WorldWideScience

Sample records for automated image analysis

  1. Automated image analysis techniques for cardiovascular magnetic resonance imaging

    NARCIS (Netherlands)

    Geest, Robertus Jacobus van der

    2011-01-01

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

  2. 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

  3. 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.

  4. 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...

  5. 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

  6. Automated image analysis for quantification of filamentous bacteria

    DEFF Research Database (Denmark)

    Fredborg, Marlene; Rosenvinge, Flemming Schønning; Spillum, Erik;

    2015-01-01

    Background Antibiotics of the β-lactam group are able to alter the shape of the bacterial cell wall, e.g. filamentation or a spheroplast formation. Early determination of antimicrobial susceptibility may be complicated by filamentation of bacteria as this can be falsely interpreted as growth...... in systems relying on colorimetry or turbidometry (such as Vitek-2, Phoenix, MicroScan WalkAway). The objective was to examine an automated image analysis algorithm for quantification of filamentous bacteria using the 3D digital microscopy imaging system, oCelloScope. Results Three E. coli strains displaying...... different resistant profiles and differences in filamentation kinetics were used to study a novel image analysis algorithm to quantify length of bacteria and bacterial filamentation. A total of 12 β-lactam antibiotics or β-lactam–β-lactamase inhibitor combinations were analyzed for their ability to induce...

  7. 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

  8. 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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jianfang Cao

    2015-01-01

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

  11. 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.

  12. Application of automated image analysis to coal petrography

    Science.gov (United States)

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

    1982-01-01

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

  13. An automated system for whole microscopic image acquisition and analysis.

    Science.gov (United States)

    Bueno, Gloria; Déniz, Oscar; Fernández-Carrobles, María Del Milagro; Vállez, Noelia; Salido, Jesús

    2014-09-01

    The field of anatomic pathology has experienced major changes over the last decade. Virtual microscopy (VM) systems have allowed experts in pathology and other biomedical areas to work in a safer and more collaborative way. VMs are automated systems capable of digitizing microscopic samples that were traditionally examined one by one. The possibility of having digital copies reduces the risk of damaging original samples, and also makes it easier to distribute copies among other pathologists. This article describes the development of an automated high-resolution whole slide imaging (WSI) system tailored to the needs and problems encountered in digital imaging for pathology, from hardware control to the full digitization of samples. The system has been built with an additional digital monochromatic camera together with the color camera by default and LED transmitted illumination (RGB). Monochrome cameras are the preferred method of acquisition for fluorescence microscopy. The system is able to digitize correctly and form large high resolution microscope images for both brightfield and fluorescence. The quality of the digital images has been quantified using three metrics based on sharpness, contrast and focus. It has been proved on 150 tissue samples of brain autopsies, prostate biopsies and lung cytologies, at five magnifications: 2.5×, 10×, 20×, 40×, and 63×. The article is focused on the hardware set-up and the acquisition software, although results of the implemented image processing techniques included in the software and applied to the different tissue samples are also presented.

  14. 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.

  15. Quantization of polyphenolic compounds in histological sections of grape berries by automated color image analysis

    Science.gov (United States)

    Clement, Alain; Vigouroux, Bertnand

    2003-04-01

    We present new results in applied color image analysis that put in evidence the significant influence of soil on localization and appearance of polyphenols in grapes. These results have been obtained with a new unsupervised classification algorithm founded on hierarchical analysis of color histograms. The process is automated thanks to a software platform we developed specifically for color image analysis and it's applications.

  16. 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...

  17. 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...

  18. 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.

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

    Science.gov (United States)

    Piattoni, Jacopo; Ceruti, Alessandro; Piergentili, Fabrizio

    2014-10-01

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

  20. 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.

  1. 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.

  2. 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)

  3. 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)

  4. 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.

  5. 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...

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

    Science.gov (United States)

    Mallard, François; Le Bourlot, Vincent; Tully, Thomas

    2013-01-01

    1. Because of recent technological improvements in the way computer and digital camera perform, the potential use of imaging for contributing to the study of communities, populations or individuals in laboratory microcosms has risen enormously. However its limited use is due to difficulties in the automation of image analysis. 2. We present an accurate and flexible method of image analysis for detecting, counting and measuring moving particles on a fixed but heterogeneous substrate. This method has been specifically designed to follow individuals, or entire populations, in experimental laboratory microcosms. It can be used in other applications. 3. The method consists in comparing multiple pictures of the same experimental microcosm in order to generate an image of the fixed background. This background is then used to extract, measure and count the moving organisms, leaving out the fixed background and the motionless or dead individuals. 4. We provide different examples (springtails, ants, nematodes, daphnia) to show that this non intrusive method is efficient at detecting organisms under a wide variety of conditions even on faintly contrasted and heterogeneous substrates. 5. The repeatability and reliability of this method has been assessed using experimental populations of the Collembola Folsomia candida. 6. We present an ImageJ plugin to automate the analysis of digital pictures of laboratory microcosms. The plugin automates the successive steps of the analysis and recursively analyses multiple sets of images, rapidly producing measurements from a large number of replicated microcosms. PMID:23734199

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

    Directory of Open Access Journals (Sweden)

    François Mallard

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

  8. Automation of Axisymmetric Drop Shape Analysis Using Digital Image Processing

    Science.gov (United States)

    Cheng, Philip Wing Ping

    The Axisymmetric Drop Shape Analysis - Profile (ADSA-P) technique, as initiated by Rotenberg, is a user -oriented scheme to determine liquid-fluid interfacial tensions and contact angles from the shape of axisymmetric menisci, i.e., from sessile as well as pendant drops. The ADSA -P program requires as input several coordinate points along the drop profile, the value of the density difference between the bulk phases, and gravity. The solution yields interfacial tension and contact angle. Although the ADSA-P technique was in principle complete, it was found that it was of very limited practical use. The major difficulty with the method is the need for very precise coordinate points along the drop profile, which, up to now, could not be obtained readily. In the past, the coordinate points along the drop profile were obtained by manual digitization of photographs or negatives. From manual digitization data, the surface tension values obtained had an average error of +/-5% when compared with literature values. Another problem with the ADSA-P technique was that the computer program failed to converge for the case of very elongated pendant drops. To acquire the drop profile coordinates automatically, a technique which utilizes recent developments in digital image acquisition and analysis was developed. In order to determine the drop profile coordinates as precisely as possible, the errors due to optical distortions were eliminated. In addition, determination of drop profile coordinates to pixel and sub-pixel resolution was developed. It was found that high precision could be obtained through the use of sub-pixel resolution and a spline fitting method. The results obtained using the automatic digitization technique in conjunction with ADSA-P not only compared well with the conventional methods, but also outstripped the precision of conventional methods considerably. To solve the convergence problem of very elongated pendant drops, it was found that the reason for the

  9. 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.

  10. Automated cell colony counting and analysis using the circular Hough image transform algorithm (CHiTA)

    Energy Technology Data Exchange (ETDEWEB)

    Bewes, J M; Suchowerska, N; McKenzie, D R [School of Physics, University of Sydney, Sydney, NSW (Australia)], E-mail: jbewes@physics.usyd.edu.au

    2008-11-07

    We present an automated cell colony counting method that is flexible, robust and capable of providing more in-depth clonogenic analysis than existing manual and automated approaches. The full form of the Hough transform without approximation has been implemented, for the first time. Improvements in computing speed have facilitated this approach. Colony identification was achieved by pre-processing the raw images of the colonies in situ in the flask, including images of the flask edges, by erosion, dilation and Gaussian smoothing processes. Colony edges were then identified by intensity gradient field discrimination. Our technique eliminates the need for specialized hardware for image capture and enables the use of a standard desktop scanner for distortion-free image acquisition. Additional parameters evaluated included regional colony counts, average colony area, nearest neighbour distances and radial distribution. This spatial and qualitative information extends the utility of the clonogenic assay, allowing analysis of spatially-variant cytotoxic effects. To test the automated system, two flask types and three cell lines with different morphology, cell size and plating density were examined. A novel Monte Carlo method of simulating cell colony images, as well as manual counting, were used to quantify algorithm accuracy. The method was able to identify colonies with unusual morphology, to successfully resolve merged colonies and to correctly count colonies adjacent to flask edges.

  11. Automated cell colony counting and analysis using the circular Hough image transform algorithm (CHiTA)

    Science.gov (United States)

    Bewes, J. M.; Suchowerska, N.; McKenzie, D. R.

    2008-11-01

    We present an automated cell colony counting method that is flexible, robust and capable of providing more in-depth clonogenic analysis than existing manual and automated approaches. The full form of the Hough transform without approximation has been implemented, for the first time. Improvements in computing speed have facilitated this approach. Colony identification was achieved by pre-processing the raw images of the colonies in situ in the flask, including images of the flask edges, by erosion, dilation and Gaussian smoothing processes. Colony edges were then identified by intensity gradient field discrimination. Our technique eliminates the need for specialized hardware for image capture and enables the use of a standard desktop scanner for distortion-free image acquisition. Additional parameters evaluated included regional colony counts, average colony area, nearest neighbour distances and radial distribution. This spatial and qualitative information extends the utility of the clonogenic assay, allowing analysis of spatially-variant cytotoxic effects. To test the automated system, two flask types and three cell lines with different morphology, cell size and plating density were examined. A novel Monte Carlo method of simulating cell colony images, as well as manual counting, were used to quantify algorithm accuracy. The method was able to identify colonies with unusual morphology, to successfully resolve merged colonies and to correctly count colonies adjacent to flask edges.

  12. 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

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

    Science.gov (United States)

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

    2015-11-01

    This paper outlines a numerical scheme for accurate, detailed, and high-throughput image analysis of plant roots. In contrast to existing root image analysis tools that focus on root system-average traits, a novel, fully automated and robust approach for the detailed characterization of root traits, based on a graph optimization process is presented. The scheme, firstly, distinguishes primary roots from lateral roots and, secondly, quantifies a broad spectrum of root traits for each identified primary and lateral root. Thirdly, it associates lateral roots and their properties with the specific primary root from which the laterals emerge. The performance of this approach was evaluated through comparisons with other automated and semi-automated software solutions as well as against results based on manual measurements. The comparisons and subsequent application of the algorithm to an array of experimental data demonstrate that this method outperforms existing methods in terms of accuracy, robustness, and the ability to process root images under high-throughput conditions.

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

    Science.gov (United States)

    White, Joshua S.; Matthews, Jeanna N.; Stacy, John L.

    2012-06-01

    Phishing website analysis is largely still a time-consuming manual process of discovering potential phishing sites, verifying if suspicious sites truly are malicious spoofs and if so, distributing their URLs to the appropriate blacklisting services. Attackers increasingly use sophisticated systems for bringing phishing sites up and down rapidly at new locations, making automated response essential. In this paper, we present a method for rapid, automated detection and analysis of phishing websites. Our method relies on near real-time gathering and analysis of URLs posted on social media sites. We fetch the pages pointed to by each URL and characterize each page with a set of easily computed values such as number of images and links. We also capture a screen-shot of the rendered page image, compute a hash of the image and use the Hamming distance between these image hashes as a form of visual comparison. We provide initial results demonstrate the feasibility of our techniques by comparing legitimate sites to known fraudulent versions from Phishtank.com, by actively introducing a series of minor changes to a phishing toolkit captured in a local honeypot and by performing some initial analysis on a set of over 2.8 million URLs posted to Twitter over a 4 days in August 2011. We discuss the issues encountered during our testing such as resolvability and legitimacy of URL's posted on Twitter, the data sets used, the characteristics of the phishing sites we discovered, and our plans for future work.

  15. 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)

  16. 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.

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

    DEFF Research Database (Denmark)

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

    1995-01-01

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

  18. 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...

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

    Directory of Open Access Journals (Sweden)

    Franziska Mech

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

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

    Science.gov (United States)

    Jiang, Luan; Hu, Xiaoxin; Gu, Yajia; Li, Qiang

    2015-03-01

    Amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) in dynamic contrast enhanced magnetic resonance (DCE-MR) images are two important indices for breast cancer risk assessment in the clinical practice. The purpose of this study is to develop and evaluate a fully automated scheme for quantitative analysis of FGT and BPE in DCE-MR images. Our fully automated method consists of three steps, i.e., segmentation of whole breast, fibroglandular tissues, and enhanced fibroglandular tissues. Based on the volume of interest extracted automatically, dynamic programming method was applied in each 2-D slice of a 3-D MR scan to delineate the chest wall and breast skin line for segmenting the whole breast. This step took advantages of the continuity of chest wall and breast skin line across adjacent slices. We then further used fuzzy c-means clustering method with automatic selection of cluster number for segmenting the fibroglandular tissues within the segmented whole breast area. Finally, a statistical method was used to set a threshold based on the estimated noise level for segmenting the enhanced fibroglandular tissues in the subtraction images of pre- and post-contrast MR scans. Based on the segmented whole breast, fibroglandular tissues, and enhanced fibroglandular tissues, FGT and BPE were automatically computed. Preliminary results of technical evaluation and clinical validation showed that our fully automated scheme could obtain good segmentation of the whole breast, fibroglandular tissues, and enhanced fibroglandular tissues to achieve accurate assessment of FGT and BPE for quantitative analysis of breast cancer risk.

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Tözeren Aydin

    2007-03-01

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

  3. 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

  4. 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.

  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. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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.

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

    Science.gov (United States)

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

    2016-07-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-07-15

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

  14. 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...

  15. Automated medical image segmentation techniques

    OpenAIRE

    Sharma Neeraj; Aggarwal Lalit

    2010-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Peter Kiehl

    1999-01-01

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

  17. 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

  18. 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...

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

    Science.gov (United States)

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

    2016-01-01

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

  20. Automated Digital Image Analysis (TrichoScan®) for Human Hair Growth Analysis: Ease versus Errors

    OpenAIRE

    Saraogi, Punit P; Rachita S Dhurat

    2010-01-01

    Background: TrichoScan® is considered to be time-saving, easy to perform and consistent for quantifying hair loss/growth. Conflicting results of our study lead us to closely observe the image analysis, and certain repeated errors in the detection of hair were highlighted. Aims: To assess the utility of TrichoScan in quantification of diffuse hair loss in males with androgenetic alopecia (AGA) and females with diffuse telogen hair loss, with regard to total hair density (THD), telogen and vell...

