosis.

  2. High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection.

    Directory of Open Access Journals (Sweden)

    Priya Choudhry

    Full Text Available Counting cells and colonies is an integral part of high-throughput screens and quantitative cellular assays. Due to its subjective and time-intensive nature, manual counting has hindered the adoption of cellular assays such as tumor spheroid formation in high-throughput screens. The objective of this study was to develop an automated method for quick and reliable counting of cells and colonies from digital images. For this purpose, I developed an ImageJ macro Cell Colony Edge and a CellProfiler Pipeline Cell Colony Counting, and compared them to other open-source digital methods and manual counts. The ImageJ macro Cell Colony Edge is valuable in counting cells and colonies, and measuring their area, volume, morphology, and intensity. In this study, I demonstrate that Cell Colony Edge is superior to other open-source methods, in speed, accuracy and applicability to diverse cellular assays. It can fulfill the need to automate colony/cell counting in high-throughput screens, colony forming assays, and cellular assays.

  3. Digital cell counting device integrated with a single-cell array.

    Science.gov (United States)

    Saeki, Tatsuya; Hosokawa, Masahito; Lim, Tae-kyu; Harada, Manabu; Matsunaga, Tadashi; Tanaka, Tsuyoshi

    2014-01-01

    In this paper, we present a novel cell counting method accomplished using a single-cell array fabricated on an image sensor, complementary metal oxide semiconductor sensor. The single-cell array was constructed using a microcavity array, which can trap up to 7,500 single cells on microcavities periodically arranged on a plane metallic substrate via the application of a negative pressure. The proposed method for cell counting is based on shadow imaging, which uses a light diffraction pattern generated by the microcavity array and trapped cells. Under illumination, the cell-occupied microcavities are visualized as shadow patterns in an image recorded by the complementary metal oxide semiconductor sensor due to light attenuation. The cell count is determined by enumerating the uniform shadow patterns created from one-on-one relationships with single cells trapped on the microcavities in digital format. In the experiment, all cell counting processes including entrapment of non-labeled HeLa cells from suspensions on the array and image acquisition of a wide-field-of-view of 30 mm(2) in 1/60 seconds were implemented in a single integrated device. As a result, the results from the digital cell counting had a linear relationship with those obtained from microscopic observation (r(2)  = 0.99). This platform could be used at extremely low cell concentrations, i.e., 25-15,000 cells/mL. Our proposed system provides a simple and rapid miniaturized cell counting device for routine laboratory use.

  4. Single-cell analysis of population context advances RNAi screening at multiple levels

    NARCIS (Netherlands)

    Snijder, Berend; Sacher, Raphael; Rämö, Pauli; Liberali, Prisca; Mench, Karin; Wolfrum, Nina; Burleigh, Laura; Scott, Cameron C; Verheije, Monique H; Mercer, Jason; Moese, Stefan; Heger, Thomas; Theusner, Kristina; Jurgeit, Andreas; Lamparter, David; Balistreri, Giuseppe; Schelhaas, Mario; De Haan, Cornelis A M; Marjomäki, Varpu; Hyypiä, Timo; Rottier, Peter J M; Sodeik, Beate; Marsh, Mark; Gruenberg, Jean; Amara, Ali; Greber, Urs; Helenius, Ari; Pelkmans, Lucas

    2012-01-01

    Isogenic cells in culture show strong variability, which arises from dynamic adaptations to the microenvironment of individual cells. Here we study the influence of the cell population context, which determines a single cell's microenvironment, in image-based RNAi screens. We developed a comprehensi

  5. Automated collection of imaging and phenotypic data to centralized and distributed data repositories.

    Science.gov (United States)

    King, Margaret D; Wood, Dylan; Miller, Brittny; Kelly, Ross; Landis, Drew; Courtney, William; Wang, Runtang; Turner, Jessica A; Calhoun, Vince D

    2014-01-01

    Accurate data collection at the ground level is vital to the integrity of neuroimaging research. Similarly important is the ability to connect and curate data in order to make it meaningful and sharable with other investigators. Collecting data, especially with several different modalities, can be time consuming and expensive. These issues have driven the development of automated collection of neuroimaging and clinical assessment data within COINS (Collaborative Informatics and Neuroimaging Suite). COINS is an end-to-end data management system. It provides a comprehensive platform for data collection, management, secure storage, and flexible data retrieval (Bockholt et al., 2010; Scott et al., 2011). It was initially developed for the investigators at the Mind Research Network (MRN), but is now available to neuroimaging institutions worldwide. Self Assessment (SA) is an application embedded in the Assessment Manager (ASMT) tool in COINS. It is an innovative tool that allows participants to fill out assessments via the web-based Participant Portal. It eliminates the need for paper collection and data entry by allowing participants to submit their assessments directly to COINS. Instruments (surveys) are created through ASMT and include many unique question types and associated SA features that can be implemented to help the flow of assessment administration. SA provides an instrument queuing system with an easy-to-use drag and drop interface for research staff to set up participants' queues. After a queue has been created for the participant, they can access the Participant Portal via the internet to fill out their assessments. This allows them the flexibility to participate from home, a library, on site, etc. The collected data is stored in a PostgresSQL database at MRN. This data is only accessible by users that have explicit permission to access the data through their COINS user accounts and access to MRN network. This allows for high volume data collection and

  6. Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer

    NARCIS (Netherlands)

    L. Bondar (Luiza); M.S. Hoogeman (Mischa); W. Schillemans; B.J.M. Heijmen (Ben)

    2013-01-01

    textabstractFor online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and

  7. Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location

    DEFF Research Database (Denmark)

    Fennema-Notestine, Christine; Ozyurt, I Burak; Clark, Camellia P;

    2006-01-01

    Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of fou...

