WorldWideScience

Sample records for automated single-cell image

  1. 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

  2. Automated single cell sorting and deposition in submicroliter drops

    Science.gov (United States)

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

    2014-08-01

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

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

    OpenAIRE

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  5. Automated single cell isolation from suspension with computer vision

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2015-07-15

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

  7. Automated single cell isolation from suspension with computer vision

    OpenAIRE

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

    2016-01-01

    Current robots can manipulate only surface-attached cells seriously limiting the fields of their application for single cell handling. We developed a computer vision-based robot applying a motorized microscope and micropipette to recognize and gently isolate intact individual cells for subsequent analysis, e.g., DNA/RNA sequencing in 1–2 nanoliters from a thin (~100 μm) layer of cell suspension. It can retrieve rare cells, needs minimal sample preparation, and can be applied for virtually any...

  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. Fast and high resolution single-cell BRET imaging

    OpenAIRE

    Elise Goyet; Nathalie Bouquier; Vincent Ollendorff; Julie Perroy

    2016-01-01

    Resonance Energy Transfer (RET)-based technologies are used to report protein-protein interactions in living cells. Among them, Bioluminescence-initiated RET (BRET) provides excellent sensitivity but the low light intensity intrinsic to the bioluminescent process hampers its use for the localization of protein complexes at the sub-cellular level. Herein we have characterized the methodological conditions required to reliably perform single-cell BRET imaging using an extremely bright luciferas...

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

    Science.gov (United States)

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

    2016-06-01

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

  13. 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.

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

    Science.gov (United States)

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

    2016-01-01

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

  15. A high sensitivity, high throughput, automated single-cell gel electrophoresis ('Comet') DNA damage assay

    International Nuclear Information System (INIS)

    A fully automated microscopy machine vision image capture and analysis system for the collection of data from slides of 'comets' has been developed. The novel image processing algorithms employed in delineating the 'comet head' from the 'comet tail' allow us to determine accurately very low levels of damage. In conjunction with calibrated and automated image capture methods, we are able to eliminate operator subjectivity and analyse large numbers of cells (>2500) in a short time (<1 hour). The image processing algorithm is designed to handle particularly difficult nuclei containing a high degree of structure, due to DNA clumping. We also present techniques used to extend the assay's dynamic range by removing interfering background fluorescence and to define a region of interest. If subtle biological variations are to be quantified (e.g. cell cycle dependant damage), then the use of large cell populations is dictated. Under those circumstances, the use of a fully automated system is particularly advantageous providing that the manner in which data is extracted does not introduce any inadvertent bias. In practice, it is essential that the image processing steps are geared towards the correct recognition of an acceptable cell nucleus, i.e. comet 'head'. We acknowledge the financial support of CRUK, Programme Grant C133/A1812 - SP 2195-01/02 and the US Department of Energy Low Dose Radiation Research Program grant DE-FG07-99ER62878

  16. Single Cell Imaging of Metabolism with Fluorescent Biosensors

    OpenAIRE

    Hung, Yin Pun

    2012-01-01

    Cells utilize various signal transduction networks to regulate metabolism. Nevertheless, a quantitative understanding of the relationship between growth factor signaling and metabolic state at the single cell level has been lacking. The signal transduction and metabolic states could vary widely among individual cells. However, such cell-to-cell variation might be masked by the bulk measurements obtained from conventional biochemical methods. To assess the spatiotemporal dynamics of metabolism...

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

    Science.gov (United States)

    Hölzel, R

    1998-10-23

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

  18. 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...

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

    Science.gov (United States)

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

    2016-06-17

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

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

    Science.gov (United States)

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

    2013-01-01

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

  1. Mass spectrometry imaging and profiling of single cells

    OpenAIRE

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

    2012-01-01

    Mass spectrometry imaging and profiling of individual cells and subcellular structures provide unique analytical capabilities for biological and biomedical research, including determination of the biochemical heterogeneity of cellular populations and intracellular localization of pharmaceuticals. Two mass spectrometry technologies—secondary ion mass spectrometry (SIMS) and matrix assisted laser desorption ionization mass spectrometry (MALDI MS)—are most often used in micro-bioanalytical inves...

  2. Automated ship image acquisition

    Science.gov (United States)

    Hammond, T. R.

    2008-04-01

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

  3. Design of a Single-Cell Positioning Controller Using Electroosmotic Flow and Image Processing

    OpenAIRE

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

    2013-01-01

    The objective of the current research was not only to provide a fast and automatic positioning platform for single cells, but also improved biomolecular manipulation techniques. In this study, an automatic platform for cell positioning using electroosmotic flow and image processing technology was designed. The platform was developed using a PCI image acquisition interface card for capturing images from a microscope and then transferring them to a computer using human-machine interface softwar...

  4. 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

  5. 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. PMID:26505210

  6. 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.

  7. 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.

  8. Long-Term In Vivo Imaging of Multiple Organs at the Single Cell Level

    OpenAIRE

    Chen, Benny J.; Jiao, Yiqun; Zhang, Ping; Sun, Albert Y.; Pitt, Geoffrey S.; DeOliveira, Divino; Drago, Nicholas; Ye, Tong; Liu, Chen; Chao, Nelson J.

    2013-01-01

    Two-photon microscopy has enabled the study of individual cell behavior in live animals. Many organs and tissues cannot be studied, especially longitudinally, because they are located too deep, behind bony structures or too close to the lung and heart. Here we report a novel mouse model that allows long-term single cell imaging of many organs. A wide variety of live tissues were successfully engrafted in the pinna of the mouse ear. Many of these engrafted tissues maintained the normal tissue ...

  9. High-recovery visual identification and single-cell retrieval of circulating tumor cells for genomic analysis using a dual-technology platform integrated with automated immunofluorescence staining

    International Nuclear Information System (INIS)

    Circulating tumor cells (CTCs) are malignant cells that have migrated from solid cancers into the blood, where they are typically present in rare numbers. There is great interest in using CTCs to monitor response to therapies, to identify clinically actionable biomarkers, and to provide a non-invasive window on the molecular state of a tumor. Here we characterize the performance of the AccuCyte® – CyteFinder® system, a comprehensive, reproducible and highly sensitive platform for collecting, identifying and retrieving individual CTCs from microscopic slides for molecular analysis after automated immunofluorescence staining for epithelial markers. All experiments employed a density-based cell separation apparatus (AccuCyte) to separate nucleated cells from the blood and transfer them to microscopic slides. After staining, the slides were imaged using a digital scanning microscope (CyteFinder). Precisely counted model CTCs (mCTCs) from four cancer cell lines were spiked into whole blood to determine recovery rates. Individual mCTCs were removed from slides using a single-cell retrieval device (CytePicker™) for whole genome amplification and subsequent analysis by PCR and Sanger sequencing, whole exome sequencing, or array-based comparative genomic hybridization. Clinical CTCs were evaluated in blood samples from patients with different cancers in comparison with the CellSearch® system. AccuCyte – CyteFinder presented high-resolution images that allowed identification of mCTCs by morphologic and phenotypic features. Spike-in mCTC recoveries were between 90 and 91%. More than 80% of single-digit spike-in mCTCs were identified and even a single cell in 7.5 mL could be found. Analysis of single SKBR3 mCTCs identified presence of a known TP53 mutation by both PCR and whole exome sequencing, and confirmed the reported karyotype of this cell line. Patient sample CTC counts matched or exceeded CellSearch CTC counts in a small feasibility cohort. The AccuCyte

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Bourret, Stéphane [Univ. Bordeaux, CENBG, UMR 5797, F-33170 Gradignan (France); CNRS, IN2P3, CENBG, UMR 5797, F-33170 Gradignan (France); Vianna, François [Univ. Bordeaux, CENBG, UMR 5797, F-33170 Gradignan (France); CNRS, IN2P3, CENBG, UMR 5797, F-33170 Gradignan (France); IRSN, BP 3, F-13115 Saint-Paul Lez Durance (France); Devès, Guillaume [Univ. Bordeaux, CENBG, UMR 5797, F-33170 Gradignan (France); CNRS, IN2P3, CENBG, UMR 5797, F-33170 Gradignan (France); Atallah, Vincent [Univ. Bordeaux, CENBG, UMR 5797, F-33170 Gradignan (France); CNRS, IN2P3, CENBG, UMR 5797, F-33170 Gradignan (France); Department of Radiation Oncology, Institut Bergonié, Bordeaux (France); Univ. Victor Segalen, Bordeaux (France); Moretto, Philippe; Seznec, Hervé [Univ. Bordeaux, CENBG, UMR 5797, F-33170 Gradignan (France); CNRS, IN2P3, CENBG, UMR 5797, F-33170 Gradignan (France); Barberet, Philippe, E-mail: barberet@cenbg.in2p3.fr [Univ. Bordeaux, CENBG, UMR 5797, F-33170 Gradignan (France); CNRS, IN2P3, CENBG, UMR 5797, F-33170 Gradignan (France)

    2014-04-01

    Charged particle microbeams provide unique features to study targeted and non-targeted radiation response and have recently emerged as a powerful tool to investigate radiation-induced DNA damage and repair. We have developed a charged particle microbeam delivering protons and alpha particles in the MeV energy range equipped with online time-lapse imaging capabilities. The beam is focused to a sub-micrometer beam spot under vacuum by means of a triplet of magnetic quadrupoles and extracted in air through a 200 nm Si{sub 3}N{sub 4} window. The end-station is equipped with an automated fluorescence microscope used for single cell targeting and online time-lapse imaging. Cells are kept in their medium during the irradiation procedure and the sample temperature is regulated to 37 °C. An overall targeting accuracy of 2.0 ± 0.7 μm has been measured by tracking the re-localization of the XRCC1 protein. First measurements of this re-localization shows the ability of our system to follow online the radiation-induced re-localization of proteins in the first minutes after irradiation.

  12. 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...... production are discussed on the example of Denmark. Details on the developed methods for interior and exterior orientation are described. Practical examples like the measurement of réseau images, updating of topographic databases and renewal of orthoimages are used to prove the feasibility of the developed...

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

    Science.gov (United States)

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

    2013-01-01

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

  14. Design of a Single-Cell Positioning Controller Using Electroosmotic Flow and Image Processing

    Directory of Open Access Journals (Sweden)

    Jhong-Yin Chen

    2013-05-01

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

  15. High-throughput and single-cell imaging of NF-κB oscillations using monoclonal cell lines

    Directory of Open Access Journals (Sweden)

    Machuy Nikolaus

    2010-03-01

    Full Text Available Abstract Background The nuclear factor-κB (NF-κB family of transcription factors plays a role in a wide range of cellular processes including the immune response and cellular growth. In addition, deregulation of the NF-κB system has been associated with a number of disease states, including cancer. Therefore, insight into the regulation of NF-κB activation has crucial medical relevance, holding promise for novel drug target discovery. Transcription of NF-κB-induced genes is regulated by differential dynamics of single NF-κB subunits, but only a few methods are currently being applied to study dynamics. In particular, while oscillations of NF-κB activation have been observed in response to the cytokine tumor necrosis factor α (TNFα, little is known about the occurrence of oscillations in response to bacterial infections. Results To quantitatively assess NF-κB dynamics we generated human and murine monoclonal cell lines that stably express the NF-κB subunit p65 fused to GFP. Furthermore, a high-throughput assay based on automated microscopy coupled to image analysis to quantify p65-nuclear translocation was established. Using this assay, we demonstrate a stimulus- and cell line-specific temporal control of p65 translocation, revealing, for the first time, oscillations of p65 translocation in response to bacterial infection. Oscillations were detected at the single-cell level using real-time microscopy as well as at the population level using high-throughput image analysis. In addition, mathematical modeling of NF-κB dynamics during bacterial infections predicted masking of oscillations on the population level in asynchronous activations, which was experimentally confirmed. Conclusions Taken together, this simple and cost effective assay constitutes an integrated approach to infer the dynamics of NF-κB kinetics in single cells and cell populations. Using a single system, novel factors modulating NF-κB can be identified and analyzed

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

    Science.gov (United States)

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

    2016-08-01

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

  17. 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)

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

    International Nuclear Information System (INIS)

    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)

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

    Science.gov (United States)

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

    2016-04-01

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

  20. An automated imaging system for radiation biodosimetry.

    Science.gov (United States)

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

    2015-07-01

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

  1. 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.

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

    OpenAIRE

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

    2015-01-01

    Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivate...

  3. Close Clustering Based Automated Color Image Annotation

    CERN Document Server

    Garg, Ankit; Asawa, Krishna

    2010-01-01

    Most image-search approaches today are based on the text based tags associated with the images which are mostly human generated and are subject to various kinds of errors. The results of a query to the image database thus can often be misleading and may not satisfy the requirements of the user. In this work we propose our approach to automate this tagging process of images, where image results generated can be fine filtered based on a probabilistic tagging mechanism. We implement a tool which helps to automate the tagging process by maintaining a training database, wherein the system is trained to identify certain set of input images, the results generated from which are used to create a probabilistic tagging mechanism. Given a certain set of segments in an image it calculates the probability of presence of particular keywords. This probability table is further used to generate the candidate tags for input images.

  4. Yeast Replicator: A High-Throughput Multiplexed Microfluidics Platform for Automated Measurements of Single-Cell Aging

    Directory of Open Access Journals (Sweden)

    Ping Liu

    2015-10-01

    Full Text Available The yeast Saccharomyces cerevisiae is a model organism for replicative aging studies; however, conventional lifespan measurement platforms have several limitations. Here, we present a microfluidics platform that facilitates simultaneous lifespan and gene expression measurements of aging yeast cells. Our multiplexed high-throughput platform offers the capability to perform independent lifespan experiments using different yeast strains or growth media. Using this platform in minimal media environments containing glucose, we measured the full lifespan of individual yeast cells in wild-type and canonical gene deletion backgrounds. Compared to glucose, in galactose we observed a 16.8% decrease in replicative lifespan accompanied by an ∼2-fold increase in single-cell oxidative stress levels reported by PSOD1-mCherry. Using PGAL1-YFP to measure the activity of the bistable galactose network, we saw that OFF and ON cells are similar in their lifespan. Our work shows that aging cells are committed to a single phenotypic state throughout their lifespan.

  5. Radiographic examination takes on an automated image

    International Nuclear Information System (INIS)

    Automation can be effectively applied to nondestructive testing (NDT). Until recently, film radiography used in NDT was largely a manual process, involving the shooting of a series of x-rays, manually positioned and manually processed. In other words, much radiographic work is being done the way it was over 50 years ago. Significant advances in automation have changed the face of manufacturing, and industry has shared in the benefits brought by such progress. The handling of parts, which was once responsible for a large measure of labor costs, is now assigned to robotic equipment. In nondestructive testing processes, some progress has been achieved in automation - for example, in real-time imaging systems. However, only recently have truly automated NDT begun to emerge. There are two major reasons to introduce automation into NDT - reliability and productivity. Any process or technique that can improve the reliability of parts testing could easily justify the capital investments required

  6. 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.

  7. Development of automated test bench for measurement of the field distribution in single cell elliptical superconducting cavity

    International Nuclear Information System (INIS)

    RRCAT Indore has initiated a program for the development of superconducting cavities and establishing the required infrastructure. A fully computer controlled bench for the field distribution measurement of a 1.3 GHz prototype cavity has been developed. The setup is based on the bead perturbation method, where the acquisition of the s21 transmission coefficient phase shift is synchronized with the displacement of a bead. The measured raw data are then converted to a quantity proportional to the electromagnetic field magnitude. It is very important to measure the resonant frequency and study the field distribution in the cavity at various stages of fabrication viz; after drawing, EB welding, various cleaning processes and most important during tuning of multi cell cavities for field flatness. The bench was used to test a prototype 1.3 GHz OFE Copper elliptical cavity developed at RRCAT. The results were verified from the simulation performed on CST Microwave studio and ANSYS. The paper describes the development of mechanical setup, control system for stepper motor, data acquisition and automation and measurement results for the Copper elliptical cavity. (author)

  8. 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.

  9. Close Clustering Based Automated Color Image Annotation

    OpenAIRE

    Garg, Ankit; Dwivedi, Rahul; Asawa, Krishna

    2010-01-01

    Most image-search approaches today are based on the text based tags associated with the images which are mostly human generated and are subject to various kinds of errors. The results of a query to the image database thus can often be misleading and may not satisfy the requirements of the user. In this work we propose our approach to automate this tagging process of images, where image results generated can be fine filtered based on a probabilistic tagging mechanism. We implement a tool which...

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

    Science.gov (United States)

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

    2009-02-01

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

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

    International Nuclear Information System (INIS)

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

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

    Science.gov (United States)

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

    2012-06-01

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

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

    Science.gov (United States)

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

    1999-05-01

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

  14. Automated Functional Analysis in Dynamic Medical Imaging

    Czech Academy of Sciences Publication Activity Database

    Tichý, Ondřej

    Praha : Katedra matematiky, FSv ČVUT v Praze, 2012, s. 19-20. [Aplikovaná matematika – Rektorysova soutěž. Praha (CZ), 07.12.2012] Institutional support: RVO:67985556 Keywords : Factor Analysis * Dynamic Sequence * Scintigraphy Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2012/AS/tichy-automated functional analysis in dynamic medical imaging.pdf

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-03-18

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

  16. Automated image analysis of uterine cervical images

    Science.gov (United States)

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

    2007-03-01

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

  17. Live-Cell Imaging Tool Optimization To Study Gene Expression Levels and Dynamics in Single Cells of Bacillus cereus

    NARCIS (Netherlands)

    Eijlander, Robyn T.; Kuipers, Oscar P.

    2013-01-01

    Single-cell methods are a powerful application in microbial research to study the molecular mechanism underlying phenotypic heterogeneity and cell-to-cell variability. Here, we describe the optimization and application of single-cell time-lapse fluorescence microscopy for the food spoilage bacterium

  18. Automated image segmentation using information theory

    International Nuclear Information System (INIS)

    Full text: Our development of automated contouring of CT images for RT planning is based on maximum a posteriori (MAP) analyses of region textures, edges, and prior shapes, and assumes stationary Gaussian distributions for voxel textures and contour shapes. Since models may not accurately represent image data, it would be advantageous to compute inferences without relying on models. The relative entropy (RE) from information theory can generate inferences based solely on the similarity of probability distributions. The entropy of a distribution of a random variable X is defined as -Σx p(x)log2p(x) for all the values x which X may assume. The RE (Kullback-Liebler divergence) of two distributions p(X), q(X), over X is Σx p(x)log2{p(x)/q(x)}. The RE is a kind of 'distance' between p,q, equaling zero when p=q and increasing as p,q are more different. Minimum-error MAP and likelihood ratio decision rules have RE equivalents: minimum error decisions obtain with functions of the differences between REs of compared distributions. One applied result is the contour ideally separating two regions is that which maximizes the relative entropy of the two regions' intensities. A program was developed that automatically contours the outlines of patients in stereotactic headframes, a situation most often requiring manual drawing. The relative entropy of intensities inside the contour (patient) versus outside (background) was maximized by conjugate gradient descent over the space of parameters of a deformable contour. shows the computed segmentation of a patient from headframe backgrounds. This program is particularly useful for preparing images for multimodal image fusion. Relative entropy and allied measures of distribution similarity provide automated contouring criteria that do not depend on statistical models of image data. This approach should have wide utility in medical image segmentation applications. Copyright (2001) Australasian College of Physical Scientists and

  19. 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.

  20. Computerized Station For Semi-Automated Testing Image Intensifier Tubes

    OpenAIRE

    Chrzanowski Krzysztof

    2015-01-01

    Testing of image intensifier tubes is still done using mostly manual methods due to a series of both technical and legal problems with test automation. Computerized stations for semi-automated testing of IITs are considered as novelty and are under continuous improvements. This paper presents a novel test station that enables semi-automated measurement of image intensifier tubes. Wide test capabilities and advanced design solutions rise the developed test station significantly above the curre...

  1. Automated quantitative image analysis of nanoparticle assembly

    Science.gov (United States)

    Murthy, Chaitanya R.; Gao, Bo; Tao, Andrea R.; Arya, Gaurav

    2015-05-01

    The ability to characterize higher-order structures formed by nanoparticle (NP) assembly is critical for predicting and engineering the properties of advanced nanocomposite materials. Here we develop a quantitative image analysis software to characterize key structural properties of NP clusters from experimental images of nanocomposites. This analysis can be carried out on images captured at intermittent times during assembly to monitor the time evolution of NP clusters in a highly automated manner. The software outputs averages and distributions in the size, radius of gyration, fractal dimension, backbone length, end-to-end distance, anisotropic ratio, and aspect ratio of NP clusters as a function of time along with bootstrapped error bounds for all calculated properties. The polydispersity in the NP building blocks and biases in the sampling of NP clusters are accounted for through the use of probabilistic weights. This software, named Particle Image Characterization Tool (PICT), has been made publicly available and could be an invaluable resource for researchers studying NP assembly. To demonstrate its practical utility, we used PICT to analyze scanning electron microscopy images taken during the assembly of surface-functionalized metal NPs of differing shapes and sizes within a polymer matrix. PICT is used to characterize and analyze the morphology of NP clusters, providing quantitative information that can be used to elucidate the physical mechanisms governing NP assembly.The ability to characterize higher-order structures formed by nanoparticle (NP) assembly is critical for predicting and engineering the properties of advanced nanocomposite materials. Here we develop a quantitative image analysis software to characterize key structural properties of NP clusters from experimental images of nanocomposites. This analysis can be carried out on images captured at intermittent times during assembly to monitor the time evolution of NP clusters in a highly automated

  2. Automated object detection for astronomical images

    Science.gov (United States)

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

    2005-10-01

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

  3. 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.

  4. An automated digital imaging system for environmental monitoring applications

    Science.gov (United States)

    Bogle, Rian; Velasco, Miguel; Vogel, John

    2013-01-01

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

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

    OpenAIRE

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

    2013-01-01

    We investigate single-cell reduction of toxic Cr(VI) by the dissimilatory metal-reducing bacterium Shewanella oneidensis MR-1 (MR-1), an important bioremediation process, using Raman spectroscopy and scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDX). Our experiments indicate that the toxic and highly soluble Cr(VI) can be efficiently reduced to the less toxic and non-soluble Cr2O3 nanoparticles by MR-1. Cr2O3 is observed to emerge as nanoparticles ads...

  6. 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 < 0.05). Regression analysis of the frequency distribution of aggregate size in leaf washes with and without Tween 80 showed that the surfactant promoted the dispersal of cells from large aggregates into smaller ones and into single cells (P < 0.05). Our study underlines the importance of investigating bacterial behavior at the scale of single cells in order to uncover trends undetectable at the population level by bacterial plate counts. Such an approach may provide valuable information to devise strategies aimed at enhancing the efficacy of produce sanitization treatments. PMID:24907336

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

    Directory of Open Access Journals (Sweden)

    Tsuyoshi Hirashima

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

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

    Science.gov (United States)

    Hirashima, Tsuyoshi; Adachi, Taiji

    2015-01-01

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

  9. 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

  10. Computerized Station For Semi-Automated Testing Image Intensifier Tubes

    Directory of Open Access Journals (Sweden)

    Chrzanowski Krzysztof

    2015-09-01

    Full Text Available Testing of image intensifier tubes is still done using mostly manual methods due to a series of both technical and legal problems with test automation. Computerized stations for semi-automated testing of IITs are considered as novelty and are under continuous improvements. This paper presents a novel test station that enables semi-automated measurement of image intensifier tubes. Wide test capabilities and advanced design solutions rise the developed test station significantly above the current level of night vision metrology.

  11. Automated feature extraction and classification from image sources

    Science.gov (United States)

    U.S. Geological Survey

    1995-01-01

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

  12. Image segmentation for automated dental identification

    Science.gov (United States)

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

    2006-02-01

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

  13. 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

  14. 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.

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

    Science.gov (United States)

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

    2016-05-16

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

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

    OpenAIRE

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

    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 capture...

  17. 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...

  18. Automated identification of animal species in camera trap images

    NARCIS (Netherlands)

    Yu, X.; Wang, J.; Kays, R.; Jansen, P.A.; Wang, T.; Huang, T.

    2013-01-01

    Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challenge for the advancement of this field. Here, we present an automated species identif

  19. Automated diabetic retinopathy imaging in Indian eyes: A pilot study

    Directory of Open Access Journals (Sweden)

    Rupak Roy

    2014-01-01

    Full Text Available Aim: To evaluate the efficacy of an automated retinal image grading system in diabetic retinopathy (DR screening. Materials and Methods: Color fundus images of patients of a DR screening project were analyzed for the purpose of the study. For each eye two set of images were acquired, one centerd on the disk and the other centerd on the macula. All images were processed by automated DR screening software (Retmarker. The results were compared to ophthalmologist grading of the same set of photographs. Results: 5780 images of 1445 patients were analyzed. Patients were screened into two categories DR or no DR. Image quality was high, medium and low in 71 (4.91%, 1117 (77.30% and 257 (17.78% patients respectively. Specificity and sensitivity for detecting DR in the high, medium and low group were (0.59, 0.91; (0.11, 0.95 and (0.93, 0.14. Conclusion: Automated retinal image screening system for DR had a high sensitivity in high and medium quality images. Automated DR grading software′s hold promise in future screening programs.

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

    Science.gov (United States)

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

    2012-03-01

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

  1. 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.

  2. 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.

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

    Science.gov (United States)

    Coe, Ryan L.; Seibel, Eric J.

    2013-02-01

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

  4. 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.

  5. 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. PMID:25838818

  6. 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.

  7. Automated image capture and defects detection by cavity inspection camera

    International Nuclear Information System (INIS)

    The defects as pit and scar make electric/magnetic field enhance and it cause field emission and quench in superconducting cavities. We used inspection camera to find these defects, but the current system which operated by human often mistake file naming and require long acquisition time. This study aims to solve these problems with introduction of cavity driving automation and defect inspection. We used rs232c of serial communication to drive of motor and camera for the automation of the inspection camera, and we used defect inspection software with defects reference images and pattern match software with the OpenCV lib. By the automation, we cut down the acquisition time from 8 hours to 2 hours, however defect inspection software is under preparation. The defect inspection software has a problem of complexity of image back ground. (author)

  8. 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.

  9. Single-cell resolution imaging of retinal ganglion cell apoptosis in vivo using a cell-penetrating caspase-activatable peptide probe.

    Directory of Open Access Journals (Sweden)

    Xudong Qiu

    Full Text Available Peptide probes for imaging retinal ganglion cell (RGC apoptosis consist of a cell-penetrating peptide targeting moiety and a fluorophore-quencher pair flanking an effector caspase consensus sequence. Using ex vivo fluorescence imaging, we previously validated the capacity of these probes to identify apoptotic RGCs in cell culture and in an in vivo rat model of N-methyl- D-aspartate (NMDA-induced neurotoxicity. Herein, using TcapQ488, a new probe designed and synthesized for compatibility with clinically-relevant imaging instruments, and real time imaging of a live rat RGC degeneration model, we fully characterized time- and dose-dependent probe activation, signal-to-noise ratios, and probe safety profiles in vivo. Adult rats received intravitreal injections of four NMDA concentrations followed by varying TcapQ488 doses. Fluorescence fundus imaging was performed sequentially in vivo using a confocal scanning laser ophthalmoscope and individual RGCs displaying activated probe were counted and analyzed. Rats also underwent electroretinography following intravitreal injection of probe. In vivo fluorescence fundus imaging revealed distinct single-cell probe activation as an indicator of RGC apoptosis induced by intravitreal NMDA injection that corresponded to the identical cells observed in retinal flat mounts of the same eye. Peak activation of probe in vivo was detected 12 hours post probe injection. Detectable fluorescent RGCs increased with increasing NMDA concentration; sensitivity of detection generally increased with increasing TcapQ488 dose until saturating at 0.387 nmol. Electroretinography following intravitreal injections of TcapQ488 showed no significant difference compared with control injections. We optimized the signal-to-noise ratio of a caspase-activatable cell penetrating peptide probe for quantitative non-invasive detection of RGC apoptosis in vivo. Full characterization of probe performance in this setting creates an important in

  10. Automated Segmentation of Cardiac Magnetic Resonance Images

    DEFF Research Database (Denmark)

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

    2001-01-01

    Magnetic resonance imaging (MRI) has been shown to be an accurate and precise technique to assess cardiac volumes and function in a non-invasive manner and is generally considered to be the current gold-standard for cardiac imaging [1]. Measurement of ventricular volumes, muscle mass and function...

  11. 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

  12. 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

    advantages and applicability of this technique. Our present data on assessment of low radiation doses, repair kinetics, spontaneous DNA damage in cancer cells, as well as constitutive and replication stress-induced HR events and their dependence on upstream factors within the DDR machinery document the......Maintenance of genome integrity is essential for homeostasis and survival as impaired DNA damage response (DDR) may predispose to grave pathologies such as neurodegenerative and immunodeficiency syndromes, cancer and premature aging. Therefore, accurate assessment of DNA damage caused 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 and...

  13. Automated radiopharmaceutical production systems for positron imaging

    International Nuclear Information System (INIS)

    This study provides information that will lead towards the widespread availability of systems for routine production of positron emitting isotopes and radiopharmaceuticals in a medical setting. The first part describes the collection, evaluation, and preparation in convenient form of the pertinent physical, engineering, and chemical data related to reaction yields and isotope production. The emphasis is on the production of the four short-lived isotopes C-11, N-13, O-15 and F-18. The second part is an assessment of radiation sources including cyclotrons, linear accelerators, and other more exotic devices. Various aspects of instrumentation including ease of installation, cost, and shielding are included. The third part of the study reviews the preparation of precursors and radiopharmaceuticals by automated chemical systems. 182 refs., 3 figs., 15 tabs

  14. An automated and simple method for brain MR image extraction

    OpenAIRE

    Zhu Zixin; Liu Jiafeng; Zhang Haiyan; Li Haiyun

    2011-01-01

    Abstract Background The extraction of brain tissue from magnetic resonance head images, is an important image processing step for the analyses of neuroimage data. The authors have developed an automated and simple brain extraction method using an improved geometric active contour model. Methods The method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity. The method defines the initial fu...

  15. An automated vessel segmentation of retinal images using multiscale vesselness

    International Nuclear Information System (INIS)

    The ocular fundus image can provide information on pathological changes caused by local ocular diseases and early signs of certain systemic diseases, such as diabetes and hypertension. Automated analysis and interpretation of fundus images has become a necessary and important diagnostic procedure in ophthalmology. The extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. In this paper, we introduce an implementation of the anisotropic diffusion which allows reducing the noise and better preserving small structures like vessels in 2D images. A vessel detection filter, based on a multi-scale vesselness function, is then applied to enhance vascular structures.

  16. 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.

  17. Patch-clamp recordings and calcium imaging followed by single-cell PCR reveal the developmental profile of 13 genes in iPSC-derived human neurons

    Directory of Open Access Journals (Sweden)

    Glenn S. Belinsky

    2014-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Amit, Guy [Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G2M9 (Canada); Purdie, Thomas G., E-mail: tom.purdie@rmp.uhn.ca [Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G2M9 (Canada); Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2 (Canada); Techna Institute, University Health Network, University of Toronto, Toronto, Ontario M5G 1P5 (Canada)

    2015-02-15

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

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

    Science.gov (United States)

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

    2016-01-01

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

  20. An Automated Image Processing System for Concrete Evaluation

    Energy Technology Data Exchange (ETDEWEB)

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

    1998-11-23

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

  1. An Automated, Image Processing System for Concrete Evaluation

    International Nuclear Information System (INIS)

    Allied Signal Federal Manufacturing ampersand Technologies (FM ampersand T) was asked to perform a proof-of-concept study for the Missouri Highway and Transportation Department (MHTD), Research Division, in June 1997. The goal of this proof-of-concept study was to ascertain if automated scanning and imaging techniques might be applied effectively to the problem of concrete evaluation. In the current evaluation process, a concrete sample core is manually scanned under a microscope. Voids (or air spaces) within the concrete are then detected visually by a human operator by incrementing the sample under the cross-hairs of a microscope and by counting the number of ''pixels'' which fall within a void. Automation of the scanning and image analysis processes is desired to improve the speed of the scanning process, to improve evaluation consistency, and to reduce operator fatigue. An initial, proof-of-concept image analysis approach was successfully developed and demonstrated using acquired black and white imagery of concrete samples. In this paper, the automated scanning and image capture system currently under development will be described and the image processing approach developed for the proof-of-concept study will be demonstrated. A development update and plans for future enhancements are also presented

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

    Science.gov (United States)

    Brauner, Paul; Jäckel, Udo

    2016-06-01

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

  3. 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.