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

    Directory of Open Access Journals (Sweden)

    Jacopo Aguzzi

    2011-11-01

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

  2. 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

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

    Directory of Open Access Journals (Sweden)

    Aleksandar Bogovac

    2010-02-01

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

  4. Automated kidney morphology measurements from ultrasound images using texture and edge analysis

    Science.gov (United States)

    Ravishankar, Hariharan; Annangi, Pavan; Washburn, Michael; Lanning, Justin

    2016-04-01

    In a typical ultrasound scan, a sonographer measures Kidney morphology to assess renal abnormalities. Kidney morphology can also help to discriminate between chronic and acute kidney failure. The caliper placements and volume measurements are often time consuming and an automated solution will help to improve accuracy, repeatability and throughput. In this work, we developed an automated Kidney morphology measurement solution from long axis Ultrasound scans. Automated kidney segmentation is challenging due to wide variability in kidney shape, size, weak contrast of the kidney boundaries and presence of strong edges like diaphragm, fat layers. To address the challenges and be able to accurately localize and detect kidney regions, we present a two-step algorithm that makes use of edge and texture information in combination with anatomical cues. First, we use an edge analysis technique to localize kidney region by matching the edge map with predefined templates. To accurately estimate the kidney morphology, we use textural information in a machine learning algorithm framework using Haar features and Gradient boosting classifier. We have tested the algorithm on 45 unseen cases and the performance against ground truth is measured by computing Dice overlap, % error in major and minor axis of kidney. The algorithm shows successful performance on 80% cases.

  5. Automated systemic-cognitive analysis of images pixels (generalization, abstraction, classification and identification

    Directory of Open Access Journals (Sweden)

    Lutsenko Y. V.

    2015-09-01

    Full Text Available In the article the application of systemic-cognitive analysis and its mathematical model i.e. the system theory of the information and its program toolkit which is "Eidos" system for loading images from graphics files, synthesis of the generalized images of classes, their abstraction, classification of the generalized images (clusters and constructs comparisons of concrete images with the generalized images (identification are examined. We suggest using the theory of information for processing the data and its size for every pixel which indicates that the image is of a certain class. A numerical example is given in which on the basis of a number of specific examples of images belonging to different classes, forming generalized images of these classes, independent of their specific implementations, i.e., the "Eidoses" of these images (in the definition of Plato – the prototypes or archetypes of images (in the definition of Jung. But the "Eidos" system provides not only the formation of prototype images, which quantitatively reflects the amount of information in the elements of specific images on their belonging to a particular proto-types, but a comparison of specific images with generic (identification and the generalization of pictures images with each other (classification

  6. 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

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

    Science.gov (United States)

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

    2004-06-01

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

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

    Science.gov (United States)

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

    2014-01-01

    In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries. PMID:24808857

  9. 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.

  10. 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

  11. 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.

  12. Automated identification of diploid reference cells in cervical smears using image analysis.

    NARCIS (Netherlands)

    Laak, J.A.W.M. van der; Siebers, A.G.; Cuijpers, V.M.J.I.; Pahlplatz, M.M.M.; Wilde, P.C.M. de; Hanselaar, A.G.J.M.

    2002-01-01

    BACKGROUND: Acquisition of DNA ploidy histograms by image analysis may yield important information regarding the behavior of premalignant cervical lesions. Accurate selection of nuclei for DNA measurement is an important prerequisite for obtaining reliable data. Traditionally, manual selection of nu

  13. 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...

  14. Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers

    OpenAIRE

    Shu, Jie; Dolman, G. E.; Duan, Jiang; Qiu, Guoping; Ilyas, Mohammad

    2016-01-01

    Background Colour is the most important feature used in quantitative immunohistochemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to confirm malignancy. Methods Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was first trained by massive colour pixels collected semi-automatically. To ...

  15. Automated analysis of phantom images for the evaluation of long-term reproducibility in digital mammography

    Energy Technology Data Exchange (ETDEWEB)

    Gennaro, G [Department of Oncological and Surgical Sciences, University of Padova, via Gattamelata 64, 35128 Padova (Italy); Ferro, F [Department of Oncological and Surgical Sciences, University of Padova, via Gattamelata 64, 35128 Padova (Italy); Contento, G [Cyberqual S.r.l., Gorizia (Italy); Fornasin, F [Cyberqual S.r.l., Gorizia (Italy); Di Maggio, C [Department of Oncological and Surgical Sciences, University of Padova, via Gattamelata 64, 35128 Padova (Italy)

    2007-03-07

    The performance of an automatic software package was evaluated with phantom images acquired by a full-field digital mammography unit. After the validation, the software was used, together with a Leeds TORMAS test object, to model the image acquisition process. Process modelling results were used to evaluate the sensitivity of the method in detecting changes of exposure parameters from routine image quality measurements in digital mammography, which is the ultimate purpose of long-term reproducibility tests. Image quality indices measured by the software included the mean pixel value and standard deviation of circular details and surrounding background, contrast-to-noise ratio and relative contrast; detail counts were also collected. The validation procedure demonstrated that the software localizes the phantom details correctly and the difference between automatic and manual measurements was within few grey levels. Quantitative analysis showed sufficient sensitivity to relate fluctuations in exposure parameters (kV{sub p} or mAs) to variations in image quality indices. In comparison, detail counts were found less sensitive in detecting image quality changes, even when limitations due to observer subjectivity were overcome by automatic analysis. In conclusion, long-term reproducibility tests provided by the Leeds TORMAS phantom with quantitative analysis of multiple IQ indices have been demonstrated to be effective in predicting causes of deviation from standard operating conditions and can be used to monitor stability in full-field digital mammography.

  16. Automated Classification Of Scanning Electron Microscope Particle Images Using Morphological Analysis

    Science.gov (United States)

    Lamarche, B. L.; Lewis, R. R.; Girvin, D. C.; McKinley, J. P.

    2008-12-01

    We are developing a software tool that can automatically classify anthropogenic and natural aerosol particulates using morphological analysis. Our method was developed using SEM (background and secondary electron) images of single particles. Particle silhouettes are detected and converted into polygons using Intel's OpenCV image processing library. Our analysis then proceeds independently for the two kinds of images. Analysis of secondary images concerns itself solely with the silhouette and seeks to quantify its shape and roughness. Traversing the polygon with spline interpolation, we uniformly sample k(s), the signed curvature of the silhouette's path as a function of distance along the perimeter s. k(s) is invariant under rotation and translation. The power spectrum of k(s) qualitatively shows both shape and roughness: more power at low frequencies indicates variation in shape; more power at higher frequencies indicates a rougher silhouette. We present a series of filters (low-, band-, and high-pass) which we convolve with k(s) to yield a set of parameters that characterize the shape and roughness numerically. Analysis of backscatter images focuses on the (visual) texture, which is the result of both composition and geometry. Using the silhouette as a boundary, we compute the variogram, a statistical measure of inter-pixel covariance as a function of distance. Variograms take on characteristic curves, which we fit with a heuristic, asymptotic function that uses a small set of parameters. The combination of silhouette and variogram fit parameters forms the basis of a multidimensional classification space whose dimensionality we may reduce by principal component analysis and whose region boundaries allow us to classify new particles. This analysis is performed without a priori knowledge of other physical, chemical, or climatic properties. The method will be adapted to multi-particulate images.

  17. Automated spectral imaging for clinical diagnostics

    Science.gov (United States)

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

    1998-04-01

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

  18. Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography

    OpenAIRE

    Guo, Yanhui; Zhou, Chuan; Chan, Heang-Ping; Chughtai, Aamer; Wei, Jun; Hadjiiski, Lubomir M.; Kazerooni, Ella A.

    2013-01-01

    Purpose: Lung segmentation is a fundamental step in many image analysis applications for lung diseases and abnormalities in thoracic computed tomography (CT). The authors have previously developed a lung segmentation method based on expectation-maximization (EM) analysis and morphological operations (EMM) for our computer-aided detection (CAD) system for pulmonary embolism (PE) in CT pulmonary angiography (CTPA). However, due to the large variations in pathology that may be present in thoraci...

  19. 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 ...

  20. A method for the automated processing and analysis of images of ULVWF-platelet strings.

    Science.gov (United States)

    Reeve, Scott R; Abbitt, Katherine B; Cruise, Thomas D; Hose, D Rodney; Lawford, Patricia V

    2013-01-01

    We present a method for identifying and analysing unusually large von Willebrand factor (ULVWF)-platelet strings in noisy low-quality images. The method requires relatively inexpensive, non-specialist equipment and allows multiple users to be employed in the capture of images. Images are subsequently enhanced and analysed, using custom-written software to perform the processing tasks. The formation and properties of ULVWF-platelet strings released in in vitro flow-based assays have recently become a popular research area. Endothelial cells are incorporated into a flow chamber, chemically stimulated to induce ULVWF release and perfused with isolated platelets which are able to bind to the ULVWF to form strings. The numbers and lengths of the strings released are related to characteristics of the flow. ULVWF-platelet strings are routinely identified by eye from video recordings captured during experiments and analysed manually using basic NIH image software to determine the number of strings and their lengths. This is a laborious, time-consuming task and a single experiment, often consisting of data from four to six dishes of endothelial cells, can take 2 or more days to analyse. The method described here allows analysis of the strings to provide data such as the number and length of strings, number of platelets per string and the distance between each platelet to be found. The software reduces analysis time, and more importantly removes user subjectivity, producing highly reproducible results with an error of less than 2% when compared with detailed manual analysis.

  1. 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.

  2. Automated analysis of retinal imaging using machine learning techniques for computer vision [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Jeffrey De Fauw

    2016-07-01

    Full Text Available There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases.   Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet” age-related macular degeneration (wet AMD and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves. Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges.   This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients.   Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, Google DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

  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 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.

  5. Digital Rocks Portal: a sustainable platform for imaged dataset sharing, translation and automated analysis

    Science.gov (United States)

    Prodanovic, M.; Esteva, M.; Hanlon, M.; Nanda, G.; Agarwal, P.

    2015-12-01

    Recent advances in imaging have provided a wealth of 3D datasets that reveal pore space microstructure (nm to cm length scale) and allow investigation of nonlinear flow and mechanical phenomena from first principles using numerical approaches. This framework has popularly been called "digital rock physics". Researchers, however, have trouble storing and sharing the datasets both due to their size and the lack of standardized image types and associated metadata for volumetric datasets. This impedes scientific cross-validation of the numerical approaches that characterize large scale porous media properties, as well as development of multiscale approaches required for correct upscaling. A single research group typically specializes in an imaging modality and/or related modeling on a single length scale, and lack of data-sharing infrastructure makes it difficult to integrate different length scales. We developed a sustainable, open and easy-to-use repository called the Digital Rocks Portal, that (1) organizes images and related experimental measurements of different porous materials, (2) improves access to them for a wider community of geosciences or engineering researchers not necessarily trained in computer science or data analysis. Once widely accepter, the repository will jumpstart productivity and enable scientific inquiry and engineering decisions founded on a data-driven basis. This is the first repository of its kind. We show initial results on incorporating essential software tools and pipelines that make it easier for researchers to store and reuse data, and for educators to quickly visualize and illustrate concepts to a wide audience. For data sustainability and continuous access, the portal is implemented within the reliable, 24/7 maintained High Performance Computing Infrastructure supported by the Texas Advanced Computing Center (TACC) at the University of Texas at Austin. Long-term storage is provided through the University of Texas System Research

  6. Screening of subfertile men for testicular carcinoma in situ by an automated image analysis-based cytological test of the ejaculate

    DEFF Research Database (Denmark)

    Almstrup, K; Lippert, Marianne; Mogensen, Hanne O;

    2011-01-01

    and detected in ejaculates with specific CIS markers. We have built a high throughput framework involving automated immunocytochemical staining, scanning microscopy and in silico image analysis allowing automated detection and grading of CIS-like stained objects in semen samples. In this study, 1175 ejaculates...... from 765 subfertile men were tested using this framework. In 5/765 (0.65%) cases, CIS-like cells were identified in the ejaculate. Three of these had bilateral testicular biopsies performed and CIS was histologically confirmed in two. In total, 63 bilateral testicular biopsy were performed...... a slightly lower sensitivity (0.51), possibly because of obstruction. We conclude that this novel non-invasive test combining automated immunocytochemistry and advanced image analysis allows identification of TC at the CIS stage with a high specificity, but a negative test does not completely exclude CIS...