  8. Study of a Microfluidic Chip Integrating Single Cell Trap and 3D Stable Rotation Manipulation

    Directory of Open Access Journals (Sweden)

    Liang Huang

    2016-08-01

    Full Text Available Single cell manipulation technology has been widely applied in biological fields, such as cell injection/enucleation, cell physiological measurement, and cell imaging. Recently, a biochip platform with a novel configuration of electrodes for cell 3D rotation has been successfully developed by generating rotating electric fields. However, the rotation platform still has two major shortcomings that need to be improved. The primary problem is that there is no on-chip module to facilitate the placement of a single cell into the rotation chamber, which causes very low efficiency in experiment to manually pipette single 10-micron-scale cells into rotation position. Secondly, the cell in the chamber may suffer from unstable rotation, which includes gravity-induced sinking down to the chamber bottom or electric-force-induced on-plane movement. To solve the two problems, in this paper we propose a new microfluidic chip with manipulation capabilities of single cell trap and single cell 3D stable rotation, both on one chip. The new microfluidic chip consists of two parts. The top capture part is based on the least flow resistance principle and is used to capture a single cell and to transport it to the rotation chamber. The bottom rotation part is based on dielectrophoresis (DEP and is used to 3D rotate the single cell in the rotation chamber with enhanced stability. The two parts are aligned and bonded together to form closed channels for microfluidic handling. Using COMSOL simulation and preliminary experiments, we have verified, in principle, the concept of on-chip single cell traps and 3D stable rotation, and identified key parameters for chip structures, microfluidic handling, and electrode configurations. The work has laid a solid foundation for on-going chip fabrication and experiment validation.

  9. Analysis of specific RNA in cultured cells through quantitative integration of q-PCR and N-SIM single cell FISH images: Application to hormonal stimulation of StAR transcription.

    Science.gov (United States)

    Lee, Jinwoo; Foong, Yee Hoon; Musaitif, Ibrahim; Tong, Tiegang; Jefcoate, Colin

    2016-07-01

    The steroidogenic acute regulatory protein (StAR) has been proposed to serve as the switch that can turn on/off steroidogenesis. We investigated the events that facilitate dynamic StAR transcription in response to cAMP stimulation in MA-10 Leydig cells, focusing on splicing anomalies at StAR gene loci. We used 3' reverse primers in a single reaction to respectively quantify StAR primary (p-RNA), spliced (sp-RNA/mRNA), and extended 3' untranslated region (UTR) transcripts, which were quantitatively imaged by high-resolution fluorescence in situ hybridization (FISH). This approach delivers spatio-temporal resolution of initiation and splicing at single StAR loci, and transfers individual mRNA molecules to cytoplasmic sites. Gene expression was biphasic, initially showing slow splicing, transitioning to concerted splicing. The alternative 3.5-kb mRNAs were distinguished through the use of extended 3'UTR probes, which exhibited distinctive mitochondrial distribution. Combining quantitative PCR and FISH enables imaging of localization of RNA expression and analysis of RNA processing rates.

  10. Automated estimation of progression of interstitial lung disease in CT images.

    NARCIS (Netherlands)

    Arzhaeva, Y.; Prokop, M.; Murphy, K.; Rikxoort, E.M. van; Jong, P.A. de; Gietema, H.A.; Viergever, M.A.; Ginneken, B. van

    2010-01-01

    PURPOSE: A system is presented for automated estimation of progression of interstitial lung disease in serial thoracic CT scans. METHODS: The system compares corresponding 2D axial sections from baseline and follow-up scans and concludes whether this pair of sections represents regression, progressi

  11. RootAnalyzer: A Cross-Section Image Analysis Tool for Automated Characterization of Root Cells and Tissues.

    Directory of Open Access Journals (Sweden)

    Joshua Chopin

    Full Text Available The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.

  12. RootAnalyzer: A Cross-Section Image Analysis Tool for Automated Characterization of Root Cells and Tissues.

    Science.gov (United States)

    Chopin, Joshua; Laga, Hamid; Huang, Chun Yuan; Heuer, Sigrid; Miklavcic, Stanley J

    2015-01-01

    The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.

  13. The potential of single-cell profiling in plants.

    Science.gov (United States)

    Efroni, Idan; Birnbaum, Kenneth D

    2016-04-05

    Single-cell transcriptomics has been employed in a growing number of animal studies, but the technique has yet to be widely used in plants. Nonetheless, early studies indicate that single-cell RNA-seq protocols developed for animal cells produce informative datasets in plants. We argue that single-cell transcriptomics has the potential to provide a new perspective on plant problems, such as the nature of the stem cells or initials, the plasticity of plant cells, and the extent of localized cellular responses to environmental inputs. Single-cell experimental outputs require different analytical approaches compared with pooled cell profiles and new tools tailored to single-cell assays are being developed. Here, we highlight promising new single-cell profiling approaches, their limitations as applied to plants, and their potential to address fundamental questions in plant biology.

  14. Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd.

    Science.gov (United States)

    Irshad, H; Montaser-Kouhsari, L; Waltz, G; Bucur, O; Nowak, J A; Dong, F; Knoblauch, N W; Beck, A H

    2015-01-01

    The development of tools in computational pathology to assist physicians and biomedical scientists in the diagnosis of disease requires access to high-quality annotated images for algorithm learning and evaluation. Generating high-quality expert-derived annotations is time-consuming and expensive. We explore the use of crowdsourcing for rapidly obtaining annotations for two core tasks in com- putational pathology: nucleus detection and nucleus segmentation. We designed and implemented crowdsourcing experiments using the CrowdFlower platform, which provides access to a large set of labor channel partners that accesses and manages millions of contributors worldwide. We obtained annotations from four types of annotators and compared concordance across these groups. We obtained: crowdsourced annotations for nucleus detection and segmentation on a total of 810 images; annotations using automated methods on 810 images; annotations from research fellows for detection and segmentation on 477 and 455 images, respectively; and expert pathologist-derived annotations for detection and segmentation on 80 and 63 images, respectively. For the crowdsourced annotations, we evaluated performance across a range of contributor skill levels (1, 2, or 3). The crowdsourced annotations (4,860 images in total) were completed in only a fraction of the time and cost required for obtaining annotations using traditional methods. For the nucleus detection task, the research fellow-derived annotations showed the strongest concordance with the expert pathologist- derived annotations (F-M =93.68%), followed by the crowd-sourced contributor levels 1,2, and 3 and the automated method, which showed relatively similar performance (F-M = 87.84%, 88.49%, 87.26%, and 86.99%, respectively). For the nucleus segmentation task, the crowdsourced contributor level 3-derived annotations, research fellow-derived annotations, and automated method showed the strongest concordance with the expert pathologist

  15. Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Dürr, Oliver; Sick, Beate

    2016-10-01

    Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening-based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.