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

    Science.gov (United States)

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

    2005-01-01

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

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

    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.

  6. 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 ...

  7. 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.

  8. 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.

  9. 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

  10. Automated techniques for quality assurance of radiological image modalities

    Science.gov (United States)

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

    1991-05-01

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

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

    Science.gov (United States)

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

    2015-12-01

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

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

    Science.gov (United States)

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

    2015-12-01

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

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

    Science.gov (United States)

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

    2016-06-01

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

  14. Usefulness of automated biopsy guns in image-guided biopsy

    International Nuclear Information System (INIS)

    To evaluate the usefulness of automated biopsy guns in image-guided biopsy of lung, liver, pancreas and other organs. Using automated biopsy devices, 160 biopsies of variable anatomic sites were performed: Biopsies were performed under ultrasonographic(US) guidance in 95 and computed tomographic (CT) guidance in 65. We retrospectively analyzed histologic results and complications. Specimens were adequate for histopathologic diagnosis in 143 of the 160 patients(89.4%)-Diagnostic tissue was obtained in 130 (81.3%), suggestive tissue obtained in 13(8.1%), and non-diagnostic tissue was obtained in 14(8.7%). Inadequate tissue was obtained in only 3(1.9%). There was no statistically significant difference between US-guided and CT-guided percutaneous biopsy. There was no occurrence of significant complication. We have experienced mild complications in only 5 patients-2 hematuria and 2 hematochezia in transrectal prostatic biopsy, and 1 minimal pneumothorax in CT-guided percutaneous lung biopsy. All of them were resolved spontaneously. The image-guided biopsy using the automated biopsy gun was a simple, safe and accurate method of obtaining adequate specimen for the histopathologic diagnosis

  15. Magnetic levitation of single cells.

    Science.gov (United States)

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

    2015-07-14

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

  16. AUTOMATED DATA ANALYSIS FOR CONSECUTIVE IMAGES FROM DROPLET COMBUSTION EXPERIMENTS

    Directory of Open Access Journals (Sweden)

    Christopher Lee Dembia

    2012-09-01

    Full Text Available A simple automated image analysis algorithm has been developed that processes consecutive images from high speed, high resolution digital images of burning fuel droplets. The droplets burn under conditions that promote spherical symmetry. The algorithm performs the tasks of edge detection of the droplet’s boundary using a grayscale intensity threshold, and shape fitting either a circle or ellipse to the droplet’s boundary. The results are compared to manual measurements of droplet diameters done with commercial software. Results show that it is possible to automate data analysis for consecutive droplet burning images even in the presence of a significant amount of noise from soot formation. An adaptive grayscale intensity threshold provides the ability to extract droplet diameters for the wide range of noise encountered. In instances where soot blocks portions of the droplet, the algorithm manages to provide accurate measurements if a circle fit is used instead of an ellipse fit, as an ellipse can be too accommodating to the disturbance.

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

    Science.gov (United States)

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

    2015-03-01

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

  18. Automated curved planar reformation of 3D spine images

    International Nuclear Information System (INIS)

    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

  19. Automated Dsm Extraction from Uav Images and Performance Analysis

    Science.gov (United States)

    Rhee, S.; Kim, T.

    2015-08-01

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

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

    Science.gov (United States)

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

    2014-11-01

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

  1. Automated image analysis in the study of collagenous colitis

    DEFF Research Database (Denmark)

    Fiehn, Anne-Marie Kanstrup; 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...... agreement between the four pathologists and the VG app was κ=0.71. CONCLUSION: In conclusion, the Visiopharm VG app is able to measure the thickness of a sub-epithelial collagenous band in colon biopsies with an accuracy comparable to the performance of a pathologist and thereby provides a promising...

  2. An automated 3D reconstruction method of UAV images

    Science.gov (United States)

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

    2015-10-01

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

  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 angiogenesis quantification through advanced image processing techniques.

    Science.gov (United States)

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

    2006-01-01

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

  5. Pancreas++ : Automated Quantification of Pancreatic Islet Cells in Microscopy Images

    Directory of Open Access Journals (Sweden)

    StuartMaudsley

    2013-01-01

    Full Text Available The microscopic image analysis of pancreatic Islet of Langerhans morphology is crucial for the investigation of diabetes and metabolic diseases. Besides the general size of the islet, the percentage and relative position of glucagon-containing alpha-, and insulin-containing beta-cells is also important for pathophysiological analyses, especially in rodents. Hence, the ability to identify, quantify and spatially locate peripheral and ‘involuted’ alpha-cells in the islet core is an important analytical goal. There is a dearth of software available for the automated and sophisticated positional-quantification of multiple cell types in the islet core. Manual analytical methods for these analyses, while relatively accurate, can suffer from a slow throughput rate as well as user-based biases. Here we describe a newly developed pancreatic islet analytical software program, Pancreas++, which facilitates the fully-automated, non-biased, and highly reproducible investigation of islet area and alpha- and beta-cell quantity as well as position within the islet for either single or large batches of fluorescent images. We demonstrate the utility and accuracy of Pancreas++ by comparing its performance to other pancreatic islet size and cell type (alpha, beta quantification methods. Our Pancreas++ analysis was significantly faster than other methods, while still retaining low error rates and a high degree of result correlation with the manually generated reference standard.

  6. 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...

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

    Science.gov (United States)

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

    2013-11-01

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

  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. Image Processing for Automated Analysis of the Fluorescence In-Situ Hybridization (FISH) Microscopic Images

    Czech Academy of Sciences Publication Activity Database

    Schier, Jan; Kovář, Bohumil; Kočárek, E.; Kuneš, Michal

    Berlin Heidelberg: Springer-Verlag, 2011, s. 622-633. (Lecture Notes in Computer Science ). ISBN 978-3-642-24081-2. [5th International Conference, ICHIT 2011. Daejeon (KR), 22.09.2011-24.09.2011] R&D Projects: GA TA ČR TA01010931 Institutional research plan: CEZ:AV0Z10750506 Keywords : fluorescence in-situ hybridization * image processing * image segmentation Subject RIV: IN - Informatics, Computer Science http://library.utia.cas.cz/separaty/2011/ZS/shier-image processing for automated analysis of the fluorescence in-situ hybridization (fish) microscopic images.pdf

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

    Science.gov (United States)

    Hong, Wei

    2008-03-01

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

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

    Science.gov (United States)

    Shankar, Vivek

    2009-01-01

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

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

    Science.gov (United States)

    Baumgart, Chris W.; Ciarcia, Christopher A.

    1994-03-01

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

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

    Science.gov (United States)

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

    2008-07-01

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

  15. Automated Recognition of 3D Features in GPIR Images

    Science.gov (United States)

    Park, Han; Stough, Timothy; Fijany, Amir

    2007-01-01

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

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

    Science.gov (United States)

    Krista, Larisza Diana; Reinard, Alysha

    2016-05-01

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

  17. 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.

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

    Science.gov (United States)

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

    2016-04-01

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

  19. 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.

  20. Automated transient detection in the STEREO Heliospheric Imagers.

    Science.gov (United States)

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

    2014-05-01

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

  1. Automated CT marker segmentation for image registration in radionuclide therapy

    International Nuclear Information System (INIS)

    In this paper a novel, automated CT marker segmentation technique for image registration is described. The technique, which is based on analysing each CT slice contour individually, treats the cross sections of the external markers as protrusions of the slice contour. Knowledge-based criteria, using the shape and dimensions of the markers, are defined to enable marker identification and segmentation. Following segmentation, the three-dimensional (3D) markers' centroids are localized using an intensity-weighted algorithm. Finally, image registration is performed using a least-squares fit algorithm. The technique was applied to both simulated and patient studies. The patients were undergoing 131I-mIBG radionuclide therapy with each study comprising several 99mTc single photon emission computed tomography (SPECT) scans and one CT marker scan. The mean residual 3D registration errors (±1 SD) computed for the simulated and patient studies were 1.8±0.3 mm and 4.3±0.5 mm respectively. (author)

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

    DEFF Research Database (Denmark)

    Gutte, Henrik; Jakobsson, David; Olofsson, Fredrik; Ohlsson, Mattias; Valind, Sven; Loft, Annika; Edenbrandt, Lars; Kjaer, Andreas

    2007-01-01

    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......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...... standard' image interpretation. The training group was used in the development of the automated method. The image processing techniques included algorithms for segmentation of the lungs based on the CT images and detection of lesions in the PET images. Lung boundaries from the CT images were used for...

  3. Automated movement correction for dynamic PET/CT images: Evaluation with phantom and patient data

    OpenAIRE

    Ye, H.; Wong, KP; Wardak, M; Dahlbom, M.; Kepe, V; Barrio, JR; Nelson, LD; Small, GW; Huang, SC

    2014-01-01

    Head movement during a dynamic brain PET/CT imaging results in mismatch between CT and dynamic PET images. It can cause artifacts in CT-based attenuation corrected PET images, thus affecting both the qualitative and quantitative aspects of the dynamic PET images and the derived parametric images. In this study, we developed an automated retrospective image-based movement correction (MC) procedure. The MC method first registered the CT image to each dynamic PET frames, then re-reconstructed th...

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

    OpenAIRE

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

    2016-01-01

    Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This sy...

  5. 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. PMID:26262345

  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 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.

  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. PMID:26729128

  9. Exploiting image registration for automated resonance assignment in NMR

    International Nuclear Information System (INIS)

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

  10. High-throughput single-cell manipulation in brain tissue.

    Directory of Open Access Journals (Sweden)

    Joseph D Steinmeyer

    Full Text Available The complexity of neurons and neuronal circuits in brain tissue requires the genetic manipulation, labeling, and tracking of single cells. However, current methods for manipulating cells in brain tissue are limited to either bulk techniques, lacking single-cell accuracy, or manual methods that provide single-cell accuracy but at significantly lower throughputs and repeatability. Here, we demonstrate high-throughput, efficient, reliable, and combinatorial delivery of multiple genetic vectors and reagents into targeted cells within the same tissue sample with single-cell accuracy. Our system automatically loads nanoliter-scale volumes of reagents into a micropipette from multiwell plates, targets and transfects single cells in brain tissues using a robust electroporation technique, and finally preps the micropipette by automated cleaning for repeating the transfection cycle. We demonstrate multi-colored labeling of adjacent cells, both in organotypic and acute slices, and transfection of plasmids encoding different protein isoforms into neurons within the same brain tissue for analysis of their effects on linear dendritic spine density. Our platform could also be used to rapidly deliver, both ex vivo and in vivo, a variety of genetic vectors, including optogenetic and cell-type specific agents, as well as fast-acting reagents such as labeling dyes, calcium sensors, and voltage sensors to manipulate and track neuronal circuit activity at single-cell resolution.

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

    International Nuclear Information System (INIS)

    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 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.

  13. Performance and Analysis of the Automated Semantic Object and Spatial Relationships Extraction in Traffic Images

    OpenAIRE

    Wang Hui Hui

    2013-01-01

    Extraction and representation of spatial relations semantics among objects are important as it can convey important information about the image and to further increase the confidence in image understanding which contributes to richer querying and retrieval facilities. This paper discusses the performance of the automated object spatial relationships semantic information extraction as proposed. Experiments have been conducted to demonstrate that the proposed automated object spatial relations...

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

    OpenAIRE

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

    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 ...

  15. Use of automated image registration to generate mean brain SPECT image of Alzheimer's patients

    International Nuclear Information System (INIS)

    The purpose of this study was to compute and compare the group mean HMPAO brain SPECT images of patients with senile dementia of Alzheimer's type (SDAT) and age matched control subjects after transformation of the individual images to a standard size and shape. Ten patients with Alzheimer's disease (age 71.6±5.0 yr) and ten age matched normal subjects (age 71.0±6.1 yr) participated in this study. Tc-99m HMPAO brain SPECT and X-ray CT scans were acquired for each subject. SPECT images were normalized to an average activity of 100 counts/pixel. Individual brain images were transformed to a standard size and shape with the help of Automated Image Registration (AIR). Realigned brain SPECT images of both groups were used to generate mean and standard deviation images by arithmetic operations on voxel based numerical values. Mean images of both groups were compared by applying the unpaired t-test on a voxel by voxel basis to generate three dimensional T-maps. X-ray CT images of individual subjects were evaluated by means of a computer program for brain atrophy. A significant decrease in relative radioisotope (RI) uptake was present in the bilateral superior and inferior parietal lobules (p<0.05), bilateral inferior temporal gyri, and the bilateral superior and middle frontal gyri (p<0.001). The mean brain atrophy indices for patients and normal subjects were 0.853±0.042 and 0.933±0.017 respectively, the difference being statistically significant (p<0.001). The use of a brain image standardization procedure increases the accuracy of voxel based group comparisons. Thus, intersubject averaging enhances the capacity for detection of abnormalities in functional brain images by minimizing the influence of individual variation. (author)

  16. Imaging Automation and Volume Tomographic Visualization at Texas Neutron Imaging Facility

    International Nuclear Information System (INIS)

    A thermal neutron imaging facility for real-time neutron radiography and computed tomography has been developed at the University of Texas reactor. The facility produced good-quality radiographs and two-dimensional tomograms. Further developments have been recently accomplished. A computer software has been developed to automate and expedite the data acquisition and reconstruction processes. Volume tomographic visualization using Interactive Data Language (IDL) software has been demonstrated and will be further developed. Volume tomography provides the additional flexibility of producing slices of the object using software and thus avoids redoing the measurements

  17. Imaging automation and volume tomographic visualization at Texas Neutron Imaging Facility

    International Nuclear Information System (INIS)

    A thermal neutron imaging facility for real-time neutron radiography and computed tomography has been developed at the University of Texas reactor. The facility produced a good-quality radiographs and two-dimensional tomograms. Further developments have been recently accomplished. Further developments have been recently accomplished. A computer software has been developed to automate and expedite the data acquisition and reconstruction processes. Volume tomographic visualization using Interactive Data Language (IDL) software has been demonstrated and will be further developed. Volume tomography provides the additional flexibility of producing slices of the object using software and thus avoids redoing the measurements

  18. 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.

    2012-06-01

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

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

    International Nuclear Information System (INIS)

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

  20. Automated Algorithm for Carotid Lumen Segmentation and 3D Reconstruction in B-mode images

    OpenAIRE

    Jorge M. S. Pereira; João Manuel R. S. Tavares

    2011-01-01

    The B-mode image system is one of the most popular systems used in the medical area; however it imposes several difficulties in the image segmentation process due to low contrast and noise. Although these difficulties, this image mode is often used in the study and diagnosis of the carotid artery diseases.In this paper, it is described the a novel automated algorithm for carotid lumen segmentation and 3-D reconstruction in B- mode images.

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

    Science.gov (United States)

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

    2015-01-01

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

  2. 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...

  3. Automated defect recognition method based on neighbor layer slice images of ICT

    International Nuclear Information System (INIS)

    The current automated defect recognition of industrial computerized tomography(ICT) slice images is mostly carried out in individual image. Certain false detections would exist for some isolated noises can not be wiped off without considering the information of neighbor layer images. To solve this problem,a new automated defect recognition method is presented based on a two-step analysis of consecutive slice images. First, all potential defects are segmented using a classic method in each image. Second, real defects and false defects are recognized by all potential defect matching of neighbor layer images in two steps based on the continuity of real defects characteristic and the non-continuity of false defects between the neighbor images. The method is verified by experiments and results prove that the real defects can be detected with high probability and false detections can be reduced effectively. (authors)

  4. 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.

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

    International Nuclear Information System (INIS)

    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. 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. 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. 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

  6. Automated striatal uptake analysis of 18F-FDOPA PET images applied to Parkinson's disease patients

    International Nuclear Information System (INIS)

    6-[18F]Fluoro-L-DOPA (FDOPA) is a radiopharmaceutical valuable for assessing the presynaptic dopaminergic function when used with positron emission tomography (PET). More specifically, the striatal-to-occipital ratio (SOR) of FDOPA uptake images has been extensively used as a quantitative parameter in these PET studies. Our aim was to develop an easy, automated method capable of performing objective analysis of SOR in FDOPA PET images of Parkinson's disease (PD) patients. Brain images from FDOPA PET studies of 21 patients with PD and 6 healthy subjects were included in our automated striatal analyses. Images of each individual were spatially normalized into an FDOPA template. Subsequently, the image slice with the highest level of basal ganglia activity was chosen among the series of normalized images. Also, the immediate preceding and following slices of the chosen image were then selected. Finally, the summation of these three images was used to quantify and calculate the SOR values. The results obtained by automated analysis were compared with manual analysis by a trained and experienced image processing technologist. The SOR values obtained from the automated analysis had a good agreement and high correlation with manual analysis. The differences in caudate, putamen, and striatum were -0.023, -0.029, and -0.025, respectively; correlation coefficients 0.961, 0.957, and 0.972, respectively. We have successfully developed a method for automated striatal uptake analysis of FDOPA PET images. There was no significant difference between the SOR values obtained from this method and using manual analysis. Yet it is an unbiased time-saving and cost-effective program and easy to implement on a personal computer. (author)

  7. 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. PMID:26358759

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

    Science.gov (United States)

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

    2016-01-01

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

  9. A novel automated image analysis method for accurate adipocyte quantification

    OpenAIRE

    Osman, Osman S.; Selway, Joanne L; Kępczyńska, Małgorzata A; Stocker, Claire J.; O’Dowd, Jacqueline F; Cawthorne, Michael A.; Arch, Jonathan RS; Jassim, Sabah; Langlands, Kenneth

    2013-01-01

    Increased adipocyte size and number are associated with many of the adverse effects observed in metabolic disease states. While methods to quantify such changes in the adipocyte are of scientific and clinical interest, manual methods to determine adipocyte size are both laborious and intractable to large scale investigations. Moreover, existing computational methods are not fully automated. We, therefore, developed a novel automatic method to provide accurate measurements of the cross-section...

  10. Microfluidics for single cell analysis

    DEFF Research Database (Denmark)

    Jensen, Marie Pødenphant

    cells, and simultaneously be fabricated and operated at low costs and be user-friendly. These challenges were addressed through development of two microfluidic devices, one for rare cell isolation based on pinched flow fractionation (PFF) and one for single cell capture based on hydrodynamic trapping....... Both devices were fabricated by injection moulding with a nickel master. CTC isolation was realised using PFF, which is a passive, size-based microfluidic technique. The focus was mainly on experimental work; however designs were based on flow calculations and analysed with numerical simulations to...

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

    Science.gov (United States)

    Chien, Steve; Mortensen, Helen

    1994-01-01

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

  12. Automated examination notification of Emergency Department images in a picture archiving and communication system

    OpenAIRE

    Andriole, Katherine P.; Avrin, David E.; Weber, Ellen; Luth, David M.; Bazzill, Todd M.

    2001-01-01

    This study compares the timeliness of radiology interpretation of Emergency Department (ED) imaging examinations in a picture archiving and communication system (PACS) before and after implementation of an automated paging system for notification of image availability. An alphanumeric pager for each radiology subspecialty (chest, pediatrics, bone, neuroradiology, and body) was used to alert the responsible radiologist that an ED imaging examination is available to be viewed on the PACS. The p...

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

    OpenAIRE

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

    2006-01-01

    The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features ...

  14. NeuronMetrics: Software for Semi-Automated Processing of Cultured-Neuron Images

    OpenAIRE

    Narro, Martha L.; Yang, Fan; Kraft, Robert; Wenk, Carola; Efrat, Alon; Restifo, Linda L.

    2007-01-01

    Using primary cell culture to screen for changes in neuronal morphology requires specialized analysis software. We developed NeuronMetrics™ for semi-automated, quantitative analysis of two-dimensional (2D) images of fluorescently labeled cultured neurons. It skeletonizes the neuron image using two complementary image-processing techniques, capturing fine terminal neurites with high fidelity. An algorithm was devised to span wide gaps in the skeleton. NeuronMetrics uses a novel strategy based ...

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

    Science.gov (United States)

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

    2015-11-01

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

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

    International Nuclear Information System (INIS)

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Siketić, Zdravko; Bogdanović Radović, Ivančica; Jakšić, Milko; Popović Hadžija, Marijana; Hadžija, Mirko [Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb (Croatia)

    2015-08-31

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

  18. 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. PMID:23465523

  19. Automative Multi Classifier Framework for Medical Image Analysis

    Directory of Open Access Journals (Sweden)

    R. Edbert Rajan

    2015-04-01

    Full Text Available Medical image processing is the technique used to create images of the human body for medical purposes. Nowadays, medical image processing plays a major role and a challenging solution for the critical stage in the medical line. Several researches have done in this area to enhance the techniques for medical image processing. However, due to some demerits met by some advanced technologies, there are still many aspects that need further development. Existing study evaluate the efficacy of the medical image analysis with the level-set shape along with fractal texture and intensity features to discriminate PF (Posterior Fossa tumor from other tissues in the brain image. To develop the medical image analysis and disease diagnosis, to devise an automotive subjective optimality model for segmentation of images based on different sets of selected features from the unsupervised learning model of extracted features. After segmentation, classification of images is done. The classification is processed by adapting the multiple classifier frameworks in the previous work based on the mutual information coefficient of the selected features underwent for image segmentation procedures. In this study, to enhance the classification strategy, we plan to implement enhanced multi classifier framework for the analysis of medical images and disease diagnosis. The performance parameter used for the analysis of the proposed enhanced multi classifier framework for medical image analysis is Multiple Class intensity, image quality, time consumption.

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

    Science.gov (United States)

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

    2016-04-01

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

  1. Automated image analysis of lateral lumber X-rays by a form model

    International Nuclear Information System (INIS)

    Development of a software for fully automated image analysis of lateral lumbar spine X-rays. Material and method: Using the concept of active shape models, we developed a software that produces a form model of the lumbar spine from lateral lumbar spine radiographs and runs an automated image segmentation. This model is able to detect lumbar vertebrae automatically after the filtering of digitized X-ray images. The model was trained with 20 lateral lumbar spine radiographs with no pathological findings before we evaluated the software with 30 further X-ray images which were sorted by image quality ranging from one (best) to three (worst). There were 10 images for each quality. Results: Image recognition strongly depended on image quality. In group one 52 and in group two 51 out of 60 vertebral bodies including the sacrum were recognized, but in group three only 18 vertebral bodies were properly identified. Conclusion: Fully automated and reliable recognition of vertebral bodies from lateral spine radiographs using the concept of active shape models is possible. The precision of this technique is limited by the superposition of different structures. Further improvements are necessary. Therefore standardized image quality and enlargement of the training data set are required. (orig.)

  2. Evaluation of an improved technique for automated center lumen line definition in cardiovascular image data

    International Nuclear Information System (INIS)

    The aim of the study was to evaluate a new method for automated definition of a center lumen line in vessels in cardiovascular image data. This method, called VAMPIRE, is based on improved detection of vessel-like structures. A multiobserver evaluation study was conducted involving 40 tracings in clinical CTA data of carotid arteries to compare VAMPIRE with an established technique. This comparison showed that VAMPIRE yields considerably more successful tracings and improved handling of stenosis, calcifications, multiple vessels, and nearby bone structures. We conclude that VAMPIRE is highly suitable for automated definition of center lumen lines in vessels in cardiovascular image data. (orig.)

  3. Evaluation of an improved technique for automated center lumen line definition in cardiovascular image data

    Energy Technology Data Exchange (ETDEWEB)

    Gratama van Andel, Hugo A.F. [Erasmus MC-University Medical Center Rotterdam, Department of Medical Informatics, Rotterdam (Netherlands); Erasmus MC-University Medical Center Rotterdam, Department of Radiology, Rotterdam (Netherlands); Academic Medical Centre-University of Amsterdam, Department of Medical Physics, Amsterdam (Netherlands); Meijering, Erik; Vrooman, Henri A.; Stokking, Rik [Erasmus MC-University Medical Center Rotterdam, Department of Medical Informatics, Rotterdam (Netherlands); Erasmus MC-University Medical Center Rotterdam, Department of Radiology, Rotterdam (Netherlands); Lugt, Aad van der; Monye, Cecile de [Erasmus MC-University Medical Center Rotterdam, Department of Radiology, Rotterdam (Netherlands)

    2006-02-01

    The aim of the study was to evaluate a new method for automated definition of a center lumen line in vessels in cardiovascular image data. This method, called VAMPIRE, is based on improved detection of vessel-like structures. A multiobserver evaluation study was conducted involving 40 tracings in clinical CTA data of carotid arteries to compare VAMPIRE with an established technique. This comparison showed that VAMPIRE yields considerably more successful tracings and improved handling of stenosis, calcifications, multiple vessels, and nearby bone structures. We conclude that VAMPIRE is highly suitable for automated definition of center lumen lines in vessels in cardiovascular image data. (orig.)

  4. Histogram analysis with automated extraction of brain-tissue region from whole-brain CT images

    OpenAIRE

    Kondo, Masatoshi; Yamashita, Koji; Yoshiura, Takashi; Hiwatash, Akio; Shirasaka, Takashi; Arimura, Hisao; Nakamura, Yasuhiko; Honda, Hiroshi

    2015-01-01

    To determine whether an automated extraction of the brain-tissue region from CT images is useful for the histogram analysis of the brain-tissue region was studied. We used the CT images of 11 patients. We developed an automatic brain-tissue extraction algorithm. We evaluated the similarity index of this automated extraction method relative to manual extraction, and we compared the mean CT number of all extracted pixels and the kurtosis and skewness of the distribution of CT numbers of all ext...

  5. Automated analysis of protein subcellular location in time series images

    OpenAIRE

    Hu, Yanhua; Osuna-Highley, Elvira; Hua, Juchang; Nowicki, Theodore Scott; Stolz, Robert; McKayle, Camille; Murphy, Robert F.

    2010-01-01

    Motivation: Image analysis, machine learning and statistical modeling have become well established for the automatic recognition and comparison of the subcellular locations of proteins in microscope images. By using a comprehensive set of features describing static images, major subcellular patterns can be distinguished with near perfect accuracy. We now extend this work to time series images, which contain both spatial and temporal information. The goal is to use temporal features to improve...

  6. 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...

  7. 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 Institute of Technology

    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

  8. Improved automated synthesis and preliminary animal PET/CT imaging of 11C-acetate

    International Nuclear Information System (INIS)

    To study a simple and rapid automated synthetic technology of 11C-acetate (11C- AC), automated synthesis of 11C-AC was performed by carboxylation of MeMgBr/tetrahydrofuran (THF) on a polyethylene loop with 11C-CO2, followed by hydrolysis and purification on solid-phase extraction cartridges using a 11C-Choline/Methionine synthesizer made in China. A high and reproducible radiochemical yield of above 40% (decay corrected) was obtained within the whole synthesis time about 8 min from 11C-CO2. The radiochemical purity of 11C-AC was over 95%. The novel, simple and rapid on-column hydrolysis-purification procedure should adaptable to the fully automated synthesis of 11C-AC at several commercial synthesis module. 11C-AC injection produced by the automated procedure is safe and effective, and can be used for PET imaging of animals and humans. (authors)

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

    Science.gov (United States)

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

    1999-10-26

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

  10. Comparison of semi-automated image analysis and manual methods for tissue quantification in pancreatic carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Sims, A.J. [Regional Medical Physics Department, Freeman Hospital, Newcastle upon Tyne (United Kingdom)]. E-mail: a.j.sims@newcastle.ac.uk; Murray, A. [Regional Medical Physics Department, Freeman Hospital, Newcastle upon Tyne (United Kingdom); Bennett, M.K. [Department of Histopathology, Newcastle upon Tyne Hospitals NHS Trust, Newcastle upon Tyne (United Kingdom)

    2002-04-21

    Objective measurements of tissue area during histological examination of carcinoma can yield valuable prognostic information. However, such measurements are not made routinely because the current manual approach is time consuming and subject to large statistical sampling error. In this paper, a semi-automated image analysis method for measuring tissue area in histological samples is applied to the measurement of stromal tissue, cell cytoplasm and lumen in samples of pancreatic carcinoma and compared with the standard manual point counting method. Histological samples from 26 cases of pancreatic carcinoma were stained using the sirius red, light-green method. Images from each sample were captured using two magnifications. Image segmentation based on colour cluster analysis was used to subdivide each image into representative colours which were classified manually into one of three tissue components. Area measurements made using this technique were compared to corresponding manual measurements and used to establish the comparative accuracy of the semi-automated image analysis technique, with a quality assurance study to measure the repeatability of the new technique. For both magnifications and for each tissue component, the quality assurance study showed that the semi-automated image analysis algorithm had better repeatability than its manual equivalent. No significant bias was detected between the measurement techniques for any of the comparisons made using the 26 cases of pancreatic carcinoma. The ratio of manual to semi-automatic repeatability errors varied from 2.0 to 3.6. Point counting would need to be increased to be between 400 and 1400 points to achieve the same repeatability as for the semi-automated technique. The results demonstrate that semi-automated image analysis is suitable for measuring tissue fractions in histological samples prepared with coloured stains and is a practical alternative to manual point counting. (author)

  11. Comparison of semi-automated image analysis and manual methods for tissue quantification in pancreatic carcinoma

    International Nuclear Information System (INIS)

    Objective measurements of tissue area during histological examination of carcinoma can yield valuable prognostic information. However, such measurements are not made routinely because the current manual approach is time consuming and subject to large statistical sampling error. In this paper, a semi-automated image analysis method for measuring tissue area in histological samples is applied to the measurement of stromal tissue, cell cytoplasm and lumen in samples of pancreatic carcinoma and compared with the standard manual point counting method. Histological samples from 26 cases of pancreatic carcinoma were stained using the sirius red, light-green method. Images from each sample were captured using two magnifications. Image segmentation based on colour cluster analysis was used to subdivide each image into representative colours which were classified manually into one of three tissue components. Area measurements made using this technique were compared to corresponding manual measurements and used to establish the comparative accuracy of the semi-automated image analysis technique, with a quality assurance study to measure the repeatability of the new technique. For both magnifications and for each tissue component, the quality assurance study showed that the semi-automated image analysis algorithm had better repeatability than its manual equivalent. No significant bias was detected between the measurement techniques for any of the comparisons made using the 26 cases of pancreatic carcinoma. The ratio of manual to semi-automatic repeatability errors varied from 2.0 to 3.6. Point counting would need to be increased to be between 400 and 1400 points to achieve the same repeatability as for the semi-automated technique. The results demonstrate that semi-automated image analysis is suitable for measuring tissue fractions in histological samples prepared with coloured stains and is a practical alternative to manual point counting. (author)

  12. 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.

  13. Automated, non-linear registration between 3-dimensional brain map and medical head image

    International Nuclear Information System (INIS)

    In this paper, we propose an automated, non-linear registration method between 3-dimensional medical head image and brain map in order to efficiently extract the regions of interest. In our method, input 3-dimensional image is registered into a reference image extracted from a brain map. The problems to be solved are automated, non-linear image matching procedure, and cost function which represents the similarity between two images. Non-linear matching is carried out by dividing the input image into connected partial regions, transforming the partial regions preserving connectivity among the adjacent images, evaluating the image similarity between the transformed regions of the input image and the correspondent regions of the reference image, and iteratively searching the optimal transformation of the partial regions. In order to measure the voxelwise similarity of multi-modal images, a cost function is introduced, which is based on the mutual information. Some experiments using MR images presented the effectiveness of the proposed method. (author)

  14. 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. PMID:26406038

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

    International Nuclear Information System (INIS)

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Wells, J; Wilson, J; Zhang, Y; Samei, E; Ravin, Carl E. [Advanced Imaging Laboratories, Duke Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC (United States)

    2014-06-01

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

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

    OpenAIRE

    Nathan S. Alexander; 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 f...