  7. An automated form of video image analysis applied to classification of movement disorders.

    Science.gov (United States)

    Chang, R; Guan, L; Burne, J A

    Video image analysis is able to provide quantitative data on postural and movement abnormalities and thus has an important application in neurological diagnosis and management. The conventional techniques require patients to be videotaped while wearing markers in a highly structured laboratory environment. This restricts the utility of video in routine clinical practise. We have begun development of intelligent software which aims to provide a more flexible system able to quantify human posture and movement directly from whole-body images without markers and in an unstructured environment. The steps involved are to extract complete human profiles from video frames, to fit skeletal frameworks to the profiles and derive joint angles and swing distances. By this means a given posture is reduced to a set of basic parameters that can provide input to a neural network classifier. To test the system's performance we videotaped patients with dopa-responsive Parkinsonism and age-matched normals during several gait cycles, to yield 61 patient and 49 normal postures. These postures were reduced to their basic parameters and fed to the neural network classifier in various combinations. The optimal parameter sets (consisting of both swing distances and joint angles) yielded successful classification of normals and patients with an accuracy above 90%. This result demonstrated the feasibility of the approach. The technique has the potential to guide clinicians on the relative sensitivity of specific postural/gait features in diagnosis. Future studies will aim to improve the robustness of the system in providing accurate parameter estimates from subjects wearing a range of clothing, and to further improve discrimination by incorporating more stages of the gait cycle into the analysis. PMID:10661762

  8. Automated identification and location analysis of marked stem cells colonies in optical microscopy images.

    Directory of Open Access Journals (Sweden)

    Vincenzo Paduano

    Full Text Available Embryonic stem cells (ESCs are characterized by two remarkable peculiarities: the capacity to propagate as undifferentiated cells (self-renewal and the ability to differentiate in ectoderm, endoderm, and mesoderm derivatives (pluripotency. Although the majority of ESCs divide without losing the pluripotency, it has become evident that ESC cultures consists of multiple cell populations highlighted by the expression of early germ lineage markers during spontaneous differentiation. Hence, the identification and characterization of ESCs subpopulations represents an efficient approach to improve the comprehension of correlation between gene expression and cell specification status. To study markers of ESCs heterogeneity, we developed an analysis pipeline which can automatically process images of stem cell colonies in optical microscopy. The question we try to address is to find out the statistically significant preferred locations of the marked cells. We tested our algorithm on a set of images of stem cell colonies to analyze the expression pattern of the Zscan4 gene, which was an elite candidate gene to be studied because it is specifically expressed in subpopulation of ESCs. To validate the proposed method we analyzed the behavior of control genes whose pattern had been associated to biological status such as differentiation (EndoA, pluripotency (Pou5f1, and pluripotency fluctuation (Nanog. We found that Zscan4 is not uniformly expressed inside a stem cell colony, and that it tends to be expressed towards the center of the colony, moreover cells expressing Zscan4 cluster each other. This is of significant importance because it allows us to hypothesize a biological status where the cells expressing Zscan4 are preferably associated to the inner of colonies suggesting pluripotent cell status features, and the clustering between themselves suggests either a colony paracrine effect or an early phase of cell specification through proliferation. Also, the

  9. Automated Motivic Analysis

    DEFF Research Database (Denmark)

    Lartillot, Olivier

    2016-01-01

    Motivic analysis provides very detailed understanding of musical composi- tions, but is also particularly difficult to formalize and systematize. A computational automation of the discovery of motivic patterns cannot be reduced to a mere extraction of all possible sequences of descriptions....... The systematic approach inexorably leads to a proliferation of redundant structures that needs to be addressed properly. Global filtering techniques cause a drastic elimination of interesting structures that damages the quality of the analysis. On the other hand, a selection of closed patterns allows...... for lossless compression. The structural complexity resulting from successive repetitions of patterns can be controlled through a simple modelling of cycles. Generally, motivic patterns cannot always be defined solely as sequences of descriptions in a fixed set of dimensions: throughout the descriptions...

  10. Automation of aggregate characterization using laser profiling and digital image analysis

    Science.gov (United States)

    Kim, Hyoungkwan

    2002-08-01

    Particle morphological properties such as size, shape, angularity, and texture are key properties that are frequently used to characterize aggregates. The characteristics of aggregates are crucial to the strength, durability, and serviceability of the structure in which they are used. Thus, it is important to select aggregates that have proper characteristics for each specific application. Use of improper aggregate can cause rapid deterioration or even failure of the structure. The current standard aggregate test methods are generally labor-intensive, time-consuming, and subject to human errors. Moreover, important properties of aggregates may not be captured by the standard methods due to a lack of an objective way of quantifying critical aggregate properties. Increased quality expectations of products along with recent technological advances in information technology are motivating new developments to provide fast and accurate aggregate characterization. The resulting information can enable a real time quality control of aggregate production as well as lead to better design and construction methods of portland cement concrete and hot mix asphalt. This dissertation presents a system to measure various morphological characteristics of construction aggregates effectively. Automatic measurement of various particle properties is of great interest because it has the potential to solve such problems in manual measurements as subjectivity, labor intensity, and slow speed. The main efforts of this research are placed on three-dimensional (3D) laser profiling, particle segmentation algorithms, particle measurement algorithms, and generalized particle descriptors. First, true 3D data of aggregate particles obtained by laser profiling are transformed into digital images. Second, a segmentation algorithm and a particle measurement algorithm are developed to separate particles and process each particle data individually with the aid of various kinds of digital image

  11. Automated hotspot analysis with aerial image CD metrology for advanced logic devices

    Science.gov (United States)

    Buttgereit, Ute; Trautzsch, Thomas; Kim, Min-ho; Seo, Jung-Uk; Yoon, Young-Keun; Han, Hak-Seung; Chung, Dong Hoon; Jeon, Chan-Uk; Meyers, Gary

    2014-09-01

    Continuously shrinking designs by further extension of 193nm technology lead to a much higher probability of hotspots especially for the manufacturing of advanced logic devices. The CD of these potential hotspots needs to be precisely controlled and measured on the mask. On top of that, the feature complexity increases due to high OPC load in the logic mask design which is an additional challenge for CD metrology. Therefore the hotspot measurements have been performed on WLCD from ZEISS, which provides the benefit of reduced complexity by measuring the CD in the aerial image and qualifying the printing relevant CD. This is especially of advantage for complex 2D feature measurements. Additionally, the data preparation for CD measurement becomes more critical due to the larger amount of CD measurements and the increasing feature diversity. For the data preparation this means to identify these hotspots and mark them automatically with the correct marker required to make the feature specific CD measurement successful. Currently available methods can address generic pattern but cannot deal with the pattern diversity of the hotspots. The paper will explore a method how to overcome those limitations and to enhance the time-to-result in the marking process dramatically. For the marking process the Synopsys WLCD Output Module was utilized, which is an interface between the CATS mask data prep software and the WLCD metrology tool. It translates the CATS marking directly into an executable WLCD measurement job including CD analysis. The paper will describe the utilized method and flow for the hotspot measurement. Additionally, the achieved results on hotspot measurements utilizing this method will be presented.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

    by digital image analysis. This study aims to detect the difference in accuracy and precision between manual indices of single and double stains, to develop an automated quantification of double stains, and to explore the relation between automated indices and tumor characteristics when quantified...... in different regions: hot spots, global tumor areas, and invasive fronts. MATERIALS AND METHODS: Paraffin-embedded, formalin-fixed tissue from 100 consecutive patients with invasive breast cancer was immunohistochemically stained for Ki-67 and Ki-67/KL1. Ki-67 was manually scored in different regions by 2...

  13. Automated image enhancement using power law transformations

    Indian Academy of Sciences (India)

    S P Vimal; P K Thiruvikraman

    2012-12-01

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

  14. Application of automated image analysis to the identification and extraction of recyclable plastic bottles

    Institute of Scientific and Technical Information of China (English)

    Edgar SCAVINO; Dzuraidah Abdul WAHAB; Aini HUSSAIN; Hassan BASRI; Mohd Marzuki MUSTAFA

    2009-01-01

    An experimental machine vision apparatus was used to identify and extract recyclable plastic bottles out of a conveyor belt. Color images were taken with a commercially available Webcam, and the recognition was performed by our homemade software, based on the shape and dimensions of object images. The software was able to manage multiple bottles in a single image and was additionally extended to cases involving touching bottles. The identification was fulfilled by comparing the set of measured features with an existing database and meanwhile integrating various recognition techniques such as minimum distance in the feature space, self-organized maps, and neural networks. The recognition system was tested on a set of 50 different bottles and provided so far an accuracy of about 97% on bottle identification. The extraction of the bottles was performed by means of a pneumatic arm, which was activated according to the plastic type; polyethylene-terephthalate (PET) bottles were left on the conveyor belt, while non-PET boules were extracted. The software was designed to provide the best compromise between reliability and speed for real-time applications in view of the commercialization of the system at existing recycling plants.

  15. Automated quantitative analysis for pneumoconiosis

    Science.gov (United States)

    Kondo, Hiroshi; Zhao, Bin; Mino, Masako

    1998-09-01

    Automated quantitative analysis for pneumoconiosis is presented. In this paper Japanese standard radiographs of pneumoconiosis are categorized by measuring the area density and the number density of small rounded opacities. And furthermore the classification of the size and shape of the opacities is made from the measuring of the equivalent radiuses of each opacity. The proposed method includes a bi- level unsharp masking filter with a 1D uniform impulse response in order to eliminate the undesired parts such as the images of blood vessels and ribs in the chest x-ray photo. The fuzzy contrast enhancement is also introduced in this method for easy and exact detection of small rounded opacities. Many simulation examples show that the proposed method is more reliable than the former method.

  16. Semi-automated 3D leaf reconstruction and analysis of trichome patterning from light microscopic images.

    Directory of Open Access Journals (Sweden)

    Henrik Failmezger

    2013-04-01

    Full Text Available Trichomes are leaf hairs that are formed by single cells on the leaf surface. They are known to be involved in pathogen resistance. Their patterning is considered to emerge from a field of initially equivalent cells through the action of a gene regulatory network involving trichome fate promoting and inhibiting factors. For a quantitative analysis of single and double mutants or the phenotypic variation of patterns in different ecotypes, it is imperative to statistically evaluate the pattern reliably on a large number of leaves. Here we present a method that enables the analysis of trichome patterns at early developmental leaf stages and the automatic analysis of various spatial parameters. We focus on the most challenging young leaf stages that require the analysis in three dimensions, as the leaves are typically not flat. Our software TrichEratops reconstructs 3D surface models from 2D stacks of conventional light-microscope pictures. It allows the GUI-based annotation of different stages of trichome development, which can be analyzed with respect to their spatial distribution to capture trichome patterning events. We show that 3D modeling removes biases of simpler 2D models and that novel trichome patterning features increase the sensitivity for inter-accession comparisons.

  17. A method to quantify movement activity of groups of animals using automated image analysis

    Science.gov (United States)

    Xu, Jianyu; Yu, Haizhen; Liu, Ying

    2009-07-01

    Most physiological and environmental changes are capable of inducing variations in animal behavior. The behavioral parameters have the possibility to be measured continuously in-situ by a non-invasive and non-contact approach, and have the potential to be used in the actual productions to predict stress conditions. Most vertebrates tend to live in groups, herds, flocks, shoals, bands, packs of conspecific individuals. Under culture conditions, the livestock or fish are in groups and interact on each other, so the aggregate behavior of the group should be studied rather than that of individuals. This paper presents a method to calculate the movement speed of a group of animal in a enclosure or a tank denoted by body length speed that correspond to group activity using computer vision technique. Frame sequences captured at special time interval were subtracted in pairs after image segmentation and identification. By labeling components caused by object movement in difference frame, the projected area caused by the movement of every object in the capture interval was calculated; this projected area was divided by the projected area of every object in the later frame to get body length moving distance of each object, and further could obtain the relative body length speed. The average speed of all object can well respond to the activity of the group. The group activity of a tilapia (Oreochromis niloticus) school to high (2.65 mg/L) levels of unionized ammonia (UIA) concentration were quantified based on these methods. High UIA level condition elicited a marked increase in school activity at the first hour (P<0.05) exhibiting an avoidance reaction (trying to flee from high UIA condition), and then decreased gradually.

  18. Automation analysis of cardiac wall deformation from tagged magnetic resonance images; Analise automatica de deformacao do miocardio em imagens marcadas por ressonancia magnetica

    Energy Technology Data Exchange (ETDEWEB)

    Piva, R.M.V. [Hospital das Clinicas, Sao Paulo, SP (Brazil). Instituto do Coracao. Div. de Informatica; Kitney, R.I. [Imperial College of Science, Technology and Medicine, London (United Kingdom)

    1998-07-01

    Automation of cardiac wall deformation analysis from tagged magnetic resonance images (MRI) derives, basically, from the automatic detection of MR tags and left ventricle contours. In this work, it was adopted an approach based on image processing techniques and fuzzy logic to extract and classify image features as belonging to tags or ventricular borders. The use of fuzzy logic and IF-THEN rules, which involve image features such as length and curvature of valleys and gradients, allow the estimation of the membership of the pixels in the searched classes. The myocardial deformation is estimated in regions circumvented by contiguous tag intersections. The proposed method was applied to cine SPAMM (Spatial Modulation of Magnetization) short-axis images of the left ventricle obtained from human volunteers. (author)

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

    Directory of Open Access Journals (Sweden)

    Joshua Chopin

    Full Text Available The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.

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

    Science.gov (United States)

    Chopin, Joshua; Laga, Hamid; Huang, Chun Yuan; Heuer, Sigrid; Miklavcic, Stanley J

    2015-01-01

    The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.

  1. 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

  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. A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna

    Directory of Open Access Journals (Sweden)

    Paolo Menesatti

    2009-10-01

    Full Text Available The understanding of ecosystem dynamics in deep-sea areas is to date limited by technical constraints on sampling repetition. We have elaborated a morphometry-based protocol for automated video-image analysis where animal movement tracking (by frame subtraction is accompanied by species identification from animals’ outlines by Fourier Descriptors and Standard K-Nearest Neighbours methods. One-week footage from a permanent video-station located at 1,100 m depth in Sagami Bay (Central Japan was analysed. Out of 150,000 frames (1 per 4 s, a subset of 10.000 was analyzed by a trained operator to increase the efficiency of the automated procedure. Error estimation of the automated and trained operator procedure was computed as a measure of protocol performance. Three displacing species were identified as the most recurrent: Zoarcid fishes (eelpouts, red crabs (Paralomis multispina, and snails (Buccinum soyomaruae. Species identification with KNN thresholding produced better results in automated motion detection. Results were discussed assuming that the technological bottleneck is to date deeply conditioning the exploration of the deep-sea.

  4. Analysis of magnetosome chains in magnetotactic bacteria by magnetic measurements and automated image analysis of electron micrographs.