  16. Systematic single-cell analysis of Pichia pastoris reveals secretory capacity limits productivity.

    Directory of Open Access Journals (Sweden)

    Kerry Routenberg Love

    Full Text Available Biopharmaceuticals represent the fastest growing sector of the global pharmaceutical industry. Cost-efficient production of these biologic drugs requires a robust host organism for generating high titers of protein during fermentation. Understanding key cellular processes that limit protein production and secretion is, therefore, essential for rational strain engineering. Here, with single-cell resolution, we systematically analysed the productivity of a series of Pichia pastoris strains that produce different proteins both constitutively and inducibly. We characterized each strain by qPCR, RT-qPCR, microengraving, and imaging cytometry. We then developed a simple mathematical model describing the flux of folded protein through the ER. This combination of single-cell measurements and computational modelling shows that protein trafficking through the secretory machinery is often the rate-limiting step in single-cell production, and strategies to enhance the overall capacity of protein secretion within hosts for the production of heterologous proteins may improve productivity.

  17. Single-cell transcriptome analysis of endometrial tissue

    OpenAIRE

    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 fo...

  18. Single-cell detection of mRNA expression using nanofountain-probe electroporated molecular beacons.

    Science.gov (United States)

    Giraldo-Vela, Juan P; Kang, Wonmo; McNaughton, Rebecca L; Zhang, Xuemei; Wile, Brian M; Tsourkas, Andrew; Bao, Gang; Espinosa, Horacio D

    2015-05-01

    New techniques for single-cell analysis enable new discoveries in gene expression and systems biology. Time-dependent measurements on individual cells are necessary, yet the common single-cell analysis techniques used today require lysing the cell, suspending the cell, or long incubation times for transfection, thereby interfering with the ability to track an individual cell over time. Here a method for detecting mRNA expression in live single cells using molecular beacons that are transfected into single cells by means of nanofountain probe electroporation (NFP-E) is presented. Molecular beacons are oligonucleotides that emit fluorescence upon binding to an mRNA target, rendering them useful for spatial and temporal studies of live cells. The NFP-E is used to transfect a DNA-based beacon that detects glyceraldehyde 3-phosphate dehydrogenase and an RNA-based beacon that detects a sequence cloned in the green fluorescence protein mRNA. It is shown that imaging analysis of transfection and mRNA detection can be performed within seconds after electroporation and without disturbing adhered cells. In addition, it is shown that time-dependent detection of mRNA expression is feasible by transfecting the same single cell at different time points. This technique will be particularly useful for studies of cell differentiation, where several measurements of mRNA expression are required over time.

  19. Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq.

    Science.gov (United States)

    Macaulay, Iain C; Teng, Mabel J; Haerty, Wilfried; Kumar, Parveen; Ponting, Chris P; Voet, Thierry

    2016-11-01

    Parallel sequencing of a single cell's genome and transcriptome provides a powerful tool for dissecting genetic variation and its relationship with gene expression. Here we present a detailed protocol for G&T-seq, a method for separation and parallel sequencing of genomic DNA and full-length polyA(+) mRNA from single cells. We provide step-by-step instructions for the isolation and lysis of single cells; the physical separation of polyA(+) mRNA from genomic DNA using a modified oligo-dT bead capture and the respective whole-transcriptome and whole-genome amplifications; and library preparation and sequence analyses of these amplification products. The method allows the detection of thousands of transcripts in parallel with the genetic variants captured by the DNA-seq data from the same single cell. G&T-seq differs from other currently available methods for parallel DNA and RNA sequencing from single cells, as it involves physical separation of the DNA and RNA and does not require bespoke microfluidics platforms. The process can be implemented manually or through automation. When performed manually, paired genome and transcriptome sequencing libraries from eight single cells can be produced in ∼3 d by researchers experienced in molecular laboratory work. For users with experience in the programming and operation of liquid-handling robots, paired DNA and RNA libraries from 96 single cells can be produced in the same time frame. Sequence analysis and integration of single-cell G&T-seq DNA and RNA data requires a high level of bioinformatics expertise and familiarity with a wide range of informatics tools.

  20. Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis

    Institute of Scientific and Technical Information of China (English)

    Lian Yanyun; Song Zhijian

    2014-01-01

    Background Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning,treatment planning,monitoring of therapy.However,manual tumor segmentation commonly used in clinic is time-consuming and challenging,and none of the existed automated methods are highly robust,reliable and efficient in clinic application.An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results.Methods Based on the symmetry of human brain,we employed sliding-window technique and correlation coefficient to locate the tumor position.At first,the image to be segmented was normalized,rotated,denoised,and bisected.Subsequently,through vertical and horizontal sliding-windows technique in turn,that is,two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image,along with calculating of correlation coefficient of two windows,two windows with minimal correlation coefficient were obtained,and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor.At last,the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length,and threshold segmentation and morphological operations were used to acquire the final tumor region.Results The method was evaluated on 3D FSPGR brain MR images of 10 patients.As a result,the average ratio of correct location was 93.4% for 575 slices containing tumor,the average Dice similarity coefficient was 0.77 for one scan,and the average time spent on one scan was 40 seconds.Conclusions An fully automated,simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use.Correlation coefficient is a new and effective feature for tumor

  1. Single-cell analysis: Advances and future perspectives

    Directory of Open Access Journals (Sweden)

    Emir Hodzic

    2016-11-01

    Full Text Available The last several years have seen rapid development of technologies and methods that permit a detailed analysis of the genome and transcriptome of a single cell. Recent evidence from studies of single cells reveals that each cell type has a distinct lineage and function. The lineage and stage of development of each cell determine how they respond to each other and the environment. Experimental approaches that utilize single-cell analysis are effective means to understand how cell networks work in concert to coordinate a response at the population level; recent progress in single-cell analysis is offering a glimpse at the future.