  18. 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

  19. 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 ($\

  20. Automated and unbiased image analyses as tools in phenotypic classification of small-spored Alternaria species

    DEFF Research Database (Denmark)

    Andersen, Birgitte; Hansen, Michael Edberg; Smedsgaard, Jørn

    2005-01-01

    often has been broadly applied to various morphologically and chemically distinct groups of isolates from different hosts. The purpose of this study was to develop and evaluate automated and unbiased image analysis systems that will analyze different phenotypic characters and facilitate testing...

  1. Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

    OpenAIRE

    D. Joshua Liao; Yusheng Huang; Xiaofen Xing; Hua Wang; Jian Liu; Hui Xiao; Zhuocai Wang; Xiaojun Ding; Xiangmin Xu

    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 cla...

  2. 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

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

    International Nuclear Information System (INIS)

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

  4. An image-processing program for automated counting

    Science.gov (United States)

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

    1996-01-01

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

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

    Science.gov (United States)

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

    1999-07-01

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

  6. Automated Drusen Segmentation and Quantification in SD-OCT Images

    OpenAIRE

    Chen, Qiang; Leng, Theodore; Zheng, Luoluo; Kutzscher, Lauren; Ma, Jeffrey; de Sisternes, Luis; Rubin, Daniel L.

    2013-01-01

    Spectral domain optical coherence tomography (SD-OCT) is a useful tool for the visualization of drusen, a retinal abnormality seen in patients with age-related macular degeneration (AMD); however, objective assessment of drusen is thwarted by the lack of a method to robustly quantify these lesions on serial OCT images. Here, we describe an automatic drusen segmentation method for SD-OCT retinal images, which leverages a priori knowledge of normal retinal morphology and anatomical features. Th...

  7. Automated detection of BB pixel clusters in digital fluoroscopic images

    International Nuclear Information System (INIS)

    Small ball bearings (BBs) are often used to characterize and correct for geometric distortion of x-ray image intensifiers. For quantitative applications the number of BBs required for accurate distortion correction is prohibitively large for manual detection. A method to automatically determine the BB coordinates is described. The technique consists of image segmentation, pixel coalescing and centroid calculation. The dependence of calculated BB coordinates on segmentation threshold was also evaluated and found to be within the uncertainty of measurement. (author)

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

    International Nuclear Information System (INIS)

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

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

    International Nuclear Information System (INIS)

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

  10. 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

  11. Single Cell Transfection through Precise Microinjection with Quantitatively Controlled Injection Volumes

    OpenAIRE

    Yu Ting Chow; Shuxun Chen; Ran Wang; Chichi Liu; Chi-wing Kong; Li, Ronald A.; Shuk Han Cheng; Dong Sun

    2016-01-01

    Cell transfection is a technique wherein foreign genetic molecules are delivered into cells. To elucidate distinct responses during cell genetic modification, methods to achieve transfection at the single-cell level are of great value. Herein, we developed an automated micropipette-based quantitative microinjection technology that can deliver precise amounts of materials into cells. The developed microinjection system achieved precise single-cell microinjection by pre-patterning cells in an a...

  12. 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.

  13. An Automated Platform for High-Resolution Tissue Imaging Using Nanospray Desorption Electrospray Ionization Mass Spectrometry

    Energy Technology Data Exchange (ETDEWEB)

    Lanekoff, Ingela T.; Heath, Brandi S.; Liyu, Andrey V.; Thomas, Mathew; Carson, James P.; Laskin, Julia

    2012-10-02

    An automated platform has been developed for acquisition and visualization of mass spectrometry imaging (MSI) data using nanospray desorption electrospray ionization (nano-DESI). The new system enables robust operation of the nano-DESI imaging source over many hours. This is achieved by controlling the distance between the sample and the probe by mounting the sample holder onto an automated XYZ stage and defining the tilt of the sample plane. This approach is useful for imaging of relatively flat samples such as thin tissue sections. Custom software called MSI QuickView was developed for visualization of large data sets generated in imaging experiments. MSI QuickView enables fast visualization of the imaging data during data acquisition and detailed processing after the entire image is acquired. The performance of the system is demonstrated by imaging rat brain tissue sections. High resolution mass analysis combined with MS/MS experiments enabled identification of lipids and metabolites in the tissue section. In addition, high dynamic range and sensitivity of the technique allowed us to generate ion images of low-abundance isobaric lipids. High-spatial resolution image acquired over a small region of the tissue section revealed the spatial distribution of an abundant brain metabolite, creatine, in the white and gray matter that is consistent with the literature data obtained using magnetic resonance spectroscopy.

  14. 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.

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

    Science.gov (United States)

    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.

  16. Automated detection of meteors in observed image sequence

    Science.gov (United States)

    Šimberová, Stanislava; Suk, Tomáš

    2015-12-01

    We propose a new detection technique based on statistical characteristics of images in the video sequence. These characteristics displayed in time enable to catch any bright track during the whole sequence. We applied our method to the image datacubes that are created from camera pictures of the night sky. Meteor flying through the Earth's atmosphere leaves a light trail lasting a few seconds on the sky background. We developed a special technique to recognize this event automatically in the complete observed video sequence. For further analysis leading to the precise recognition of object we suggest to apply Fourier and Hough transformations.

  17. Microfluidics for investigating single-cell biodynamics

    OpenAIRE

    Cookson, Scott Warren

    2008-01-01

    Progress in synthetic biology requires the development of novel techniques for investigating long-term dynamics in single cells. Here, we demonstrate the utility of microfluidics for investigating single-cell biodynamics within tightly-controlled environments in the model organisms Saccharomyces cerevisiae and Escherichia coli. First, we develop a microfluidic chemostat for monitoring single-cell gene expression within large populations of S. cerevisiae over many cellular generations. We over...

  18. Automation of the method gamma of comparison dosimetry images

    International Nuclear Information System (INIS)

    The objective of this work was the development of JJGAMMA application analysis software, which enables this task systematically, minimizing intervention specialist and therefore the variability due to the observer. Both benefits, allow comparison of images is done in practice with the required frequency and objectivity. (Author)

  19. Semi-automated recognition of protozoa by image analysis

    OpenAIRE

    A.L. Amaral; Baptiste, C; Pons, M. N.; Nicolau, Ana; Lima, Nelson; Ferreira, E. C.; Mota, M.; H. Vivier

    1999-01-01

    A programme was created to semi-automatically analyse protozoal digitised images. Principal Component Analysis technique was used for species identification. After data collection and mathematical treatment, a threedimensional representation was generated and several protozoa (Opercularia, Colpidium, Tetrahymena, Prorodon, Glaucoma and Trachelophyllum) species could be positively identified.

  20. 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.

  1. 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.

  2. Automated and Accurate Detection of Soma Location and Surface Morphology in Large-Scale 3D Neuron Images

    OpenAIRE

    Cheng Yan; Anan Li; Bin Zhang,; Wenxiang Ding; Qingming Luo; Hui Gong

    2013-01-01

    Automated and accurate localization and morphometry of somas in 3D neuron images is essential for quantitative studies of neural networks in the brain. However, previous methods are limited in obtaining the location and surface morphology of somas with variable size and uneven staining in large-scale 3D neuron images. In this work, we proposed a method for automated soma locating in large-scale 3D neuron images that contain relatively sparse soma distributions. This method involves three step...

  3. 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.

  4. Phenotype classification of single cells using SRS microscopy, RNA sequencing, and microfluidics (Conference Presentation)

    Science.gov (United States)

    Streets, Aaron M.; Cao, Chen; Zhang, Xiannian; Huang, Yanyi

    2016-03-01

    Phenotype classification of single cells reveals biological variation that is masked in ensemble measurement. This heterogeneity is found in gene and protein expression as well as in cell morphology. Many techniques are available to probe phenotypic heterogeneity at the single cell level, for example quantitative imaging and single-cell RNA sequencing, but it is difficult to perform multiple assays on the same single cell. In order to directly track correlation between morphology and gene expression at the single cell level, we developed a microfluidic platform for quantitative coherent Raman imaging and immediate RNA sequencing (RNA-Seq) of single cells. With this device we actively sort and trap cells for analysis with stimulated Raman scattering microscopy (SRS). The cells are then processed in parallel pipelines for lysis, and preparation of cDNA for high-throughput transcriptome sequencing. SRS microscopy offers three-dimensional imaging with chemical specificity for quantitative analysis of protein and lipid distribution in single cells. Meanwhile, the microfluidic platform facilitates single-cell manipulation, minimizes contamination, and furthermore, provides improved RNA-Seq detection sensitivity and measurement precision, which is necessary for differentiating biological variability from technical noise. By combining coherent Raman microscopy with RNA sequencing, we can better understand the relationship between cellular morphology and gene expression at the single-cell level.

  5. 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.

  6. Semi-automated segmentation of carotid artery total plaque volume from three dimensional ultrasound carotid imaging

    Science.gov (United States)

    Buchanan, D.; Gyacskov, I.; Ukwatta, E.; Lindenmaier, T.; Fenster, A.; Parraga, G.

    2012-03-01

    Carotid artery total plaque volume (TPV) is a three-dimensional (3D) ultrasound (US) imaging measurement of carotid atherosclerosis, providing a direct non-invasive and regional estimation of atherosclerotic plaque volume - the direct determinant of carotid stenosis and ischemic stroke. While 3DUS measurements of TPV provide the potential to monitor plaque in individual patients and in populations enrolled in clinical trials, until now, such measurements have been performed manually which is laborious, time-consuming and prone to intra-observer and inter-observer variability. To address this critical translational limitation, here we describe the development and application of a semi-automated 3DUS plaque volume measurement. This semi-automated TPV measurement incorporates three user-selected boundaries in two views of the 3DUS volume to generate a geometric approximation of TPV for each plaque measured. We compared semi-automated repeated measurements to manual segmentation of 22 individual plaques ranging in volume from 2mm3 to 151mm3. Mean plaque volume was 43+/-40mm3 for semi-automated and 48+/-46mm3 for manual measurements and these were not significantly different (p=0.60). Mean coefficient of variation (CV) was 12.0+/-5.1% for the semi-automated measurements.

  7. 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.

  8. Automated segmentation of pigmented skin lesions in multispectral imaging

    International Nuclear Information System (INIS)

    The aim of this study was to develop an algorithm for the automatic segmentation of multispectral images of pigmented skin lesions. The study involved 1700 patients with 1856 cutaneous pigmented lesions, which were analysed in vivo by a novel spectrophotometric system, before excision. The system is able to acquire a set of 15 different multispectral images at equally spaced wavelengths between 483 and 951 nm. An original segmentation algorithm was developed and applied to the whole set of lesions and was able to automatically contour them all. The obtained lesion boundaries were shown to two expert clinicians, who, independently, rejected 54 of them. The 97.1% contour accuracy indicates that the developed algorithm could be a helpful and effective instrument for the automatic segmentation of skin pigmented lesions. (note)

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

    DEFF Research Database (Denmark)

    Kling, Jens; Vestergaard, Jacob Schack; Dahl, Anders Bjorholm; Hansen, Thomas Willum; Larsen, Rasmus; Wagner, Jakob Birkedal

    structure in the image. The centers of the C-hexagons are displayed as nodes. To segment the image into “pure” and “impure” regions, like areas with residual amorphous contamination or defects e.g. holes, a sliding window approach is used. The magnitude of the Fourier transformation within a window is...... tensor B-splines is employed, which is deformed by matching model grid points with the C-hexagon centers. Dependent on the Cs and defocus-settings during microscopy these centers appear either dark or bright. One ends up with a deformed hexagonal tessellation, which can easily be transformed into a...... length scale, and at the same time lattice deformations can be visualized. The method will be refined to facilitate the detection of larger defects like holes and the determination of the edge terminations....

  10. 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

    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...... object class description, which can be employed to rapidly search images for new object instances. The proposed extensions concern enhanced shape representation, handling of homogeneous and heterogeneous textures, refinement optimization using Simulated Annealing and robust statistics. Finally, an...

  11. Pancreas++: Automated Quantification of Pancreatic Islet Cells in Microscopy Images

    OpenAIRE

    StuartMaudsley; BronwenMartin; JenniferLFiori

    2013-01-01

    The microscopic image analysis of pancreatic Islet of Langerhans morphology is crucial for the investigation of diabetes and metabolic diseases. Besides the general size of the islet, the percentage and relative position of glucagon-containing alpha-, and insulin-containing beta-cells is also important for pathophysiological analyses, especially in rodents. Hence, the ability to identify, quantify and spatially locate peripheral, and “involuted” alpha-cells in the islet core is an important a...

  12. Automation of disbond detection in aircraft fuselage through thermal image processing

    Science.gov (United States)

    Prabhu, D. R.; Winfree, W. P.

    1992-01-01

    A procedure for interpreting thermal images obtained during the nondestructive evaluation of aircraft bonded joints is presented. The procedure operates on time-derivative thermal images and resulted in a disbond image with disbonds highlighted. The size of the 'black clusters' in the output disbond image is a quantitative measure of disbond size. The procedure is illustrated using simulation data as well as data obtained through experimental testing of fabricated samples and aircraft panels. Good results are obtained, and, except in pathological cases, 'false calls' in the cases studied appeared only as noise in the output disbond image which was easily filtered out. The thermal detection technique coupled with an automated image interpretation capability will be a very fast and effective method for inspecting bonded joints in an aircraft structure.

  13. Imaging System for the Automated Determination of Microscopical Properties in Hardened Portland Concrete

    Energy Technology Data Exchange (ETDEWEB)

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

    2000-03-08

    During this CRADA, Honeywell FM and T and MoDOT personnel designed a unique scanning system (including both hardware and software) that can be used to perform an automated scan and evaluation of a concrete sample. The specific goals of the CRADA were: (1) Develop a combined system integration, image acquisition, and image analysis approach to mimic the manual scanning and evaluation process. Produce a prototype system which can: (a) automate the scanning process to improve its speed and efficiency; (b) reduce operator fatigue; and (c) improve the consistency of the evaluation process. (2) Capture and preserve the baseline knowledge used by the MoDOT experts in performing the evaluation process. At the present time, the evaluation expertise resides in two MoDOT personnel. Automation of the evaluation process will allow that knowledge to be captured, preserved, and used for training purposes. (3) Develop an approach for the image analysis which is flexible and extensible in order to accommodate the inevitable pathologies that arise in the evaluation process. Such pathologies include features such as cracks and fissures, voids filled with paste or debris, and multiple, overlapping voids. FM and T personnel used image processing, pattern recognition, and system integration skills developed for other Department of Energy applications to develop and test a prototype of an automated scanning system for concrete evaluation. MoDOT personnel provided all the basic hardware (microscope, camera, computer-controlled stage, etc.) for the prototype, supported FM and T in the acquisition of image data for software development, and provided their critical expert knowledge of the process of concrete evaluation. This combination of expertise was vital to the successful development of the prototype system.

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

    DEFF Research Database (Denmark)

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

    2015-01-01

    each containing 50 frames. The scans are acquired using a recently commercialized BK3000 ultrasound scanner (BK Ultrasound, Denmark). Matching pairs of in-vivo sequences, unprocessed and processed with the proposed method were visualized side by side and evaluated by 4 radiologists for image quality....... Wilcoxon signed-rank test was then applied to the ratings provided by radiologists. The average VAS score was highly positive 12.16 (p-value: 2.09 x 10-23) favoring the gain-adjusted scans with the proposed algorithm....

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

    DEFF Research Database (Denmark)

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

    2015-01-01

    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. Automation of image data processing. (Polish Title: Automatyzacja proces u przetwarzania danych obrazowych)

    Science.gov (United States)

    Preuss, R.

    2014-12-01

    This article discusses the current capabilities of automate processing of the image data on the example of using PhotoScan software by Agisoft. At present, image data obtained by various registration systems (metric and non - metric cameras) placed on airplanes, satellites, or more often on UAVs is used to create photogrammetric products. Multiple registrations of object or land area (large groups of photos are captured) are usually performed in order to eliminate obscured area as well as to raise the final accuracy of the photogrammetric product. Because of such a situation t he geometry of the resulting image blocks is far from the typical configuration of images. For fast images georeferencing automatic image matching algorithms are currently applied. They can create a model of a block in the local coordinate system or using initial exterior orientation and measured control points can provide image georeference in an external reference frame. In the case of non - metric image application, it is also possible to carry out self - calibration process at this stage. Image matching algorithm is also used in generation of dense point clouds reconstructing spatial shape of the object (area). In subsequent processing steps it is possible to obtain typical photogrammetric products such as orthomosaic, DSM or DTM and a photorealistic solid model of an object . All aforementioned processing steps are implemented in a single program in contrary to standard commercial software dividing all steps into dedicated modules. Image processing leading to final geo referenced products can be fully automated including sequential implementation of the processing steps at predetermined control parameters. The paper presents the practical results of the application fully automatic generation of othomosaic for both images obtained by a metric Vexell camera and a block of images acquired by a non - metric UAV system

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

    International Nuclear Information System (INIS)

    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. 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). 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. 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

  18. Automated Peripheral Neuropathy Assessment Using Optical Imaging and Foot Anthropometry.

    Science.gov (United States)

    Siddiqui, Hafeez-U R; Spruce, Michelle; Alty, Stephen R; Dudley, Sandra

    2015-08-01

    A large proportion of individuals who live with type-2 diabetes suffer from plantar sensory neuropathy. Regular testing and assessment for the condition is required to avoid ulceration or other damage to patient's feet. Currently accepted practice involves a trained clinician testing a patient's feet manually with a hand-held nylon monofilament probe. The procedure is time consuming, labor intensive, requires special training, is prone to error, and repeatability is difficult. With the vast increase in type-2 diabetes, the number of plantar sensory neuropathy sufferers has already grown to such an extent as to make a traditional manual test problematic. This paper presents the first investigation of a novel approach to automatically identify the pressure points on a given patient's foot for the examination of sensory neuropathy via optical image processing incorporating plantar anthropometry. The method automatically selects suitable test points on the plantar surface that correspond to those repeatedly chosen by a trained podiatrist. The proposed system automatically identifies the specific pressure points at different locations, namely the toe (hallux), metatarsal heads and heel (Calcaneum) areas. The approach is generic and has shown 100% reliability on the available database used. The database consists of Chinese, Asian, African, and Caucasian foot images. PMID:26186748

  19. 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.

  20. Micro-PIXE for single cell analysis

    International Nuclear Information System (INIS)

    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 combination of micro-PIXE with RBS and STIM (Scanning Transmission Ion Microscopy) allows the quantitative determination of trace metal content within sub-cellular compartments. The application of STIM analysis 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. Examples of micro-PIXE studies for sub-cellular imaging of trace elements in various fields of interest will be presented: in patho-physiology of trace elements involved in neurodegenerative diseases such as Parkinson's disease, and in toxicology of metals such as cobalt. (author)

  1. Integrated Electrowetting Nanoinjector for Single Cell Transfection

    OpenAIRE

    Elaheh Shekaramiz; Ganeshkumar Varadarajalu; Day, Philip J.; Kumar Wickramasinghe, H.

    2016-01-01

    Single cell transfection techniques are essential to understand the heterogeneity between cells. We have developed an integrated electrowetting nanoinjector (INENI) to transfect single cells. The high transfection efficiency, controlled dosage delivery and ease of INENI fabrication promote the widespread application of the INENI in cell transfection assays.

  2. Automated extraction of metastatic liver cancer regions from abdominal contrast CT images

    International Nuclear Information System (INIS)

    In this paper, automated extraction of metastatic liver cancer regions from abdominal contrast X-ray CT images is investigated. Because even in Japan, cases of metastatic liver cancers are increased due to recent Europeanization and/or Americanization of Japanese eating habits, development of a system for computer aided diagnosis of them is strongly expected. Our automated extraction procedure consists of following four steps; liver region extraction, density transformation for enhancement of cancer regions, segmentation for obtaining candidate cancer regions, and reduction of false positives by shape feature. Parameter values used in each step of the procedure are decided based on density and shape features of typical metastatic liver cancers. In experiments using practical 20 cases of metastatic liver tumors, it is shown that 56% of true cancers can be detected successfully from CT images by the proposed procedure. (author)

  3. An image-based, dual fluorescence reporter assay to evaluate the efficacy of shRNA for gene silencing at the single-cell level [v1; ref status: indexed, http://f1000r.es/2tt

    Directory of Open Access Journals (Sweden)

    Shin-ichiro Kojima

    2014-02-01

    Full Text Available RNA interference (RNAi is widely used to suppress gene expression in a specific manner. The efficacy of RNAi is mainly dependent on the sequence of small interfering RNA (siRNA in relation to the target mRNA. Although several algorithms have been developed for the design of siRNA, it is still difficult to choose a really effective siRNA from among multiple candidates. In this article, we report the development of an image-based, quantitative, ratiometric fluorescence reporter assay to evaluate the efficacy of RNAi at the single-cell level. Two fluorescence reporter constructs are used. One expresses the candidate small hairpin RNA (shRNA together with an enhanced green fluorescent protein (EGFP; the other expresses a 19-nt target sequence inserted into a cassette expressing a red fluorescent protein (either DsRed or mCherry. Effectiveness of the candidate shRNA is evaluated as the extent to which it knocks down expression of the red fluorescent protein. Thus, the red-to-green fluorescence intensity ratio (appropriately normalized to controls is used as the read-out for quantifying the siRNA efficacy at the individual cell level. We tested this dual fluorescence assay and compared predictions to actual endogenous knockdown levels for three different genes (vimentin, lamin A/C and Arp3 and twenty different shRNAs. For each of the genes, our assay successfully predicted the target sequences for effective RNAi. To further facilitate testing of RNAi efficacy, we developed a negative selection marker (ccdB method for construction of shRNA and red fluorescent reporter plasmids that allowed us to purify these plasmids directly from transformed bacteria without the need for colony selection and DNA sequencing verification.

  4. An image-based, dual fluorescence reporter assay to evaluate the efficacy of shRNA for gene silencing at the single-cell level [v2; ref status: indexed, http://f1000r.es/39j

    Directory of Open Access Journals (Sweden)

    Shin-ichiro Kojima

    2014-05-01

    Full Text Available RNA interference (RNAi is widely used to suppress gene expression in a specific manner. The efficacy of RNAi is mainly dependent on the sequence of small interfering RNA (siRNA in relation to the target mRNA. Although several algorithms have been developed for the design of siRNA, it is still difficult to choose a really effective siRNA from among multiple candidates. In this article, we report the development of an image-based, quantitative, ratiometric fluorescence reporter assay to evaluate the efficacy of RNAi at the single-cell level. Two fluorescence reporter constructs are used. One expresses the candidate small hairpin RNA (shRNA together with an enhanced green fluorescent protein (EGFP; the other expresses a 19-nt target sequence inserted into a cassette expressing a red fluorescent protein (either DsRed or mCherry. Effectiveness of the candidate shRNA is evaluated as the extent to which it knocks down expression of the red fluorescent protein. Thus, the red-to-green fluorescence intensity ratio (appropriately normalized to controls is used as the read-out for quantifying the siRNA efficacy at the individual cell level. We tested this dual fluorescence assay and compared predictions to actual endogenous knockdown levels for three different genes (vimentin, lamin A/C and Arp3 and twenty different shRNAs. For each of the genes, our assay successfully predicted the target sequences for effective RNAi. To further facilitate testing of RNAi efficacy, we developed a negative selection marker (ccdB method for construction of shRNA and red fluorescent reporter plasmids that allowed us to purify these plasmids directly from transformed bacteria without the need for colony selection and DNA sequencing verification.

  5. 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.

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

    International Nuclear Information System (INIS)

    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. 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. 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. 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 (graded) by different pathologists. The region of interest

  7. Simple Tool for Semi-automated Evaluation of Yeast Colony Images

    Czech Academy of Sciences Publication Activity Database

    Schier, Jan; Kovář, Bohumil

    Berlin: Springer, 2013 - (Fred, A.; Filipe, J.; Gamboa, H.), s. 110-125. (Communications in Computer and Information Science. 273). ISBN 978-3-642-29751-9 R&D Projects: GA TA ČR TA01010931 Institutional support: RVO:67985556 Keywords : Colony counting * Petri dish evaluation * software tool Subject RIV: JC - Computer Hardware ; Software http://library.utia.cas.cz/separaty/2012/ZS/schier-simple tool for semi-automated evaluation of yeast colony image s.pdf

  8. Characterization of the microstructure of dairy systems using automated image analysis

    OpenAIRE

    Silva, Juliana V.C.; Legland, David; Cauty, Chantal; Kolotuev, Irina; Floury, Juliane

    2015-01-01

    A sound understanding of the microstructure of dairy products is of great importance in order to predict and control their properties and final quality. The aim of this study was to develop an automated image analysis procedure to characterize the microstructure of different dairy systems. A high pressure freezing coupled with freeze-substitution (HPF-FS) protocol was applied prior to transmission electron microscopy(TEM) in order to minimize any modification of the microstructure of the dair...

  9. The impact of air pollution on the level of micronuclei measured by automated image analysis

    Czech Academy of Sciences Publication Activity Database

    Rössnerová, Andrea; Špátová, Milada; Rossner, P.; Solanský, I.; Šrám, Radim

    2009-01-01

    Roč. 669, 1-2 (2009), s. 42-47. ISSN 0027-5107 R&D Projects: GA AV ČR 1QS500390506; GA MŠk 2B06088; GA MŠk 2B08005 Institutional research plan: CEZ:AV0Z50390512 Keywords : micronuclei * binucleated cells * automated image analysis Subject RIV: DN - Health Impact of the Environment Quality Impact factor: 3.556, year: 2009

  10. 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. PMID:10805018

  11. Development of a methodology for automated assessment of the quality of digitized images in mammography

    International Nuclear Information System (INIS)

    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. The purpose of this study is to develop a computational methodology to automate the process of assessing the quality of mammography images through techniques of digital imaging processing (PDI), using an existing image processing environment (ImageJ). With the application of PDI techniques was possible to extract geometric and radiometric characteristics of the images evaluated. The evaluated parameters include spatial resolution, high-contrast detail, low contrast threshold, linear detail of low contrast, tumor masses, contrast ratio and background optical density. The results obtained by this method were compared with the results presented in the visual evaluations performed by the Health Surveillance of Minas Gerais. Through this comparison was possible to demonstrate that the automated methodology is presented as a promising alternative for the reduction or elimination of existing subjectivity in the visual assessment methodology currently in use. (author)

  12. 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.

  13. Automating PACS quality control with the Vanderbilt image processing enterprise resource

    Science.gov (United States)

    Esparza, Michael L.; Welch, E. Brian; Landman, Bennett A.

    2012-02-01

    Precise image acquisition is an integral part of modern patient care and medical imaging research. Periodic quality control using standardized protocols and phantoms ensures that scanners are operating according to specifications, yet such procedures do not ensure that individual datasets are free from corruption; for example due to patient motion, transient interference, or physiological variability. If unacceptable artifacts are noticed during scanning, a technologist can repeat a procedure. Yet, substantial delays may be incurred if a problematic scan is not noticed until a radiologist reads the scans or an automated algorithm fails. Given scores of slices in typical three-dimensional scans and widevariety of potential use cases, a technologist cannot practically be expected inspect all images. In large-scale research, automated pipeline systems have had great success in achieving high throughput. However, clinical and institutional workflows are largely based on DICOM and PACS technologies; these systems are not readily compatible with research systems due to security and privacy restrictions. Hence, quantitative quality control has been relegated to individual investigators and too often neglected. Herein, we propose a scalable system, the Vanderbilt Image Processing Enterprise Resource (VIPER) to integrate modular quality control and image analysis routines with a standard PACS configuration. This server unifies image processing routines across an institutional level and provides a simple interface so that investigators can collaborate to deploy new analysis technologies. VIPER integrates with high performance computing environments has successfully analyzed all standard scans from our institutional research center over the course of the last 18 months.

  14. An Automated System for the Detection of Stratified Squamous Epithelial Cancer Cell Using Image Processing Techniques

    Directory of Open Access Journals (Sweden)

    Ram Krishna Kumar

    2013-06-01

    Full Text Available Early detection of cancer disease is a difficult problem and if it is not detected in starting phase the cancer can be fatal. Current medical procedures which are used to diagnose the cancer in body partsare time taking and more laboratory work is required for them. This work is an endeavor to possible recognition of cancer cells in the body part. The process consists of image taken of the affected area and digital image processing of the images to get a morphological pattern which differentiate normal cell to cancer cell. The technique is different than visual inspection and biopsy process. Image processing enables the visualization of cellular structure with substantial resolution. The aim of the work is to exploit differences in cellular organization between cancerous and normal tissue using image processing technique, thus allowing for automated, fast and accurate diagnosis.

  15. Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

    Science.gov (United States)

    Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

    2009-09-01

    We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

  16. 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. PMID:26636114

  17. Automated Functional Morphology Measurement Using Cardiac SPECT Images

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Seok Yoon; Ko, Seong Jin; Kang, Se Sik; Kim, Chang Soo; Kim, Jung Hoon [Dept. of Radiological Science, College of Health Sciences, Catholic University of Pusan, Pusan (Korea, Republic of)

    2012-06-15

    For the examination of nuclear medicine, myocardial scan is a good method to evaluate a hemodynamic importance of coronary heart disease. but, the automatized qualitative measurement is additionally necessary to improve the decoding efficiency. we suggests the creation of cardiac three-dimensional model and model of three-dimensional cardiac thickness as a new measurement. For the experiment, cardiac reduced cross section was obtained from SPECT. Next, the pre-process was performed and image segmentation was fulfilled by level set. for the modeling of left cardiac thickness, it was realized by applying difference equation of two-dimensional laplace equation. As the result of experiment, it was successful to measure internal wall and external wall and three-dimensional modeling was realized by coordinate. and, with laplace formula, it was successful to develop the thickness of cardiac wall. through the three-dimensional model, defects were observed easily and position of lesion was grasped rapidly by the revolution of model. The model which was developed as the support index of decoding will provide decoding information to doctor additionally and reduce the rate of false diagnosis as well as play a great role for diagnosing IHD early.

  18. 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

  19. 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

  20. Automation Aspects for the Georeferencing of Photogrammetric Aerial Image Archives in Forested Scenes

    Directory of Open Access Journals (Sweden)

    Kimmo Nurminen

    2015-02-01

    Full Text Available Photogrammetric aerial film image archives are scanned into digital form in many countries. These data sets offer an interesting source of information for scientists from different disciplines. The objective of this investigation was to contribute to the automation of a generation of 3D environmental model time series when using small-scale airborne image archives, especially in forested scenes. Furthermore, we investigated the usability of dense digital surface models (DSMs generated using these data sets as well as the uncertainty propagation of the DSMs. A key element in the automation is georeferencing. It is obvious that for images captured years apart, it is essential to find ground reference locations that have changed as little as possible. We studied a 68-year-long aerial image time series in a Finnish Karelian forestland. The quality of candidate ground locations was evaluated by comparing digital DSMs created from the images to an airborne laser scanning (ALS-originated reference DSM. The quality statistics of DSMs were consistent with the expectations; the estimated median root mean squared error for height varied between 0.3 and 2 m, indicating a photogrammetric modelling error of 0.1‰ with respect to flying height for data sets collected since the 1980s, and 0.2‰ for older data sets. The results show that of the studied land cover classes, “peatland without trees” changed the least over time and is one of the most promising candidates to serve as a location for automatic ground control measurement. Our results also highlight some potential challenges in the process as well as possible solutions. Our results indicate that using modern photogrammetric techniques, it is possible to reconstruct 3D environmental model time series using photogrammetric image archives in a highly automated way.

  1. 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.