    Science.gov (United States)

    Katzmann, E; Eibauer, M; Lin, W; Pan, Y; Plitzko, J M; Schüler, D

    2013-12-01

    Magnetotactic bacteria (MTB) align along the Earth's magnetic field by the activity of intracellular magnetosomes, which are membrane-enveloped magnetite or greigite particles that are assembled into well-ordered chains. Formation of magnetosome chains was found to be controlled by a set of specific proteins in Magnetospirillum gryphiswaldense and other MTB. However, the contribution of abiotic factors on magnetosome chain assembly has not been fully explored. Here, we first analyzed the effect of growth conditions on magnetosome chain formation in M. gryphiswaldense by electron microscopy. Whereas higher temperatures (30 to 35°C) and high oxygen concentrations caused increasingly disordered chains and smaller magnetite crystals, growth at 20°C and anoxic conditions resulted in long chains with mature cuboctahedron-shaped crystals. In order to analyze the magnetosome chain in electron microscopy data sets in a more quantitative and unbiased manner, we developed a computerized image analysis algorithm. The collected data comprised the cell dimensions and particle size and number as well as the intracellular position and extension of the magnetosome chain. The chain analysis program (CHAP) was used to evaluate the effects of the genetic and growth conditions on magnetosome chain formation. This was compared and correlated to data obtained from bulk magnetic measurements of wild-type (WT) and mutant cells displaying different chain configurations. These techniques were used to differentiate mutants due to magnetosome chain defects on a bulk scale. PMID:24096429

  5. 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.

  6. An automated multi-modal object analysis approach to coronary calcium scoring of adaptive heart isolated MSCT images

    Science.gov (United States)

    Wu, Jing; Ferns, Gordon; Giles, John; Lewis, Emma

    2012-02-01

    Inter- and intra- observer variability is a problem often faced when an expert or observer is tasked with assessing the severity of a disease. This issue is keenly felt in coronary calcium scoring of patients suffering from atherosclerosis where in clinical practice, the observer must identify firstly the presence, followed by the location of candidate calcified plaques found within the coronary arteries that may prevent oxygenated blood flow to the heart muscle. This can be challenging for a human observer as it is difficult to differentiate calcified plaques that are located in the coronary arteries from those found in surrounding anatomy such as the mitral valve or pericardium. The inclusion or exclusion of false positive or true positive calcified plaques respectively will alter the patient calcium score incorrectly, thus leading to the possibility of incorrect treatment prescription. In addition to the benefits to scoring accuracy, the use of fast, low dose multi-slice CT imaging to perform the cardiac scan is capable of acquiring the entire heart within a single breath hold. Thus exposing the patient to lower radiation dose, which for a progressive disease such as atherosclerosis where multiple scans may be required, is beneficial to their health. Presented here is a fully automated method for calcium scoring using both the traditional Agatston method, as well as the Volume scoring method. Elimination of the unwanted regions of the cardiac image slices such as lungs, ribs, and vertebrae is carried out using adaptive heart isolation. Such regions cannot contain calcified plaques but can be of a similar intensity and their removal will aid detection. Removal of both the ascending and descending aortas, as they contain clinical insignificant plaques, is necessary before the final calcium scores are calculated and examined against ground truth scores of three averaged expert observer results. The results presented here are intended to show the requirement and

  7. Automated activation-analysis system

    International Nuclear Information System (INIS)

    An automated delayed neutron counting and instrumental neutron activation analysis system has been developed at Los Alamos National Laboratory's Omega West Reactor (OWR) to analyze samples for uranium and 31 additional elements with a maximum throughput of 400 samples per day. The system and its mode of operation for a large reconnaissance survey are described

  8. Strong Prognostic Value of Tumor-infiltrating Neutrophils and Lymphocytes Assessed by Automated Digital Image Analysis in Early Stage Cervical Cancer

    DEFF Research Database (Denmark)

    Carus, Andreas; Donskov, Frede; Switten Nielsen, Patricia;

    2014-01-01

    INTRODUCTION Manual observer-assisted stereological (OAS) assessments of tumor-infiltrating neutrophils and lymphocytes are prognostic, accurate, but cumbersome. We assessed the applicability of automated digital image analysis (DIA). METHODS Visiomorph software was used to obtain DIA densities...... to lymphocyte (TA–NL) index accurately predicted the risk of relapse, ranging from 8% to 52% (P = 0.001). CONCLUSIONS DIA is a potential assessment technique. The TA–NL index obtained by DIA is a strong prognostic variable with possible routine clinical application....

  9. AUTOMATED ANALYSIS OF BREAKERS

    Directory of Open Access Journals (Sweden)

    E. M. Farhadzade

    2014-01-01

    Full Text Available Breakers relate to Electric Power Systems’ equipment, the reliability of which influence, to a great extend, on reliability of Power Plants. In particular, the breakers determine structural reliability of switchgear circuit of Power Stations and network substations. Failure in short-circuit switching off by breaker with further failure of reservation unit or system of long-distance protection lead quite often to system emergency.The problem of breakers’ reliability improvement and the reduction of maintenance expenses is becoming ever more urgent in conditions of systematic increasing of maintenance cost and repair expenses of oil circuit and air-break circuit breakers. The main direction of this problem solution is the improvement of diagnostic control methods and organization of on-condition maintenance. But this demands to use a great amount of statistic information about nameplate data of breakers and their operating conditions, about their failures, testing and repairing, advanced developments (software of computer technologies and specific automated information system (AIS.The new AIS with AISV logo was developed at the department: “Reliability of power equipment” of AzRDSI of Energy. The main features of AISV are:· to provide the security and data base accuracy;· to carry out systematic control of breakers conformity with operating conditions;· to make the estimation of individual  reliability’s value and characteristics of its changing for given combination of characteristics variety;· to provide personnel, who is responsible for technical maintenance of breakers, not only with information but also with methodological support, including recommendations for the given problem solving  and advanced methods for its realization.

  10. 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.

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

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

    Science.gov (United States)

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

    2014-07-01

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

  13. Automated detection of hepatotoxic compounds in human hepatocytes using HepaRG cells and image-based analysis of mitochondrial dysfunction with JC-1 dye

    International Nuclear Information System (INIS)

    In this study, our goal was to develop an efficient in situ test adapted to screen hepatotoxicity of various chemicals, a process which remains challenging during the early phase of drug development. The test was based on functional human hepatocytes using the HepaRG cell line, and automation of quantitative fluorescence microscopy coupled with automated imaging analysis. Differentiated HepaRG cells express most of the specific liver functions at levels close to those found in primary human hepatocytes, including detoxifying enzymes and drug transporters. A triparametric analysis was first used to evaluate hepatocyte purity and differentiation status, mainly detoxication capacity of cells before toxicity testing. We demonstrated that culturing HepaRG cells at high density maintained high hepatocyte purity and differentiation level. Moreover, evidence was found that isolating hepatocytes from 2-week-old confluent cultures limited variations associated with an ageing process occurring over time in confluent cells. Then, we designed a toxicity test based on detection of early mitochondrial depolarisation associated with permeability transition (MPT) pore opening, using JC-1 as a metachromatic fluorescent dye. Maximal dye dimerization that would have been strongly hampered by efficient efflux due to the active, multidrug-resistant (MDR) pump was overcome by coupling JC-1 with the MDR inhibitor verapamil. Specificity of this test was demonstrated and its usefulness appeared directly dependent on conditions supporting hepatic cell competence. This new hepatotoxicity test adapted to automated, image-based detection should be useful to evaluate the early MPT event common to cell apoptosis and necrosis and simultaneously to detect involvement of the multidrug resistant pump with target drugs in a human hepatocyte environment. - Highlights: → We define conditions to preserve differentiation of selective pure HepaRG hepatocyte cultures. → In these conditions, CYPs

  14. Automated image analysis with the potential for process quality control applications in stem cell maintenance and differentiation.

    Science.gov (United States)

    Smith, David; Glen, Katie; Thomas, Robert

    2016-01-01

    The translation of laboratory processes into scaled production systems suitable for manufacture is a significant challenge for cell based therapies; in particular there is a lack of analytical methods that are informative and efficient for process control. Here the potential of image analysis as one part of the solution to this issue is explored, using pluripotent stem cell colonies as a valuable and challenging exemplar. The Cell-IQ live cell imaging platform was used to build image libraries of morphological culture attributes such as colony "edge," "core periphery" or "core" cells. Conventional biomarkers, such as Oct3/4, Nanog, and Sox-2, were shown to correspond to specific morphologies using immunostaining and flow cytometry techniques. Quantitative monitoring of these morphological attributes in-process using the reference image libraries showed rapid sensitivity to changes induced by different media exchange regimes or the addition of mesoderm lineage inducing cytokine BMP4. The imaging sample size to precision relationship was defined for each morphological attribute to show that this sensitivity could be achieved with a relatively low imaging sample. Further, the morphological state of single colonies could be correlated to individual colony outcomes; smaller colonies were identified as optimum for homogenous early mesoderm differentiation, while larger colonies maintained a morphologically pluripotent core. Finally, we show the potential of the same image libraries to assess cell number in culture with accuracy comparable to sacrificial digestion and counting. The data supports a potentially powerful role for quantitative image analysis in the setting of in-process specifications, and also for screening the effects of process actions during development, which is highly complementary to current analysis in optimization and manufacture.

  15. Automated landmark-guided deformable image registration

    Science.gov (United States)

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

    2015-01-01

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

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

    DEFF Research Database (Denmark)

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

    analysis. Single-layer graphene with its regular honeycomb lattice is a perfect model structure to apply automated structure detection. By utilizing Fourier analysis the initial perfect hexagonal structure can easily be recognized. The recorded hexagonal tessellation reflects the unperturbed structure...... challenging to interpret. In order to increase the signal-to-noise ratio of the images two routes can be pursued: 1) the exposure time can be increased; or 2) acquiring series of images and summarize them after alignment. Both methods have the disadvantage of summing images acquired over a certain period...... 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 compared...

  17. Automated vector selection of SIVQ and parallel computing integration MATLAB TM : Innovations supporting large-scale and high-throughput image analysis studies

    Directory of Open Access Journals (Sweden)

    Jerome Cheng

    2011-01-01

    Full Text Available Introduction: Spatially invariant vector quantization (SIVQ is a texture and color-based image matching algorithm that queries the image space through the use of ring vectors. In prior studies, the selection of one or more optimal vectors for a particular feature of interest required a manual process, with the user initially stochastically selecting candidate vectors and subsequently testing them upon other regions of the image to verify the vector′s sensitivity and specificity properties (typically by reviewing a resultant heat map. In carrying out the prior efforts, the SIVQ algorithm was noted to exhibit highly scalable computational properties, where each region of analysis can take place independently of others, making a compelling case for the exploration of its deployment on high-throughput computing platforms, with the hypothesis that such an exercise will result in performance gains that scale linearly with increasing processor count. Methods: An automated process was developed for the selection of optimal ring vectors to serve as the predicate matching operator in defining histopathological features of interest. Briefly, candidate vectors were generated from every possible coordinate origin within a user-defined vector selection area (VSA and subsequently compared against user-identified positive and negative "ground truth" regions on the same image. Each vector from the VSA was assessed for its goodness-of-fit to both the positive and negative areas via the use of the receiver operating characteristic (ROC transfer function, with each assessment resulting in an associated area-under-the-curve (AUC figure of merit. Results: Use of the above-mentioned automated vector selection process was demonstrated in two cases of use: First, to identify malignant colonic epithelium, and second, to identify soft tissue sarcoma. For both examples, a very satisfactory optimized vector was identified, as defined by the AUC metric. Finally, as an

  18. High-resolution Time-lapse Imaging and Automated Analysis of Microtubule Dynamics in Living Human Umbilical Vein Endothelial Cells.

    Science.gov (United States)

    Braun, Alexander; Caesar, Nicole M; Dang, Kyvan; Myers, Kenneth A

    2016-01-01

    The physiological process by which new vasculature forms from existing vasculature requires specific signaling events that trigger morphological changes within individual endothelial cells (ECs). These processes are critical for homeostatic maintenance such as wound healing, and are also crucial in promoting tumor growth and metastasis. EC morphology is defined by the organization of the cytoskeleton, a tightly regulated system of actin and microtubule (MT) dynamics that is known to control EC branching, polarity and directional migration, essential components of angiogenesis. To study MT dynamics, we used high-resolution fluorescence microscopy coupled with computational image analysis of fluorescently-labeled MT plus-ends to investigate MT growth dynamics and the regulation of EC branching morphology and directional migration. Time-lapse imaging of living Human Umbilical Vein Endothelial Cells (HUVECs) was performed following transfection with fluorescently-labeled MT End Binding protein 3 (EB3) and Mitotic Centromere Associated Kinesin (MCAK)-specific cDNA constructs to evaluate effects on MT dynamics. PlusTipTracker software was used to track EB3-labeled MT plus ends in order to measure MT growth speeds and MT growth lifetimes in time-lapse images. This methodology allows for the study of MT dynamics and the identification of how localized regulation of MT dynamics within sub-cellular regions contributes to the angiogenic processes of EC branching and migration. PMID:27584860

  19. Automated pipelines for spectroscopic analysis

    Science.gov (United States)

    Allende Prieto, C.

    2016-09-01

    The Gaia mission will have a profound impact on our understanding of the structure and dynamics of the Milky Way. Gaia is providing an exhaustive census of stellar parallaxes, proper motions, positions, colors and radial velocities, but also leaves some glaring holes in an otherwise complete data set. The radial velocities measured with the on-board high-resolution spectrograph will only reach some 10 % of the full sample of stars with astrometry and photometry from the mission, and detailed chemical information will be obtained for less than 1 %. Teams all over the world are organizing large-scale projects to provide complementary radial velocities and chemistry, since this can now be done very efficiently from the ground thanks to large and mid-size telescopes with a wide field-of-view and multi-object spectrographs. As a result, automated data processing is taking an ever increasing relevance, and the concept is applying to many more areas, from targeting to analysis. In this paper, I provide a quick overview of recent, ongoing, and upcoming spectroscopic surveys, and the strategies adopted in their automated analysis pipelines.