  2. Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease

    Science.gov (United States)

    Kim, Youngwoo; Bae, Sonu K.; Cheng, Tianming; Tao, Cheng; Ge, Yinghui; Chapman, Arlene B.; Torres, Vincente E.; Yu, Alan S. L.; Mrug, Michal; Bennett, William M.; Flessner, Michael F.; Landsittel, Doug P.; Bae, Kyongtae T.

    2016-11-01

    Liver and liver cyst volume measurements are important quantitative imaging biomarkers for assessment of disease progression in autosomal dominant polycystic kidney disease (ADPKD) and polycystic liver disease (PLD). To date, no study has presented automated segmentation and volumetric computation of liver and liver cysts in these populations. In this paper, we proposed an automated segmentation framework for liver and liver cysts from bounded abdominal MR images in patients with ADPKD. To model the shape and variations in ADPKD livers, the spatial prior probability map (SPPM) of liver location and the tissue prior probability maps (TPPMs) of liver parenchymal tissue intensity and cyst morphology were generated. Formulated within a three-dimensional level set framework, the TPPMs successfully captured liver parenchymal tissues and cysts, while the SPPM globally constrained the initial surfaces of the liver into the desired boundary. Liver cysts were extracted by combined operations of the TPPMs, thresholding, and false positive reduction based on spatial prior knowledge of kidney cysts and distance map. With cross-validation for the liver segmentation, the agreement between the radiology expert and the proposed method was 84% for shape congruence and 91% for volume measurement assessed by the intra-class correlation coefficient (ICC). For the liver cyst segmentation, the agreement between the reference method and the proposed method was ICC  =  0.91 for cyst volumes and ICC  =  0.94 for % cyst-to-liver volume.

  3. A simple viability analysis for unicellular cyanobacteria using a new autofluorescence assay, automated microscopy, and ImageJ

    Directory of Open Access Journals (Sweden)

    Schulze Katja

    2011-11-01

    Full Text Available Abstract Background Currently established methods to identify viable and non-viable cells of cyanobacteria are either time-consuming (eg. plating or preparation-intensive (eg. fluorescent staining. In this paper we present a new and fast viability assay for unicellular cyanobacteria, which uses red chlorophyll fluorescence and an unspecific green autofluorescence for the differentiation of viable and non-viable cells without the need of sample preparation. Results The viability assay for unicellular cyanobacteria using red and green autofluorescence was established and validated for the model organism Synechocystis sp. PCC 6803. Both autofluorescence signals could be observed simultaneously allowing a direct classification of viable and non-viable cells. The results were confirmed by plating/colony count, absorption spectra and chlorophyll measurements. The use of an automated fluorescence microscope and a novel ImageJ based image analysis plugin allow a semi-automated analysis. Conclusions The new method simplifies the process of viability analysis and allows a quick and accurate analysis. Furthermore results indicate that a combination of the new assay with absorption spectra or chlorophyll concentration measurements allows the estimation of the vitality of cells.

  4. Mission Design Evaluation Using Automated Planning for High Resolution Imaging of Dynamic Surface Processes from the ISS

    Science.gov (United States)

    Knight, Russell; Donnellan, Andrea; Green, Joseph J.

    2013-01-01

    A challenge for any proposed mission is to demonstrate convincingly that the proposed systems will in fact deliver the science promised. Funding agencies and mission design personnel are becoming ever more skeptical of the abstractions that form the basis of the current state of the practice with respect to approximating science return. To address this, we have been using automated planning and scheduling technology to provide actual coverage campaigns that provide better predictive performance with respect to science return for a given mission design and set of mission objectives given implementation uncertainties. Specifically, we have applied an adaptation of ASPEN and SPICE to the Eagle-Eye domain that demonstrates the performance of the mission design with respect to coverage of science imaging targets that address climate change and disaster response. Eagle-Eye is an Earth-imaging telescope that has been proposed to fly aboard the International Space Station (ISS).

  5. Semi-automated Acanthamoeba polyphaga detection and computation of Salmonella typhimurium concentration in spatio-temporal images.

    Science.gov (United States)

    Tsibidis, George D; Burroughs, Nigel J; Gaze, William; Wellington, Elizabeth M H

    2011-12-01

    Interaction between bacteria and protozoa is an increasing area of interest, however there are a few systems that allow extensive observation of the interactions. A semi-automated approach is proposed to analyse a large amount of experimental data and avoid a time demanding manual object classification. We examined a surface system consisting of non nutrient agar with a uniform bacterial lawn that extended over the agar surface, and a spatially localised central population of amoebae. Location and identification of protozoa and quantification of bacteria population are performed by the employment of image analysis techniques in a series of spatial images. The quantitative tools are based on intensity thresholding, or on probabilistic models. To accelerate organism identification, correct classification errors and attain quantitative details of all objects a custom written Graphical User Interfaces has also been developed.

  6. Automated two- and three-dimensional, fine-resolution radar imaging of rigid targets with arbitrary unknown motion

    Science.gov (United States)

    Stuff, Mark A.; Sullivan, Richard C., Jr.; Thelen, Brian J.; Werness, Susan A.

    1994-06-01

    An automated system for the SAR/ISAR imaging of rigid bodies which are undergoing arbitrarily complicated unknown motions is being developed. This system determines, from only the radar data, all observable parameters of motion, on a pulse by pulse basis. The approach makes it possible to: (1) exploit any type of relative motion: translational, rotational, two dimensional, three dimensional, deterministic, or stochastic; no prior parametric assumptions on the functional form of the motion are required; (2) require only the radar data; no ancillary motion measurement system on either the radar platform or on the target is required; (3) automatically provide all the motion information needed to form correctly scaled images, without cross range scale ambiguities; (4) make full use of all the radar data; no signals returning from a target are discarded; and (5) require a known computation time, which is not signal dependent, as all iterative processes used have known, guaranteed convergence rates.