  2. 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. 

  3. An algorithm for automated analysis of ultrasound images to measure tendon excursion in vivo.

    Science.gov (United States)

    Lee, Sabrina S M; Lewis, Gregory S; Piazza, Stephen J

    2008-02-01

    The accuracy of an algorithm for the automated tracking of tendon excursion from ultrasound images was tested in three experiments. Because the automated method could not be tested against direct measurements of tendon excursion in vivo, an indirect validation procedure was employed. In one experiment, a wire "phantom" was moved a known distance across the ultrasound probe and the automated tracking results were compared with the known distance. The excursion of the musculotendinous junction of the gastrocnemius during frontal and sagittal plane movement of the ankle was assessed in a single cadaver specimen both by manual tracking and with a cable extensometer sutured to the gastrocnemius muscle. A third experiment involved estimation of Achilles tendon excursion in vivo with both manual and automated tracking. Root mean squared (RMS) error was calculated between pairs of measurements after each test. Mean RMS errors of less than 1 mm were observed for the phantom experiments. For the in vitro experiment, mean RMS errors of 8-9% of the total tendon excursion were observed. Mean RMS errors of 6-8% of the total tendon excursion were found in vivo. The results indicate that the proposed algorithm accurately tracks Achilles tendon excursion, but further testing is necessary to determine its general applicability. PMID:18309186

  4. Automated collection of medical images for research from heterogeneous systems: trials and tribulations

    Science.gov (United States)

    Patel, M. N.; Looney, P.; Young, K.; Halling-Brown, M. D.

    2014-03-01

    Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. Over the past two decades both diagnostic and therapeutic imaging have undergone a rapid growth, the ability to be able to harness this large influx of medical images can provide an essential resource for research and training. Traditionally, the systematic collection of medical images for research from heterogeneous sites has not been commonplace within the NHS and is fraught with challenges including; data acquisition, storage, secure transfer and correct anonymisation. Here, we describe a semi-automated system, which comprehensively oversees the collection of both unprocessed and processed medical images from acquisition to a centralised database. The provision of unprocessed images within our repository enables a multitude of potential research possibilities that utilise the images. Furthermore, we have developed systems and software to integrate these data with their associated clinical data and annotations providing a centralised dataset for research. Currently we regularly collect digital mammography images from two sites and partially collect from a further three, with efforts to expand into other modalities and sites currently ongoing. At present we have collected 34,014 2D images from 2623 individuals. In this paper we describe our medical image collection system for research and discuss the wide spectrum of challenges faced during the design and implementation of such systems.

  5. 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

  6. 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.

  7. ATOM - an OMERO add-on for automated import of image data

    Directory of Open Access Journals (Sweden)

    Lipp Peter

    2011-10-01

    Full Text Available Abstract Background Modern microscope platforms are able to generate multiple gigabytes of image data in a single experimental session. In a routine research laboratory workflow, these data are initially stored on the local acquisition computer from which files need to be transferred to the experimenter's (remote image repository (e.g., DVDs, portable hard discs or server-based storage because of limited local data storage. Although manual solutions for this migration, such as OMERO - a client-server software for visualising and managing large amounts of image data - exist, this import process may be a time-consuming and tedious task. Findings We have developed ATOM, a Java-based and thus platform-independent add-on for OMERO enabling automated transfer of image data from a wide variety of acquisition software packages into OMERO. ATOM provides a graphical user interface and allows pre-organisation of experimental data for the transfer. Conclusions ATOM is a convenient extension of the OMERO software system. An automated interface to OMERO will be a useful tool for scientists working with file formats supported by the Bio-Formats file format library, a platform-independent library for reading the most common file formats of microscope images.

  8. 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.

  9. High-Throughput Secondary Screening at the Single-Cell Level

    OpenAIRE

    Robinson, J. Paul; Patsekin, Valery; Holdman, Cheryl; Ragheb, Kathy; Sturgis, Jennifer; Fatig, Ray; Avramova, Larisa V.; Rajwa, Bartek; Davisson, V. Jo; Lewis, Nicole; Narayanan, Padma; Li, Nianyu; Qualls, C. W.

    2012-01-01

    We have developed an automated system for drug screening using a single-cell–multiple functional response technology. The approach uses a semiautomated preparatory system, high-speed sample collection, and a unique analytical tool that provides instantaneous results for compound dilutions using 384-well plates. The combination of automation and rapid robotic sampling increases quality control and robustness. High-speed flow cytometry is used to collect single-cell results together with a newl...

  10. Automated quantification technology for cerebrospinal fluid dynamics based on magnetic resonance image analysis

    International Nuclear Information System (INIS)

    Time-spatial labeling inversion pulse (Time-SLIP) technology, which is a non-contrast-enhanced magnetic resonance imaging (MRI) technology for the visualization of blood flow and cerebrospinal fluid (CSF) dynamics, is used for diagnosis of neurological diseases related to CSF including idiopathic normal-pressure hydrocephalus (iNPH), one of the causes of dementia. However, physicians must subjectively evaluate the velocity of CSF dynamics through observation of Time-SLIP images because no quantification technology exists that can express the values numerically. To address this issue, Toshiba, in cooperation with Toshiba Medical Systems Corporation and Toshiba Rinkan Hospital, has developed an automated quantification technology for CSF dynamics utilizing MR image analysis. We have confirmed the effectiveness of this technology through verification tests using a water phantom and quantification experiments using images of healthy volunteers. (author)

  11. Magnetic levitation of single cells

    OpenAIRE

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

    2015-01-01

    Cells consist of micro- and nanoscale components and materials that contribute to their fundamental magnetic and density signatures. Previous studies have claimed that magnetic levitation can only be used to measure density signatures of nonliving materials. Here, we demonstrate that both eukaryotic and prokaryotic cells can be levitated and that each cell has a unique levitation profile. Furthermore, our levitation platform uniquely enables ultrasensitive density measurements, imaging, and p...

  12. Protein expression analyses at the single cell level.

    Science.gov (United States)

    Ohno, Masae; Karagiannis, Peter; Taniguchi, Yuichi

    2014-01-01

    The central dogma of molecular biology explains how genetic information is converted into its end product, proteins, which are responsible for the phenotypic state of the cell. Along with the protein type, the phenotypic state depends on the protein copy number. Therefore, quantification of the protein expression in a single cell is critical for quantitative characterization of the phenotypic states. Protein expression is typically a dynamic and stochastic phenomenon that cannot be well described by standard experimental methods. As an alternative, fluorescence imaging is being explored for the study of protein expression, because of its high sensitivity and high throughput. Here we review key recent progresses in fluorescence imaging-based methods and discuss their application to proteome analysis at the single cell level. PMID:25197931

  13. Protein Expression Analyses at the Single Cell Level

    Directory of Open Access Journals (Sweden)

    Masae Ohno

    2014-09-01

    Full Text Available The central dogma of molecular biology explains how genetic information is converted into its end product, proteins, which are responsible for the phenotypic state of the cell. Along with the protein type, the phenotypic state depends on the protein copy number. Therefore, quantification of the protein expression in a single cell is critical for quantitative characterization of the phenotypic states. Protein expression is typically a dynamic and stochastic phenomenon that cannot be well described by standard experimental methods. As an alternative, fluorescence imaging is being explored for the study of protein expression, because of its high sensitivity and high throughput. Here we review key recent progresses in fluorescence imaging-based methods and discuss their application to proteome analysis at the single cell level.

  14. 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.

  15. Can Automated Imaging for Optic Disc and Retinal Nerve Fiber Layer Analysis Aid Glaucoma Detection?

    Science.gov (United States)

    Banister, Katie; Boachie, Charles; Bourne, Rupert; Cook, Jonathan; Burr, Jennifer M.; Ramsay, Craig; Garway-Heath, David; Gray, Joanne; McMeekin, Peter; Hernández, Rodolfo; Azuara-Blanco, Augusto

    2016-01-01

    Purpose To compare the diagnostic performance of automated imaging for glaucoma. Design Prospective, direct comparison study. Participants Adults with suspected glaucoma or ocular hypertension referred to hospital eye services in the United Kingdom. Methods We evaluated 4 automated imaging test algorithms: the Heidelberg Retinal Tomography (HRT; Heidelberg Engineering, Heidelberg, Germany) glaucoma probability score (GPS), the HRT Moorfields regression analysis (MRA), scanning laser polarimetry (GDx enhanced corneal compensation; Glaucoma Diagnostics (GDx), Carl Zeiss Meditec, Dublin, CA) nerve fiber indicator (NFI), and Spectralis optical coherence tomography (OCT; Heidelberg Engineering) retinal nerve fiber layer (RNFL) classification. We defined abnormal tests as an automated classification of outside normal limits for HRT and OCT or NFI ≥ 56 (GDx). We conducted a sensitivity analysis, using borderline abnormal image classifications. The reference standard was clinical diagnosis by a masked glaucoma expert including standardized clinical assessment and automated perimetry. We analyzed 1 eye per patient (the one with more advanced disease). We also evaluated the performance according to severity and using a combination of 2 technologies. Main Outcome Measures Sensitivity and specificity, likelihood ratios, diagnostic, odds ratio, and proportion of indeterminate tests. Results We recruited 955 participants, and 943 were included in the analysis. The average age was 60.5 years (standard deviation, 13.8 years); 51.1% were women. Glaucoma was diagnosed in at least 1 eye in 16.8%; 32% of participants had no glaucoma-related findings. The HRT MRA had the highest sensitivity (87.0%; 95% confidence interval [CI], 80.2%–92.1%), but lowest specificity (63.9%; 95% CI, 60.2%–67.4%); GDx had the lowest sensitivity (35.1%; 95% CI, 27.0%–43.8%), but the highest specificity (97.2%; 95% CI, 95.6%–98.3%). The HRT GPS sensitivity was 81.5% (95% CI, 73.9%–87.6%), and

  16. Single cell enzyme diagnosis on the chip

    DEFF Research Database (Denmark)

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

    2013-01-01

    Conventional diagnosis based on ensemble measurements often overlooks the variation among cells. Here, we present a droplet-microfluidics based platform to investigate single cell activities. Adopting a previously developed isothermal rolling circle amplification-based assay, we demonstrate detec...

  17. Automated detection of galaxy-scale gravitational lenses in high resolution imaging data

    CERN Document Server

    Marshall, Philip J; Moustakas, Leonidas A; Fassnacht, Christopher D; Bradac, Marusa; Schrabback, Tim; Blandford, Roger D

    2008-01-01

    Lens modeling is the key to successful and meaningful automated strong galaxy-scale gravitational lens detection. We have implemented a lens-modeling "robot" that treats every bright red galaxy (BRG) in a large imaging survey as a potential gravitational lens system. Using a simple model optimized for "typical" galaxy-scale lenses, we generate four assessments of model quality that are used in an automated classification. The robot infers the lens classification parameter H that a human would have assigned; the inference is performed using a probability distribution generated from a human-classified training set, including realistic simulated lenses and known false positives drawn from the HST/EGS survey. We compute the expected purity, completeness and rejection rate, and find that these can be optimized for a particular application by changing the prior probability distribution for H, equivalent to defining the robot's "character." Adopting a realistic prior based on the known abundance of lenses, we find t...

  18. 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.

  19. 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...

  20. Automated classification of optical coherence tomography images of human atrial tissue.

    Science.gov (United States)

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

    2016-10-01

    Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions. PMID:26926869

  1. AUTOMATED DETECTION OF OIL DEPOTS FROM HIGH RESOLUTION IMAGES: A NEW PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    A. O. Ok

    2015-03-01

    Full Text Available This paper presents an original approach to identify oil depots from single high resolution aerial/satellite images in an automated manner. The new approach considers the symmetric nature of circular oil depots, and it computes the radial symmetry in a unique way. An automated thresholding method to focus on circular regions and a new measure to verify circles are proposed. Experiments are performed on six GeoEye-1 test images. Besides, we perform tests on 16 Google Earth images of an industrial test site acquired in a time series manner (between the years 1995 and 2012. The results reveal that our approach is capable of detecting circle objects in very different/difficult images. We computed an overall performance of 95.8% for the GeoEye-1 dataset. The time series investigation reveals that our approach is robust enough to locate oil depots in industrial environments under varying illumination and environmental conditions. The overall performance is computed as 89.4% for the Google Earth dataset, and this result secures the success of our approach compared to a state-of-the-art approach.

  2. Fully automated muscle quality assessment by Gabor filtering of second harmonic generation images

    Science.gov (United States)

    Paesen, Rik; Smolders, Sophie; Vega, José Manolo de Hoyos; Eijnde, Bert O.; Hansen, Dominique; Ameloot, Marcel

    2016-02-01

    Although structural changes on the sarcomere level of skeletal muscle are known to occur due to various pathologies, rigorous studies of the reduced sarcomere quality remain scarce. This can possibly be explained by the lack of an objective tool for analyzing and comparing sarcomere images across biological conditions. Recent developments in second harmonic generation (SHG) microscopy and increasing insight into the interpretation of sarcomere SHG intensity profiles have made SHG microscopy a valuable tool to study microstructural properties of sarcomeres. Typically, sarcomere integrity is analyzed by fitting a set of manually selected, one-dimensional SHG intensity profiles with a supramolecular SHG model. To circumvent this tedious manual selection step, we developed a fully automated image analysis procedure to map the sarcomere disorder for the entire image at once. The algorithm relies on a single-frequency wavelet-based Gabor approach and includes a newly developed normalization procedure allowing for unambiguous data interpretation. The method was validated by showing the correlation between the sarcomere disorder, quantified by the M-band size obtained from manually selected profiles, and the normalized Gabor value ranging from 0 to 1 for decreasing disorder. Finally, to elucidate the applicability of our newly developed protocol, Gabor analysis was used to study the effect of experimental autoimmune encephalomyelitis on the sarcomere regularity. We believe that the technique developed in this work holds great promise for high-throughput, unbiased, and automated image analysis to study sarcomere integrity by SHG microscopy.

  3. MAGNETIC RESONANCE IMAGING COMPATIBLE ROBOTIC SYSTEM FOR FULLY AUTOMATED BRACHYTHERAPY SEED PLACEMENT

    Science.gov (United States)

    Muntener, Michael; Patriciu, Alexandru; Petrisor, Doru; Mazilu, Dumitru; Bagga, Herman; Kavoussi, Louis; Cleary, Kevin; Stoianovici, Dan

    2011-01-01

    Objectives To introduce the development of the first magnetic resonance imaging (MRI)-compatible robotic system capable of automated brachytherapy seed placement. Methods An MRI-compatible robotic system was conceptualized and manufactured. The entire robot was built of nonmagnetic and dielectric materials. The key technology of the system is a unique pneumatic motor that was specifically developed for this application. Various preclinical experiments were performed to test the robot for precision and imager compatibility. Results The robot was fully operational within all closed-bore MRI scanners. Compatibility tests in scanners of up to 7 Tesla field intensity showed no interference of the robot with the imager. Precision tests in tissue mockups yielded a mean seed placement error of 0.72 ± 0.36 mm. Conclusions The robotic system is fully MRI compatible. The new technology allows for automated and highly accurate operation within MRI scanners and does not deteriorate the MRI quality. We believe that this robot may become a useful instrument for image-guided prostate interventions. PMID:17169653

  4. An Automated Images-to-Graphs Framework for High Resolution Connectomics

    Directory of Open Access Journals (Sweden)

    William R Gray Roncal

    2015-08-01

    Full Text Available Reconstructing a map of neuronal connectivity is a critical challenge in contemporary neuroscience. Recent advances in high-throughput serial section electron microscopy (EM have produced massive 3D image volumes of nanoscale brain tissue for the first time. The resolution of EM allows for individual neurons and their synaptic connections to be directly observed. Recovering neuronal networks by manually tracing each neuronal process at this scale is unmanageable, and therefore researchers are developing automated image processing modules. Thus far, state-of-the-art algorithms focus only on the solution to a particular task (e.g., neuron segmentation or synapse identification. In this manuscript we present the first fully automated images-to-graphs pipeline (i.e., a pipeline that begins with an imaged volume of neural tissue and produces a brain graph without any human interaction. To evaluate overall performance and select the best parameters and methods, we also develop a metric to assess the quality of the output graphs. We evaluate a set of algorithms and parameters, searching possible operating points to identify the best available brain graph for our assessment metric. Finally, we deploy a reference end-to-end version of the pipeline on a large, publicly available data set. This provides a baseline result and framework for community analysis and future algorithm development and testing. All code and data derivatives have been made publicly available toward eventually unlocking new biofidelic computational primitives and understanding of neuropathologies.

  5. Efficient synergistic single-cell genome assembly

    Directory of Open Access Journals (Sweden)

    Narjes S. Movahedi

    2016-05-01

    Full Text Available As the vast majority of all microbes are unculturable, single-cell sequencing has become a significant method to gain insight into microbial physiology. Single-cell sequencing methods, currently powered by multiple displacement genome amplification (MDA, have passed important milestones such as finishing and closing the genome of a prokaryote. However, the quality and reliability of genome assemblies from single cells are still unsatisfactory due to uneven coverage depth and the absence of scattered chunks of the genome in the final collection of reads caused by MDA bias. In this work, our new algorithm Hybrid De novo Assembler (HyDA demonstrates the power of co-assembly of multiple single-cell genomic data sets through significant improvement of the assembly quality in terms of predicted functional elements and length statistics. Co-assemblies contain significantly more base pairs and protein coding genes, cover more subsystems, and consist of longer contigs compared to individual assemblies by the same algorithm as well as state-of-the-art single-cell assemblers SPAdes and IDBA-UD. Hybrid emph{De novo} Assembler (HyDA is also able to avoid chimeric assemblies by detecting and separating shared and exclusive pieces of sequence for input data sets. By replacing one deep single-cell sequencing experiment with a few single-cell sequencing experiments of lower depth, the co-assembly method can hedge against the risk of failure and loss of the sample, without significantly increasing sequencing cost. Application of the single-cell co-assembler HyDA to the study of three uncultured members of an alkane-degrading methanogenic community validated the usefulness of the co-assembly concept. HyDA is open source and publicly available at http://chitsazlab.org/software.html and the raw reads are available at http://chitsazlab.org/research.html.

  6. Efficient Synergistic Single-Cell Genome Assembly.

    Science.gov (United States)

    Movahedi, Narjes S; Embree, Mallory; Nagarajan, Harish; Zengler, Karsten; Chitsaz, Hamidreza

    2016-01-01

    As the vast majority of all microbes are unculturable, single-cell sequencing has become a significant method to gain insight into microbial physiology. Single-cell sequencing methods, currently powered by multiple displacement genome amplification (MDA), have passed important milestones such as finishing and closing the genome of a prokaryote. However, the quality and reliability of genome assemblies from single cells are still unsatisfactory due to uneven coverage depth and the absence of scattered chunks of the genome in the final collection of reads caused by MDA bias. In this work, our new algorithm Hybrid De novo Assembler (HyDA) demonstrates the power of coassembly of multiple single-cell genomic data sets through significant improvement of the assembly quality in terms of predicted functional elements and length statistics. Coassemblies contain significantly more base pairs and protein coding genes, cover more subsystems, and consist of longer contigs compared to individual assemblies by the same algorithm as well as state-of-the-art single-cell assemblers SPAdes and IDBA-UD. Hybrid De novo Assembler (HyDA) is also able to avoid chimeric assemblies by detecting and separating shared and exclusive pieces of sequence for input data sets. By replacing one deep single-cell sequencing experiment with a few single-cell sequencing experiments of lower depth, the coassembly method can hedge against the risk of failure and loss of the sample, without significantly increasing sequencing cost. Application of the single-cell coassembler HyDA to the study of three uncultured members of an alkane-degrading methanogenic community validated the usefulness of the coassembly concept. HyDA is open source and publicly available at http://chitsazlab.org/software.html, and the raw reads are available at http://chitsazlab.org/research.html. PMID:27243002

  7. Single cell genomics: advances and future perspectives.

    OpenAIRE

    Macaulay, Iain C.; Thierry Voet

    2014-01-01

    Advances in whole-genome and whole-transcriptome amplification have permitted the sequencing of the minute amounts of DNA and RNA present in a single cell, offering a window into the extent and nature of genomic and transcriptomic heterogeneity which occurs in both normal development and disease. Single-cell approaches stand poised to revolutionise our capacity to understand the scale of genomic, epigenomic, and transcriptomic diversity that occurs during the lifetime of an individual organis...

  8. Efficient Synergistic Single-Cell Genome Assembly

    Science.gov (United States)

    Movahedi, Narjes S.; Embree, Mallory; Nagarajan, Harish; Zengler, Karsten; Chitsaz, Hamidreza

    2016-01-01

    As the vast majority of all microbes are unculturable, single-cell sequencing has become a significant method to gain insight into microbial physiology. Single-cell sequencing methods, currently powered by multiple displacement genome amplification (MDA), have passed important milestones such as finishing and closing the genome of a prokaryote. However, the quality and reliability of genome assemblies from single cells are still unsatisfactory due to uneven coverage depth and the absence of scattered chunks of the genome in the final collection of reads caused by MDA bias. In this work, our new algorithm Hybrid De novo Assembler (HyDA) demonstrates the power of coassembly of multiple single-cell genomic data sets through significant improvement of the assembly quality in terms of predicted functional elements and length statistics. Coassemblies contain significantly more base pairs and protein coding genes, cover more subsystems, and consist of longer contigs compared to individual assemblies by the same algorithm as well as state-of-the-art single-cell assemblers SPAdes and IDBA-UD. Hybrid De novo Assembler (HyDA) is also able to avoid chimeric assemblies by detecting and separating shared and exclusive pieces of sequence for input data sets. By replacing one deep single-cell sequencing experiment with a few single-cell sequencing experiments of lower depth, the coassembly method can hedge against the risk of failure and loss of the sample, without significantly increasing sequencing cost. Application of the single-cell coassembler HyDA to the study of three uncultured members of an alkane-degrading methanogenic community validated the usefulness of the coassembly concept. HyDA is open source and publicly available at http://chitsazlab.org/software.html, and the raw reads are available at http://chitsazlab.org/research.html.

  9. Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function.

    Science.gov (United States)

    Shrestha, Ravi; Mohammed, Shahed K; Hasan, Md Mehedi; Zhang, Xuechao; Wahid, Khan A

    2016-08-01

    Wireless capsule endoscopy (WCE) plays an important role in the diagnosis of gastrointestinal (GI) diseases by capturing images of human small intestine. Accurate diagnosis of endoscopic images depends heavily on the quality of captured images. Along with image and frame rate, brightness of the image is an important parameter that influences the image quality which leads to the design of an efficient illumination system. Such design involves the choice and placement of proper light source and its ability to illuminate GI surface with proper brightness. Light emitting diodes (LEDs) are normally used as sources where modulated pulses are used to control LED's brightness. In practice, instances like under- and over-illumination are very common in WCE, where the former provides dark images and the later provides bright images with high power consumption. In this paper, we propose a low-power and efficient illumination system that is based on an automated brightness algorithm. The scheme is adaptive in nature, i.e., the brightness level is controlled automatically in real-time while the images are being captured. The captured images are segmented into four equal regions and the brightness level of each region is calculated. Then an adaptive sigmoid function is used to find the optimized brightness level and accordingly a new value of duty cycle of the modulated pulse is generated to capture future images. The algorithm is fully implemented in a capsule prototype and tested with endoscopic images. Commercial capsules like Pillcam and Mirocam were also used in the experiment. The results show that the proposed algorithm works well in controlling the brightness level accordingly to the environmental condition, and as a result, good quality images are captured with an average of 40% brightness level that saves power consumption of the capsule. PMID:27333609

  10. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks.

    Science.gov (United States)

    Reddick, W E; Glass, J O; Cook, E N; Elkin, T D; Deaton, R J

    1997-12-01

    We present a fully automated process for segmentation and classification of multispectral magnetic resonance (MR) images. This hybrid neural network method uses a Kohonen self-organizing neural network for segmentation and a multilayer backpropagation neural network for classification. To separate different tissue types, this process uses the standard T1-, T2-, and PD-weighted MR images acquired in clinical examinations. Volumetric measurements of brain structures, relative to intracranial volume, were calculated for an index transverse section in 14 normal subjects (median age 25 years; seven male, seven female). This index slice was at the level of the basal ganglia, included both genu and splenium of the corpus callosum, and generally, showed the putamen and lateral ventricle. An intraclass correlation of this automated segmentation and classification of tissues with the accepted standard of radiologist identification for the index slice in the 14 volunteers demonstrated coefficients (ri) of 0.91, 0.95, and 0.98 for white matter, gray matter, and ventricular cerebrospinal fluid (CSF), respectively. An analysis of variance for estimates of brain parenchyma volumes in five volunteers imaged five times each demonstrated high intrasubject reproducibility with a significance of at least p < 0.05 for white matter, gray matter, and white/gray partial volumes. The population variation, across 14 volunteers, demonstrated little deviation from the averages for gray and white matter, while partial volume classes exhibited a slightly higher degree of variability. This fully automated technique produces reliable and reproducible MR image segmentation and classification while eliminating intra- and interobserver variability. PMID:9533591

  11. 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

  12. 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.

  13. 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.

  14. Experiences and achievements in automated image sequence orientation for close-range photogrammetric projects

    Science.gov (United States)

    Barazzetti, Luigi; Forlani, Gianfranco; Remondino, Fabio; Roncella, Riccardo; Scaioni, Marco

    2011-07-01

    Automatic image orientation of close-range image blocks is becoming a task of increasing importance in the practice of photogrammetry. Although image orientation procedures based on interactive tie point measurements do not require any preferential block structure, the use of structured sequences can help to accomplish this task in an automated way. Automatic orientation of image sequences has been widely investigated in the Computer Vision community. Here the method is generally named "Structure from Motion" (SfM), or "Structure and Motion". These refer to the simultaneous estimation of the image orientation parameters and 3D object points of a scene from a set of image correspondences. Such approaches, that generally disregard camera calibration data, do not ensure an accurate 3D reconstruction, which is a requirement for photogrammetric projects. The major contribution of SfM is therefore viewed in the photogrammetric community as a powerful tool to automatically provide a dense set of tie points as well as initial parameters for a final rigorous bundle adjustment. The paper, after a brief overview of automatic procedures for close-range image sequence orientation, will show some characteristic examples. Although powerful and reliable image orientation solutions are nowadays available at research level, there are certain questions that are still open. Thus the paper will also report some open issues, like the geometric characteristics of the sequences, scene's texture and shape, ground constraints (control points and/or free-network adjustment), feature matching techniques, outlier rejection and bundle adjustment models.

  15. Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium

    Directory of Open Access Journals (Sweden)

    Konsti Juho

    2012-03-01

    Full Text Available Abstract Background Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC stainings and automated tumor segmentation. Methods Two tissue microarray (TMA slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images. Results Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and

  16. 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

  17. Newly found pulmonary pathophysiology from automated breath-hold perfusion-SPECT-CT fusion image

    International Nuclear Information System (INIS)

    Pulmonary perfusion single photon emission computed tomography (SPECT)-CT fusion image largely contributes to objective and detailed correlation between lung morphologic and perfusion impairment in various lung diseases. However, traditional perfusion SPECT obtained during rest breathing usually shows a significant mis-registration on fusion image with conventional CT obtained during deep-inspiratory phase. There are also other adverse effects caused by respiratory lung motion such as blurring or smearing of small perfusion defects. To resolve these disadvantages of traditional perfusion SPECT, an innovative method of deep-inspiratory breath-hold (DIBrH) SPECT scan is developed in the Nuclear Medicine Institute of Yamaguchi University Hospital. This review article briefly describes the new findings of pulmonary pathophysiology which has been reveled by detailed lung morphologic-perfusion correlation on automated reliable DIBrH perfusion SPECT-CT fusion image. (author)

  18. PAPNET TM: an automated cytology screener using image processing and neural networks

    Science.gov (United States)

    Luck, Randall L.; Tjon-Fo-Sang, Robert; Mango, Laurie; Recht, Joel R.; Lin, Eunice; Knapp, James

    1992-04-01

    The Pap smear is the universally accepted test used for cervical cancer screening. In the United States alone, about 50 to 70 million of these test are done annually. Every one of the tests is done manually be a cytotechnologist looking at cells on a glass slide under a microscope. This paper describes PAPNET, an automated microscope system that combines a high speed image processor and a neural network processor. The image processor performs an algorithmic primary screen of each image. The neural network performs a non-algorithmic secondary classification of candidate cells. The final output of the system is not a diagnosis. Rather it is a display screen of suspicious cells from which a decision about the status of the case can be made.

  19. Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study, Kenya

    DEFF Research Database (Denmark)

    Juul Bøgelund Hansen, Morten; Abramoff, M. D.; Folk, J. C.;

    2015-01-01

    Objective Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased...... workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields...... gave an AUC of 0.878 (95% CI 0.850-0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment. Conclusions In this epidemiological sample, the IDP's grading was comparable to that...

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

    International Nuclear Information System (INIS)

    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. (paper)

  1. An integrated and interactive decision support system for automated melanoma recognition of dermoscopic images.

    Science.gov (United States)

    Rahman, M M; Bhattacharya, P

    2010-09-01

    This paper presents an integrated and interactive decision support system for the automated melanoma recognition of the dermoscopic images based on image retrieval by content and multiple expert fusion. In this context, the ultimate aim is to support the decision making by retrieving and displaying the relevant past cases as well as predicting the image categories (e.g., melanoma, benign and dysplastic nevi) by combining outputs from different classifiers. However, the most challenging aspect in this domain is to detect the lesion from the healthy background skin and extract the lesion-specific local image features. A thresholding-based segmentation method is applied on the intensity images generated from two different schemes to detect the lesion. For the fusion-based image retrieval and classification, the lesion-specific local color and texture features are extracted and represented in the form of the mean and variance-covariance of color channels and in a combined feature space. The performance is evaluated by using both the precision-recall and classification accuracies. Experimental results on a dermoscopic image collection demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application. PMID:19942406

  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. 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

  4. Advances in hardware, software, and automation for 193nm aerial image measurement systems

    Science.gov (United States)

    Zibold, Axel M.; Schmid, R.; Seyfarth, A.; Waechter, M.; Harnisch, W.; Doornmalen, H. v.

    2005-05-01

    A new, second generation AIMS fab 193 system has been developed which is capable of emulating lithographic imaging of any type of reticles such as binary and phase shift masks (PSM) including resolution enhancement technologies (RET) such as optical proximity correction (OPC) or scatter bars. The system emulates the imaging process by adjustment of the lithography equivalent illumination and imaging conditions of 193nm wafer steppers including circular, annular, dipole and quadrupole type illumination modes. The AIMS fab 193 allows a rapid prediction of wafer printability of critical mask features, including dense patterns and contacts, defects or repairs by acquiring through-focus image stacks by means of a CCD camera followed by quantitative image analysis. Moreover the technology can be readily applied to directly determine the process window of a given mask under stepper imaging conditions. Since data acquisition is performed electronically, AIMS in many applications replaces the need for costly and time consuming wafer prints using a wafer stepper/ scanner followed by CD SEM resist or wafer analysis. The AIMS fab 193 second generation system is designed for 193nm lithography mask printing predictability down to the 65nm node. In addition to hardware improvements a new modular AIMS software is introduced allowing for a fully automated operation mode. Multiple pre-defined points can be visited and through-focus AIMS measurements can be executed automatically in a recipe based mode. To increase the effectiveness of the automated operation mode, the throughput of the system to locate the area of interest, and to acquire the through-focus images is increased by almost a factor of two in comparison with the first generation AIMS systems. In addition a new software plug-in concept is realised for the tools. One new feature has been successfully introduced as "Global CD Map", enabling automated investigation of global mask quality based on the local determination of

  5. Automated measurement of CT noise in patient images with a novel structure coherence feature.

    Science.gov (United States)

    Chun, Minsoo; Choi, Young Hun; Kim, Jong Hyo

    2015-12-01

    While the assessment of CT noise constitutes an important task for the optimization of scan protocols in clinical routine, the majority of noise measurements in practice still rely on manual operation, hence limiting their efficiency and reliability. This study presents an algorithm for the automated measurement of CT noise in patient images with a novel structure coherence feature. The proposed algorithm consists of a four-step procedure including subcutaneous fat tissue selection, the calculation of structure coherence feature, the determination of homogeneous ROIs, and the estimation of the average noise level. In an evaluation with 94 CT scans (16 517 images) of pediatric and adult patients along with the participation of two radiologists, ROIs were placed on a homogeneous fat region at 99.46% accuracy, and the agreement of the automated noise measurements with the radiologists' reference noise measurements (PCC  =  0.86) was substantially higher than the within and between-rater agreements of noise measurements (PCCwithin  =  0.75, PCCbetween  =  0.70). In addition, the absolute noise level measurements matched closely the theoretical noise levels generated by a reduced-dose simulation technique. Our proposed algorithm has the potential to be used for examining the appropriateness of radiation dose and the image quality of CT protocols for research purposes as well as clinical routine. PMID:26561914

  6. 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.