  20. Reload safety analysis automation tools

    International Nuclear Information System (INIS)

    Performing core physics calculations for the sake of reload safety analysis is a very demanding and time consuming process. This process generally begins with the preparation of libraries for the core physics code using a lattice code. The next step involves creating a very large set of calculations with the core physics code. Lastly, the results of the calculations must be interpreted, correctly applying uncertainties and checking whether applicable limits are satisfied. Such a procedure requires three specialized experts. One must understand the lattice code in order to correctly calculate and interpret its results. The next expert must have a good understanding of the physics code in order to create libraries from the lattice code results and to correctly define all the calculations involved. The third expert must have a deep knowledge of the power plant and the reload safety analysis procedure in order to verify, that all the necessary calculations were performed. Such a procedure involves many steps and is very time consuming. At ÚJV Řež, a.s., we have developed a set of tools which can be used to automate and simplify the whole process of performing reload safety analysis. Our application QUADRIGA automates lattice code calculations for library preparation. It removes user interaction with the lattice code and reduces his task to defining fuel pin types, enrichments, assembly maps and operational parameters all through a very nice and user-friendly GUI. The second part in reload safety analysis calculations is done by CycleKit, a code which is linked with our core physics code ANDREA. Through CycleKit large sets of calculations with complicated interdependencies can be performed using simple and convenient notation. CycleKit automates the interaction with ANDREA, organizes all the calculations, collects the results, performs limit verification and displays the output in clickable html format. Using this set of tools for reload safety analysis simplifies

  1. Automated vertebra identification in CT images

    Science.gov (United States)

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

    2009-02-01

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

  2. Analysis of an automated background correction method for cardiovascular MR phase contrast imaging in children and young adults

    Energy Technology Data Exchange (ETDEWEB)

    Rigsby, Cynthia K.; Hilpipre, Nicholas; Boylan, Emma E.; Popescu, Andrada R.; Deng, Jie [Ann and Robert H. Lurie Children' s Hospital of Chicago, Department of Medical Imaging, Chicago, IL (United States); McNeal, Gary R. [Siemens Medical Solutions USA Inc., Customer Solutions Group, Cardiovascular MR R and D, Chicago, IL (United States); Zhang, Gang [Ann and Robert H. Lurie Children' s Hospital of Chicago Research Center, Biostatistics Research Core, Chicago, IL (United States); Choi, Grace [Ann and Robert H. Lurie Children' s Hospital of Chicago, Department of Pediatrics, Chicago, IL (United States); Greiser, Andreas [Siemens AG Healthcare Sector, Erlangen (Germany)

    2014-03-15

    Phase contrast magnetic resonance imaging (MRI) is a powerful tool for evaluating vessel blood flow. Inherent errors in acquisition, such as phase offset, eddy currents and gradient field effects, can cause significant inaccuracies in flow parameters. These errors can be rectified with the use of background correction software. To evaluate the performance of an automated phase contrast MRI background phase correction method in children and young adults undergoing cardiac MR imaging. We conducted a retrospective review of patients undergoing routine clinical cardiac MRI including phase contrast MRI for flow quantification in the aorta (Ao) and main pulmonary artery (MPA). When phase contrast MRI of the right and left pulmonary arteries was also performed, these data were included. We excluded patients with known shunts and metallic implants causing visible MRI artifact and those with more than mild to moderate aortic or pulmonary stenosis. Phase contrast MRI of the Ao, mid MPA, proximal right pulmonary artery (RPA) and left pulmonary artery (LPA) using 2-D gradient echo Fast Low Angle SHot (FLASH) imaging was acquired during normal respiration with retrospective cardiac gating. Standard phase image reconstruction and the automatic spatially dependent background-phase-corrected reconstruction were performed on each phase contrast MRI dataset. Non-background-corrected and background-phase-corrected net flow, forward flow, regurgitant volume, regurgitant fraction, and vessel cardiac output were recorded for each vessel. We compared standard non-background-corrected and background-phase-corrected mean flow values for the Ao and MPA. The ratio of pulmonary to systemic blood flow (Qp:Qs) was calculated for the standard non-background and background-phase-corrected data and these values were compared to each other and for proximity to 1. In a subset of patients who also underwent phase contrast MRI of the MPA, RPA, and LPA a comparison was made between standard non

  3. Computer automated movement detection for the analysis of behavior

    OpenAIRE

    Ramazani, Roseanna B.; Harish R Krishnan; BERGESON, SUSAN E.; Atkinson, Nigel S.

    2007-01-01

    Currently, measuring ethanol behaviors in flies depends on expensive image analysis software or time intensive experimenter observation. We have designed an automated system for the collection and analysis of locomotor behavior data, using the IEEE 1394 acquisition program dvgrab, the image toolkit ImageMagick and the programming language Perl. In the proposed method, flies are placed in a clear container and a computer-controlled camera takes pictures at regular intervals. Digital subtractio...

  4. 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...

  5. Automated migration analysis based on cell texture: method & reliability

    Directory of Open Access Journals (Sweden)

    Chittenden Thomas W

    2005-03-01

    Full Text Available Abstract Background In this paper, we present and validate a way to measure automatically the extent of cell migration based on automated examination of a series of digital photographs. It was designed specifically to identify the impact of Second Hand Smoke (SHS on endothelial cell migration but has broader applications. The analysis has two stages: (1 preprocessing of image texture, and (2 migration analysis. Results The output is a graphic overlay that indicates the front lines of cell migration superimposed on each original image, with automated reporting of the distance traversed vs. time. Expert preference compares to manual placement of leading edge shows complete equivalence of automated vs. manual leading edge definition for cell migration measurement. Conclusion Our method is indistinguishable from careful manual determinations of cell front lines, with the advantages of full automation, objectivity, and speed.

  6. Simplified Automated Image Analysis for Detection and Phenotyping of Mycobacterium tuberculosis on Porous Supports by Monitoring Growing Microcolonies

    OpenAIRE

    den Hertog, Alice L.; Dennis W Visser; Ingham, Colin J.; Frank H A G Fey; Paul R Klatser; Anthony, Richard M.

    2010-01-01

    BACKGROUND: Even with the advent of nucleic acid (NA) amplification technologies the culture of mycobacteria for diagnostic and other applications remains of critical importance. Notably microscopic observed drug susceptibility testing (MODS), as opposed to traditional culture on solid media or automated liquid culture, has shown potential to both speed up and increase the provision of mycobacterial culture in high burden settings. METHODS: Here we explore the growth of Mycobacterial tubercul...

  7. Total Mini-Mental State Examination score and regional cerebral blood flow using Z score imaging and automated ROI analysis software in subjects with memory impairment

    International Nuclear Information System (INIS)

    The Mini-Mental State Examination (MMSE) is considered a useful supplementary method to diagnose dementia and evaluate the severity of cognitive disturbance. However, the region of the cerebrum that correlates with the MMSE score is not clear. Recently, a new method was developed to analyze regional cerebral blood flow (rCBF) using a Z score imaging system (eZIS). This system shows changes of rCBF when compared with a normal database. In addition, a three-dimensional stereotaxic region of interest (ROI) template (3DSRT), fully automated ROI analysis software was developed. The objective of this study was to investigate the correlation between rCBF changes and total MMSE score using these new methods. The association between total MMSE score and rCBF changes was investigated in 24 patients (mean age±standard deviation (SD) 71.5±9.2 years; 6 men and 18 women) with memory impairment using eZIS and 3DSRT. Step-wise multiple regression analysis was used for multivariate analysis, with the total MMSE score as the dependent variable and rCBF change in 24 areas as the independent variable. Total MMSE score was significantly correlated only with the reduction of left hippocampal perfusion but not with right (P<0.01). Total MMSE score is an important indicator of left hippocampal function. (author)

  8. On Automating and Standardising Corpus Callosum Analysis in Brain MRI

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Skoglund, Karl

    2005-01-01

    Corpus callosum analysis is influenced by many factors. The effort in controlling these has previously been incomplete and scattered. This paper sketches a complete pipeline for automated corpus callosum analysis from magnetic resonance images, with focus on measurement standardisation. The prese...

  9. Automation for System Safety Analysis

    Science.gov (United States)

    Malin, Jane T.; Fleming, Land; Throop, David; Thronesbery, Carroll; Flores, Joshua; Bennett, Ted; Wennberg, Paul

    2009-01-01

    This presentation describes work to integrate a set of tools to support early model-based analysis of failures and hazards due to system-software interactions. The tools perform and assist analysts in the following tasks: 1) extract model parts from text for architecture and safety/hazard models; 2) combine the parts with library information to develop the models for visualization and analysis; 3) perform graph analysis and simulation to identify and evaluate possible paths from hazard sources to vulnerable entities and functions, in nominal and anomalous system-software configurations and scenarios; and 4) identify resulting candidate scenarios for software integration testing. There has been significant technical progress in model extraction from Orion program text sources, architecture model derivation (components and connections) and documentation of extraction sources. Models have been derived from Internal Interface Requirements Documents (IIRDs) and FMEA documents. Linguistic text processing is used to extract model parts and relationships, and the Aerospace Ontology also aids automated model development from the extracted information. Visualizations of these models assist analysts in requirements overview and in checking consistency and completeness.

  10. Automated 3D renal segmentation based on image partitioning

    Science.gov (United States)

    Yeghiazaryan, Varduhi; Voiculescu, Irina D.

    2016-03-01

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

  11. Mesh Processing in Medical Image Analysis

    DEFF Research Database (Denmark)

    The following topics are dealt with: mesh processing; medical image analysis; interactive freeform modeling; statistical shape analysis; clinical CT images; statistical surface recovery; automated segmentation; cerebral aneurysms; and real-time particle-based representation....

  12. An approach to automated chromosome analysis

    International Nuclear Information System (INIS)

    The methods of approach developed with a view to automatic processing of the different stages of chromosome analysis are described in this study divided into three parts. Part 1 relates the study of automated selection of metaphase spreads, which operates a decision process in order to reject ail the non-pertinent images and keep the good ones. This approach has been achieved by Computing a simulation program that has allowed to establish the proper selection algorithms in order to design a kit of electronic logical units. Part 2 deals with the automatic processing of the morphological study of the chromosome complements in a metaphase: the metaphase photographs are processed by an optical-to-digital converter which extracts the image information and writes it out as a digital data set on a magnetic tape. For one metaphase image this data set includes some 200 000 grey values, encoded according to a 16, 32 or 64 grey-level scale, and is processed by a pattern recognition program isolating the chromosomes and investigating their characteristic features (arm tips, centromere areas), in order to get measurements equivalent to the lengths of the four arms. Part 3 studies a program of automated karyotyping by optimized pairing of human chromosomes. The data are derived from direct digitizing of the arm lengths by means of a BENSON digital reader. The program supplies' 1/ a list of the pairs, 2/ a graphic representation of the pairs so constituted according to their respective lengths and centromeric indexes, and 3/ another BENSON graphic drawing according to the author's own representation of the chromosomes, i.e. crosses with orthogonal arms, each branch being the accurate measurement of the corresponding chromosome arm. This conventionalized karyotype indicates on the last line the really abnormal or non-standard images unpaired by the program, which are of special interest for the biologist. (author)

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

    Science.gov (United States)

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

    2016-03-01

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

  14. Mesh Processing in Medical Image Analysis

    DEFF Research Database (Denmark)

    The following topics are dealt with: mesh processing; medical image analysis; interactive freeform modeling; statistical shape analysis; clinical CT images; statistical surface recovery; automated segmentation; cerebral aneurysms; and real-time particle-based representation.......The following topics are dealt with: mesh processing; medical image analysis; interactive freeform modeling; statistical shape analysis; clinical CT images; statistical surface recovery; automated segmentation; cerebral aneurysms; and real-time particle-based representation....

  15. 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.

  16. Automated Pipelines for Spectroscopic Analysis

    CERN Document Server

    Prieto, Carlos Allende

    2016-01-01

    The Gaia mission will have a profound impact on our understanding of the structure and dynamics of the Milky Way. Gaia is providing an exhaustive census of stellar parallaxes, proper motions, positions, colors and radial velocities, but also leaves some flaring holes in an otherwise complete data set. The radial velocities measured with the on-board high-resolution spectrograph will only reach some 10% of the full sample of stars with astrometry and photometry from the mission, and detailed chemical information will be obtained for less than 1%. Teams all over the world are organizing large-scale projects to provide complementary radial velocities and chemistry, since this can now be done very efficiently from the ground thanks to large and mid-size telescopes with a wide field-of-view and multi-object spectrographs. As a result, automated data processing is taking an ever increasing relevance, and the concept is applying to many more areas, from targeting to analysis. In this paper, I provide a quick overvie...

  17. An Automated Approach to Agricultural Tile Drain Detection and Extraction Utilizing High Resolution Aerial Imagery and Object-Based Image Analysis

    Science.gov (United States)

    Johansen, Richard A.

    Subsurface drainage from agricultural fields in the Maumee River watershed is suspected to adversely impact the water quality and contribute to the formation of harmful algal blooms (HABs) in Lake Erie. In early August of 2014, a HAB developed in the western Lake Erie Basin that resulted in over 400,000 people being unable to drink their tap water due to the presence of a toxin from the bloom. HAB development in Lake Erie is aided by excess nutrients from agricultural fields, which are transported through subsurface tile and enter the watershed. Compounding the issue within the Maumee watershed, the trend within the watershed has been to increase the installation of tile drains in both total extent and density. Due to the immense area of drained fields, there is a need to establish an accurate and effective technique to monitor subsurface farmland tile installations and their associated impacts. This thesis aimed at developing an automated method in order to identify subsurface tile locations from high resolution aerial imagery by applying an object-based image analysis (OBIA) approach utilizing eCognition. This process was accomplished through a set of algorithms and image filters, which segment and classify image objects by their spectral and geometric characteristics. The algorithms utilized were based on the relative location of image objects and pixels, in order to maximize the robustness and transferability of the final rule-set. These algorithms were coupled with convolution and histogram image filters to generate results for a 10km2 study area located within Clay Township in Ottawa County, Ohio. The eCognition results were compared to previously collected tile locations from an associated project that applied heads-up digitizing of aerial photography to map field tile. The heads-up digitized locations were used as a baseline for the accuracy assessment. The accuracy assessment generated a range of agreement values from 67.20% - 71.20%, and an average

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

    DEFF Research Database (Denmark)

    Lomsky, Milan; Richter, Jens; Johansson, Lena;

    2005-01-01

    SIMIND were used to simulate the studies. Finally CAFU was validated on ten rest studies from patients referred for routine stress/rest myocardial perfusion scintigraphy and compared with Cedar-Sinai quantitative gated-SPECT (QGS), a commercially available program for quantification of gated-SPECT images...