  7. Comparison of the automated evaluation of phantom mama in digital and digitalized images; Comparacao da avaliacao automatizada do phantom mama em imagens digitais e digitalizadas

    Energy Technology Data Exchange (ETDEWEB)

    Santana, Priscila do Carmo, E-mail: pcs@cdtn.b [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Dept. de Engenharia Nuclear. Programa de Pos-Graduacao em Ciencias e Tecnicas Nucleares; Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Fac. de Medicina. Dept. de Propedeutica Complementar; Gomes, Danielle Soares; Oliveira, Marcio Alves; Nogueira, Maria do Socorro, E-mail: mnogue@cdtn.b [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

    2011-07-01

    Mammography is an essential tool for diagnosis and early detection of breast cancer if it is provided as a very good quality service. The process of evaluating the quality of radiographic images in general, and mammography in particular, can be much more accurate, practical and fast with the help of computer analysis tools. This work compare the automated methodology for the evaluation of scanned digital images the phantom mama. By applied the DIP method techniques was possible determine geometrical and radiometric images evaluated. The evaluated parameters include circular details of low contrast, contrast ratio, spatial resolution, tumor masses, optical density and background in Phantom Mama scanned and digitized images. The both results of images were evaluated. Through this comparison was possible to demonstrate that this automated methodology is presented as a promising alternative for the reduction or elimination of subjectivity in both types of images, but the Phantom Mama present insufficient parameters for spatial resolution evaluation. (author)

  8. Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification

    Science.gov (United States)

    Ding, Yifu; Tavolara, Thomas; Cheng, Keith

    2016-03-01

    Our group is developing a method to examine biological specimens in cellular detail using synchrotron microCT. The method can acquire 3D images of tissue at micrometer-scale resolutions, allowing for individual cell types to be visualized in the context of the entire specimen. For model organism research, this tool will enable the rapid characterization of tissue architecture and cellular morphology from every organ system. This characterization is critical for proposed and ongoing "phenome" projects that aim to phenotype whole-organism mutants and diseased tissues from different organisms including humans. With the envisioned collection of hundreds to thousands of images for a phenome project, it is important to develop quantitative image analysis tools for the automated scoring of organism phenotypes across organ systems. Here we present a first step towards that goal, demonstrating the use of support vector machines (SVM) in detecting retinal cell nuclei in 3D images of wild-type zebrafish. In addition, we apply the SVM classifier on a mutant zebrafish to examine whether SVMs can be used to capture phenotypic differences in these images. The longterm goal of this work is to allow cellular and tissue morphology to be characterized quantitatively for many organ systems, at the level of the whole-organism.

  9. Laser tweezers Raman spectroscopy of single cells

    Science.gov (United States)

    Chen, De

    Raman scattering is an inelastic collision between the vibrating molecules inside the sample and the incident photons. During this process, energy exchange takes place between the photon and the scattering molecule. By measuring the energy change of the photon, the molecular vibration mode can be probed. The vibrational spectrum contains valuable information about the disposition of atomic nuclei and chemical bonds within a molecule, the chemical compositions and the interactions between the molecule and its surroundings. In this dissertation, laser tweezers Raman spectroscopy (LTRS) technique is applied for the analysis of biological cells and human cells at single cell level. In LTRS, an individual cell is trapped in aqueous medium with laser tweezers, and Raman scattering spectra from the trapped cell are recorded in real-time. The Raman spectra of these cells can be used to reveal the dynamical processes of cell growth, cell response to environment changes, and can be used as the finger print for the identification of a bacterial cell species. Several biophysical experiments were carried out using LTRS: (1) the dynamic germination process of individual spores of Bacillus thuringiensis was detected via Ca-DPA, a spore-specific biomarker molecule; (2) inactivation and killing of Bacillus subtilis spores by microwave irradiation and wet heat were studied at single cell level; (3) the heat shock activation process of single B. subtilis spores were analyzed, in which the reversible transition from glass-like state at low temperature to liquid-like state at high temperature in spore was revealed at the molecular level; (4) the kinetic processes of bacterial cell lysis of E. coli by lysozyme and by temperature induction of lambda phage were detected real-time; (5) the fixation and rehydration of human platelets were quantitatively evaluated and characterized with Raman spectroscopy method, which provided a rapid way to quantify the quality of freeze-dried therapeutic

  10. Different approaches to synovial membrane volume determination by magnetic resonance imaging: manual versus automated segmentation

    DEFF Research Database (Denmark)

    Østergaard, Mikkel

    1997-01-01

    Automated fast (5-20 min) synovial membrane volume determination by MRI, based on pre-set post-gadolinium-DTPA enhancement thresholds, was evaluated as a substitute for a time-consuming (45-120 min), previously validated, manual segmentation method. Twenty-nine knees [rheumatoid arthritis (RA) 13...... or synovial membrane volume, e.g. no systematic errors were found. The inter-MRI variation, evaluated in three knees and three wrists, was higher than by manual segmentation, particularly due to sensitivity to malalignment artefacts. Examination of test objects proved the high accuracy of the general...... methodology for volume determinations (maximal error 6.3%). Preceded by the determination of reproducibility and the optimal threshold at the available MR unit, automated 'threshold' segmentation appears to be acceptable when changes rather than absolute values of synovial membrane volumes are most important...

  11. Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry.

    Science.gov (United States)

    Lacson, Ronilda; Harris, Kimberly; Brawarsky, Phyllis; Tosteson, Tor D; Onega, Tracy; Tosteson, Anna N A; Kaye, Abby; Gonzalez, Irina; Birdwell, Robyn; Haas, Jennifer S

    2015-10-01

    Breast cancer screening is central to early breast cancer detection. Identifying and monitoring process measures for screening is a focus of the National Cancer Institute's Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative, which requires participating centers to report structured data across the cancer screening continuum. We evaluate the accuracy of automated information extraction of imaging findings from radiology reports, which are available as unstructured text. We present prevalence estimates of imaging findings for breast imaging received by women who obtained care in a primary care network participating in PROSPR (n = 139,953 radiology reports) and compared automatically extracted data elements to a "gold standard" based on manual review for a validation sample of 941 randomly selected radiology reports, including mammograms, digital breast tomosynthesis, ultrasound, and magnetic resonance imaging (MRI). The prevalence of imaging findings vary by data element and modality (e.g., suspicious calcification noted in 2.6% of screening mammograms, 12.1% of diagnostic mammograms, and 9.4% of tomosynthesis exams). In the validation sample, the accuracy of identifying imaging findings, including suspicious calcifications, masses, and architectural distortion (on mammogram and tomosynthesis); masses, cysts, non-mass enhancement, and enhancing foci (on MRI); and masses and cysts (on ultrasound), range from 0.8 to1.0 for recall, precision, and F-measure. Information extraction tools can be used for accurate documentation of imaging findings as structured data elements from text reports for a variety of breast imaging modalities. These data can be used to populate screening registries to help elucidate more effective breast cancer screening processes.