  7. 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.

  8. Automated measurement of CT noise in patient images with a novel structure coherence feature

    International Nuclear Information System (INIS)

    While the assessment of CT noise constitutes an important task for the optimization of scan protocols in clinical routine, the majority of noise measurements in practice still rely on manual operation, hence limiting their efficiency and reliability. This study presents an algorithm for the automated measurement of CT noise in patient images with a novel structure coherence feature. The proposed algorithm consists of a four-step procedure including subcutaneous fat tissue selection, the calculation of structure coherence feature, the determination of homogeneous ROIs, and the estimation of the average noise level. In an evaluation with 94 CT scans (16 517 images) of pediatric and adult patients along with the participation of two radiologists, ROIs were placed on a homogeneous fat region at 99.46% accuracy, and the agreement of the automated noise measurements with the radiologists’ reference noise measurements (PCC  =  0.86) was substantially higher than the within and between-rater agreements of noise measurements (PCCwithin  =  0.75, PCCbetween  =  0.70). In addition, the absolute noise level measurements matched closely the theoretical noise levels generated by a reduced-dose simulation technique. Our proposed algorithm has the potential to be used for examining the appropriateness of radiation dose and the image quality of CT protocols for research purposes as well as clinical routine. (paper)

  9. Automated measurement of CT noise in patient images with a novel structure coherence feature

    Science.gov (United States)

    Chun, Minsoo; Choi, Young Hun; Hyo Kim, Jong

    2015-12-01

    While the assessment of CT noise constitutes an important task for the optimization of scan protocols in clinical routine, the majority of noise measurements in practice still rely on manual operation, hence limiting their efficiency and reliability. This study presents an algorithm for the automated measurement of CT noise in patient images with a novel structure coherence feature. The proposed algorithm consists of a four-step procedure including subcutaneous fat tissue selection, the calculation of structure coherence feature, the determination of homogeneous ROIs, and the estimation of the average noise level. In an evaluation with 94 CT scans (16 517 images) of pediatric and adult patients along with the participation of two radiologists, ROIs were placed on a homogeneous fat region at 99.46% accuracy, and the agreement of the automated noise measurements with the radiologists’ reference noise measurements (PCC  =  0.86) was substantially higher than the within and between-rater agreements of noise measurements (PCCwithin  =  0.75, PCCbetween  =  0.70). In addition, the absolute noise level measurements matched closely the theoretical noise levels generated by a reduced-dose simulation technique. Our proposed algorithm has the potential to be used for examining the appropriateness of radiation dose and the image quality of CT protocols for research purposes as well as clinical routine.

  10. A molecular scanner to automate proteomic research and to display proteome images.

    Science.gov (United States)

    Binz, P A; Müller, M; Walther, D; Bienvenut, W V; Gras, R; Hoogland, C; Bouchet, G; Gasteiger, E; Fabbretti, R; Gay, S; Palagi, P; Wilkins, M R; Rouge, V; Tonella, L; Paesano, S; Rossellat, G; Karmime, A; Bairoch, A; Sanchez, J C; Appel, R D; Hochstrasser, D F

    1999-11-01

    Identification and characterization of all proteins expressed by a genome in biological samples represent major challenges in proteomics. Today's commonly used high-throughput approaches combine two-dimensional electrophoresis (2-DE) with peptide mass fingerprinting (PMF) analysis. Although automation is often possible, a number of limitations still adversely affect the rate of protein identification and annotation in 2-DE databases: the sequential excision process of pieces of gel containing protein; the enzymatic digestion step; the interpretation of mass spectra (reliability of identifications); and the manual updating of 2-DE databases. We present a highly automated method that generates a fully annoated 2-DE map. Using a parallel process, all proteins of a 2-DE are first simultaneously digested proteolytically and electro-transferred onto a poly(vinylidene difluoride) membrane. The membrane is then directly scanned by MALDI-TOF MS. After automated protein identification from the obtained peptide mass fingerprints using PeptIdent software (http://www.expasy.ch/tools/peptident.html + ++), a fully annotated 2-D map is created on-line. It is a multidimensional representation of a proteome that contains interpreted PMF data in addition to protein identification results. This "MS-imaging" method represents a major step toward the development of a clinical molecular scanner. PMID:10565287

  11. 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.

  12. Automated aortic calcification detection in low-dose chest CT images

    Science.gov (United States)

    Xie, Yiting; Htwe, Yu Maw; Padgett, Jennifer; Henschke, Claudia; Yankelevitz, David; Reeves, Anthony P.

    2014-03-01

    The extent of aortic calcification has been shown to be a risk indicator for vascular events including cardiac events. We have developed a fully automated computer algorithm to segment and measure aortic calcification in low-dose noncontrast, non-ECG gated, chest CT scans. The algorithm first segments the aorta using a pre-computed Anatomy Label Map (ALM). Then based on the segmented aorta, aortic calcification is detected and measured in terms of the Agatston score, mass score, and volume score. The automated scores are compared with reference scores obtained from manual markings. For aorta segmentation, the aorta is modeled as a series of discrete overlapping cylinders and the aortic centerline is determined using a cylinder-tracking algorithm. Then the aortic surface location is detected using the centerline and a triangular mesh model. The segmented aorta is used as a mask for the detection of aortic calcification. For calcification detection, the image is first filtered, then an elevated threshold of 160 Hounsfield units (HU) is used within the aorta mask region to reduce the effect of noise in low-dose scans, and finally non-aortic calcification voxels (bony structures, calcification in other organs) are eliminated. The remaining candidates are considered as true aortic calcification. The computer algorithm was evaluated on 45 low-dose non-contrast CT scans. Using linear regression, the automated Agatston score is 98.42% correlated with the reference Agatston score. The automated mass and volume score is respectively 98.46% and 98.28% correlated with the reference mass and volume score.

  13. Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

    Science.gov (United States)

    Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

    2010-02-01

    Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

  14. 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.

  15. 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

  16. 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

    2015-08-01

    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

  17. 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.

  18. 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

  19. Automated image-based assay for evaluation of HIV neutralization and cell-to-cell fusion inhibition

    OpenAIRE

    Sheik-Khalil, Enas; Bray, Mark-Anthony; Özkaya Sahin, Gülsen; Scarlatti, Gabriella; Jansson, Marianne; Carpenter, Anne E.; Fenyö, Eva Maria

    2014-01-01

    Background Standardized techniques to detect HIV-neutralizing antibody responses are of great importance in the search for an HIV vaccine. Methods Here, we present a high-throughput, high-content automated plaque reduction (APR) assay based on automated microscopy and image analysis that allows evaluation of neutralization and inhibition of cell-cell fusion within the same assay. Neutralization of virus particles is measured as a reduction in the number of fluorescent plaques, and inhibition ...

  20. Knee x-ray image analysis method for automated detection of osteoarthritis.

    Science.gov (United States)

    Shamir, Lior; Ling, Shari M; Scott, William W; Bos, Angelo; Orlov, Nikita; Macura, Tomasz J; Eckley, D Mark; Ferrucci, Luigi; Goldberg, Ilya G

    2009-02-01

    We describe a method for automated detection of radiographic osteoarthritis (OA) in knee X-ray images. The detection is based on the Kellgren-Lawrence (KL) classification grades, which correspond to the different stages of OA severity. The classifier was built using manually classified X-rays, representing the first four KL grades (normal, doubtful, minimal, and moderate). Image analysis is performed by first identifying a set of image content descriptors and image transforms that are informative for the detection of OA in the X-rays and assigning weights to these image features using Fisher scores. Then, a simple weighted nearest neighbor rule is used in order to predict the KL grade to which a given test X-ray sample belongs. The dataset used in the experiment contained 350 X-ray images classified manually by their KL grades. Experimental results show that moderate OA (KL grade 3) and minimal OA (KL grade 2) can be differentiated from normal cases with accuracy of 91.5% and 80.4%, respectively. Doubtful OA (KL grade 1) was detected automatically with a much lower accuracy of 57%. The source code developed and used in this study is available for free download at www.openmicroscopy.org. PMID:19342330

  1. Evaluation of a content-based retrieval system for blood cell images with automated methods.

    Science.gov (United States)

    Seng, Woo Chaw; Mirisaee, Seyed Hadi

    2011-08-01

    Content-based image retrieval techniques have been extensively studied for the past few years. With the growth of digital medical image databases, the demand for content-based analysis and retrieval tools has been increasing remarkably. Blood cell image is a key diagnostic tool for hematologists. An automated system that can retrieved relevant blood cell images correctly and efficiently would save the effort and time of hematologists. The purpose of this work is to develop such a content-based image retrieval system. Global color histogram and wavelet-based methods are used in the prototype. The system allows users to search by providing a query image and select one of four implemented methods. The obtained results demonstrate the proposed extended query refinement has the potential to capture a user's high level query and perception subjectivity by dynamically giving better query combinations. Color-based methods performed better than wavelet-based methods with regard to precision, recall rate and retrieval time. Shape and density of blood cells are suggested as measurements for future improvement. The system developed is useful for undergraduate education. PMID:20703533

  2. 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.

  3. Automated bone segmentation from large field of view 3D MR images of the hip joint

    International Nuclear Information System (INIS)

    Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head–neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone–cartilage interfaces for potential cartilage segmentation. (paper)

  4. An automated segmentation method for three-dimensional carotid ultrasound images

    Science.gov (United States)

    Zahalka, Abir; Fenster, Aaron

    2001-04-01

    We have developed an automated segmentation method for three-dimensional vascular ultrasound images. The method consists of two steps: an automated initial contour identification, followed by application of a geometrically deformable model (GDM). The formation of the initial contours requires the input of a single seed point by the user, and was shown to be insensitive to the placement of the seed within a structure. The GDM minimizes contour energy, providing a smoothed final result. It requires only three simple parameters, all with easily selectable values. The algorithm is fast, performing segmentation on a 336×352×200 volume in 25 s when running on a 100 MHz 9500 Power Macintosh prototype. The segmentation algorithm was tested on stenosed vessel phantoms with known geometry, and the segmentation of the cross-sectional areas was found to be within 3% of the true area. The algorithm was also applied to two sets of patient carotid images, one acquired with a mechanical scanner and the other with a freehand scanning system, with good results on both.

  5. The objective evaluation of the phase image: a comparison of different automated methods

    International Nuclear Information System (INIS)

    Patients with suspected or proven coronary artery disease were investigated using both X-ray ventriculography and equilibrium gated radionuclide angiography. In order to diagnose regional wall motion abnormalities, the parametric images obtained by Fourier analysis of the radionuclide images were analysed by different automated methods based on the measurement of the homogeneity of the phase values within the LV ROI. The effect of a diastolic frames exclusion, smoothing the original data, weighting the phase histogram, using Bacharach's error corrected phase distribution functions, using different descriptors of the spread of the phase histograms or distribution functions were tested. (Receiver operating characteristic ROC) curves were plotted for each method. The results show that the diagnostic value of the automated methods depends mainly on the way the histograms or distribution functions are described and to a lesser extent on the type of histograms or distribution functions used. The best result is obtained after smoothing, diastolic frames exclusion, weighting the phase histogram by the amplitude and describing it by its standard deviation. Nevertheless, this result is not significantly better than the visual method. (author)

  6. Automated model-based bias field correction of MR images of the brain.

    Science.gov (United States)

    Van Leemput, K; Maes, F; Vandermeulen, D; Suetens, P

    1999-10-01

    We propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method we propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. We have validated the bias correction algorithm on simulated data and we illustrate its performance on various MR images with important field inhomogeneities. We also relate the proposed algorithm to other bias correction algorithms. PMID:10628948

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

    Directory of Open Access Journals (Sweden)

    Marcin Andrzej KUREK

    2015-01-01

    Full Text Available 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 measured at two points: dry and after water soaking. The most significant water holding capacity (7.00 g water/g solid was achieved by the smaller sized oat fiber. Conversely, the water holding capacity was highest (4.20 g water/g solid in larger sized beet fiber. There was evidence for water absorption increasing with a decrease in particle size in regards to the same fiber source. Very strong correlations were drawn between particle shape parameters, such as fiber length, straightness, width and hydration properties measured conventionally. The regression analysis provided the opportunity to estimate whether the automated static image analysis method could be an efficient tool in describing the hydration properties of dietary fiber. The application of the method was validated using mathematical model which was verified in comparison to conventional WHC measurement results.

  8. 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. PMID:26910307

  9. Automated image analyzer for batch processing of CR-39 foils for fast neutron dosimetry

    International Nuclear Information System (INIS)

    An automated image analysis system has been developed for counting of tracks generated in CR-39 detectors after processing by Electro-chemical etching (ECE). The tracks are caused by exposure to fast neutron, and is used for measuring the neutron dose received by the radiation workers. The system is capable of batch processing a group of 20 foils in a single cycle, rendering the measurement process elegant and efficient. Thus, the system provides a marked improvement over the earlier one, which has provision of handling one foil at a time. The image analysis software of this system is empowered with the capability to resolve the overlapping tracks, which are commonly found in foils exposed to higher levels of neutron dose. The algorithm employed to resolve the tracks is an enhancement over that utilized in the earlier system. This results in improved accuracy of dosimetry. (author)

  10. Automated segmentation of wood fibres in micro-CT images of paper.

    Science.gov (United States)

    Sharma, Y; Phillion, A B; Martinez, D M

    2015-12-01

    A novel algorithm has been developed and validated to isolate individual papermaking fibres in micro-computed tomographic images of paper handsheets as a first step to characterize the structure of the paper. The three-step fibre segmentation algorithm segments the papermaking fibres by (i) tracking the hollow inside the fibres via a modified connected component methodology, (ii) extracting the fibre walls using a distance transform and (iii) labelling the fibres through collapsed sections by a final refinement step. Furthermore, postprocessing algorithms have been developed to calculate the length and coarseness of the segmented fibres. The fibre segmentation algorithm is the first ever reported method for the automated segmentation of the tortuous three-dimensional morphology of papermaking fibres within microstructural images of paper handsheets. The method is not limited to papermaking fibres, but can be applied to any material consisting of tortuous and hollow fibres. PMID:26301453

  11. Modelling and representation issues in automated feature extraction from aerial and satellite images

    Science.gov (United States)

    Sowmya, Arcot; Trinder, John

    New digital systems for the processing of photogrammetric and remote sensing images have led to new approaches to information extraction for mapping and Geographic Information System (GIS) applications, with the expectation that data can become more readily available at a lower cost and with greater currency. Demands for mapping and GIS data are increasing as well for environmental assessment and monitoring. Hence, researchers from the fields of photogrammetry and remote sensing, as well as computer vision and artificial intelligence, are bringing together their particular skills for automating these tasks of information extraction. The paper will review some of the approaches used in knowledge representation and modelling for machine vision, and give examples of their applications in research for image understanding of aerial and satellite imagery.

  12. 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

  13. Semi-automated characterization of the γ' phase in Ni-based superalloys via high-resolution backscatter imaging

    International Nuclear Information System (INIS)

    The size distribution and volume fraction of the γ' phase in Ni-based superalloys plays a critical role in microstructural evolution and mechanical properties. Automated analysis of images is often desired for rapid quantitative characterization of these microstructures. Backscatter electron imaging of specimens in which the γ' phase has been selectively etched yields images that can be more readily segmented with image processing algorithms than other imaging techniques. Utilization of this combination of sample preparation and imaging technique allows for the rapid collection of microstructural data.

  14. Automated materials discrimination using 3D dual energy X ray images

    International Nuclear Information System (INIS)

    The ability of a human observer to identify an explosive device concealed in complex arrangements of objects routinely encountered in the 2D x-ray screening of passenger baggage at airports is often problematic. Standard dual-energy x-ray techniques enable colour encoding of the resultant images in terms of organic, inorganic and metal substances. This transmission imaging technique produces colour information computed from a high-energy x-ray signal and a low energy x-ray signal (80keVeff ≤ 13) to be automatically discriminated from many layers of overlapping substances. This is achieved by applying a basis materials subtraction technique to the data provided by a wavelet image segmentation algorithm. This imaging technique is reliant upon the image data for the masking substances to be discriminated independently of the target material. Further work investigated the extraction of depth data from stereoscopic images to estimate the mass density of the target material. A binocular stereoscopic dual-energy x-ray machine previously developed by the Vision Systems Group at The Nottingham Trent University in collaboration with The Home Office Science and Technology Group provided the image data for the empirical investigation. This machine utilises a novel linear castellated dual-energy x-ray detector recently developed by the Vision Systems Group. This detector array employs half the number of scintillator-photodiode sensors in comparison to a conventional linear dual-energy sensor. The castellated sensor required the development of an image enhancement algorithm to remove the spatial interlace effect in the resultant images prior to the calibration of the system for materials discrimination. To automate the basis materials subtraction technique a wavelet image segmentation and classification algorithm was developed. This enabled overlapping image structures in the x-rayed baggage to be partitioned. A series of experiments was conducted to investigate the

  15. Semi-automated segmentation of a glioblastoma multiforme on brain MR images for radiotherapy planning

    International Nuclear Information System (INIS)

    We propose a computerized method for semi-automated segmentation of the gross tumor volume (GTV) of a glioblastoma multiforme (GBM) on brain MR images for radiotherapy planning (RTP). Three-dimensional (3D) MR images of 28 cases with a GBM were used in this study. First, a sphere volume of interest (VOI) including the GBM was selected by clicking a part of the GBM region in the 3D image. Then, the sphere VOI was transformed to a two-dimensional (2D) image by use of a spiral-scanning technique. We employed active contour models (ACM) to delineate an optimal outline of the GBM in the transformed 2D image. After inverse transform of the optimal outline to the 3D space, a morphological filter was applied to smooth the shape of the 3D segmented region. For evaluation of our computerized method, we compared the computer output with manually segmented regions, which were obtained by a therapeutic radiologist using a manual tracking method. In evaluating our segmentation method, we employed the Jaccard similarity coefficient (JSC) and the true segmentation coefficient (TSC) in volumes between the computer output and the manually segmented region. The mean and standard deviation of JSC and TSC were 74.2±9.8% and 84.1±7.1%, respectively. Our segmentation method provided a relatively accurate outline for GBM and would be useful for radiotherapy planning. (author)

  16. An automated voxelized dosimetry tool for radionuclide therapy based on serial quantitative SPECT/CT imaging

    Energy Technology Data Exchange (ETDEWEB)

    Jackson, Price A.; Kron, Tomas [Department of Physical Sciences, Peter MacCallum Cancer Centre, East Melbourne 3002 (Australia); Beauregard, Jean-Mathieu [Department of Radiology, Université Laval, Quebec City G1V 0A6 (Canada); Hofman, Michael S.; Hogg, Annette; Hicks, Rodney J. [Department of Molecular Imaging, Peter MacCallum Cancer Centre, East Melbourne 3002 (Australia)

    2013-11-15

    Purpose: To create an accurate map of the distribution of radiation dose deposition in healthy and target tissues during radionuclide therapy.Methods: Serial quantitative SPECT/CT images were acquired at 4, 24, and 72 h for 28 {sup 177}Lu-octreotate peptide receptor radionuclide therapy (PRRT) administrations in 17 patients with advanced neuroendocrine tumors. Deformable image registration was combined with an in-house programming algorithm to interpolate pharmacokinetic uptake and clearance at a voxel level. The resultant cumulated activity image series are comprised of values representing the total number of decays within each voxel's volume. For PRRT, cumulated activity was translated to absorbed dose based on Monte Carlo-determined voxel S-values at a combination of long and short ranges. These dosimetric image sets were compared for mean radiation absorbed dose to at-risk organs using a conventional MIRD protocol (OLINDA 1.1).Results: Absorbed dose values to solid organs (liver, kidneys, and spleen) were within 10% using both techniques. Dose estimates to marrow were greater using the voxelized protocol, attributed to the software incorporating crossfire effect from nearby tumor volumes.Conclusions: The technique presented offers an efficient, automated tool for PRRT dosimetry based on serial post-therapy imaging. Following retrospective analysis, this method of high-resolution dosimetry may allow physicians to prescribe activity based on required dose to tumor volume or radiation limits to healthy tissue in individual patients.

  17. 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.

  18. Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images

    Directory of Open Access Journals (Sweden)

    Pachiyappan Arulmozhivarman

    2012-06-01

    Full Text Available Abstract We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized.

  19. Prospective Image Registration for Automated Scan Prescription of Follow-up Knee Images in Quantitative Studies

    OpenAIRE

    Goldenstein, Janet; Schooler, Joseph; Crane, Jason C; Ozhinsky, Eugene; Pialat, Jean-Baptiste; Carballido-Gamio, Julio; Majumdar, Sharmila

    2011-01-01

    Consistent scan prescription for MRI of the knee is very important for accurate comparison of images in a longitudinal study. However, consistent scan region selection is difficult due the complexity of the knee joint. We propose a novel method for registering knee images using a mutual information registration algorithm to align images in a baseline and follow-up exam. The output of the registration algorithm, three translations and three Euler angles, is then used to redefine the region to ...

  20. An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.

    Science.gov (United States)

    van 't Klooster, Ronald; Patterson, Andrew J; Young, Victoria E; Gillard, Jonathan H; Reiber, Johan H C; van der Geest, Rob J

    2013-01-01

    A typical MR imaging protocol to study the status of atherosclerosis in the carotid artery consists of the application of multiple MR sequences. Since scanner time is limited, a balance has to be reached between the duration of the applied MR protocol and the quantity and quality of the resulting images which are needed to assess the disease. In this study an objective method to optimize the MR sequence set for classification of soft plaque in vessel wall images of the carotid artery using automated image segmentation was developed. The automated method employs statistical pattern recognition techniques and was developed based on an extensive set of MR contrast weightings and corresponding manual segmentations of the vessel wall and soft plaque components, which were validated by histological sections. Evaluation of the results from nine contrast weightings showed the tradeoff between scan duration and automated image segmentation performance. For our dataset the best segmentation performance was achieved by selecting five contrast weightings. Similar performance was achieved with a set of three contrast weightings, which resulted in a reduction of scan time by more than 60%. The presented approach can help others to optimize MR imaging protocols by investigating the tradeoff between scan duration and automated image segmentation performance possibly leading to shorter scanning times and better image interpretation. This approach can potentially also be applied to other research fields focusing on different diseases and anatomical regions. PMID:24194941

  1. Single-cell twitching chemotaxis in developing biofilms.

    Science.gov (United States)

    Oliveira, Nuno M; Foster, Kevin R; Durham, William M

    2016-06-01

    Bacteria form surface-attached communities, known as biofilms, which are central to bacterial biology and how they affect us. Although surface-attached bacteria often experience strong chemical gradients, it remains unclear whether single cells can effectively perform chemotaxis on surfaces. Here we use microfluidic chemical gradients and massively parallel automated tracking to study the behavior of the pathogen Pseudomonas aeruginosa during early biofilm development. We show that individual cells can efficiently move toward chemoattractants using pili-based "twitching" motility and the Chp chemosensory system. Moreover, we discovered the behavioral mechanism underlying this surface chemotaxis: Cells reverse direction more frequently when moving away from chemoattractant sources. These corrective maneuvers are triggered rapidly, typically before a wayward cell has ventured a fraction of a micron. Our work shows that single bacteria can direct their motion with submicron precision and reveals the hidden potential for chemotaxis within bacterial biofilms. PMID:27222583

  2. Easily fabricated magnetic traps for single-cell applications

    Science.gov (United States)

    Koschwanez, John H.; Carlson, Robert H.; Meldrum, Deirdre R.

    2007-04-01

    We describe a simple and inexpensive method of fabricating single cell magnetic traps within a polydimethylsiloxane (PDMS) device. These traps were developed as part of an automated system that captures individual yeast cells in a microfluidic device and analyzes each cell as it buds. To make the traps, PdCl2 catalyst is rubbed with vinyl foam onto plasma-patterned PDMS, and then Co-Ni-B alloy is electrolessly deposited onto the catalyst at a moderate temperature. We demonstrate individual yeast cell capture and estimate the capture force (1.9-4.4 pN) by measuring the flow speed required to remove the cell from its trap in a microfluidic channel.

  3. RootAnalyzer: A Cross-Section Image Analysis Tool for Automated Characterization of Root Cells and Tissues

    OpenAIRE

    Joshua Chopin; Hamid Laga; Chun Yuan Huang; Sigrid Heuer; 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 processi...

  4. 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

  5. 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.

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

    International Nuclear Information System (INIS)

    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

  7. Semi-automated scar detection in delayed enhanced cardiac magnetic resonance images

    Science.gov (United States)

    Morisi, Rita; Donini, Bruno; Lanconelli, Nico; Rosengarden, James; Morgan, John; Harden, Stephen; Curzen, Nick

    2015-06-01

    Late enhancement cardiac magnetic resonance images (MRI) has the ability to precisely delineate myocardial scars. We present a semi-automated method for detecting scars in cardiac MRI. This model has the potential to improve routine clinical practice since quantification is not currently offered due to time constraints. A first segmentation step was developed for extracting the target regions for potential scar and determining pre-candidate objects. Pattern recognition methods are then applied to the segmented images in order to detect the position of the myocardial scar. The database of late gadolinium enhancement (LE) cardiac MR images consists of 111 blocks of images acquired from 63 patients at the University Hospital Southampton NHS Foundation Trust (UK). At least one scar was present for each patient, and all the scars were manually annotated by an expert. A group of images (around one third of the entire set) was used for training the system which was subsequently tested on all the remaining images. Four different classifiers were trained (Support Vector Machine (SVM), k-nearest neighbor (KNN), Bayesian and feed-forward neural network) and their performance was evaluated by using Free response Receiver Operating Characteristic (FROC) analysis. Feature selection was implemented for analyzing the importance of the various features. The segmentation method proposed allowed the region affected by the scar to be extracted correctly in 96% of the blocks of images. The SVM was shown to be the best classifier for our task, and our system reached an overall sensitivity of 80% with less than 7 false positives per patient. The method we present provides an effective tool for detection of scars on cardiac MRI. This may be of value in clinical practice by permitting routine reporting of scar quantification.

  8. Automated Analysis of 123I-beta-CIT SPECT Images with Statistical Probabilistic Anatomical Mapping

    International Nuclear Information System (INIS)

    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-(4-123I-iodophenyl)tropane (123I-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 123I-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

  9. Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

    Energy Technology Data Exchange (ETDEWEB)

    Drukker, Karen, E-mail: kdrukker@uchicago.edu; Sennett, Charlene A.; Giger, Maryellen L. [Department of Radiology, MC2026, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637 (United States)

    2014-01-15

    Purpose: Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts. Methods: The HIPAA compliant study involved a dataset of volumetric ultrasound image data, “views,” acquired with an automated U-Systems Somo•V{sup ®} ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patients (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of “marks” (detections) per view. Results: At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2—similar to radiologists’ performance sensitivity (49.9%) for this dataset from a prior reader study—and 45.9% (28/61) ± 4% for all patients. Conclusions: Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation.

  10. A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field.

    Science.gov (United States)

    Wasson, Anton; Bischof, Leanne; Zwart, Alec; Watt, Michelle

    2016-02-01

    Root architecture traits are a target for pre-breeders. Incorporation of root architecture traits into new cultivars requires phenotyping. It is attractive to rapidly and directly phenotype root architecture in the field, avoiding laboratory studies that may not translate to the field. A combination of soil coring with a hydraulic push press and manual core-break counting can directly phenotype root architecture traits of depth and distribution in the field through to grain development, but large teams of people are required and labour costs are high with this method. We developed a portable fluorescence imaging system (BlueBox) to automate root counting in soil cores with image analysis software directly in the field. The lighting system was optimized to produce high-contrast images of roots emerging from soil cores. The correlation of the measurements with the root length density of the soil cores exceeded the correlation achieved by human operator measurements (R (2)=0.68 versus 0.57, respectively). A BlueBox-equipped team processed 4.3 cores/hour/person, compared with 3.7 cores/hour/person for the manual method. The portable, automated in-field root architecture phenotyping system was 16% more labour efficient, 19% more accurate, and 12% cheaper than manual conventional coring, and presents an opportunity to directly phenotype root architecture in the field as part of pre-breeding programs. The platform has wide possibilities to capture more information about root health and other root traits in the field. PMID:26826219

  11. A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field

    Science.gov (United States)

    Wasson, Anton; Bischof, Leanne; Zwart, Alec; Watt, Michelle

    2016-01-01

    Root architecture traits are a target for pre-breeders. Incorporation of root architecture traits into new cultivars requires phenotyping. It is attractive to rapidly and directly phenotype root architecture in the field, avoiding laboratory studies that may not translate to the field. A combination of soil coring with a hydraulic push press and manual core-break counting can directly phenotype root architecture traits of depth and distribution in the field through to grain development, but large teams of people are required and labour costs are high with this method. We developed a portable fluorescence imaging system (BlueBox) to automate root counting in soil cores with image analysis software directly in the field. The lighting system was optimized to produce high-contrast images of roots emerging from soil cores. The correlation of the measurements with the root length density of the soil cores exceeded the correlation achieved by human operator measurements (R 2=0.68 versus 0.57, respectively). A BlueBox-equipped team processed 4.3 cores/hour/person, compared with 3.7 cores/hour/person for the manual method. The portable, automated in-field root architecture phenotyping system was 16% more labour efficient, 19% more accurate, and 12% cheaper than manual conventional coring, and presents an opportunity to directly phenotype root architecture in the field as part of pre-breeding programs. The platform has wide possibilities to capture more information about root health and other root traits in the field. PMID:26826219

  12. 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.

  13. Automated Detection of Galaxy-Scale Gravitational Lenses in High-Resolution Imaging Data

    Science.gov (United States)

    Marshall, Philip J.; Hogg, David W.; Moustakas, Leonidas A.; Fassnacht, Christopher D.; Bradač, Maruša; Schrabback, Tim; Blandford, Roger D.

    2009-04-01

    We expect direct lens modeling to be the key to successful and meaningful automated strong galaxy-scale gravitational lens detection. We have implemented a lens-modeling "robot" that treats every bright red galaxy (BRG) in a large imaging survey as a potential gravitational lens system. Having optimized a simple model for "typical" galaxy-scale gravitational lenses, we generate four assessments of model quality that are then used in an automated classification. The robot infers from these four data the lens classification parameter H that a human would have assigned; the inference is performed using a probability distribution generated from a human-classified training set of candidates, including realistic simulated lenses and known false positives drawn from the Hubble Space Telescope (HST) Extended Groth Strip (EGS) survey. We compute the expected purity, completeness, and rejection rate, and find that these statistics can be optimized for a particular application by changing the prior probability distribution for H; this is equivalent to defining the robot's "character." Adopting a realistic prior based on expectations for the abundance of lenses, we find that a lens sample may be generated that is ~100% pure, but only ~20% complete. This shortfall is due primarily to the oversimplicity of the model of both the lens light and mass. With a more optimistic robot, ~90% completeness can be achieved while rejecting ~90% of the candidate objects. The remaining candidates must be classified by human inspectors. Displaying the images used and produced by the robot on a custom "one-click" web interface, we are able to inspect and classify lens candidates at a rate of a few seconds per system, suggesting that a future 1000 deg2 imaging survey containing 107 BRGs, and some 104 lenses, could be successfully, and reproducibly, searched in a modest amount of time. We have verified our projected survey statistics, albeit at low significance, using the HST EGS data, discovering

  14. 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.