  19. Statistical Analysis of Filament Features Based on the Hα Solar Images from 1988 to 2013 by Computer Automated Detection Method

    Science.gov (United States)

    Hao, Q.; Fang, C.; Cao, W.; Chen, P. F.

    2015-12-01

    We improve our filament automated detection method which was proposed in our previous works. It is then applied to process the full disk Hα data mainly obtained by the Big Bear Solar Observatory from 1988 to 2013, spanning nearly three solar cycles. The butterfly diagrams of the filaments, showing the information of the filament area, spine length, tilt angle, and the barb number, are obtained. The variations of these features with the calendar year and the latitude band are analyzed. The drift velocities of the filaments in different latitude bands are calculated and studied. We also investigate the north-south (N-S) asymmetries of the filament numbers in total and in each subclass classified according to the filament area, spine length, and tilt angle. The latitudinal distribution of the filament number is found to be bimodal. About 80% of all the filaments have tilt angles within [0°, 60°]. For the filaments within latitudes lower (higher) than 50°, the northeast (northwest) direction is dominant in the northern hemisphere and the southeast (southwest) direction is dominant in the southern hemisphere. The latitudinal migrations of the filaments experience three stages with declining drift velocities in each of solar cycles 22 and 23, and it seems that the drift velocity is faster in shorter solar cycles. Most filaments in latitudes lower (higher) than 50° migrate toward the equator (polar region). The N-S asymmetry indices indicate that the southern hemisphere is the dominant hemisphere in solar cycle 22 and the northern hemisphere is the dominant one in solar cycle 23.

  20. An automated image analysis framework for segmentation and division plane detection of single live Staphylococcus aureus cells which can operate at millisecond sampling time scales using bespoke Slimfield microscopy

    CERN Document Server

    Wollman, Adam J M; Foster, Simon; Leake, Mark C

    2016-01-01

    Staphylococcus aureus is an important pathogen, giving rise to antimicrobial resistance in cell strains such as Methicillin Resistant S. aureus (MRSA). Here we report an image analysis framework for automated detection and image segmentation of cells in S. aureus cell clusters, and explicit identification of their cell division planes. We use a new combination of several existing analytical tools of image analysis to detect cellular and subcellular morphological features relevant to cell division from millisecond time scale sampled images of live pathogens at a detection precision of single molecules. We demonstrate this approach using a fluorescent reporter GFP fused to the protein EzrA that localises to a mid-cell plane during division and is involved in regulation of cell size and division. This image analysis framework presents a valuable platform from which to study candidate new antimicrobials which target the cell division machinery, but may also have more general application in detecting morphological...

  1. Fully automated TV-image analysis of the cell-cycle: comparison of the PLM method with determinations of the percentage and the DNA content of labelled cells.

    Science.gov (United States)

    Wachsmuth, E D; van Golitschek, M; Macht, F; Maurer-Schultze, B

    1988-01-01

    A cell-cycle analysis based on a fully automated TV-image scanning system is proposed to replace the laborious PLM method. To compare the efficiency of the two procedures, cell-cycle parameters were assessed in Ehrlich (diploid and hyperdiploid), L-1210, and JB-1 mouse ascites tumours and in rat jejunal crypts. The percentages of labelled mitoses (PLM) were counted visually on Feulgen-stained autoradiographs obtained at various times after a single 3H-thymidine pulse. The fraction of labelled cells (P) and the DNA ratio of labelled and unlabelled cells were measured by TV-image analysis in the same slides and plotted against time. Within practical limits, TV-image analysis using the P-curve gives the same results as the PLM method. Using the P-curve has the important advantage that its first part, beginning at the time of 3H-thymidine injection and ending at the first maximum, furnishes more information about the cell cycle than the corresponding part of the PLM curve. It can be used to compute tG2M tS and the ratio of the growth faction index to the cell-cycle time (IP/tC) whereas the first part of the PLM-curve reveals only the length of the S-phase (tS). The IP/tC ratio is a readily accessible measure of growth and increases when the cells divide more frequently. Cell death rates may be neglected since the ratio is determined within less than the duration of one cell cycle. Moreover, the data from the first part of the P curve indicate whether there is a large non-growth fraction. If the non-growth fraction is small, i.e. if IP approximately 1, the P curve need only be measured until the first maximum is reached so that fewer samples and animals are required. If the non-growth fraction is large or unknown, the cell-cycle parameters are calculated by reference to the position and size not only of the first minimum and the first maximum, but also of the second minimum of the P curve.

  2. Automated Image Processing for Spatially Resolved Analysis of Lipid Droplets in Cultured 3T3-L1 Adipocytes

    OpenAIRE

    Sims, James Kenneth; Rohr, Brian; Miller, Eric; Lee, Kyongbum

    2014-01-01

    Cellular hypertrophy of adipose tissue underlies many of the proposed proinflammatory mechanisms for obesity-related diseases. Adipose hypertrophy results from an accumulation of esterified lipids (triglycerides) into membrane-enclosed intracellular lipid droplets (LDs). The coupling between adipocyte metabolism and LD morphology could be exploited to investigate biochemical regulation of lipid pathways by monitoring the dynamics of LDs. This article describes an image processing method to id...

  3. 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.

  4. 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.

  5. 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.

  6. Automated analysis of Xe-133 pulmonary ventilation (AAPV) in children

    Science.gov (United States)

    Cao, Xinhua; Treves, S. Ted

    2011-03-01

    In this study, an automated analysis of pulmonary ventilation (AAPV) was developed to visualize the ventilation in pediatric lungs using dynamic Xe-133 scintigraphy. AAPV is a software algorithm that converts a dynamic series of Xe- 133 images into four functional images: equilibrium, washout halftime, residual, and clearance rate by analyzing pixelbased activity. Compared to conventional methods of calculating global or regional ventilation parameters, AAPV provides a visual representation of pulmonary ventilation functions.

  7. Feasibility Analysis of Crane Automation

    Institute of Scientific and Technical Information of China (English)

    DONG Ming-xiao; MEI Xue-song; JIANG Ge-dong; ZHANG Gui-qing

    2006-01-01

    This paper summarizes the modeling methods, open-loop control and closed-loop control techniques of various forms of cranes, worldwide, and discusses their feasibilities and limitations in engineering. Then the dynamic behaviors of cranes are analyzed. Finally, we propose applied modeling methods and feasible control techniques and demonstrate the feasibilities of crane automation.

  8. Distribution system analysis and automation

    CERN Document Server

    Gers, Juan

    2013-01-01

    A comprehensive guide to techniques that allow engineers to simulate, analyse and optimise power distribution systems which combined with automation, underpin the emerging concept of the "smart grid". This book is supported by theoretical concepts with real-world applications and MATLAB exercises.

  9. 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

  10. 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.

  11. 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

  12. 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.

  13. 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...

  14. Automated analysis of 3D echocardiography

    NARCIS (Netherlands)

    Stralen, Marijn van

    2009-01-01

    In this thesis we aim at automating the analysis of 3D echocardiography, mainly targeting the functional analysis of the left ventricle. Manual analysis of these data is cumbersome, time-consuming and is associated with inter-observer and inter-institutional variability. Methods for reconstruction o

  15. An automated confirmatory system for analysis of mammograms.

    Science.gov (United States)

    Peng, W; Mayorga, R V; Hussein, E M A

    2016-03-01

    This paper presents an integrated system for the automatic analysis of mammograms to assist radiologists in confirming their diagnosis in mammography screening. The proposed automated confirmatory system (ACS) can process a digitalized mammogram online, and generates a high quality filtered segmentation of an image for biological interpretation and a texture-feature based diagnosis. We use a serial of image pre-processing and segmentation techniques, including 2D median filtering, seeded region growing (SRG) algorithm, image contrast enhancement, to remove noise, delete radiopaque artifacts and eliminate the projection of the pectoral muscle from a digitalized mammogram. We also develop an entire-image texture-feature based classification method, by combining a Rough-set approach to extract five fundamental texture features from images, and then an Artificial Neural Network technique to classify a mammogram as: normal; indicating the presence of a benign lump; or representing a malignant tumor. Here, 222 random images from the Mammographic Image Analysis Society (MIAS) database are used for the offline ACS training. Once the system is tuned and trained, it is ready for the automated use for the analysis and diagnosis of new mammograms. To test the trained system, a separate set of 100 random images from the MIAS and another set of 100 random images from the independent BancoWeb database are selected. The proposed ACS is shown to be successful in confirming diagnosis of mammograms from the two independent databases. PMID:26742491

  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. 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

  18. 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.

  19. On Automating and Standardising Corpus Callosum Analysis in Brain MRI

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Skoglund, Karl

    2005-01-01

    Corpus callosum analysis is influenced by many factors. The effort in controlling these has previously been incomplete and scattered. This paper sketches a complete pipeline for automated corpus callosum analysis from magnetic resonance images, with focus on measurement standardisation. The...... presented pipeline deals with i) estimation of the mid-sagittal plane, ii) localisation and registration of the corpus callosum, iii) parameterisation and representation of its contour, and iv) means of standardising the traditional reference area measurements....

  20. A 3D image analysis tool for SPECT imaging

    Science.gov (United States)

    Kontos, Despina; Wang, Qiang; Megalooikonomou, Vasileios; Maurer, Alan H.; Knight, Linda C.; Kantor, Steve; Fisher, Robert S.; Simonian, Hrair P.; Parkman, Henry P.

    2005-04-01

    We have developed semi-automated and fully-automated tools for the analysis of 3D single-photon emission computed tomography (SPECT) images. The focus is on the efficient boundary delineation of complex 3D structures that enables accurate measurement of their structural and physiologic properties. We employ intensity based thresholding algorithms for interactive and semi-automated analysis. We also explore fuzzy-connectedness concepts for fully automating the segmentation process. We apply the proposed tools to SPECT image data capturing variation of gastric accommodation and emptying. These image analysis tools were developed within the framework of a noninvasive scintigraphic test to measure simultaneously both gastric emptying and gastric volume after ingestion of a solid or a liquid meal. The clinical focus of the particular analysis was to probe associations between gastric accommodation/emptying and functional dyspepsia. Employing the proposed tools, we outline effectively the complex three dimensional gastric boundaries shown in the 3D SPECT images. We also perform accurate volume calculations in order to quantitatively assess the gastric mass variation. This analysis was performed both with the semi-automated and fully-automated tools. The results were validated against manual segmentation performed by a human expert. We believe that the development of an automated segmentation tool for SPECT imaging of the gastric volume variability will allow for other new applications of SPECT imaging where there is a need to evaluate complex organ function or tumor masses.

  1. Automated Technology for Verificiation and Analysis

    DEFF Research Database (Denmark)

    This volume contains the papers presented at the 7th International Symposium on Automated Technology for Verification and Analysis held during October 13-16 in Macao SAR, China. The primary objective of the ATVA conferences remains the same: to exchange and promote the latest advances of state-of-the-art...... research on theoretical and practical aspects of automated analysis, verification, and synthesis. Among 74 research papers and 10 tool papers submitted to ATVA 2009, the Program Committee accepted 23 as regular papers and 3 as tool papers. In all, 33 experts from 17 countries worked hard to make sure...

  2. Image Analysis

    DEFF Research Database (Denmark)

    . The topics of the accepted papers range from novel applications of vision systems, pattern recognition, machine learning, feature extraction, segmentation, 3D vision, to medical and biomedical image analysis. The papers originate from all the Scandinavian countries and several other European countries......The 19th Scandinavian Conference on Image Analysis was held at the IT University of Copenhagen in Denmark during June 15-17, 2015. The SCIA conference series has been an ongoing biannual event for more than 30 years and over the years it has nurtured a world-class regional research and development...

  3. 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

  4. 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.

  5. 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

  6. 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

  7. Automated image registration for FDOPA PET studies

    Science.gov (United States)

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

    1996-12-01

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

  8. Automation of the proximate analysis of coals

    Energy Technology Data Exchange (ETDEWEB)

    1985-01-01

    A study is reported of the feasibility of using a multi-jointed general-purpose robot for the automated analysis of moisture, volatile matter, ash and total post-combustion sulfur in coal and coke. The results obtained with an automated system are compared with those of conventional manual methods. The design of the robot hand and the safety measures provided are now both fully satisfactory, and the analytic values obtained exhibit little scatter. It is concluded that the use of this robot system results in a better working environment and in considerable labour saving. Applications to other tasks are under development.

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

    Science.gov (United States)

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

    2013-06-01

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

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

    Directory of Open Access Journals (Sweden)

    Mohendra Roy

    2016-05-01

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

  11. 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

  12. 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)

  13. Automated quantitative analysis of ventilation-perfusion lung scintigrams

    International Nuclear Information System (INIS)

    An automated computer analysis of ventilation (Kr-81m) and perfusion (Tc-99m) lung images has been devised that produces a graphical image of the distribution of ventilation and perfusion, and of ventilation-perfusion ratios. The analysis has overcome the following problems: the identification of the midline between two lungs and the lung boundaries, the exclusion of extrapulmonary radioactivity, the superimposition of lung images of different sizes, and the format for presentation of the data. Therefore, lung images of different sizes and shapes may be compared with each other. The analysis has been used to develop normal ranges from 55 volunteers. Comparison of younger and older age groups of men and women show small but significant differences in the distribution of ventilation and perfusion, but no differences in ventilation-perfusion ratios

  14. High-resolution image analysis.

    Science.gov (United States)

    Preston, K

    1986-01-01

    In many departments of cytology, cytogenetics, hematology, and pathology, research projects using high-resolution computerized microscopy are now being mounted for computation of morphometric measurements on various structural components, as well as for determination of cellular DNA content. The majority of these measurements are made in a partially automated, computer-assisted mode, wherein there is strong interaction between the user and the computerized microscope. At the same time, full automation has been accomplished for both sample preparation and sample examination for clinical determination of the white blood cell differential count. At the time of writing, approximately 1,000 robot differential counting microscopes are in the field, analyzing images of human white blood cells, red blood cells, and platelets at the overall rate of about 100,000 slides per day. This mammoth through-put represents a major accomplishment in the application of machine vision to automated microscopy for hematology. In other areas of automated high-resolution microscopy, such as cytology and cytogenetics, no commercial instruments are available (although a few metaphase-finding machines are available and other new machines have been announced during the past year). This is a disappointing product, considering the nearly half century of research effort in these areas. This paper provides examples of the state of the art in automation of cell analysis for blood smears, cervical smears, and chromosome preparations. Also treated are new developments in multi-resolution automated microscopy, where images are now being generated and analyzed by a single machine over a range of 64:1 magnification and from 10,000 X 20,000 to 500 X 500 in total picture elements (pixels). Examples of images of human lymph node and liver tissue are presented. Semi-automated systems are not treated, although there is mention of recent research in the automation of tissue analysis.