  12. Single cell genomics of subsurface microorganisms

    Science.gov (United States)

    Stepanauskas, R.; Onstott, T. C.; Lau, C.; Kieft, T. L.; Woyke, T.; Rinke, C.; Sczyrba, A.; van Heerden, E.

    2012-12-01

    Recent studies have revealed unexpected abundance and diversity of microorganisms in terrestrial and marine subsurface, providing new perspectives over their biogeochemical significance, evolution, and the limits of life. The now commonly used research tools, such as metagenomics and PCR-based gene surveys enabled cultivation-unbiased analysis of genes encoded by natural microbial communities. However, these methods seldom provide direct evidence for how the discovered genes are organized inside genomes and from which organisms do they come from. Here we evaluated the feasibility of an alternative, single cell genomics approach, in the analysis of subsurface microbial community composition, metabolic potential and microevolution at the Sanford Underground Research Facility (SURF), South Dakota, and the Witwaterstrand Basin, South Africa. We successfully recovered genomic DNA from individual microbial cells from multiple locations, including ultra-deep (down to 3,500 m) and low-biomass (down to 10^3 cells mL^-1) fracture water. The obtained single amplified genomes (SAGs) from SURF contained multiple representatives of the candidate divisions OP3, OP11, OD1 and uncharacterized archaea. By sequencing eight of these SAGs, we obtained the first genome content information for these phylum-level lineages that do not contain a single cultured representative. The Witwaterstrand samples were collected from deep fractures, biogeochemical dating of which suggests isolation from tens of thousands to tens of millions of years. Thus, these fractures may be viewed as "underground Galapagos", a natural, long-term experiment of microbial evolution within well-defined temporal and spatial boundaries. We are analyzing multiple SAGs from these environments, which will provide detailed information about adaptations to life in deep subsurface, mutation rates, selective pressures and gene flux within and across microbial populations.

  13. Single cell mechanics of keratinocyte cells.

    Science.gov (United States)

    Lulevich, Valentin; Yang, Hsin-ya; Isseroff, R Rivkah; Liu, Gang-yu

    2010-11-01

    Keratinocytes represent the major cell type of the uppermost layer of human skin, the epidermis. Using AFM-based single cell compression, the ability of individual keratinocytes to resist external pressure and global rupturing forces is investigated and compared with various cell types. Keratinocytes are found to be 6-70 times stiffer than other cell types, such as white blood, breast epithelial, fibroblast, or neuronal cells, and in contrast to other cell types they retain high mechanic strength even after the cell's death. The absence of membrane rupturing peaks in the force-deformation profiles of keratinocytes and their high stiffness during a second load cycle suggests that their unique mechanical resistance is dictated by the cytoskeleton. A simple analytical model enables the quantification of Young's modulus of keratinocyte cytoskeleton, as high as 120-340 Pa. Selective disruption of the two major cytoskeletal networks, actin filaments and microtubules, does not significantly affect keratinocyte mechanics. F-actin is found to impact cell deformation under pressure. During keratinocyte compression, the plasma membrane stretches to form peripheral blebs. Instead of blebbing, cells with depolymerized F-actin respond to pressure by detaching the plasma membrane from the cytoskeleton underneath. On the other hand, the compression force of keratinocytes expressing a mutated keratin (cell line, KEB-7) is 1.6-2.2 times less than that for the control cell line that has normal keratin networks. Therefore, we infer that the keratin intermediate filament network is responsible for the extremely high keratinocyte stiffness and resilience. This could manifest into the rugged protective nature of the human epidermis.

  14. An Automated Approach to Extracting River Bank Locations from Aerial Imagery Using Image Texture

    Science.gov (United States)

    2015-11-04

    consuming and labour intensive, and the quality is dependent on the individual doing the task. This paper describes a quick and fully automated method for...33: 4–24. Novikov A, Bagtzoglou A. 2006. Hydrodynamic model of the Lower Hud- son River estuarine system and its application for water quality manage ...ment. Water Resources Management 20(2): 257–276. Pasternak G, Wang C, Merz J. 2003. Application of a 2D hydrodynamic model to design of reach-scale

  15. Automated kidney morphology measurements from ultrasound images using texture and edge analysis

    Science.gov (United States)

    Ravishankar, Hariharan; Annangi, Pavan; Washburn, Michael; Lanning, Justin

    2016-04-01

    In a typical ultrasound scan, a sonographer measures Kidney morphology to assess renal abnormalities. Kidney morphology can also help to discriminate between chronic and acute kidney failure. The caliper placements and volume measurements are often time consuming and an automated solution will help to improve accuracy, repeatability and throughput. In this work, we developed an automated Kidney morphology measurement solution from long axis Ultrasound scans. Automated kidney segmentation is challenging due to wide variability in kidney shape, size, weak contrast of the kidney boundaries and presence of strong edges like diaphragm, fat layers. To address the challenges and be able to accurately localize and detect kidney regions, we present a two-step algorithm that makes use of edge and texture information in combination with anatomical cues. First, we use an edge analysis technique to localize kidney region by matching the edge map with predefined templates. To accurately estimate the kidney morphology, we use textural information in a machine learning algorithm framework using Haar features and Gradient boosting classifier. We have tested the algorithm on 45 unseen cases and the performance against ground truth is measured by computing Dice overlap, % error in major and minor axis of kidney. The algorithm shows successful performance on 80% cases.

  16. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.

    Science.gov (United States)

    Barker, Jocelyn; Hoogi, Assaf; Depeursinge, Adrien; Rubin, Daniel L

    2016-05-01

    Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p < 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.