  15. High-throughput automated home-cage mesoscopic functional imaging of mouse cortex

    Science.gov (United States)

    Murphy, Timothy H.; Boyd, Jamie D.; Bolaños, Federico; Vanni, Matthieu P.; Silasi, Gergely; Haupt, Dirk; LeDue, Jeff M.

    2016-01-01

    Mouse head-fixed behaviour coupled with functional imaging has become a powerful technique in rodent systems neuroscience. However, training mice can be time consuming and is potentially stressful for animals. Here we report a fully automated, open source, self-initiated head-fixation system for mesoscopic functional imaging in mice. The system supports five mice at a time and requires minimal investigator intervention. Using genetically encoded calcium indicator transgenic mice, we longitudinally monitor cortical functional connectivity up to 24 h per day in >7,000 self-initiated and unsupervised imaging sessions up to 90 days. The procedure provides robust assessment of functional cortical maps on the basis of both spontaneous activity and brief sensory stimuli such as light flashes. The approach is scalable to a number of remotely controlled cages that can be assessed within the controlled conditions of dedicated animal facilities. We anticipate that home-cage brain imaging will permit flexible and chronic assessment of mesoscale cortical function. PMID:27291514

  16. 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. PMID:23958645

  17. Portable automated imaging in complex ceramics with a microwave interference scanning system

    Science.gov (United States)

    Goitia, Ryan M.; Schmidt, Karl F.; Little, Jack R.; Ellingson, William A.; Green, William; Franks, Lisa P.

    2013-01-01

    An improved portable microwave interferometry system has been automated to permit rapid examination of components with minimal operator attendance. Functionalities include stereo and multiplexed, frequency-modulated at multiple frequencies, producing layered volumetric images of complex ceramic structures. The technique has been used to image composite ceramic armor and ceramic matrix composite components, as well as other complex dielectric materials. The system utilizes Evisive Scan microwave interference scanning technique. Validation tests include artificial and in-service damage of ceramic armor, surrogates and ceramic matrix composite samples. Validation techniques include micro-focus x-ray and computed tomography imaging. The microwave interference scanning technique has demonstrated detection of cracks, interior laminar features and variations in material properties such as density. The image yields depth information through phase angle manipulation, and shows extent of feature and relative dielectric property information. It requires access to only one surface, and no coupling medium. Data are not affected by separation of layers of dielectric material, such as outer over-wrap. Test panels were provided by the US Army Research Laboratory, and the US Army Tank Automotive Research, Development and Engineering Center (TARDEC), who with the US Air Force Research Laboratory have supported this work.

  18. An automated target recognition technique for image segmentation and scene analysis

    Energy Technology Data Exchange (ETDEWEB)

    Baumgart, C.W.; Ciarcia, C.A.

    1994-02-01

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

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

    International Nuclear Information System (INIS)

    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

  20. High-throughput automated home-cage mesoscopic functional imaging of mouse cortex.

    Science.gov (United States)

    Murphy, Timothy H; Boyd, Jamie D; Bolaños, Federico; Vanni, Matthieu P; Silasi, Gergely; Haupt, Dirk; LeDue, Jeff M

    2016-01-01

    Mouse head-fixed behaviour coupled with functional imaging has become a powerful technique in rodent systems neuroscience. However, training mice can be time consuming and is potentially stressful for animals. Here we report a fully automated, open source, self-initiated head-fixation system for mesoscopic functional imaging in mice. The system supports five mice at a time and requires minimal investigator intervention. Using genetically encoded calcium indicator transgenic mice, we longitudinally monitor cortical functional connectivity up to 24 h per day in >7,000 self-initiated and unsupervised imaging sessions up to 90 days. The procedure provides robust assessment of functional cortical maps on the basis of both spontaneous activity and brief sensory stimuli such as light flashes. The approach is scalable to a number of remotely controlled cages that can be assessed within the controlled conditions of dedicated animal facilities. We anticipate that home-cage brain imaging will permit flexible and chronic assessment of mesoscale cortical function. PMID:27291514

  1. 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.

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

    OpenAIRE

    2014-01-01

    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 inc...

  3. Automated parameterisation for multi-scale image segmentation on multiple layers

    Science.gov (United States)

    Drăguţ, L.; Csillik, O.; Eisank, C.; Tiede, D.

    2014-01-01

    We introduce a new automated approach to parameterising multi-scale image segmentation of multiple layers, and we implemented it as a generic tool for the eCognition® software. This approach relies on the potential of the local variance (LV) to detect scale transitions in geospatial data. The tool detects the number of layers added to a project and segments them iteratively with a multiresolution segmentation algorithm in a bottom-up approach, where the scale factor in the segmentation, namely, the scale parameter (SP), increases with a constant increment. The average LV value of the objects in all of the layers is computed and serves as a condition for stopping the iterations: when a scale level records an LV value that is equal to or lower than the previous value, the iteration ends, and the objects segmented in the previous level are retained. Three orders of magnitude of SP lags produce a corresponding number of scale levels. Tests on very high resolution imagery provided satisfactory results for generic applicability. The tool has a significant potential for enabling objectivity and automation of GEOBIA analysis. PMID:24748723

  4. NeuronMetrics: Software for Semi-Automated Processing of Cultured-Neuron Images

    Science.gov (United States)

    Narro, Martha L.; Yang, Fan; Kraft, Robert; Wenk, Carola; Efrat, Alon; Restifo, Linda L.

    2007-01-01

    Using primary cell culture to screen for changes in neuronal morphology requires specialized analysis software. We developed NeuronMetrics™ for semi-automated, quantitative analysis of two-dimensional (2D) images of fluorescently labeled cultured neurons. It skeletonizes the neuron image using two complementary image-processing techniques, capturing fine terminal neurites with high fidelity. An algorithm was devised to span wide gaps in the skeleton. NeuronMetrics uses a novel strategy based on geometric features called faces to extract a branch-number estimate from complex arbors with numerous neurite-to-neurite contacts, without creating a precise, contact-free representation of the neurite arbor. It estimates total neurite length, branch number, primary neurite number, territory (the area of the convex polygon bounding the skeleton and cell body), and Polarity Index (a measure of neuronal polarity). These parameters provide fundamental information about the size and shape of neurite arbors, which are critical factors for neuronal function. NeuronMetrics streamlines optional manual tasks such as removing noise, isolating the largest primary neurite, and correcting length for self-fasciculating neurites. Numeric data are output in a single text file, readily imported into other applications for further analysis. Written as modules for ImageJ, NeuronMetrics provides practical analysis tools that are easy to use and support batch processing. Depending on the need for manual intervention, processing time for a batch of ~60 2D images is 1.0–2.5 hours, from a folder of images to a table of numeric data. NeuronMetrics’ output accelerates the quantitative detection of mutations and chemical compounds that alter neurite morphology in vitro, and will contribute to the use of cultured neurons for drug discovery. PMID:17270152

  5. Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image

    Science.gov (United States)

    Al Fugura, A.'kif; Billa, Lawal; Pradhan, Biswajeet

    2011-12-01

    Coastline identification is important for surveying and mapping reasons. Coastline serves as the basic point of reference and is used on nautical charts for navigation purposes. Its delineation has become crucial and more important in the wake of the many recent earthquakes and tsunamis resulting in complete change and redraw of some shorelines. In a tropical country like Malaysia, presence of cloud cover hinders the application of optical remote sensing data. In this study a semi-automated technique and procedures are presented for shoreline delineation from RADARSAT-1 image. A scene of RADARSAT-1 satellite image was processed using enhanced filtering technique to identify and extract the shoreline coast of Kuala Terengganu, Malaysia. RADSARSAT image has many advantages over the optical data because of its ability to penetrate cloud cover and its night sensing capabilities. At first, speckles were removed from the image by using Lee sigma filter which was used to reduce random noise and to enhance the image and discriminate the boundary between land and water. The results showed an accurate and improved extraction and delineation of the entire coastline of Kuala Terrenganu. The study demonstrated the reliability of the image averaging filter in reducing random noise over the sea surface especially near the shoreline. It enhanced land-water boundary differentiation, enabling better delineation of the shoreline. Overall, the developed techniques showed the potential of radar imagery for accurate shoreline mapping and will be useful for monitoring shoreline changes during high and low tides as well as shoreline erosion in a tropical country like Malaysia.

  6. Prospective image registration for automated scan prescription of follow-up knee images in quantitative studies.

    Science.gov (United States)

    Goldenstein, Janet; Schooler, Joseph; Crane, Jason C; Ozhinsky, Eugene; Pialat, Jean-Baptiste; Carballido-Gamio, Julio; Majumdar, Sharmila

    2011-06-01

    Consistent scan prescription for MRI of the knee is very important for accurate comparison of images in a longitudinal study. However, consistent scan region selection is difficult due to the complexity of the knee joint. We propose a novel method for registering knee images using a mutual information registration algorithm to align images in a baseline and follow-up exam. The output of the registration algorithm, three translations and three Euler angles, is then used to redefine the region to be imaged and acquire an identical oblique imaging volume in the follow-up exam as in the baseline. This algorithm is robust to articulation of the knee and anatomical abnormalities due to disease (e.g., osteophytes). The registration method is performed only on the distal femur and is not affected by the proximal tibia or soft tissues. We have incorporated this approach in a clinical MR system and have demonstrated its utility in automatically obtaining consistent scan regions between baseline and follow-up examinations, thus improving the precision of quantitative evaluation of cartilage. Results show an improvement with prospective registration in the coefficient of variation for cartilage thickness, cartilage volume and T2 relaxation measurements. PMID:21546186

  7. 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.

  8. Feasibility of fully automated detection of fiducial markers implanted into the prostate using electronic portal imaging: A comparison of methods

    International Nuclear Information System (INIS)

    Purpose: To investigate the feasibility of fully automated detection of fiducial markers implanted into the prostate using portal images acquired with an electronic portal imaging device. Methods and Materials: We have made a direct comparison of 4 different methods (2 template matching-based methods, a method incorporating attenuation and constellation analyses and a cross correlation method) that have been published in the literature for the automatic detection of fiducial markers. The cross-correlation technique requires a-priory information from the portal images, therefore the technique is not fully automated for the first treatment fraction. Images of 7 patients implanted with gold fiducial markers (8 mm in length and 1 mm in diameter) were acquired before treatment (set-up images) and during treatment (movie images) using 1MU and 15MU per image respectively. Images included: 75 anterior (AP) and 69 lateral (LAT) set-up images and 51 AP and 83 LAT movie images. Using the different methods described in the literature, marker positions were automatically identified. Results: The method based upon cross correlation techniques gave the highest percentage detection success rate of 99% (AP) and 83% (LAT) set-up (1MU) images. The methods gave detection success rates of less than 91% (AP) and 42% (LAT) set-up images. The amount of a-priory information used and how it affects the way the techniques are implemented, is discussed. Conclusions: Fully automated marker detection in set-up images for the first treatment fraction is unachievable using these methods and that using cross-correlation is the best technique for automatic detection on subsequent radiotherapy treatment fractions

  9. Three-dimensional semi-automated segmentation of carotid atherosclerosis from three-dimensional ultrasound images

    Science.gov (United States)

    Ukwatta, E.; Awad, J.; Buchanan, D.; Parraga, G.; Fenster, A.

    2012-03-01

    Three-dimensional ultrasound (3DUS) provides non-invasive and precise measurements of carotid atherosclerosis that directly reflects arterial wall abnormalities that are thought to be related to stroke risk. Here we describe a threedimensional segmentation method based on the sparse field level set method to automate the segmentation of the mediaadventitia (MAB) and lumen-intima (LIB) boundaries of the common carotid artery from 3DUS images. To initiate the process, an expert chooses four anchor points on each boundary on a subset of transverse slices that are orthogonal to the axis of the artery. An initial surface is generated using the anchor points as initial guess for the segmentation. The MAB is segmented first using five energies: length minimization energy, local region-based energy, edge-based energy, anchor point-based energy, and local smoothness energy. Five energies are also used for the LIB segmentation: length minimization energy, local region-based energy, global region-based energy, anchor point-based energy, and boundary separation-based energy. The algorithm was evaluated with respect to manual segmentations on a slice-by-slice basis using 15 3DUS images. To generate results in this paper, inter-slice distance of 2 mm is used for the initialization. For the MAB and LIB segmentations, our method yielded Dice coefficients of more than 92% and sub-millimeter values for mean and maximum absolute distance errors. Our method also yielded a vessel wall volume error of 7.1% +/- 3.4%. The realization of a semi-automated algorithm will aid in the translation of 3DUS measurements to clinical research for the rapid, non-invasive, and economical monitoring of atherosclerotic disease.

  10. New automated image analysis method for the assessment of Ki-67 labeling index in meningiomas.

    Directory of Open Access Journals (Sweden)

    Wielisław Papierz

    2010-05-01

    Full Text Available Many studies have emphasised the importance of Ki-67 labeling index (LI as the proliferation marker in meningiomas. Several authors confirmed, that Ki-67 LI has prognostic significance and correlates with likelihood of tumour recurrences. These observations were widely accepted by pathologists, but up till now no standard method for Ki-67 LI assessment was developed and introduced for the diagnostic pathology. In this paper we present a new computerised system for automated Ki-67 LI estimation in meningiomas as an aid for histological grading of meningiomas and potential standard method of Ki-67 LI assessment. We also discuss the concordance of Ki-67 LI results obtained by presented computerized system and expert pathologist, as well as possible pitfalls and mistakes in automated counting of immunopositive or negative cells. For the quantitative evaluation of digital images of meningiomas the designed software uses an algorithm based on mathematical description of cell morphology. This solution acts together with the Support Vector Machine (SVM used in the classification mode for the recognition of immunoreactivity of cells. The applied sequential thresholding simulated well the human process of cell recognition. The same digital images of randomly selected tumour areas were parallelly analysed by computer and blindly by two expert pathologists. Ki-67 labeling indices were estimated and the results compared. The mean relative discrepancy between the levels of Ki-67 LI by our system and by the human expert did not exceed 14% in all investigated cases. These preliminary results suggest that the designed software could be an useful tool supporting the diagnostic digital pathology. However, more extended studies are needed for approval of this suggestion.

  11. 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.

  12. Comparison of manual and semi-automated delineation of regions of interest for radioligand PET imaging analysis

    International Nuclear Information System (INIS)

    As imaging centers produce higher resolution research scans, the number of man-hours required to process regional data has become a major concern. Comparison of automated vs. manual methodology has not been reported for functional imaging. We explored validation of using automation to delineate regions of interest on positron emission tomography (PET) scans. The purpose of this study was to ascertain improvements in image processing time and reproducibility of a semi-automated brain region extraction (SABRE) method over manual delineation of regions of interest (ROIs). We compared 2 sets of partial volume corrected serotonin 1a receptor binding potentials (BPs) resulting from manual vs. semi-automated methods. BPs were obtained from subjects meeting consensus criteria for frontotemporal degeneration and from age- and gender-matched healthy controls. Two trained raters provided each set of data to conduct comparisons of inter-rater mean image processing time, rank order of BPs for 9 PET scans, intra- and inter-rater intraclass correlation coefficients (ICC), repeatability coefficients (RC), percentages of the average parameter value (RM%), and effect sizes of either method. SABRE saved approximately 3 hours of processing time per PET subject over manual delineation (p < .001). Quality of the SABRE BP results was preserved relative to the rank order of subjects by manual methods. Intra- and inter-rater ICC were high (>0.8) for both methods. RC and RM% were lower for the manual method across all ROIs, indicating less intra-rater variance across PET subjects' BPs. SABRE demonstrated significant time savings and no significant difference in reproducibility over manual methods, justifying the use of SABRE in serotonin 1a receptor radioligand PET imaging analysis. This implies that semi-automated ROI delineation is a valid methodology for future PET imaging analysis

  13. Combined multiphoton imaging and automated functional enucleation of porcine oocytes using femtosecond laser pulses

    Science.gov (United States)

    Kuetemeyer, Kai; Lucas-Hahn, Andrea; Petersen, Bjoern; Lemme, Erika; Hassel, Petra; Niemann, Heiner; Heisterkamp, Alexander

    2010-07-01

    Since the birth of ``Dolly'' as the first mammal cloned from a differentiated cell, somatic cell cloning has been successful in several mammalian species, albeit at low success rates. The highly invasive mechanical enucleation step of a cloning protocol requires sophisticated, expensive equipment and considerable micromanipulation skill. We present a novel noninvasive method for combined oocyte imaging and automated functional enucleation using femtosecond (fs) laser pulses. After three-dimensional imaging of Hoechst-labeled porcine oocytes by multiphoton microscopy, our self-developed software automatically identified the metaphase plate. Subsequent irradiation of the metaphase chromosomes with the very same laser at higher pulse energies in the low-density-plasma regime was used for metaphase plate ablation (functional enucleation). We show that fs laser-based functional enucleation of porcine oocytes completely inhibited the parthenogenetic development without affecting the oocyte morphology. In contrast, nonirradiated oocytes were able to develop parthenogenetically to the blastocyst stage without significant differences to controls. Our results indicate that fs laser systems have great potential for oocyte imaging and functional enucleation and may improve the efficiency of somatic cell cloning.

  14. CT and MRI derived source localization error in a custom prostate phantom using automated image coregistration

    International Nuclear Information System (INIS)

    Dosimetric evaluation of completed brachytherapy implant procedures is crucial in developing proper technique. Additionally, accurate dosimetry may be useful in predicting the success of an implant. Accurate definition of the prostate gland and localization of the implanted radioactive sources are critical to attain meaningful dosimetric data. MRI is recognized as a superior imaging modality in delineating the prostate gland. More importantly, MRI can be used for source localization in postimplant prostates. However, the MRI derived source localization error bears further investigation. We present a useful tool in determining the source localization error as well as permitting the fusion, or coregistration, of selected data from multiple imaging modalities. We constructed a custom prostate phantom of hydrocolloid material precisely implanted with I-125 seeds. We obtained CT, the accepted modality, and MRI scans of the phantom. Subsequently, we developed an automated algorithm that employs a sequential translation of data sets to initially maximize coregistration and minimize error between data sets. This was followed by a noniterative solution for the necessary rotation transformation matrix using the Orthogonal Procrustes Solution. We applied this algorithm to CT and MRI scans of the custom phantom. CT derived source locations had source localization errors of 1.59 mm±0.64. MRI derived source locations produced similar results (1.67 mm±0.76). These errors may be attributed to the image digitization process

  15. New technologies for automated cell counting based on optical image analysis ;The Cellscreen'.

    Science.gov (United States)

    Brinkmann, Marlies; Lütkemeyer, Dirk; Gudermann, Frank; Lehmann, Jürgen

    2002-01-01

    A prototype of a newly developed apparatus for measuring cell growth characteristics of suspension cells in micro titre plates over a period of time was examined. Fully automated non-invasive cell counts in small volume cultivation vessels, e.g. 96 well plates, were performed with the Cellscreen system by Innovatis AG, Germany. The system automatically generates microscopic images of suspension cells which had sedimented on the base of the well plate. The total cell number and cell geometry was analysed without staining or sampling using the Cedex image recognition technology. Thus, time course studies of cell growth with the identical culture became possible. Basic parameters like the measurement range, the minimum number of images which were required for statistically reliable results, as well as the influence of the measurement itself and the effect of evaporation in 96 well plates on cell proliferation were determined. A comparison with standard methods including the influence of the cultured volume per well (25 mul to 200 mul) on cell growth was performed. Furthermore, the toxic substances ammonia, lactate and butyrate were used to show that the Cellscreen system is able to detect even the slightest changes in the specific growth rate. PMID:19003093

  16. Software feature enhancements for automated scanning of multiple surface geometry objects using ultrasonic imaging system

    International Nuclear Information System (INIS)

    Electronics Division, BARC in association with Metallic Fuels Division has developed an Ultrasonic Imaging System suitable for automated inspection of metallic objects with multiple surface geometry. The electronics hardware and application software for this system has been developed by Electronics Division and the design and development of the mechanical scanner was done by Metallic Fuels Division, BARC. The scanner has been successfully interfaced with the high-resolution ultrasonic imaging system (ULTIMA-200SP). A very significant feature of the ULTIMA-200SP system is the application software which performs various tasks of controlling various motors of scanner in addition to data acquisition, processing, analysis and information display. All these tasks must be carried out in a well synchronized manner for generating high resolution B Scan and C Scan images of test objects. In order to meet stringent requirements of the user, ULTIMA software has been extensively upgraded with new advanced features viz. Fast (coarse) and Slow (fine) scan for the speed optimization, Scanning of Cuboids and Cylindrical objects in the user defined region of interest, 3D view of the C-Scan, gray level, dual or multiple color plot in B-Scan, C-Scan and 3D views. This paper describes the advanced Windows based application software package developed at ED, BARC and highlights its salient features along with a brief description of the system hardware and relevant information. (author)

  17. 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.

  18. Automated optical image correlation to constrain dynamics of slow-moving landslides

    Science.gov (United States)

    Mackey, B. H.; Roering, J. J.; Lamb, M. P.

    2011-12-01

    Large, slow-moving landslides can dominate sediment flux from mountainous terrain, yet their long-term spatio-temporal behavior at the landscape scale is not well understood. Movement can be inconspicuous, episodic, persist for decades, and is challenging and time consuming to quantify using traditional methods such as stereo photogrammetry or field surveying. In the absence of large datasets documenting the movement of slow-moving landslides, we are challenged to isolate the key variables that control their movement and evolution. This knowledge gap hampers our understanding of landslide processes, landslide hazard, sediment budgets, and landscape evolution. Here we document the movement of numerous slow-moving landslides along the Eel River, northern California. These glacier-like landslides (earthflows) move seasonally (typically 1-2 m/yr), with minimal surface deformation, such that scattered shrubs can grow on the landslide surface for decades. Previous work focused on manually tracking the position of individual features (trees, rocks) on photos and LiDAR-derived digital topography to identify the extent of landslide activity. Here, we employ sub-pixel change detection software (COSI-Corr) to generate automated maps of landslide displacement by correlating successive orthorectified photos. Through creation of a detailed multi-temporal deformation field across the entire landslide surface, COSI-Corr is able to delineate zones of movement, quantify displacement, and identify domains of flow convergence and divergence. The vegetation and fine-scale landslide morphology provide excellent texture for automated comparison between successive orthorectified images, although decorrelation can occur in areas where translation between images is greater than the specified search window, or where intense ground deformation or vegetation change occurs. We automatically detected movement on dozens of active landslides (with landslide extent and displacement confirmed by

  19. Myocardial Perfusion: Near-automated Evaluation from Contrast-enhanced MR Images Obtained at Rest and during Vasodilator Stress

    OpenAIRE

    Tarroni, Giacomo; Corsi, Cristiana; Antkowiak, Patrick F; Veronesi, Federico; Kramer, Christopher M.; Epstein, Frederick H; Walter, James; Lamberti, Claudio; Lang, Roberto M.; Mor-Avi, Victor; Patel, Amit R

    2012-01-01

    This study demonstrated that despite the extreme dynamic nature of contrast-enhanced cardiac MR image sequences and respiratory motion, near-automated frame-by-frame detection of myocardial segments and high-quality quantification of myocardial contrast is feasible both at rest and during vasodilator stress.

  20. Quantification of diffusion tensor imaging in normal white matter maturation of early childhood using an automated processing pipeline

    Energy Technology Data Exchange (ETDEWEB)

    Loh, K.B.; Ramli, N.; Tan, L.K.; Roziah, M. [University of Malaya, Department of Biomedical Imaging, University Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, Kuala Lumpur (Malaysia); Rahmat, K. [University of Malaya, Department of Biomedical Imaging, University Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, Kuala Lumpur (Malaysia); University Malaya, Biomedical Imaging Department, Kuala Lumpur (Malaysia); Ariffin, H. [University of Malaya, Department of Paediatrics, Faculty of Medicine, Kuala Lumpur (Malaysia)

    2012-07-15

    The degree and status of white matter myelination can be sensitively monitored using diffusion tensor imaging (DTI). This study looks at the measurement of fractional anistropy (FA) and mean diffusivity (MD) using an automated ROI with an existing DTI atlas. Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction. DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued. DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data. (orig.)

  1. Quantification of diffusion tensor imaging in normal white matter maturation of early childhood using an automated processing pipeline

    International Nuclear Information System (INIS)

    The degree and status of white matter myelination can be sensitively monitored using diffusion tensor imaging (DTI). This study looks at the measurement of fractional anistropy (FA) and mean diffusivity (MD) using an automated ROI with an existing DTI atlas. Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction. DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued. DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data. (orig.)

  2. 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.

  3. Mass Cytometry: Single Cells, Many Features.

    Science.gov (United States)

    Spitzer, Matthew H; Nolan, Garry P

    2016-05-01

    Technology development in biological research often aims to either increase the number of cellular features that can be surveyed simultaneously or enhance the resolution at which such observations are possible. For decades, flow cytometry has balanced these goals to fill a critical need by enabling the measurement of multiple features in single cells, commonly to examine complex or hierarchical cellular systems. Recently, a format for flow cytometry has been developed that leverages the precision of mass spectrometry. This fusion of the two technologies, termed mass cytometry, provides measurement of over 40 simultaneous cellular parameters at single-cell resolution, significantly augmenting the ability of cytometry to evaluate complex cellular systems and processes. In this Primer, we review the current state of mass cytometry, providing an overview of the instrumentation, its present capabilities, and methods of data analysis, as well as thoughts on future developments and applications. PMID:27153492

  4. Quantification of circadian rhythms in single cells.

    Directory of Open Access Journals (Sweden)

    Pål O Westermark

    2009-11-01

    Full Text Available Bioluminescence techniques allow accurate monitoring of the circadian clock in single cells. We have analyzed bioluminescence data of Per gene expression in mouse SCN neurons and fibroblasts. From these data, we extracted parameters such as damping rate and noise intensity using two simple mathematical models, one describing a damped oscillator driven by noise, and one describing a self-sustained noisy oscillator. Both models describe the data well and enabled us to quantitatively characterize both wild-type cells and several mutants. It has been suggested that the circadian clock is self-sustained at the single cell level, but we conclude that present data are not sufficient to determine whether the circadian clock of single SCN neurons and fibroblasts is a damped or a self-sustained oscillator. We show how to settle this question, however, by testing the models' predictions of different phases and amplitudes in response to a periodic entrainment signal (zeitgeber.

  5. Mechanical downstream processing of Single Cell Oils

    OpenAIRE

    De Coninck, Maarten; Van Hecke, Renaat; Deprez, Koen; De Baerdemaeker, Josse

    2011-01-01

    During the last years, the third generation of bio fuels has been arousing more and more interest. Under certain conditions some micro organisms: yeasts, algae, fungi and bacteria, can accumulate up to 50% oil (based on dry weight). These so-called ‘Single cell oils’ (SCO) are well known in this context. Nowadays, harvesting and recovery of interesting products from microalgae is one of the most problematic areas of algal biofuel production technology. The traditional downstream process,...

  6. Measuring enzyme activity in single cells

    OpenAIRE

    Kovarik, Michelle L.; Allbritton, Nancy L.

    2011-01-01

    Seemingly identical cells can differ in their biochemical state, function and fate, and this variability plays an increasingly recognized role in organism-level outcomes. Cellular heterogeneity arises in part from variation in enzyme activity, which results from interplay between biological noise and multiple cellular processes. As a result, single-cell assays of enzyme activity, particularly those that measure product formation directly, are crucial. Recent innovations have yielded a range o...

  7. 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

  8. Automated melanoma detection with a novel multispectral imaging system: results of a prospective study

    International Nuclear Information System (INIS)

    The aim of this research was to evaluate the performance of a new spectroscopic system in the diagnosis of melanoma. This study involves a consecutive series of 1278 patients with 1391 cutaneous pigmented lesions including 184 melanomas. In an attempt to approach the 'real world' of lesion population, a further set of 1022 not excised clinically reassuring lesions was also considered for analysis. Each lesion was imaged in vivo by a multispectral imaging system. The system operates at wavelengths between 483 and 950 nm by acquiring 15 images at equally spaced wavelength intervals. From the images, different lesion descriptors were extracted related to the colour distribution and morphology of the lesions. Data reduction techniques were applied before setting up a neural network classifier designed to perform automated diagnosis. The data set was randomly divided into three sets: train (696 lesions, including 90 melanomas) and verify (348 lesions, including 53 melanomas) for the instruction of a proper neural network, and an independent test set (347 lesions, including 41 melanomas). The neural network was able to discriminate between melanomas and non-melanoma lesions with a sensitivity of 80.4% and a specificity of 75.6% in the 1391 histologized cases data set. No major variations were found in classification scores when train, verify and test subsets were separately evaluated. Following receiver operating characteristic (ROC) analysis, the resulting area under the curve was 0.85. No significant differences were found among areas under train, verify and test set curves, supporting the good network ability to generalize for new cases. In addition, specificity and area under ROC curve increased up to 90% and 0.90, respectively, when the additional set of 1022 lesions without histology was added to the test set. Our data show that performance of an automated system is greatly population dependent, suggesting caution in the comparison with results reported in the

  9. An automated image cytometry system for monitoring DNA ploidy and other cell features of radiotherapy and chemotherapy patients

    International Nuclear Information System (INIS)

    DNA content and distribution in cell nuclei were studied in samples of fine-needle aspiration (FNA) from 27 locally advanced breast and head and neck cancers in two going randomized trials that compared accelerated fractionation to standard fractionation radiation in locally advanced breast cancer and head and neck cancer. Two image cytometry methods were compared: a new, fully automated DNA image cytometry system (AIC) and a conventional image cytometry (CIC) system with manual selection, focusing, and segmentation of cells. The results of both techniques were compared on the basis of DNA histogram parameters including DNA index (DI), mean DNA values (MDV), and Auer's DNA histogram patterns. An excellent correlation was achieved between the two imaging techniques in terms of DI (r=0.985, p<0.001) and MDV (r=0.951, p<0.001) as well as between Auer's histogram patterns, where both methods agreed completely. It was concluded in these analyses that the two image cytometry methods were equivalent. However, the AIC offered an advantage by scanning samples in a fully automated way, which represented significant time saving for cytopathologists working with the system, as well as a larger number of cells used in the automated analysis. With the automated image cytometer, 500 relevant cells were collected and analyzed in about 10 minutes, where with the interactive (manual) method, it took typically an hour to collect and analyze only about 250 cells. Seventeen samples were sufficient for flow analysis. Image cytometry and flow cytometry showed good agreement in DI determination; however, three cases reported as diploid by flow cytometry were found to be aneuploid by image cytometry techniques. (author)

  10. 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.

  11. Development of Automated Image Analysis Tools for Verification of Radiotherapy Field Accuracy with AN Electronic Portal Imaging Device.

    Science.gov (United States)

    Dong, Lei

    1995-01-01

    The successful management of cancer with radiation relies on the accurate deposition of a prescribed dose to a prescribed anatomical volume within the patient. Treatment set-up errors are inevitable because the alignment of field shaping devices with the patient must be repeated daily up to eighty times during the course of a fractionated radiotherapy treatment. With the invention of electronic portal imaging devices (EPIDs), patient's portal images can be visualized daily in real-time after only a small fraction of the radiation dose has been delivered to each treatment field. However, the accuracy of human visual evaluation of low-contrast portal images has been found to be inadequate. The goal of this research is to develop automated image analysis tools to detect both treatment field shape errors and patient anatomy placement errors with an EPID. A moments method has been developed to align treatment field images to compensate for lack of repositioning precision of the image detector. A figure of merit has also been established to verify the shape and rotation of the treatment fields. Following proper alignment of treatment field boundaries, a cross-correlation method has been developed to detect shifts of the patient's anatomy relative to the treatment field boundary. Phantom studies showed that the moments method aligned the radiation fields to within 0.5mm of translation and 0.5^ circ of rotation and that the cross-correlation method aligned anatomical structures inside the radiation field to within 1 mm of translation and 1^ circ of rotation. A new procedure of generating and using digitally reconstructed radiographs (DRRs) at megavoltage energies as reference images was also investigated. The procedure allowed a direct comparison between a designed treatment portal and the actual patient setup positions detected by an EPID. Phantom studies confirmed the feasibility of the methodology. Both the moments method and the cross -correlation technique were

  12. Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype

    International Nuclear Information System (INIS)

    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

  13. 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.