  15. Automated morphometry of transgenic mouse brains in MR images

    NARCIS (Netherlands)

    Scheenstra, Alize Elske Hiltje

    2011-01-01

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

  16. Automated 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

  17. 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...

  18. Automated Loads Analysis System (ATLAS)

    Science.gov (United States)

    Gardner, Stephen; Frere, Scot; O’Reilly, Patrick

    2013-01-01

    ATLAS is a generalized solution that can be used for launch vehicles. ATLAS is used to produce modal transient analysis and quasi-static analysis results (i.e., accelerations, displacements, and forces) for the payload math models on a specific Shuttle Transport System (STS) flight using the shuttle math model and associated forcing functions. This innovation solves the problem of coupling of payload math models into a shuttle math model. It performs a transient loads analysis simulating liftoff, landing, and all flight events between liftoff and landing. ATLAS utilizes efficient and numerically stable algorithms available in MSC/NASTRAN.

  19. Volumetric measurements of pulmonary nodules: variability in automated analysis tools

    Science.gov (United States)

    Juluru, Krishna; Kim, Woojin; Boonn, William; King, Tara; Siddiqui, Khan; Siegel, Eliot

    2007-03-01

    Over the past decade, several computerized tools have been developed for detection of lung nodules and for providing volumetric analysis. Incidentally detected lung nodules have traditionally been followed over time by measurements of their axial dimensions on CT scans to ensure stability or document progression. A recently published article by the Fleischner Society offers guidelines on the management of incidentally detected nodules based on size criteria. For this reason, differences in measurements obtained by automated tools from various vendors may have significant implications on management, yet the degree of variability in these measurements is not well understood. The goal of this study is to quantify the differences in nodule maximum diameter and volume among different automated analysis software. Using a dataset of lung scans obtained with both "ultra-low" and conventional doses, we identified a subset of nodules in each of five size-based categories. Using automated analysis tools provided by three different vendors, we obtained size and volumetric measurements on these nodules, and compared these data using descriptive as well as ANOVA and t-test analysis. Results showed significant differences in nodule maximum diameter measurements among the various automated lung nodule analysis tools but no significant differences in nodule volume measurements. These data suggest that when using automated commercial software, volume measurements may be a more reliable marker of tumor progression than maximum diameter. The data also suggest that volumetric nodule measurements may be relatively reproducible among various commercial workstations, in contrast to the variability documented when performing human mark-ups, as is seen in the LIDC (lung imaging database consortium) study.

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

    Science.gov (United States)

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

    2016-03-01

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

  1. Automated Pointing of Cardiac Imaging Catheters.

    Science.gov (United States)

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

    2013-12-31

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

  2. Automated digital image analysis of islet cell mass using Nikon's inverted eclipse Ti microscope and software to improve engraftment may help to advance the therapeutic efficacy and accessibility of islet transplantation across centers.

    Science.gov (United States)

    Gmyr, Valery; Bonner, Caroline; Lukowiak, Bruno; Pawlowski, Valerie; Dellaleau, Nathalie; Belaich, Sandrine; Aluka, Isanga; Moermann, Ericka; Thevenet, Julien; Ezzouaoui, Rimed; Queniat, Gurvan; Pattou, Francois; Kerr-Conte, Julie

    2015-01-01

    Reliable assessment of islet viability, mass, and purity must be met prior to transplanting an islet preparation into patients with type 1 diabetes. The standard method for quantifying human islet preparations is by direct microscopic analysis of dithizone-stained islet samples, but this technique may be susceptible to inter-/intraobserver variability, which may induce false positive/negative islet counts. Here we describe a simple, reliable, automated digital image analysis (ADIA) technique for accurately quantifying islets into total islet number, islet equivalent number (IEQ), and islet purity before islet transplantation. Islets were isolated and purified from n = 42 human pancreata according to the automated method of Ricordi et al. For each preparation, three islet samples were stained with dithizone and expressed as IEQ number. Islets were analyzed manually by microscopy or automatically quantified using Nikon's inverted Eclipse Ti microscope with built-in NIS-Elements Advanced Research (AR) software. The AIDA method significantly enhanced the number of islet preparations eligible for engraftment compared to the standard manual method (p methods showed good correlations between mean values of IEQ number (r(2) = 0.91) and total islet number (r(2) = 0.88) and thus increased to r(2) = 0.93 when islet surface area was estimated comparatively with IEQ number. The ADIA method showed very high intraobserver reproducibility compared to the standard manual method (p method versus the ADIA method (p method also detected small islets between 10 and 50 µm in size. Automated digital image analysis utilizing the Nikon Instruments software is an unbiased, simple, and reliable teaching tool to comprehensively assess the individual size of each islet cell preparation prior to transplantation. Implementation of this technology to improve engraftment may help to advance the therapeutic efficacy and accessibility of islet transplantation across centers. PMID:23683575

  3. Fully automated diabetic retinopathy screening using morphological component analysis.

    Science.gov (United States)

    Imani, Elaheh; Pourreza, Hamid-Reza; Banaee, Touka

    2015-07-01

    Diabetic retinopathy is the major cause of blindness in the world. It has been shown that early diagnosis can play a major role in prevention of visual loss and blindness. This diagnosis can be made through regular screening and timely treatment. Besides, automation of this process can significantly reduce the work of ophthalmologists and alleviate inter and intra observer variability. This paper provides a fully automated diabetic retinopathy screening system with the ability of retinal image quality assessment. The novelty of the proposed method lies in the use of Morphological Component Analysis (MCA) algorithm to discriminate between normal and pathological retinal structures. To this end, first a pre-screening algorithm is used to assess the quality of retinal images. If the quality of the image is not satisfactory, it is examined by an ophthalmologist and must be recaptured if necessary. Otherwise, the image is processed for diabetic retinopathy detection. In this stage, normal and pathological structures of the retinal image are separated by MCA algorithm. Finally, the normal and abnormal retinal images are distinguished by statistical features of the retinal lesions. Our proposed system achieved 92.01% sensitivity and 95.45% specificity on the Messidor dataset which is a remarkable result in comparison with previous work. PMID:25863517

  4. Techniques for Automated Performance Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Marcus, Ryan C. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2014-09-02

    The performance of a particular HPC code depends on a multitude of variables, including compiler selection, optimization flags, OpenMP pool size, file system load, memory usage, MPI configuration, etc. As a result of this complexity, current predictive models have limited applicability, especially at scale. We present a formulation of scientific codes, nodes, and clusters that reduces complex performance analysis to well-known mathematical techniques. Building accurate predictive models and enhancing our understanding of scientific codes at scale is an important step towards exascale computing.

  5. 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.

  6. Proximate analysis by automated thermogravimetry

    Energy Technology Data Exchange (ETDEWEB)

    Elder, J.P.

    1983-05-01

    A study has been made of the use of the Perkin-Elmer thermogravimetric instrument TGS-2, under the control of the System 4 microprocessor for the automatic proximate analysis of solid fossil fuels and related matter. The programs developed are simple to operate, and do not require detailed temperature calibration of the instrumental system. They have been tested with coals of varying rank, biomass samples and Devonian oil shales all of which were of special importance to the State of Kentucky. Precise, accurate data conforming to ASTM specifications were obtained. The simplicity of the technique suggests that it may complement the classical ASTM method and could be used when this latter procedure cannot be employed. However, its adoption as a standardized method must await the development of statistical data resulting from interlaboratory testing on a variety of fossil fuels. (9 refs.)

  7. 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.

  8. 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

  9. Osteolytica: An automated image analysis software package that rapidly measures cancer-induced osteolytic lesions in in vivo models with greater reproducibility compared to other commonly used methods.

    Science.gov (United States)

    Evans, H R; Karmakharm, T; Lawson, M A; Walker, R E; Harris, W; Fellows, C; Huggins, I D; Richmond, P; Chantry, A D

    2016-02-01

    Methods currently used to analyse osteolytic lesions caused by malignancies such as multiple myeloma and metastatic breast cancer vary from basic 2-D X-ray analysis to 2-D images of micro-CT datasets analysed with non-specialised image software such as ImageJ. However, these methods have significant limitations. They do not capture 3-D data, they are time-consuming and they often suffer from inter-user variability. We therefore sought to develop a rapid and reproducible method to analyse 3-D osteolytic lesions in mice with cancer-induced bone disease. To this end, we have developed Osteolytica, an image analysis software method featuring an easy to use, step-by-step interface to measure lytic bone lesions. Osteolytica utilises novel graphics card acceleration (parallel computing) and 3-D rendering to provide rapid reconstruction and analysis of osteolytic lesions. To evaluate the use of Osteolytica we analysed tibial micro-CT datasets from murine models of cancer-induced bone disease and compared the results to those obtained using a standard ImageJ analysis method. Firstly, to assess inter-user variability we deployed four independent researchers to analyse tibial datasets from the U266-NSG murine model of myeloma. Using ImageJ, inter-user variability between the bones was substantial (±19.6%), in contrast to using Osteolytica, which demonstrated minimal variability (±0.5%). Secondly, tibial datasets from U266-bearing NSG mice or BALB/c mice injected with the metastatic breast cancer cell line 4T1 were compared to tibial datasets from aged and sex-matched non-tumour control mice. Analyses by both Osteolytica and ImageJ showed significant increases in bone lesion area in tumour-bearing mice compared to control mice. These results confirm that Osteolytica performs as well as the current 2-D ImageJ osteolytic lesion analysis method. However, Osteolytica is advantageous in that it analyses over the entirety of the bone volume (as opposed to selected 2-D images), it

  10. Osteolytica: An automated image analysis software package that rapidly measures cancer-induced osteolytic lesions in in vivo models with greater reproducibility compared to other commonly used methods.

    Science.gov (United States)

    Evans, H R; Karmakharm, T; Lawson, M A; Walker, R E; Harris, W; Fellows, C; Huggins, I D; Richmond, P; Chantry, A D

    2016-02-01

    Methods currently used to analyse osteolytic lesions caused by malignancies such as multiple myeloma and metastatic breast cancer vary from basic 2-D X-ray analysis to 2-D images of micro-CT datasets analysed with non-specialised image software such as ImageJ. However, these methods have significant limitations. They do not capture 3-D data, they are time-consuming and they often suffer from inter-user variability. We therefore sought to develop a rapid and reproducible method to analyse 3-D osteolytic lesions in mice with cancer-induced bone disease. To this end, we have developed Osteolytica, an image analysis software method featuring an easy to use, step-by-step interface to measure lytic bone lesions. Osteolytica utilises novel graphics card acceleration (parallel computing) and 3-D rendering to provide rapid reconstruction and analysis of osteolytic lesions. To evaluate the use of Osteolytica we analysed tibial micro-CT datasets from murine models of cancer-induced bone disease and compared the results to those obtained using a standard ImageJ analysis method. Firstly, to assess inter-user variability we deployed four independent researchers to analyse tibial datasets from the U266-NSG murine model of myeloma. Using ImageJ, inter-user variability between the bones was substantial (±19.6%), in contrast to using Osteolytica, which demonstrated minimal variability (±0.5%). Secondly, tibial datasets from U266-bearing NSG mice or BALB/c mice injected with the metastatic breast cancer cell line 4T1 were compared to tibial datasets from aged and sex-matched non-tumour control mice. Analyses by both Osteolytica and ImageJ showed significant increases in bone lesion area in tumour-bearing mice compared to control mice. These results confirm that Osteolytica performs as well as the current 2-D ImageJ osteolytic lesion analysis method. However, Osteolytica is advantageous in that it analyses over the entirety of the bone volume (as opposed to selected 2-D images), it

  11. Flux-P: Automating Metabolic Flux Analysis

    Directory of Open Access Journals (Sweden)

    Birgitta E. Ebert

    2012-11-01

    Full Text Available Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via 13C-based metabolic flux analysis, a model-based interpretation of stable carbon isotope patterns in intermediates of metabolism. Existing software such as FiatFlux, OpenFLUX or 13CFLUX supports experts in this complex analysis, but requires several steps that have to be carried out manually, hence restricting the use of this software for data interpretation to a rather small number of experiments. In this paper, we present Flux-P as an approach to automate and standardize 13C-based metabolic flux analysis, using the Bio-jETI workflow framework. Exemplarily based on the FiatFlux software, it demonstrates how services can be created that carry out the different analysis steps autonomously and how these can subsequently be assembled into software workflows that perform automated, high-throughput intracellular flux analysis of high quality and reproducibility. Besides significant acceleration and standardization of the data analysis, the agile workflow-based realization supports flexible changes of the analysis workflows on the user level, making it easy to perform custom analyses.