  17. SU-E-J-252: Reproducibility of Radiogenomic Image Features: Comparison of Two Semi-Automated Segmentation Methods

    Energy Technology Data Exchange (ETDEWEB)

    Lee, M; Woo, B; Kim, J [Seoul National University, Seoul (Korea, Republic of); Jamshidi, N; Kuo, M [UCLA School of Medicine, Los Angeles, CA (United States)

    2015-06-15

    Purpose: Objective and reliable quantification of imaging phenotype is an essential part of radiogenomic studies. We compared the reproducibility of two semi-automatic segmentation methods for quantitative image phenotyping in magnetic resonance imaging (MRI) of glioblastoma multiforme (GBM). Methods: MRI examinations with T1 post-gadolinium and FLAIR sequences of 10 GBM patients were downloaded from the Cancer Image Archive site. Two semi-automatic segmentation tools with different algorithms (deformable model and grow cut method) were used to segment contrast enhancement, necrosis and edema regions by two independent observers. A total of 21 imaging features consisting of area and edge groups were extracted automatically from the segmented tumor. The inter-observer variability and coefficient of variation (COV) were calculated to evaluate the reproducibility. Results: Inter-observer correlations and coefficient of variation of imaging features with the deformable model ranged from 0.953 to 0.999 and 2.1% to 9.2%, respectively, and the grow cut method ranged from 0.799 to 0.976 and 3.5% to 26.6%, respectively. Coefficient of variation for especially important features which were previously reported as predictive of patient survival were: 3.4% with deformable model and 7.4% with grow cut method for the proportion of contrast enhanced tumor region; 5.5% with deformable model and 25.7% with grow cut method for the proportion of necrosis; and 2.1% with deformable model and 4.4% with grow cut method for edge sharpness of tumor on CE-T1W1. Conclusion: Comparison of two semi-automated tumor segmentation techniques shows reliable image feature extraction for radiogenomic analysis of GBM patients with multiparametric Brain MRI.

  18. IHC Profiler: An Open Source Plugin for the Quantitative Evaluation and Automated Scoring of Immunohistochemistry Images of Human Tissue Samples

    Science.gov (United States)

    Malhotra, Renu; De, Abhijit

    2014-01-01

    In anatomic pathology, immunohistochemistry (IHC) serves as a diagnostic and prognostic method for identification of disease markers in tissue samples that directly influences classification and grading the disease, influencing patient management. However, till today over most of the world, pathological analysis of tissue samples remained a time-consuming and subjective procedure, wherein the intensity of antibody staining is manually judged and thus scoring decision is directly influenced by visual bias. This instigated us to design a simple method of automated digital IHC image analysis algorithm for an unbiased, quantitative assessment of antibody staining intensity in tissue sections. As a first step, we adopted the spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. Then the DAB stained image is displayed in a new window wherein it undergoes pixel-by-pixel analysis, and displays the full profile along with its scoring decision. Based on the mathematical formula conceptualized, the algorithm is thoroughly tested by analyzing scores assigned to thousands (n = 1703) of DAB stained IHC images including sample images taken from human protein atlas web resource. The IHC Profiler plugin developed is compatible with the open resource digital image analysis software, ImageJ, which creates a pixel-by-pixel analysis profile of a digital IHC image and further assigns a score in a four tier system. A comparison study between manual pathological analysis and IHC Profiler resolved in a match of 88.6% (P<0.0001, CI = 95%). This new tool developed for clinical histopathological sample analysis can be adopted globally for scoring most protein targets where the marker protein expression is of cytoplasmic and/or nuclear type. We foresee that this method will minimize the problem of inter-observer variations across labs and further help in

  19. IHC Profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples.

    Directory of Open Access Journals (Sweden)

    Frency Varghese

    Full Text Available In anatomic pathology, immunohistochemistry (IHC serves as a diagnostic and prognostic method for identification of disease markers in tissue samples that directly influences classification and grading the disease, influencing patient management. However, till today over most of the world, pathological analysis of tissue samples remained a time-consuming and subjective procedure, wherein the intensity of antibody staining is manually judged and thus scoring decision is directly influenced by visual bias. This instigated us to design a simple method of automated digital IHC image analysis algorithm for an unbiased, quantitative assessment of antibody staining intensity in tissue sections. As a first step, we adopted the spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. Then the DAB stained image is displayed in a new window wherein it undergoes pixel-by-pixel analysis, and displays the full profile along with its scoring decision. Based on the mathematical formula conceptualized, the algorithm is thoroughly tested by analyzing scores assigned to thousands (n = 1703 of DAB stained IHC images including sample images taken from human protein atlas web resource. The IHC Profiler plugin developed is compatible with the open resource digital image analysis software, ImageJ, which creates a pixel-by-pixel analysis profile of a digital IHC image and further assigns a score in a four tier system. A comparison study between manual pathological analysis and IHC Profiler resolved in a match of 88.6% (P<0.0001, CI = 95%. This new tool developed for clinical histopathological sample analysis can be adopted globally for scoring most protein targets where the marker protein expression is of cytoplasmic and/or nuclear type. We foresee that this method will minimize the problem of inter-observer variations across labs and

  20. Validation of noise models for single-cell transcriptomics

    NARCIS (Netherlands)

    Grün, Dominic; Kester, Lennart; van Oudenaarden, Alexander

    2014-01-01

    Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity among single cells. Here we identify two major sources of technical variability: sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to co

  1. Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning

    Science.gov (United States)

    Sun, Yankui; Li, Shan; Sun, Zhongyang

    2017-01-01

    We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects-15 normal subjects, 15 AMD patients, and 15 DME patients; and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing-168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method; for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively.

  2. Automated layer segmentation of macular OCT images via graph-based SLIC superpixels and manifold ranking approach.

    Science.gov (United States)

    Gao, Zhijun; Bu, Wei; Zheng, Yalin; Wu, Xiangqian

    2017-01-01

    Using the graph-based a simple linear iterative clustering (SLIC) superpixels and manifold ranking technology, a novel automated intra-retinal layer segmentation method is proposed in this paper. Eleven boundaries of ten retinal layers in optical coherence tomography (OCT) images are exactly, fast and reliably quantified. Instead of considering the intensity or gradient features of the single-pixel in most existing segmentation methods, the proposed method focuses on the superpixels and the connected components-based image cues. The image is represented as some weighted graphs with superpixels or connected components as nodes. Each node is ranked with the gradient and spatial distance cues via graph-based Dijkstra's method or manifold ranking. So that it can effectively overcome speckle noise, organic texture and blood vessel artifacts issues. Segmentation is carried out in a three-stage scheme to extract eleven boundaries efficiently. The segmentation algorithm is validated on 2D and 3D OCT images in three databases, and is compared with the manual tracings of two independent observers. It demonstrates promising results in term of the mean unsigned boundaries errors, the mean signed boundaries errors, and layers thickness errors.