  14. Evaluation of automated image registration algorithm for image-guided radiotherapy (IGRT)

    International Nuclear Information System (INIS)

    The performance of an image registration (IR) software was evaluated for automatically detecting known errors simulated through the movement of ExactCouch using an onboard imager. Twenty-seven set-up errors (11 translations, 10 rotations, 6 translation and rotation) were simulated by introducing offset up to ±15 mm in three principal axes and 0° to ±1° in yaw. For every simulated error, orthogonal kV radiograph and cone beam CT were acquired in half-fan (CBCTHF) and full-fan (CBCTFF) mode. The orthogonal radiographs and CBCTs were automatically co-registered to reference digitally reconstructed radiographs (DRRs) and planning CT using 2D–2D and 3D–3D matching software based on mutual information transformation. A total of 79 image sets (ten pairs of kV X-rays and 69 session of CBCT) were analyzed to determine the (a) reproducibility of IR outcome and (b) residual error, defined as the deviation between the known and IR software detected displacement in translation and rotation. The reproducibility of automatic IR of planning CT and repeat CBCTs taken with and without kilovoltage detector and kilovoltage X-ray source arm movement was excellent with mean SD of 0.1 mm in the translation and 0.0° in rotation. The average residual errors in translation and rotation were within ±0.5 mm and ±0.2°, ±0.9 mm and ±0.3°, and ±0.4 mm and ±0.2° for setup simulated only in translation, rotation, and both translation and rotation. The mean (SD) 3D vector was largest when only translational error was simulated and was 1.7 (1.1) mm for 2D–2D match of reference DRR with radiograph, 1.4 (0.6) and 1.3 (0.5) mm for 3D–3D match of reference CT and CBCT with full fan and half fan, respectively. In conclusion, the image-guided radiation therapy (IGRT) system is accurate within 1.8 mm and 0.4° and reproducible under control condition. Inherent error from any IGRT process should be taken into account while setting clinical IGRT protocol.

  15. 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

  16. Automated parasite faecal egg counting using fluorescence labelling, smartphone image capture and computational image analysis.

    Science.gov (United States)

    Slusarewicz, Paul; Pagano, Stefanie; Mills, Christopher; Popa, Gabriel; Chow, K Martin; Mendenhall, Michael; Rodgers, David W; Nielsen, Martin K

    2016-07-01

    Intestinal parasites are a concern in veterinary medicine worldwide and for human health in the developing world. Infections are identified by microscopic visualisation of parasite eggs in faeces, which is time-consuming, requires technical expertise and is impractical for use on-site. For these reasons, recommendations for parasite surveillance are not widely adopted and parasite control is based on administration of rote prophylactic treatments with anthelmintic drugs. This approach is known to promote anthelmintic resistance, so there is a pronounced need for a convenient egg counting assay to promote good clinical practice. Using a fluorescent chitin-binding protein, we show that this structural carbohydrate is present and accessible in shells of ova of strongyle, ascarid, trichurid and coccidian parasites. Furthermore, we show that a cellular smartphone can be used as an inexpensive device to image fluorescent eggs and, by harnessing the computational power of the phone, to perform image analysis to count the eggs. Strongyle egg counts generated by the smartphone system had a significant linear correlation with manual McMaster counts (R(2)=0.98), but with a significantly lower coefficient of variation (P=0.0177). Furthermore, the system was capable of differentiating equine strongyle and ascarid eggs similar to the McMaster method, but with significantly lower coefficients of variation (Psmartphones as relatively sophisticated, inexpensive and portable medical diagnostic devices. PMID:27025771

  17. Automated Data Production for a Novel Airborne Multiangle Spectropolarimetric Imager (airmspi)

    Science.gov (United States)

    Jovanovic, V. M.; Bull, M.; Diner, D. J.; Geier, S.; Rheingans, B.

    2012-07-01

    A novel polarimetric imaging technique making use of rapid retardance modulation has been developed by JPL as a part of NASA's Instrument Incubator Program. It has been built into the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) under NASA's Airborne Instrument Technology Transition Program, and is aimed primarily at remote sensing of the amounts and microphysical properties of aerosols and clouds. AirMSPI includes an 8-band (355, 380, 445, 470, 555, 660, 865, 935 nm) pushbroom camera that measures polarization in a subset of the bands (470, 660, and 865 nm). The camera is mounted on a gimbal and acquires imagery in a configurable set of along-track viewing angles ranging between +67° and -67° relative to nadir. As a result, near simultaneous multi-angle, multi-spectral, and polarimetric measurements of the targeted areas at a spatial resolution ranging from 7 m to 20 m (depending on the viewing angle) can be derived. An automated data production system is being built to support high data acquisition rate in concert with co-registration and orthorectified mapping requirements. To date, a number of successful engineering checkout flights were conducted in October 2010, August-September 2011, and January 2012. Data products resulting from these flights will be presented.

  18. A fully automated tortuosity quantification system with application to corneal nerve fibres in confocal microscopy images.

    Science.gov (United States)

    Annunziata, Roberto; Kheirkhah, Ahmad; Aggarwal, Shruti; Hamrah, Pedram; Trucco, Emanuele

    2016-08-01

    Recent clinical research has highlighted important links between a number of diseases and the tortuosity of curvilinear anatomical structures like corneal nerve fibres, suggesting that tortuosity changes might detect early stages of specific conditions. Currently, clinical studies are mainly based on subjective, visual assessment, with limited repeatability and inter-observer agreement. To address these problems, we propose a fully automated framework for image-level tortuosity estimation, consisting of a hybrid segmentation method and a highly adaptable, definition-free tortuosity estimation algorithm. The former combines an appearance model, based on a Scale and Curvature-Invariant Ridge Detector (SCIRD), with a context model, including multi-range learned context filters. The latter is based on a novel tortuosity estimation paradigm in which discriminative, multi-scale features can be automatically learned for specific anatomical objects and diseases. Experimental results on 140 in vivo confocal microscopy images of corneal nerve fibres from healthy and unhealthy subjects demonstrate the excellent performance of our method compared to state-of-the-art approaches and ground truth annotations from 3 expert observers. PMID:27136674

  19. Continuing Development of GOES-R SUVI Automated Solar Image Processing

    Science.gov (United States)

    Hill, S. M.; Darnel, J.; Vickroy, J.; Steenburgh, R. A.; Rigler, E. J.

    2013-12-01

    NOAA's Space Weather Prediction Center (SWPC) is the Nation's official source of space weather alerts, watches and warnings. In that role, the Center will be ingesting GOES-R Solar Ultraviolet Imager (SUVI) data beginning in the 2015-16 timeframe. Along with other NOAA and non-NOAA sources of solar imagery these observations are used by SWPC forecasters to inform their analysis (nowcast) and forecasts and also as sources of data to run empirical and numerical models. A supervised, multispectral, Bayesian pixel classifier has been developed and produces what are referred to as thematic maps to assist forecasters in their analysis. These maps represent classes of pixels including: space, coronal holes, quiet corona, filaments, active regions, and flares. The thematic maps product underwent initial operational test and evaluation at SWPC in 2012-13. The test used synoptic data from the Atmospheric Imaging Array (AIA) on NASA's SDO mission in near real time as a proxy for SUVI data. The thematic maps product has been upgraded and retrained to incorporate H-alpha imagery to better discriminate between filament channels and coronal holes. We present ongoing results of the operational test and evaluation for thematic maps. Also, we include initial results for automated flare location and coronal hole boundary location that depend on thematic maps as inputs.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Domenico F. Galati

    2016-01-01

    Full Text Available 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.

  2. Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

    Science.gov (United States)

    Haponen, Markus; Rasku, Jyrki

    2016-01-01

    The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies. PMID:27493680

  3. 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.

  4. Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.

    Science.gov (United States)

    Zhan, Mei; Crane, Matthew M; Entchev, Eugeni V; Caballero, Antonio; Fernandes de Abreu, Diana Andrea; Ch'ng, QueeLim; Lu, Hang

    2015-04-01

    Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision

  5. Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.

    Directory of Open Access Journals (Sweden)

    Mei Zhan

    2015-04-01

    Full Text Available Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM. These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a

  6. 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.

  7. Comparison of manual direct and automated indirect measurement of hippocampus using magnetic resonance imaging

    Energy Technology Data Exchange (ETDEWEB)

    Giesel, Frederik L. [Department of Radiology, German Cancer Research Center (Germany); MRI Unit, Department of Radiology, Sheffield (United Kingdom)], E-mail: f.giesel@dkfz.de; Thomann, Philipp A. [Section of Geriatric Psychiatry, University of Heidelberg (Germany); Hahn, Horst K. [MeVis, Bremen (Germany); Politi, Maria [Neuroradiology, Homburg/Saar (Germany); Stieltjes, Bram; Weber, Marc-Andre [Department of Radiology, German Cancer Research Center (Germany); Pantel, Johannes [Department of Psychiatry, University of Frankfurt (Germany); Wilkinson, I.D.; Griffiths, Paul D. [MRI Unit, Department of Radiology, Sheffield (United Kingdom); Schroeder, Johannes [Section of Geriatric Psychiatry, University of Heidelberg (Germany); Essig, Marco [Department of Radiology, German Cancer Research Center (Germany)

    2008-05-15

    Purpose: Objective quantification of brain structure can aid diagnosis and therapeutic monitoring in several neuropsychiatric disorders. In this study, we aimed to compare direct and indirect quantification approaches for hippocampal formation changes in patients with mild cognitive impairment and Alzheimer's disease (AD). Methods and materials: Twenty-one healthy volunteers (mean age: 66.2), 21 patients with mild cognitive impairment (mean age: 66.6), and 10 patients with AD (mean age: 65.1) were enrolled. All subjects underwent extensive neuropsychological testing and were imaged at 1.5 T (Vision, Siemens, Germany; T1w coronal TR = 4 ms, Flip = 13 deg., FOV = 250 mm, Matrix = 256 x 256, 128 contiguous slices, 1.8 mm). Direct measurement of the hippocampal formation was performed on coronal slices using a standardized protocol, while indirect temporal horn volume (THV) was calculated using a watershed algorithm-based software package (MeVis, Germany). Manual tracing took about 30 min, semi-automated measurement less than 3 min time. Results: Successful direct and indirect quantification was performed in all subjects. A significant volume difference was found between controls and AD patients (p < 0.001) with both the manual and the semi-automated approach. Group analysis showed a slight but not significant decrease of hippocampal volume and increase in temporal horn volume (THV) for subjects with mild cognitive impairment compared to volunteers (p < 0.07). A significant correlation (p < 0.001) of direct and indirect measurement was found. Conclusion: The presented indirect approach for hippocampus volumetry is equivalent to the direct approach and offers the advantages of observer independency, time reduction and thus usefulness for clinical routine.

  8. Comparison of manual direct and automated indirect measurement of hippocampus using magnetic resonance imaging

    International Nuclear Information System (INIS)

    Purpose: Objective quantification of brain structure can aid diagnosis and therapeutic monitoring in several neuropsychiatric disorders. In this study, we aimed to compare direct and indirect quantification approaches for hippocampal formation changes in patients with mild cognitive impairment and Alzheimer's disease (AD). Methods and materials: Twenty-one healthy volunteers (mean age: 66.2), 21 patients with mild cognitive impairment (mean age: 66.6), and 10 patients with AD (mean age: 65.1) were enrolled. All subjects underwent extensive neuropsychological testing and were imaged at 1.5 T (Vision, Siemens, Germany; T1w coronal TR = 4 ms, Flip = 13 deg., FOV = 250 mm, Matrix = 256 x 256, 128 contiguous slices, 1.8 mm). Direct measurement of the hippocampal formation was performed on coronal slices using a standardized protocol, while indirect temporal horn volume (THV) was calculated using a watershed algorithm-based software package (MeVis, Germany). Manual tracing took about 30 min, semi-automated measurement less than 3 min time. Results: Successful direct and indirect quantification was performed in all subjects. A significant volume difference was found between controls and AD patients (p < 0.001) with both the manual and the semi-automated approach. Group analysis showed a slight but not significant decrease of hippocampal volume and increase in temporal horn volume (THV) for subjects with mild cognitive impairment compared to volunteers (p < 0.07). A significant correlation (p < 0.001) of direct and indirect measurement was found. Conclusion: The presented indirect approach for hippocampus volumetry is equivalent to the direct approach and offers the advantages of observer independency, time reduction and thus usefulness for clinical routine

  9. 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.

  10. Single-cell Raman spectroscopy of irradiated tumour cells

    Science.gov (United States)

    Matthews, Quinn

    This work describes the development and application of a novel combination of single-cell Raman spectroscopy (RS), automated data processing, and principal component analysis (PCA) for investigating radiation induced biochemical responses in human tumour cells. The developed techniques are first validated for the analysis of large data sets (˜200 spectra) obtained from single cells. The effectiveness and robustness of the automated data processing methods is demonstrated, and potential pitfalls that may arise during the implementation of such methods are identified. The techniques are first applied to investigate the inherent sources of spectral variability between single cells of a human prostate tumour cell line (DU145) cultured in vitro. PCA is used to identify spectral differences that correlate with cell cycle progression and the changing confluency of a cell culture during the first 3-4 days after sub-culturing. Spectral variability arising from cell cycle progression is (i) expressed as varying intensities of protein and nucleic acid features relative to lipid features, (ii) well correlated with known biochemical changes in cells as they progress through the cell cycle, and (iii) shown to be the most significant source of inherent spectral variability between cells. This characterization provides a foundation for interpreting spectral variability in subsequent studies. The techniques are then applied to study the effects of ionizing radiation on human tumour cells. DU145 cells are cultured in vitro and irradiated to doses between 15 and 50 Gy with single fractions of 6 MV photons from a medical linear accelerator. Raman spectra are acquired from irradiated and unirradiated cells, up to 5 days post-irradiation. PCA is used to distinguish radiation induced spectral changes from inherent sources of spectral variability, such as those arising from cell cycle. Radiation induced spectral changes are found to correlate with both the irradiated dose and the

  11. Get to Understand More from Single-Cells: Current Studies of Microfluidic-Based Techniques for Single-Cell Analysis

    Science.gov (United States)

    Lo, Shih-Jie; Yao, Da-Jeng

    2015-01-01

    This review describes the microfluidic techniques developed for the analysis of a single cell. The characteristics of microfluidic (e.g., little sample amount required, high-throughput performance) make this tool suitable to answer and to solve biological questions of interest about a single cell. This review aims to introduce microfluidic related techniques for the isolation, trapping and manipulation of a single cell. The major approaches for detection in single-cell analysis are introduced; the applications of single-cell analysis are then summarized. The review concludes with discussions of the future directions and opportunities of microfluidic systems applied in analysis of a single cell. PMID:26213918

  12. Get to Understand More from Single-Cells: Current Studies of Microfluidic-Based Techniques for Single-Cell Analysis

    Directory of Open Access Journals (Sweden)

    Shih-Jie Lo

    2015-07-01

    Full Text Available This review describes the microfluidic techniques developed for the analysis of a single cell. The characteristics of microfluidic (e.g., little sample amount required, high-throughput performance make this tool suitable to answer and to solve biological questions of interest about a single cell. This review aims to introduce microfluidic related techniques for the isolation, trapping and manipulation of a single cell. The major approaches for detection in single-cell analysis are introduced; the applications of single-cell analysis are then summarized. The review concludes with discussions of the future directions and opportunities of microfluidic systems applied in analysis of a single cell.

  13. An automated image-registration technique based on multiple structure matching

    International Nuclear Information System (INIS)

    A new image-registration technique that matches multiple structures on complementary imaging data sets (e.g., CT and MRI) has been developed and tested with both phantom and patient data. The algorithm assumes a rigid-body transformation and is suitable for correlating structures within the cranium or at the skull base. The basic premise of the new technique is that an optimum transformation is achieved when the relative volume lying outside of the intersection between a structure and its transformed counterpart is a minimum. This relative volume is calculated numerically using a random sampling approach, and a binary searching algorithm was used to step through the nine-dimensional parameter space consisting of three rotation angles, three scaling factors and three components of a translation vector. For the nine tests using phantom data, the automated structure-matching technique was able to predict the correct rotation angles to within ±1 degree. The expected clinical performance of the new technique was assessed by comparing results obtained with the new method to those obtained using other techniques for 12 patients who were treated with charged particles at Lawrence Berkeley Laboratory (LBL) and who had image-registration studies performed as part of their treatment plan. For 9 of the 12 patients considered, the new structure-matching technique produced a significantly better registration than the older methods, as measured by the resultant average relative volume lying outside of the intersection between any structure and its transformed counterpart. For the other three patients, results were not significantly different for the new structure-matching method and the older techniques

  14. 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

  15. 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.

  16. 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.

  17. Single cell analysis of signaling molecules

    Czech Academy of Sciences Publication Activity Database

    Klepárník, Karel; Luksch, Jaroslav; Adamová, Eva; Potáčová, Anna; Matalová, E.; Foret, František

    Grupo VLS Print Solution, 2014 - (Guzman, N.; Taveres, M.). s. 49-49 [International Symposium on Electro- and Liquid Phase-Separation Techniques /21./ and Latin-American Symposium on Biotechnology, Biomedical, Biopharmaceutical, and Industrial Applications of Capillary Electrophoresis and Microchip Technology /21./. 04.10.2014-08.10.2014, Natal] R&D Projects: GA ČR(CZ) GA14-28254S Institutional support: RVO:68081715 ; RVO:67985904 Keywords : single cell analysis * signaling molecules * caspase Subject RIV: CB - Analytical Chemistry, Separation

  18. Automated recognition of body cavities in torso X-ray CT images

    International Nuclear Information System (INIS)

    Recently, it has become even more important to understand the normal anatomical structures of the human body in the field of radiological image anatomy. The recognition of body cavities is useful when organ extraction is performed. However, it is difficult to perform organ extraction and to obtain a clear understanding of anatomical structures based on CT values. For example, the distributions of the CT numbers of muscle and organs overlap each other. Therefore, the recognition of the body cavity domain can reduce the range of internal organ extraction and simplify organ segmentation. The purpose of the present study was to develop an automated method for recognizing the body cavities in torso X-ray CT images. Our body cavity recognition method is based on skeletal positions and is performed by extracting the borders of the thoracic cavity, abdominal cavity, and pelvic cavity. This method detects the initial and final points of such borders and searches for the shortest path on the surfaces of the skeletal structures. It then generates the boundary surfaces of the thoracic, abdominal, and pelvic cavities by drawing straight lines between the shortest paths and the centers of the paths. The body cavities located between the superior thoracic aperture and the diaphragm, the diaphragm and the pelvic inlet, and the pelvic inlet and the pelvic outlet are regarded as the thoracic cavity, the abdominal cavity, and the pelvic cavity, respectively. The method was applied to 20 cases in which torso X-ray CT images were obtained. The results showed that the body cavities were extracted correctly in most cases: the thoracic cavity in 17 cases, the abdominal cavity in 19 cases, and the pelvic cavity in 18 cases. We confirmed that our proposed method is effective for recognizing these body cavities. As one of the applications of our study, segmentation of muscle, fat, and the rectum in CT images was performed using the information obtained for body cavity structures. The results

  19. 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.

  20. 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.

  1. Automated parcellation of the brain surface generated from magnetic resonance images

    Directory of Open Access Journals (Sweden)

    Wen Li

    2013-10-01

    Full Text Available We have developed a fast and reliable pipeline to automatically parcellate the cortical surface into sub-regions. The pipeline can be used to study brain changes associated with psychiatric and neurological disorders. First, a genus zero cortical surface for one hemisphere is generated from the magnetic resonance images at the parametric boundary of the white matter and the gray matter. Second, a hemisphere-specific surface atlas is registered to the cortical surface using geometry features mapped in the spherical domain. The deformation field is used to warp statistic labels from the atlas to the subject surface. The Dice index of the labeled surface area is used to evaluate the similarity between the automated labels with the manual labels on the subject. The average Dice across twenty-four regions on fourteen testing subjects is 0.86. Alternative evaluations have also chosen to show the accuracy and flexibility of the present method. The point-wise accuracy of fourteen testing subjects is above 86% in average. The experiment shows that the present method is highly consistent with FreeSurfer (>99% of the surface area, using the same set of labels.

  2. An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images.

    Science.gov (United States)

    Sun, Zhuli; Chen, Haoyu; Shi, Fei; Wang, Lirong; Zhu, Weifang; Xiang, Dehui; Yan, Chenglin; Li, Liang; Chen, Xinjian

    2016-01-01

    Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorioretinal diseases, which can cause loss of central vision. In this paper, an automated framework is proposed to segment serous PED in SD-OCT images. The proposed framework consists of four main steps: first, a multi-scale graph search method is applied to segment abnormal retinal layers; second, an effective AdaBoost method is applied to refine the initial segmented regions based on 62 extracted features; third, a shape-constrained graph cut method is applied to segment serous PED, in which the foreground and background seeds are obtained automatically; finally, an adaptive structure elements based morphology method is applied to remove false positive segmented regions. The proposed framework was tested on 25 SD-OCT volumes from 25 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 90.08%, 0.22%, 91.20% and 92.62%, respectively. The proposed framework can provide clinicians with accurate quantitative information, including shape, size and position of the PED region, which can assist clinical diagnosis and treatment. PMID:26899236

  3. 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.

  4. 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.

  5. 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. Microsc. Res. Tech. 79:393-402, 2016. © 2016 Wiley Periodicals, Inc. PMID:26929213

  6. Automated Synthesis of 18F-Fluoropropoxytryptophan for Amino Acid Transporter System Imaging

    Directory of Open Access Journals (Sweden)

    I-Hong Shih

    2014-01-01

    Full Text Available Objective. This study was to develop a cGMP grade of [18F]fluoropropoxytryptophan (18F-FTP to assess tryptophan transporters using an automated synthesizer. Methods. Tosylpropoxytryptophan (Ts-TP was reacted with K18F/kryptofix complex. After column purification, solvent evaporation, and hydrolysis, the identity and purity of the product were validated by radio-TLC (1M-ammonium acetate : methanol = 4 : 1 and HPLC (C-18 column, methanol : water = 7 : 3 analyses. In vitro cellular uptake of 18F-FTP and 18F-FDG was performed in human prostate cancer cells. PET imaging studies were performed with 18F-FTP and 18F-FDG in prostate and small cell lung tumor-bearing mice (3.7 MBq/mouse, iv. Results. Radio-TLC and HPLC analyses of 18F-FTP showed that the Rf and Rt values were 0.9 and 9 min, respectively. Radiochemical purity was >99%. The radiochemical yield was 37.7% (EOS 90 min, decay corrected. Cellular uptake of 18F-FTP and 18F-FDG showed enhanced uptake as a function of incubation time. PET imaging studies showed that 18F-FTP had less tumor uptake than 18F-FDG in prostate cancer model. However, 18F-FTP had more uptake than 18F-FDG in small cell lung cancer model. Conclusion. 18F-FTP could be synthesized with high radiochemical yield. Assessment of upregulated transporters activity by 18F-FTP may provide potential applications in differential diagnosis and prediction of early treatment response.

  7. 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

  8. Automated synthesis of novel cell death imaging tracer 18F-FPDuramycin

    International Nuclear Information System (INIS)

    Background: The noninvasive imaging of cell death plays an important role in the evaluation of degenerative diseases and detection of tumor treatments. Duramycin, a peptide with 19-amino acid, is produced by Streptoverticillium cinnamoneus. It binds specifically to phosphatidylethanolamine (PE), a novel molecular target for cell death. Purpose: The aim is to develop a synthetic method to label duramycin using 18F ion. The automated synthesis was carried out by multi-step procedure on the modified PET-MF-2V-IT-I synthesizer. Methods: Firstly, the prosthetic group of 4-nitrophenyl 2-[18F]fluoropropionate (18F-NFP) was automatically synthesized by a convenient three-step procedure. Secondly, 18F-FPDuramycin was synthesized by conjunction of 18F-NFP with duramycin, which was purified by a solid-phase extraction cartridge. Orthogonal test was performed to confirm the suitable reaction conditions (solvent, base and temperature). Results: The radiochemical yields of 18F-NFP were (25±5)% (n=10, decay-uncorrected) based on[18F]fluoride in 80 min. 18F-FPDuramycin was obtained with yield of (70±3)% (n=8, decay-uncorrected) based on 18F-NFP within 20 min. The radiochemical purity of 18F-FPDuramycin was greater than 99% and the specific activity was greater than (23.7±13.7) GBq·μmol-1 (n=10). Conclusion: 18F-FPDuramycin injection is easy to be prepared with 'two-pot reaction' and is a promising radiotracer used for the clinical and scientific study on positron emission tomography (PET) imaging. (authors)

  9. Computer-aided method for automated selection of optimal imaging plane for measurement of total cerebral blood flow by MRI

    Science.gov (United States)

    Teng, Pang-yu; Bagci, Ahmet Murat; Alperin, Noam

    2009-02-01

    A computer-aided method for finding an optimal imaging plane for simultaneous measurement of the arterial blood inflow through the 4 vessels leading blood to the brain by phase contrast magnetic resonance imaging is presented. The method performance is compared with manual selection by two observers. The skeletons of the 4 vessels for which centerlines are generated are first extracted. Then, a global direction of the relatively less curved internal carotid arteries is calculated to determine the main flow direction. This is then used as a reference direction to identify segments of the vertebral arteries that strongly deviates from the main flow direction. These segments are then used to identify anatomical landmarks for improved consistency of the imaging plane selection. An optimal imaging plane is then identified by finding a plane with the smallest error value, which is defined as the sum of the angles between the plane's normal and the vessel centerline's direction at the location of the intersections. Error values obtained using the automated and the manual methods were then compared using 9 magnetic resonance angiography (MRA) data sets. The automated method considerably outperformed the manual selection. The mean error value with the automated method was significantly lower than the manual method, 0.09+/-0.07 vs. 0.53+/-0.45, respectively (p<.0001, Student's t-test). Reproducibility of repeated measurements was analyzed using Bland and Altman's test, the mean 95% limits of agreements for the automated and manual method were 0.01~0.02 and 0.43~0.55 respectively.

  10. Automated image analysis of alveolar expansion patterns in immature newborn rabbits treated with natural or artificial surfactant.

    OpenAIRE

    Halliday, H; Robertson, B.; Nilsson, R.; Rigaut, J. P.; Grossmann, G.

    1987-01-01

    Automated image analysis of histological lung sections was used to compare the efficacy of an artificial surfactant (dipalmitoylphosphatidylcholine + high-density lipoprotein, 10:1) and a natural surfactant (the phospholipid fraction of porcine surfactant, isolated by liquid-gel chromatography in ventilated immature newborn rabbits delivered after 27 days' gestation. Tidal volumes were significantly improved in each group treated with surfactant when compared with controls, but natural surfac...

  11. 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...

  12. 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.

  13. Electrodeformation for single cell mechanical characterization

    Science.gov (United States)

    Chen, Jian; Abdelgawad, Mohamed; Yu, Liming; Shakiba, Nika; Chien, Wei-Yin; Lu, Zhe; Geddie, William R.; Jewett, Michael A. S.; Sun, Yu

    2011-05-01

    This paper presents the use of electrodeformation as a method for single cell mechanical characterization in which mechanical properties of SiHa and ME180 cells (two cervical cancer cell lines) were quantified. Cells were directly placed between two microelectrodes with a rectangular ac electric field applied, and cell deformation was recorded under certain experimental conditions. Numerical simulations were performed to model cell electrodeformation based on the Maxwell stress tensor formulation. In these simulations, effects of cell electrical property variations on their electrodeformed behavior were investigated. By comparing the measured morphological changes with those obtained from numerical simulations, we were able to quantify Young's modulus of SiHa cells (601 ± 183 Pa) and ME180 cells (1463 ± 649 Pa). These values were consistent with Young's modulus values (SiHa: 400 ± 290 Pa and ME180: 1070 ± 580 Pa) obtained from conventional micropipette aspiration.

  14. Sample preparation for single cell analysis

    Czech Academy of Sciences Publication Activity Database

    Adamová, Eva; Basova, E. Y.; Potáčová, Anna; Foret, František; Matalová, Eva; Klepárník, Karel

    Brno: Institute of Analytical Chemistry AS CR, 2014 - (Foret, F.; Křenková, J.; Drobníková, I.; Guttman, A.; Klepárník, K.), s. 118-121 ISBN 978-80-904959-2-0. [CECE 2014. International Interdisciplinary Meeting on Bioanalysis /11./. Brno (CZ), 20.10.2014-22.10.2014] R&D Projects: GA ČR(CZ) GA14-28254S; GA MŠk(CZ) EE2.3.20.0182; GA ČR(CZ) GBP206/12/G014 Institutional support: RVO:68081715 Keywords : single cell analysis * micromanipulation * bioluminiscence Subject RIV: CB - Analytical Chemistry, Separation http://www.ce-ce.org/CECE2014/CECE%202014%20proceedings_full.pdf

  15. Single-cell protein from waste cellulose

    Science.gov (United States)

    Dunlap, C. E.; Callihan, C. D.

    1973-01-01

    The recycle, reuse, or reclamation of single cell protein from liquid and solid agricultural waste fibers by a fermentation process is reported. It is shown that cellulose comprises the bulk of the fibers at 50% to 55% of the dry weight of the refuse and that its biodegradability is of prime importance in the choice of a substrate. The application of sodium hydroxide followed by heat and pressure serves to de-polymerize and disrupt lignin structure while swelling the cellulose to increase water uptake and pore volume. Some of the lignin, hemi-celluloses, ash, and cellulose of the material is hydrolized and solubilized. Introduction of microorganisms to the substrate fibers mixed with nutrients produces continuous fermentation of cellulose for further protein extraction and purification.

  16. Sample preparation for single cell analysis

    Czech Academy of Sciences Publication Activity Database

    Adamová, Eva; Basova, E. Y.; Potáčová, Anna; Foret, František; Matalová, Eva; Klepárník, Karel

    Brno : Institute of Analytical Chemistry AS CR, 2014 - (Foret, F.; Křenková, J.; Drobníková, I.; Guttman, A.; Klepárník, K.), s. 118-121 ISBN 978-80-904959-2-0. [CECE 2014. International Interdisciplinary Meeting on Bioanalysis /11./. Brno (CZ), 20.10.2014-22.10.2014] R&D Projects: GA ČR(CZ) GA14-28254S; GA MŠk(CZ) EE2.3.20.0182; GA ČR(CZ) GBP206/12/G014 Institutional support: RVO:68081715 Keywords : single cell analysis * micromanipulation * bioluminiscence Subject RIV: CB - Analytical Chemistry, Separation http://www.ce-ce.org/CECE2014/CECE%202014%20proceedings_full.pdf

  17. Biological Evaluation of Single Cell Protein

    International Nuclear Information System (INIS)

    In this study, the nutritional value of single cell protein (SCP) was evaluated as a non conventional protein source produced by fermenting fungal local strains of Trichoderma longibrachiatum, Aspergillus niger, Aspergillus terreus and Penicillium funiculosum with alkali treated sugar cane bagasse. Amino acid analysis revealed that the produced SCP contains essential and non essential amino acids. Male mice were fed on normal (basal) diet which contains 18% conventional protein and served as control group. In the second (T1) and the third (T2) group, the animals were fed on a diet in which 15% and 30% of conventional protein source were replaced by SCP, respectively. At intervals of 15, 30, 45 and 60 days, mice were sacrificed and the blood samples were collected for the biochemical evaluation. The daily averages of body weight were significantly higher with group T2 than group T1. Where as, the kidney weights in groups (T1) and (T2) were significantly increased as compared with control. A non significant difference between the tested groups in the enzyme activities of AST, ALT and GSH content of liver tissue were recorded. While, cholesterol and triglycerides contents showed a significant decrease in both (T1) and (T2) groups as compared with control. The recorded values of the serum hormone (T4), ALP activities, albumin and A/G ratio did not changed by the previous treatments. Serum levels of total protein, urea, creatinine and uric acid were higher for groups (T1) and (T2) than the control group. In conclusion, partial substitution of soy bean protein in mice diet with single cell protein (15%) improved the mice growth without any adverse effects on some of the physiological functions tested

  18. Development of a semi-automated superimposing image verification system using a template matching algorithm in radiotherapy

    International Nuclear Information System (INIS)

    To analyze shifts in the isocenter of images, we developed a semi-automated superimposing image-verification system that is capable of automatically quantifying shifts in the isocenter through image analysis with a personal computer (PC). The accuracy and usefulness of this software were examined through a comparison of nine portal images with a simulation image and by comparing nine portal images with a DRR image, using a human pelvic phantom. The difference between the known magnitude of shift and the magnitude of shift detected with this method was analyzed as detection error. When the portal images were compared with the simulation image, the 95% confidence interval (95% CI) of detection errors (mean±SD) was 0.57±0.36 mm (95% CI: 0.49-0.65 mm). When the portal images were compared with the DRR image, the respective figures were 0.68±0.38 mm (95% CI: 0.59-0.77 mm). No significant difference was noted between these two categories of comparison (N.S). The absolute detection error (mean±SD) in all directions was 0.34±0.34 mm for the comparison of portal images with the simulation image and 0.41±0.36 mm for the comparison of portal images with the DRR image. This system seems to be appropriate for verification of the treatment field by improving the accuracy of radiotherapy as a method of computer-assisted landmark recognition during image comparison. (author)

  19. 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. PMID:19605307

  20. 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

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

    International Nuclear Information System (INIS)

    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

  2. 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

  3. 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

  4. 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

    2013-01-01

    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. PMID:23380664

  5. 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.

  6. Automated 3D quantitative assessment and measurement of alpha angles from the femoral head-neck junction using MR imaging

    International Nuclear Information System (INIS)

    To develop an automated approach for 3D quantitative assessment and measurement of alpha angles from the femoral head-neck (FHN) junction using bone models derived from magnetic resonance (MR) images of the hip joint.Bilateral MR images of the hip joints were acquired from 30 male volunteers (healthy active individuals and high-performance athletes, aged 18–49 years) using a water-excited 3D dual echo steady state (DESS) sequence. In a subset of these subjects (18 water-polo players), additional True Fast Imaging with Steady-state Precession (TrueFISP) images were acquired from the right hip joint. For both MR image sets, an active shape model based algorithm was used to generate automated 3D bone reconstructions of the proximal femur. Subsequently, a local coordinate system of the femur was constructed to compute a 2D shape map to project femoral head sphericity for calculation of alpha angles around the FHN junction. To evaluate automated alpha angle measures, manual analyses were performed on anterosuperior and anterior radial MR slices from the FHN junction that were automatically reformatted using the constructed coordinate system.High intra- and inter-rater reliability (intra-class correlation coefficients  >  0.95) was found for manual alpha angle measurements from the auto-extracted anterosuperior and anterior radial slices. Strong correlations were observed between manual and automatic measures of alpha angles for anterosuperior (r  =  0.84) and anterior (r  =  0.92) FHN positions. For matched DESS and TrueFISP images, there were no significant differences between automated alpha angle measures obtained from the upper anterior quadrant of the FHN junction (two-way repeated measures ANOVA, F  <  0.01, p  =  0.98).Our automatic 3D method analysed MR images of the hip joints to generate alpha angle measures around the FHN junction circumference with very good reliability and reproducibility. This work has the

  7. Hybrid Confocal Raman Fluorescence Microscopy on Single Cells Using Semiconductor Quantum Dots

    NARCIS (Netherlands)

    Manen, van Henk-Jan; Otto, Cees

    2007-01-01

    We have overcome the traditional incompatibility of Raman microscopy with fluorescence microscopy by exploiting the optical properties of semiconductor fluorescent quantum dots (QDs). Here we present a hybrid Raman fluorescence spectral imaging approach for single-cell microscopy applications. We sh

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

    International Nuclear Information System (INIS)

    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

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

    International Nuclear Information System (INIS)

    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

  10. 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

  11. Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach.

    Science.gov (United States)

    Varghese, Frency; Burket, Jessica A; Benson, Andrew D; Deutsch, Stephen I; Zemlin, Christian W

    2016-01-01

    Mouse is the preferred model organism for testing drugs designed to increase sociability. We present a method to quantify mouse sociability in which the test mouse is placed in a standardized apparatus and relevant behaviors are assessed in three different sessions (called session I, II, and III). The apparatus has three compartments (see Figure 1), the left and right compartments contain an inverted cup which can house a mouse (called "stimulus mouse"). In session I, the test mouse is placed in the cage and its mobility is characterized by the number of transitions made between compartments. In session II, a stimulus mouse is placed under one of the inverted cups and the sociability of the test mouse is quantified by the amounts of time it spends near the cup containing the enclosed stimulus mouse vs. the empty inverted cup. In session III, the inverted cups are removed and both mice interact freely. The sociability of the test mouse in session III is quantified by the number of social approaches it makes toward the stimulus mouse and by the number of times it avoids a social approach by the stimulus mouse. The automated evaluation of the movie detects the nose of the test mouse, which allows the determination of all described sociability measures in session I and II (in session III, approaches are identified automatically but classified manually). To find the nose, the image of an empty cage is digitally subtracted from each frame of the movie and the resulting image is binarized to identify the mouse pixels. The mouse tail is automatically removed and the two most distant points of the remaining mouse are determined; these are close to nose and base of tail. By analyzing the motion of the mouse and using continuity arguments, the nose is identified. Figure 1. Assessment of Sociability During 3 sessions. Session I (top): Acclimation of test mouse to the cage. Session II (middle): Test mouse moving freely in the cage while the stimulus mouse is enclosed in an

  12. 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.

  13. Automated 3D quantitative assessment and measurement of alpha angles from the femoral head-neck junction using MR imaging

    Science.gov (United States)

    Xia, Ying; Fripp, Jurgen; Chandra, Shekhar S.; Walker, Duncan; Crozier, Stuart; Engstrom, Craig

    2015-10-01

    To develop an automated approach for 3D quantitative assessment and measurement of alpha angles from the femoral head-neck (FHN) junction using bone models derived from magnetic resonance (MR) images of the hip joint. Bilateral MR images of the hip joints were acquired from 30 male volunteers (healthy active individuals and high-performance athletes, aged 18-49 years) using a water-excited 3D dual echo steady state (DESS) sequence. In a subset of these subjects (18 water-polo players), additional True Fast Imaging with Steady-state Precession (TrueFISP) images were acquired from the right hip joint. For both MR image sets, an active shape model based algorithm was used to generate automated 3D bone reconstructions of the proximal femur. Subsequently, a local coordinate system of the femur was constructed to compute a 2D shape map to project femoral head sphericity for calculation of alpha angles around the FHN junction. To evaluate automated alpha angle measures, manual analyses were performed on anterosuperior and anterior radial MR slices from the FHN junction that were automatically reformatted using the constructed coordinate system. High intra- and inter-rater reliability (intra-class correlation coefficients  >  0.95) was found for manual alpha angle measurements from the auto-extracted anterosuperior and anterior radial slices. Strong correlations were observed between manual and automatic measures of alpha angles for anterosuperior (r  =  0.84) and anterior (r  =  0.92) FHN positions. For matched DESS and TrueFISP images, there were no significant differences between automated alpha angle measures obtained from the upper anterior quadrant of the FHN junction (two-way repeated measures ANOVA, F  angle measures around the FHN junction circumference with very good reliability and reproducibility. This work has the potential to improve analyses of cam-type lesions of the FHN junction for large-scale morphometric and clinical MR

  14. Optimization of automated radiosynthesis of [18F]AV-45: a new PET imaging agent for Alzheimer's disease

    International Nuclear Information System (INIS)

    Introduction: Accumulation of β-amyloid (Aβ) aggregates in the brain is linked to the pathogenesis of Alzheimer's disease (AD). Imaging probes targeting these Aβ aggregates in the brain may provide a useful tool to facilitate the diagnosis of AD. Recently, [18F]AV-45 ([18F]5) demonstrated high binding to the Aβ aggregates in AD patients. To improve the availability of this agent for widespread clinical application, a rapid, fully automated, high-yield, cGMP-compliant radiosynthesis was necessary for production of this probe. We report herein an optimal [18F]fluorination, de-protection condition and fully automated radiosynthesis of [18F]AV-45 ([18F]5) on a radiosynthesis module (BNU F-A2). Methods: The preparation of [18F]AV-45 ([18F]5) was evaluated under different conditions, specifically by employing different precursors (-OTs and -Br as the leaving group), reagents (K222/K2CO3 vs. tributylammonium bicarbonate) and deprotection in different acids. With optimized conditions from these experiments, the automated synthesis of [18F]AV-45 ([18F]5) was accomplished by using a computer-programmed, standard operating procedure, and was purified on an on-line solid-phase cartridge (Oasis HLB). Results: The optimized reaction conditions were successfully implemented to an automated nucleophilic fluorination module. The radiochemical purity of [18F]AV-45 ([18F]5) was >95%, and the automated synthesis yield was 33.6±5.2% (no decay corrected, n=4), 50.1±7.9% (decay corrected) in 50 min at a quantity level of 10-100 mCi (370-3700 MBq). Autoradiography studies of [18F]AV-45 ([18F]5) using postmortem AD brain and Tg mouse brain sections in the presence of different concentration of 'cold' AV-136 showed a relatively low inhibition of in vitro binding of [18F]AV-45 ([18F]5) to the Aβ plaques (IC50=1-4 μM, a concentration several order of magnitude higher than the expected pseudo carrier concentration in the brain). Conclusions: Solid-phase extraction purification and

  15. An automated four-point scale scoring of segmental wall motion in echocardiography using quantified parametric images

    International Nuclear Information System (INIS)

    The aim of this paper is to develop an automated method which operates on echocardiographic dynamic loops for classifying the left ventricular regional wall motion (RWM) in a four-point scale. A non-selected group of 37 patients (2 and 4 chamber views) was studied. Each view was segmented according to the standardized segmentation using three manually positioned anatomical landmarks (the apex and the angles of the mitral annulus). The segmented data were analyzed by two independent experienced echocardiographists and the consensual RWM scores were used as a reference for comparisons. A fast and automatic parametric imaging method was used to compute and display as static color-coded parametric images both temporal and motion information contained in left ventricular dynamic echocardiograms. The amplitude and time parametric images were provided to a cardiologist for visual analysis of RWM and used for RWM quantification. A cross-validation method was applied to the segmental quantitative indices for classifying RWM in a four-point scale. A total of 518 segments were analyzed. Comparison between visual interpretation of parametric images and the reference reading resulted in an absolute agreement (Aa) of 66% and a relative agreement (Ra) of 96% and kappa (κ) coefficient of 0.61. Comparison of the automated RWM scoring against the same reference provided Aa = 64%, Ra = 96% and κ = 0.64 on the validation subset. Finally, linear regression analysis between the global quantitative index and global reference scores as well as ejection fraction resulted in correlations of 0.85 and 0.79. A new automated four-point scale scoring of RWM was developed and tested in a non-selected database. Its comparison against a consensual visual reading of dynamic echocardiograms showed its ability to classify RWM abnormalities.

  16. Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection

    International Nuclear Information System (INIS)

    Purpose: To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm. Methods: An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided doses over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm. Results: The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan. Conclusions: An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.

  17. From single-cell transcriptomics to single-molecule counting

    OpenAIRE

    Islam, Saiful

    2013-01-01

    RNA-sequencing (RNA-seq) technology has been progressing so fast in the last few years and made it possible to perform transcriptome analysis at single-cell level that was even unimaginable a few years before. Nowadays, the importance of gene expression analysis at the single-cell level is increasingly appreciated for the study of complex heterogeneous tissue. Also, in order to solve the obscure and no consensus definition of cell types, the single-cell gene expression analysis ap...

  18. A microfluidic approach to parallelized transcriptional profiling of single cells

    OpenAIRE

    Sun, Hao; Olsen, Timothy; Zhu, Jing; Tao, Jianguo; Ponnaiya, Brian; Amundson, Sally A; Brenner, David J.; Lin, Qiao

    2015-01-01

    The ability to correlate single-cell genetic information with cellular phenotypes is of great importance to biology and medicine, as it holds the potential to gain insight into disease pathways that is unavailable from ensemble measurements. We present a microfluidic approach to parallelized, rapid, quantitative analysis of messenger RNA from single cells via RT-qPCR. The approach leverages an array of single-cell RT-qPCR analysis units formed by a set of parallel microchannels concurrently c...

  19. 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.

  20. Quantifying intrinsic and extrinsic control of single-cell fates in cancer and stem/progenitor cell pedigrees with competing risks analysis

    OpenAIRE

    J. A. Cornwell; Hallett, R. M.; S. Auf der Mauer; A. Motazedian; Schroeder, T.; J. S. Draper; Harvey, R. P.; R. E. Nordon

    2016-01-01

    The molecular control of cell fate and behaviour is a central theme in biology. Inherent heterogeneity within cell populations requires that control of cell fate is studied at the single-cell level. Time-lapse imaging and single-cell tracking are powerful technologies for acquiring cell lifetime data, allowing quantification of how cell-intrinsic and extrinsic factors control single-cell fates over time. However, cell lifetime data contain complex features. Competing cell fates, censoring, an...

  1. Development of automated segmentation method for the posterior portion of the temporal lobe on coronal MR images

    International Nuclear Information System (INIS)

    Brain MRI is an important method for examining the diseases caused by various cerebral pathologies, and the measurement of temporal lobe volume is useful for identifying dementia and temporal lobe abnormalities. However, no segmentation algorithm for the temporal lobe on coronal MR images has been established. Such an algorithm is needed because the shape of the temporal lobe on coronal images varies from area to area. The purpose of this research was to develop a segmentation method for the posterior portion of the temporal lobe on coronal MR images. The subjects were 11 normal patients, whose coronal T1-weighted images were selected for this study. The preprocessing algorithm for segmentation consists of smoothing, binarization, and thinning. The first step of the segmentation process consists of recognition techniques for the temporal lobe region on thinning images. The next step is distance transformation on identified thinning images. Finally, the temporal lobe was segmented by using the original images and distance transformation images and employing the newly developed algorithm. The rate of accuracy of automated recognition was over 74% for all cases, while the average rate of accuracy was 83.2±4.0%. These results suggest that this segmentation method can clearly segment the temporal lobe and has potential for clinical use. Based on this study, although it included only 11 normal patients, we have started applying this segmentation method to many patients, with or without temporal lobe disease. (author)

  2. 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. PMID:27091298

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

    International Nuclear Information System (INIS)

    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

  4. Improving the image quality of contrast-enhanced MR angiography by automated image registration: A prospective study in peripheral arterial disease of the lower extremities

    International Nuclear Information System (INIS)

    Objective: If a patient has moved during digital subtraction angiography (DSA), manual pixel shift can improve the image quality. This study investigated whether such image registration can also improve the quality of contrast-enhanced magnetic resonance angiography (MRA) in patients with peripheral arterial disease of the lower extremities. Materials and methods: 404 leg MRAs of patients likely to have peripheral artery disease were included in this prospective study. The standard non-registered MRAs were compared to automatically linear, affine and warp registered MRAs by four image quality parameters, including the vessel detection probability (VDP) in maximum intensity projection (MIP) images and contrast-to-noise ratios (CNR). The different registration types were compared by analysis of variance. Results: All studied image quality parameters showed similar trends. Generally, registration improved the leg MRA quality significantly (P < 0.05). The 12% of lower legs with a body shift of 1 mm or more showed the highest gain in image quality when using linear registration instead of no registration, with an average VDP gain of 20-49%. Warp registration improved the image quality slightly further. Conclusion: Automated image registration can improve the MRA image quality especially in the lower legs, which is comparable to the effect of pixel shift in DSA.

  5. Different approaches to synovial membrane volume determination by magnetic resonance imaging: manual versus automated segmentation

    DEFF Research Database (Denmark)

    Østergaard, Mikkel

    1997-01-01

    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......, osteoarthritis (OA) 16] and 17 RA wrists were examined. At enhancement thresholds between 30 and 60%, the automated volumes (Syn(x%)) were highly significantly correlated to manual volumes (SynMan) (knees: rho = 0.78-0.91, P < 10(-5) to < 10(-9); wrists: rho = 0.87-0.95, P < 10(-4) to < 10(-6)). The absolute...... values of the automated estimates were extremely dependent on the threshold chosen. At the optimal threshold of 45%, the median numerical difference from SynMan was 7 ml (17%) in knees and 2 ml (25%) in wrists. At this threshold, the difference was not related to diagnosis, clinical inflammation or...

  6. 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

  7. 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.

  8. Development of an automated method for the detection of chronic lacunar infarct regions in brain MR images

    International Nuclear Information System (INIS)

    The purpose of our study is to develop an algorithm that would enable the automated detection of lacunar infarct on T1- and T2-weighted magnetic resonance (MR) images. Automated identification of the lacunar infarct regions is not only useful in assisting radiologists to detect lacunar infarcts as a computer-aided detection (CAD) system but is also beneficial in preventing the occurrence of cerebral apoplexy in high-risk patients. The lacunar infarct regions are classified into the following two types for detection: ''isolated lacunar infarct regions'' and ''lacunar infarct regions adjacent to hyperintensive structures.'' The detection of isolated lacunar infarct regions was based on the multiple-phase binarization (MPB) method. Moreover, to detect lacunar infarct regions adjacent to hyperintensive structures, we used a morphological opening processing and a subtraction technique between images produced using two types of circular structuring elements. Thereafter, candidate regions were selected based on three features -area, circularity, and gravity center. Two methods were applied to the detected candidates for eliminating false positives (FPs). The first method involved eliminating FPs that occurred along the periphery of the brain using the region-growing technique. The second method, the multi-circular regions difference method (MCRDM), was based on the comparison between the mean pixel values in a series of double circles on a T1-weighted image. A training dataset comprising 20 lacunar infarct cases was used to adjust the parameters. In addition, 673 MR images from 80 cases were used for testing the performance of our method; the sensitivity and specificity were 90.1% and 30.0% with 1.7 FPs per image, respectively. The results indicated that our CAD system for the automatic detection of lacunar infarct on MR images was effective. (author)

  9. 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.

  10. Accuracy of patient specific organ-dose estimates obtained using an automated image segmentation algorithm

    Science.gov (United States)

    Gilat-Schmidt, Taly; Wang, Adam; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

    2016-03-01

    The overall goal of this work is to develop a rapid, accurate and fully automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using a deterministic Boltzmann Transport Equation solver and automated CT segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. The investigated algorithm uses a combination of feature-based and atlas-based methods. A multiatlas approach was also investigated. We hypothesize that the auto-segmentation algorithm is sufficiently accurate to provide organ dose estimates since random errors at the organ boundaries will average out when computing the total organ dose. To test this hypothesis, twenty head-neck CT scans were expertly segmented into nine regions. A leave-one-out validation study was performed, where every case was automatically segmented with each of the remaining cases used as the expert atlas, resulting in nineteen automated segmentations for each of the twenty datasets. The segmented regions were applied to gold-standard Monte Carlo dose maps to estimate mean and peak organ doses. The results demonstrated that the fully automated segmentation algorithm estimated the mean organ dose to within 10% of the expert segmentation for regions other than the spinal canal, with median error for each organ region below 2%. In the spinal canal region, the median error was 7% across all data sets and atlases, with a maximum error of 20%. The error in peak organ dose was below 10% for all regions, with a median error below 4% for all organ regions. The multiple-case atlas reduced the variation in the dose estimates and additional improvements may be possible with more robust multi-atlas approaches. Overall, the results support potential feasibility of an automated segmentation algorithm to provide accurate organ dose estimates.

  11. An expert diagnostic system based on neural networks and image analysis techniques in the field of automated cytogenetics.

    Science.gov (United States)

    Beksaç, M S; Eskiizmirliler, S; Cakar, A N; Erkmen, A M; Dağdeviren, A; Lundsteen, C

    1996-03-01

    In this study, we introduce an expert system for intelligent chromosome recognition and classification based on artificial neural networks (ANN) and features obtained by automated image analysis techniques. A microscope equipped with a CCTV camera, integrated with an IBM-PC compatible computer environment including a frame grabber, is used for image data acquisition. Features of the chromosomes are obtained directly from the digital chromosome images. Two new algorithms for automated object detection and object skeletonizing constitute the basis of the feature extraction phase which constructs the components of the input vector to the ANN part of the system. This first version of our intelligent diagnostic system uses a trained unsupervised neural network structure and an original rule-based classification algorithm to find a karyotyped form of randomly distributed chromosomes over a complete metaphase. We investigate the effects of network parameters on the classification performance and discuss the adaptability and flexibility of the neural system in order to reach a structure giving an output including information about both structural and numerical abnormalities. Moreover, the classification performances of neural and rule-based system are compared for each class of chromosome. PMID:8705397

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

    Directory of Open Access Journals (Sweden)

    Nicolas Rey-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.

  13. 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

  14. 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

  15. Design and Analysis of Single-Cell Sequencing Experiments

    NARCIS (Netherlands)

    Grün, Dominic; van Oudenaarden, Alexander

    2015-01-01

    Recent advances in single-cell sequencing hold great potential for exploring biological systems with unprecedented resolution. Sequencing the genome of individual cells can reveal somatic mutations and allows the investigation of clonal dynamics. Single-cell transcriptome sequencing can elucidate th

  16. UV Decontamination of MDA Reagents for Single Cell Genomics

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Janey; Tighe, Damon; Sczyrba, Alexander; Malmatrom, Rex; Clingenpeel, Scott; Malfatti, Stephanie; Rinke, Christian; Wang, Zhong; Stepanauskas, Ramunas; Cheng, Jan-Fang; Woyke, Tanja

    2011-03-18

    Single cell genomics, the amplification and sequencing of genomes from single cells, can provide a glimpse into the genetic make-up and thus life style of the vast majority of uncultured microbial cells, making it an immensely powerful and increasingly popular tool. This is accomplished by use of multiple displacement amplification (MDA), which can generate billions of copies of a single bacterial genome producing microgram-range DNA required for shotgun sequencing. Here, we address a key challenge inherent to this approach and propose a solution for the improved recovery of single cell genomes. While DNA-free reagents for the amplification of a single cell genome are a prerequisite for successful single cell sequencing and analysis, DNA contamination has been detected in various reagents, which poses a considerable challenge. Our study demonstrates the effect of UV irradiation in efficient elimination of exogenous contaminant DNA found in MDA reagents, while maintaining Phi29 activity. Consequently, we also find that increased UV exposure to Phi29 does not adversely affect genome coverage of MDA amplified single cells. While additional challenges in single cell genomics remain to be resolved, the proposed methodology is relatively quick and simple and we believe that its application will be of high value for future single cell sequencing projects.

  17. Automated extraction of skeletal muscles from torso X-ray CT images based on anatomical positional information between skeleton and skeletal muscles

    International Nuclear Information System (INIS)

    We propose an automated approach to extract skeletal muscles in torso X-ray CT images. It transforms 3-D anatomy into 2-D stretched images for simplifying anatomical relationships to getting pathognomonical points. The experimental results show that the proposed method was effective to extract skeletal muscles. (author)

  18. Time efficiency and diagnostic accuracy of new automated myocardial perfusion analysis software in 320-row CT cardiac imaging

    International Nuclear Information System (INIS)

    We aimed to evaluate the time efficiency and diagnostic accuracy of automated myocardial computed tomography perfusion (CTP) image analysis software. 320-row CTP was performed in 30 patients, and analyses were conducted independently by three different blinded readers by the use of two recent software releases (version 4.6 and novel version 4.71GR001, Toshiba, Tokyo, Japan). Analysis times were compared, and automated epi- and endocardial contour detection was subjectively rated in five categories (excellent, good, fair, poor and very poor). As semi-quantitative perfusion parameters, myocardial attenuation and transmural perfusion ratio (TPR) were calculated for each myocardial segment and agreement was tested by using the intraclass correlation coefficient (ICC). Conventional coronary angiography served as reference standard. The analysis time was significantly reduced with the novel automated software version as compared with the former release (Reader 1: 43:08 ± 11:39 min vs. 09:47 ± 04:51 min, Reader 2: 42:07 ± 06:44 min vs. 09:42 ± 02:50 min and Reader 3: 21:38 ± 3:44 min vs. 07:34 ± 02:12 min; p < 0.001 for all). Epi- and endocardial contour detection for the novel software was rated to be significantly better (p < 0.001) than with the former software. ICCs demonstrated strong agreement (≥ 0.75) for myocardial attenuation in 93% and for TPR in 82%. Diagnostic accuracy for the two software versions was not significantly different (p 0.169) as compared with conventional coronary angiography. The novel automated CTP analysis software offers enhanced time efficiency with an improvement by a factor of about four, while maintaining diagnostic accuracy.

  19. SU-E-T-497: Semi-Automated in Vivo Radiochromic Film Dosimetry Using a Novel Image Processing Algorithm

    International Nuclear Information System (INIS)

    Purpose: To validate an automated image processing algorithm designed to detect the center of radiochromic film used for in vivo film dosimetry against the current gold standard of manual selection. Methods: An image processing algorithm was developed to automatically select the region of interest (ROI) in *.tiff images that contain multiple pieces of radiochromic film (0.5x1.3cm2). After a user has linked a calibration file to the processing algorithm and selected a *.tiff file for processing, an ROI is automatically detected for all films by a combination of thresholding and erosion, which removes edges and any additional markings for orientation. Calibration is applied to the mean pixel values from the ROIs and a *.tiff image is output displaying the original image with an overlay of the ROIs and the measured doses. Validation of the algorithm was determined by comparing in vivo dose determined using the current gold standard (manually drawn ROIs) versus automated ROIs for n=420 scanned films. Bland-Altman analysis, paired t-test, and linear regression were performed to demonstrate agreement between the processes. Results: The measured doses ranged from 0.2-886.6cGy. Bland-Altman analysis of the two techniques (automatic minus manual) revealed a bias of -0.28cGy and a 95% confidence interval of (5.5cGy,-6.1cGy). These values demonstrate excellent agreement between the two techniques. Paired t-test results showed no statistical differences between the two techniques, p=0.98. Linear regression with a forced zero intercept demonstrated that Automatic=0.997*Manual, with a Pearson correlation coefficient of 0.999. The minimal differences between the two techniques may be explained by the fact that the hand drawn ROIs were not identical to the automatically selected ones. The average processing time was 6.7seconds in Matlab on an IntelCore2Duo processor. Conclusion: An automated image processing algorithm has been developed and validated, which will help minimize user

  20. Probing bacterial adhesion at the single-cell level

    DEFF Research Database (Denmark)

    Zeng, Guanghong; Müller, Torsten; Meyer, Rikke Louise

    cantilever coated with the commercial cell adhesive CellTakTM. We applied the method to study adhesion of living cells to abiotic surfaces at the single-cell level. Immobilisation of single bacterial cells to the cantilever was stable for several hours, and viability was confirmed by Live/Dead staining and......Bacteria initiate attachment to surfaces with the aid of different extracellular proteins and polymeric adhesins. To quantitatively analyse the cell-cell and cell-surface interactions provided by bacterial adhesins, it is essential to go down to single cell level where cell-to-cell variation can be...... considered. We have developed a simple and versatile method to make single-cell bacterial probes for measuring single cell adhesion by force spectroscopy using atomic force microscopy (AFM). A single-cell probe was readily made by picking up a bacterial cell from a glass surface by approaching a tipless AFM...

  1. Quantum Dot Platform for Single-Cell Molecular Profiling

    Science.gov (United States)

    Zrazhevskiy, Pavel S.

    In-depth understanding of the nature of cell physiology and ability to diagnose and control the progression of pathological processes heavily rely on untangling the complexity of intracellular molecular mechanisms and pathways. Therefore, comprehensive molecular profiling of individual cells within the context of their natural tissue or cell culture microenvironment is essential. In principle, this goal can be achieved by tagging each molecular target with a unique reporter probe and detecting its localization with high sensitivity at sub-cellular resolution, primarily via microscopy-based imaging. Yet, neither widely used conventional methods nor more advanced nanoparticle-based techniques have been able to address this task up to date. High multiplexing potential of fluorescent probes is heavily restrained by the inability to uniquely match probes with corresponding molecular targets. This issue is especially relevant for quantum dot probes---while simultaneous spectral imaging of up to 10 different probes is possible, only few can be used concurrently for staining with existing methods. To fully utilize multiplexing potential of quantum dots, it is necessary to design a new staining platform featuring unique assignment of each target to a corresponding quantum dot probe. This dissertation presents two complementary versatile approaches towards achieving comprehensive single-cell molecular profiling and describes engineering of quantum dot probes specifically tailored for each staining method. Analysis of expanded molecular profiles is achieved through augmenting parallel multiplexing capacity with performing several staining cycles on the same specimen in sequential manner. In contrast to other methods utilizing quantum dots or other nanoparticles, which often involve sophisticated probe synthesis, the platform technology presented here takes advantage of simple covalent bioconjugation and non-covalent self-assembly mechanisms for straightforward probe

  2. Automated Scoring of Chromogenic Media for Detection of Methicillin-Resistant Staphylococcus aureus by Use of WASPLab Image Analysis Software.

    Science.gov (United States)

    Faron, Matthew L; Buchan, Blake W; Vismara, Chiara; Lacchini, Carla; Bielli, Alessandra; Gesu, Giovanni; Liebregts, Theo; van Bree, Anita; Jansz, Arjan; Soucy, Genevieve; Korver, John; Ledeboer, Nathan A

    2016-03-01

    Recently, systems have been developed to create total laboratory automation for clinical microbiology. These systems allow for the automation of specimen processing, specimen incubation, and imaging of bacterial growth. In this study, we used the WASPLab to validate software that discriminates and segregates positive and negative chromogenic methicillin-resistant Staphylococcus aureus (MRSA) plates by recognition of pigmented colonies. A total of 57,690 swabs submitted for MRSA screening were enrolled in the study. Four sites enrolled specimens following their standard of care. Chromogenic agar used at these sites included MRSASelect (Bio-Rad Laboratories, Redmond, WA), chromID MRSA (bioMérieux, Marcy l'Etoile, France), and CHROMagar MRSA (BD Diagnostics, Sparks, MD). Specimens were plated and incubated using the WASPLab. The digital camera took images at 0 and 16 to 24 h and the WASPLab software determined the presence of positive colonies based on a hue, saturation, and value (HSV) score. If the HSV score fell within a defined threshold, the plate was called positive. The performance of the digital analysis was compared to manual reading. Overall, the digital software had a sensitivity of 100% and a specificity of 90.7% with the specificity ranging between 90.0 and 96.0 across all sites. The results were similar using the three different agars with a sensitivity of 100% and specificity ranging between 90.7 and 92.4%. These data demonstrate that automated digital analysis can be used to accurately sort positive from negative chromogenic agar cultures regardless of the pigmentation produced. PMID:26719443

  3. 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

  4. 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.

  5. Single Cell Transfection through Precise Microinjection with Quantitatively Controlled Injection Volumes

    Science.gov (United States)

    Chow, Yu Ting; Chen, Shuxun; Wang,