  3. Fully Automated On-Chip Imaging Flow Cytometry System with Disposable Contamination-Free Plastic Re-Cultivation Chip

    Directory of Open Access Journals (Sweden)

    Tomoyuki Kaneko

    2011-06-01

    Full Text Available We have developed a novel imaging cytometry system using a poly(methyl methacrylate (PMMA based microfluidic chip. The system was contamination-free, because sample suspensions contacted only with a flammable PMMA chip and no other component of the system. The transparency and low-fluorescence of PMMA was suitable for microscopic imaging of cells flowing through microchannels on the chip. Sample particles flowing through microchannels on the chip were discriminated by an image-recognition unit with a high-speed camera in real time at the rate of 200 event/s, e.g., microparticles 2.5 μm and 3.0 μm in diameter were differentiated with an error rate of less than 2%. Desired cells were separated automatically from other cells by electrophoretic or dielectrophoretic force one by one with a separation efficiency of 90%. Cells in suspension with fluorescent dye were separated using the same kind of microfluidic chip. Sample of 5 μL with 1 × 106 particle/mL was processed within 40 min. Separated cells could be cultured on the microfluidic chip without contamination. The whole operation of sample handling was automated using 3D micropipetting system. These results showed that the novel imaging flow cytometry system is practically applicable for biological research and clinical diagnostics.

  4. Multimedia input in automated image annotation and content-based retrieval

    Science.gov (United States)

    Srihari, Rohini K.

    1995-03-01

    This research explores the interaction of linguistic and photographic information in an integrated text/image database. By utilizing linguistic descriptions of a picture (speech and text input) coordinated with pointing references to the picture, we extract information useful in two aspects: image interpretation and image retrieval. In the image interpretation phase, objects and regions mentioned in the text are identified; the annotated image is stored in a database for future use. We incorporate techniques from our previous research on photo understanding using accompanying text: a system, PICTION, which identifies human faces in a newspaper photograph based on the caption. In the image retrieval phase, images matching natural language queries are presented to a user in a ranked order. This phase combines the output of (1) the image interpretation/annotation phase, (2) statistical text retrieval methods, and (3) image retrieval methods (e.g., color indexing). The system allows both point and click querying on a given image as well as intelligent querying across the entire text/image database.

  5. HEIDI: An Automated Process for the Identification and Extraction of Photometric Light Curves from Astronomical Images

    CERN Document Server

    Todd, M; Tanga, P; Coward, D M; Zadnik, M G

    2014-01-01

    The production of photometric light curves from astronomical images is a very time-consuming task. Larger data sets improve the resolution of the light curve, however, the time requirement scales with data volume. The data analysis is often made more difficult by factors such as a lack of suitable calibration sources and the need to correct for variations in observing conditions from one image to another. Often these variations are unpredictable and corrections are based on experience and intuition. The High Efficiency Image Detection & Identification (HEIDI) pipeline software rapidly processes sets of astronomical images. HEIDI automatically selects multiple sources for calibrating the images using an algorithm that provides a reliable means of correcting for variations between images in a time series. The algorithm takes into account that some sources may intrinsically vary on short time scales and excludes these from being used as calibration sources. HEIDI processes a set of images from an entire nigh...

  6. Semi-automated 3D leaf reconstruction and analysis of trichome patterning from light microscopic images.

    Directory of Open Access Journals (Sweden)

    Henrik Failmezger

    2013-04-01

    Full Text Available Trichomes are leaf hairs that are formed by single cells on the leaf surface. They are known to be involved in pathogen resistance. Their patterning is considered to emerge from a field of initially equivalent cells through the action of a gene regulatory network involving trichome fate promoting and inhibiting factors. For a quantitative analysis of single and double mutants or the phenotypic variation of patterns in different ecotypes, it is imperative to statistically evaluate the pattern reliably on a large number of leaves. Here we present a method that enables the analysis of trichome patterns at early developmental leaf stages and the automatic analysis of various spatial parameters. We focus on the most challenging young leaf stages that require the analysis in three dimensions, as the leaves are typically not flat. Our software TrichEratops reconstructs 3D surface models from 2D stacks of conventional light-microscope pictures. It allows the GUI-based annotation of different stages of trichome development, which can be analyzed with respect to their spatial distribution to capture trichome patterning events. We show that 3D modeling removes biases of simpler 2D models and that novel trichome patterning features increase the sensitivity for inter-accession comparisons.

  7. Fully automated rodent brain MR image processing pipeline on a Midas server: from acquired images to region-based statistics.

    Science.gov (United States)

    Budin, Francois; Hoogstoel, Marion; Reynolds, Patrick; Grauer, Michael; O'Leary-Moore, Shonagh K; Oguz, Ipek

    2013-01-01

    Magnetic resonance imaging (MRI) of rodent brains enables study of the development and the integrity of the brain under certain conditions (alcohol, drugs etc.). However, these images are difficult to analyze for biomedical researchers with limited image processing experience. In this paper we present an image processing pipeline running on a Midas server, a web-based data storage system. It is composed of the following steps: rigid registration, skull-stripping, average computation, average parcellation, parcellation propagation to individual subjects, and computation of region-based statistics on each image. The pipeline is easy to configure and requires very little image processing knowledge. We present results obtained by processing a data set using this pipeline and demonstrate how this pipeline can be used to find differences between populations.

  8. Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections

    OpenAIRE

    YANG Chen; Niedieker, Daniel; Großerüschkamp, Frederik; Horn, Melanie; Tannapfel, Andrea; Kallenbach-Thieltges, Angela; Gerwert, Klaus; Mosig, Axel

    2015-01-01

    Background In recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as ...

  9. Correction of oral contrast artifacts