  12. Automated analysis of digital fundus autofluorescence images of geographic atrophy in advanced age-related macular degeneration using confocal scanning laser ophthalmoscopy (cSLO

    Directory of Open Access Journals (Sweden)

    Bindewald A

    2005-04-01

    Full Text Available Abstract Background Fundus autofluorescence (AF imaging using confocal scanning laser ophthalmoscopy (cSLO provides an accurate delineation of areas of geographic atrophy (GA. Automated computer-assisted methods for detecting and removing interfering vessels are needed to support the GA quantification process in longitudinal studies and in reading centres. Methods A test tool was implemented that uses region-growing techniques to segment GA areas. An algorithm for illuminating shadows can be used to process low-quality images. Agreement between observers and between three different methods was evaluated by two independent readers in a pilot study. Agreement and objectivity were assessed using the Bland-Altman approach. Results The new method (C identifies vascular structures that interfere with the delineation of GA. Results are comparable to those of two commonly used procedures (A, B, with a mean difference between C and A of -0.67 mm2 (95% CI [-0.99, -0.36], between B and A of -0.81 mm2, (95% CI [-1.08, -0.53], and between C and B of 0.15 mm2 (95% CI [-0.12, 0.41]. Objectivity of a method is quantified by the mean difference between observers: A 0.30 mm2 (95% CI [0.02, 0.57], B -0.11 mm2 (95% CI [-0.28, 0.10], and C 0.12 mm2 (95% CI [0.02, 0.22]. Conclusion The novel procedure is comparable with regard to objectivity and inter-reader agreement to established methods of quantifying GA. It considerably speeds up the lengthy measurement process in AF with well defined GA zones.

  13. Automated fetal spine detection in ultrasound images

    Science.gov (United States)

    Tolay, Paresh; Vajinepalli, Pallavi; Bhattacharya, Puranjoy; Firtion, Celine; Sisodia, Rajendra Singh

    2009-02-01

    A novel method is proposed for the automatic detection of fetal spine in ultrasound images along with its orientation in this paper. This problem presents a variety of challenges, including robustness to speckle noise, variations in the visible shape of the spine due to orientation of the ultrasound probe with respect to the fetus and the lack of a proper edge enclosing the entire spine on account of its composition out of distinct vertebra. The proposed method improves robustness and accuracy by making use of two independent techniques to estimate the spine, and then detects the exact location using a cross-correlation approach. Experimental results show that the proposed method is promising for fetal spine detection.

  14. 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.

  15. Multispectral tissue analysis and classification towards enabling automated robotic surgery

    Science.gov (United States)

    Triana, Brian; Cha, Jaepyeong; Shademan, Azad; Krieger, Axel; Kang, Jin U.; Kim, Peter C. W.

    2014-02-01

    Accurate optical characterization of different tissue types is an important tool for potentially guiding surgeons and enabling automated robotic surgery. Multispectral imaging and analysis have been used in the literature to detect spectral variations in tissue reflectance that may be visible to the naked eye. Using this technique, hidden structures can be visualized and analyzed for effective tissue classification. Here, we investigated the feasibility of automated tissue classification using multispectral tissue analysis. Broadband reflectance spectra (200-1050 nm) were collected from nine different ex vivo porcine tissues types using an optical fiber-probe based spectrometer system. We created a mathematical model to train and distinguish different tissue types based upon analysis of the observed spectra using total principal component regression (TPCR). Compared to other reported methods, our technique is computationally inexpensive and suitable for real-time implementation. Each of the 92 spectra was cross-referenced against the nine tissue types. Preliminary results show a mean detection rate of 91.3%, with detection rates of 100% and 70.0% (inner and outer kidney), 100% and 100% (inner and outer liver), 100% (outer stomach), and 90.9%, 100%, 70.0%, 85.7% (four different inner stomach areas, respectively). We conclude that automated tissue differentiation using our multispectral tissue analysis method is feasible in multiple ex vivo tissue specimens. Although measurements were performed using ex vivo tissues, these results suggest that real-time, in vivo tissue identification during surgery may be possible.

  16. Automating Risk Analysis of Software Design Models

    Directory of Open Access Journals (Sweden)

    Maxime Frydman

    2014-01-01

    Full Text Available The growth of the internet and networked systems has exposed software to an increased amount of security threats. One of the responses from software developers to these threats is the introduction of security activities in the software development lifecycle. This paper describes an approach to reduce the need for costly human expertise to perform risk analysis in software, which is common in secure development methodologies, by automating threat modeling. Reducing the dependency on security experts aims at reducing the cost of secure development by allowing non-security-aware developers to apply secure development with little to no additional cost, making secure development more accessible. To automate threat modeling two data structures are introduced, identification trees and mitigation trees, to identify threats in software designs and advise mitigation techniques, while taking into account specification requirements and cost concerns. These are the components of our model for automated threat modeling, AutSEC. We validated AutSEC by implementing it in a tool based on data flow diagrams, from the Microsoft security development methodology, and applying it to VOMS, a grid middleware component, to evaluate our model's performance.

  17. AUTOMATED IMAGE MATCHING WITH CODED POINTS IN STEREOVISION MEASUREMENT

    Institute of Scientific and Technical Information of China (English)

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

    2005-01-01

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

  18. 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

  19. Automated Steel Cleanliness Analysis Tool (ASCAT)

    International Nuclear Information System (INIS)

    The objective of this study was to develop the Automated Steel Cleanliness Analysis Tool (ASCATTM) to permit steelmakers to evaluate the quality of the steel through the analysis of individual inclusions. By characterizing individual inclusions, determinations can be made as to the cleanliness of the steel. Understanding the complicating effects of inclusions in the steelmaking process and on the resulting properties of steel allows the steel producer to increase throughput, better control the process, reduce remelts, and improve the quality of the product. The ASCAT (Figure 1) is a steel-smart inclusion analysis tool developed around a customized next-generation computer controlled scanning electron microscopy (NG-CCSEM) hardware platform that permits acquisition of inclusion size and composition data at a rate never before possible in SEM-based instruments. With built-in customized ''intelligent'' software, the inclusion data is automatically sorted into clusters representing different inclusion types to define the characteristics of a particular heat (Figure 2). The ASCAT represents an innovative new tool for the collection of statistically meaningful data on inclusions, and provides a means of understanding the complicated effects of inclusions in the steel making process and on the resulting properties of steel. Research conducted by RJLG with AISI (American Iron and Steel Institute) and SMA (Steel Manufactures of America) members indicates that the ASCAT has application in high-grade bar, sheet, plate, tin products, pipes, SBQ, tire cord, welding rod, and specialty steels and alloys where control of inclusions, whether natural or engineered, are crucial to their specification for a given end-use. Example applications include castability of calcium treated steel; interstitial free (IF) degasser grade slag conditioning practice; tundish clogging and erosion minimization; degasser circulation and optimization; quality assessment/steel cleanliness; slab, billet

  20. Automated Steel Cleanliness Analysis Tool (ASCAT)

    Energy Technology Data Exchange (ETDEWEB)

    Gary Casuccio (RJ Lee Group); Michael Potter (RJ Lee Group); Fred Schwerer (RJ Lee Group); Dr. Richard J. Fruehan (Carnegie Mellon University); Dr. Scott Story (US Steel)

    2005-12-30

    The objective of this study was to develop the Automated Steel Cleanliness Analysis Tool (ASCATTM) to permit steelmakers to evaluate the quality of the steel through the analysis of individual inclusions. By characterizing individual inclusions, determinations can be made as to the cleanliness of the steel. Understanding the complicating effects of inclusions in the steelmaking process and on the resulting properties of steel allows the steel producer to increase throughput, better control the process, reduce remelts, and improve the quality of the product. The ASCAT (Figure 1) is a steel-smart inclusion analysis tool developed around a customized next-generation computer controlled scanning electron microscopy (NG-CCSEM) hardware platform that permits acquisition of inclusion size and composition data at a rate never before possible in SEM-based instruments. With built-in customized ''intelligent'' software, the inclusion data is automatically sorted into clusters representing different inclusion types to define the characteristics of a particular heat (Figure 2). The ASCAT represents an innovative new tool for the collection of statistically meaningful data on inclusions, and provides a means of understanding the complicated effects of inclusions in the steel making process and on the resulting properties of steel. Research conducted by RJLG with AISI (American Iron and Steel Institute) and SMA (Steel Manufactures of America) members indicates that the ASCAT has application in high-grade bar, sheet, plate, tin products, pipes, SBQ, tire cord, welding rod, and specialty steels and alloys where control of inclusions, whether natural or engineered, are crucial to their specification for a given end-use. Example applications include castability of calcium treated steel; interstitial free (IF) degasser grade slag conditioning practice; tundish clogging and erosion minimization; degasser circulation and optimization; quality assessment

  1. Automated Radiochemical Separation, Analysis, and Sensing

    International Nuclear Information System (INIS)

    Chapter 14 for the 2nd edition of the Handbook of Radioactivity Analysis. The techniques and examples described in this chapter demonstrate that modern fluidic techniques and instrumentation can be used to develop automated radiochemical separation workstations. In many applications, these can be mechanically simple and key parameters can be controlled from software. If desired, many of the fluidic components and solution can be located remotely from the radioactive samples and other hot sample processing zones. There are many issues to address in developing automated radiochemical separation that perform reliably time after time in unattended operation. These are associated primarily with the separation and analytical chemistry aspects of the process. The relevant issues include the selectivity of the separation, decontamination factors, matrix effects, and recoveries from the separation column. In addition, flow rate effects, column lifetimes, carryover from one sample to another, and sample throughput must be considered. Nevertheless, successful approaches for addressing these issues have been developed. Radiochemical analysis is required not only for processing nuclear waste samples in the laboratory, but also for at-site or in situ applications. Monitors for nuclear waste processing operations represent an at-site application where continuous unattended monitoring is required to assure effective process radiochemical separations that produce waste streams that qualify for conversion to stable waste forms. Radionuclide sensors for water monitoring and long term stewardship represent an application where at-site or in situ measurements will be most effective. Automated radiochemical analyzers and sensors have been developed that demonstrate that radiochemical analysis beyond the analytical laboratory is both possible and practical

  2. NEW TECHNIQUES USED IN AUTOMATED TEXT ANALYSIS

    Directory of Open Access Journals (Sweden)

    M. I strate

    2010-12-01

    Full Text Available Automated analysis of natural language texts is one of the most important knowledge discovery tasks for any organization. According to Gartner Group, almost 90% of knowledge available at an organization today is dispersed throughout piles of documents buried within unstructured text. Analyzing huge volumes of textual information is often involved in making informed and correct business decisions. Traditional analysis methods based on statistics fail to help processing unstructured texts and the society is in search of new technologies for text analysis. There exist a variety of approaches to the analysis of natural language texts, but most of them do not provide results that could be successfully applied in practice. This article concentrates on recent ideas and practical implementations in this area.

  3. APSAS; an Automated Particle Size Analysis System

    Science.gov (United States)

    Poppe, Lawrence J.; Eliason, A.H.; Fredericks, J.J.

    1985-01-01

    The Automated Particle Size Analysis System integrates a settling tube and an electroresistance multichannel particle-size analyzer (Coulter Counter) with a Pro-Comp/gg microcomputer and a Hewlett Packard 2100 MX(HP 2100 MX) minicomputer. This system and its associated software digitize the raw sediment grain-size data, combine the coarse- and fine-fraction data into complete grain-size distributions, perform method of moments and inclusive graphics statistics, verbally classify the sediment, generate histogram and cumulative frequency plots, and transfer the results into a data-retrieval system. This system saves time and labor and affords greater reliability, resolution, and reproducibility than conventional methods do.

  4. 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

  5. Full second order chromatographic/spectrometric data matrices for automated sample identification and component analysis by non-data-reducing image analysis

    DEFF Research Database (Denmark)

    Nielsen, Niles-Peter Vest; Smedsgaard, Jørn; Frisvad, Jens Christian

    1999-01-01

    A data analysis method is proposed for identification and for confirmation of classification schemes, based on single- or multiple-wavelength chromatographic profiles. The proposed method works directly on the chromatographic data without data reduction procedures such as peak area or retention...... index calculation, Chromatographic matrices from analysis of previously identified samples are used for generating a reference chromatogram for each class, and unidentified samples are compared with all reference chromatograms by calculating a resemblance measure for each reference. Once the method...... yielded over 90% agreement with accepted classifications. The method is highly accurate and may be used on all sorts of chromatographic profiles. Characteristic component analysis yielded results in good agreement with existing knowledge of characteristic components, but also succeeded in identifying new...

  6. 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

  7. Individual flexor tendon identification within the carpal tunnel: A semi-automated analysis method for serial cross-section magnetic resonance images

    Directory of Open Access Journals (Sweden)

    Nicole M Kunze

    2009-12-01

    Full Text Available Nicole M Kunze1, Jessica E Goetz2, Daniel R Thedens3, Thomas E Baer2, Ericka A Lawler2, Thomas D Brown21Department of Biomedical Engineering, 2Department of Orthopaedics and Rehabilitation, 3Department of Radiology, University of Iowa, Iowa City, IA, USAAbstract: Carpal tunnel syndrome is commonly viewed as resulting from chronic mechanical insult of the median nerve by adjacent anatomical structures. Both the median nerve and its surrounding soft tissue structures are well visualized on magnetic resonance (MR images of the wrist and hand. Addressing nerve damage from impingement of flexor digitorum tendons co-occupying the tunnel is attractive, but to date has been restricted by a lack of means for making individual identifications of the respective tendons. In this image analysis work, we have developed a region-growing method to positively identify each individual digital flexor tendon within the carpal tunnel by tracking it from a more distal MR section where the respective tendon identities are unambiguous. Illustratively, the new method was applied to MRI scans from four different subjects in a variety of hand poses. Conventional shape measures yielded less discriminatory information than did evaluations of individual tendon location and arrangement. This new method of rapid identification of individual tendons will facilitate analysis of tendon/nerve interactions within the tunnel, thereby providing better information about mechanical insult of the median nerve.Keywords: carpal tunnel syndrome, magnetic resonance imaging, region growing, digital flexor tendons

  8. 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.

  9. Target identification by image analysis.

    Science.gov (United States)

    Fetz, V; Prochnow, H; Brönstrup, M; Sasse, F

    2016-05-01

    Covering: 1997 to the end of 2015Each biologically active compound induces phenotypic changes in target cells that are characteristic for its mode of action. These phenotypic alterations can be directly observed under the microscope or made visible by labelling structural elements or selected proteins of the cells with dyes. A comparison of the cellular phenotype induced by a compound of interest with the phenotypes of reference compounds with known cellular targets allows predicting its mode of action. While this approach has been successfully applied to the characterization of natural products based on a visual inspection of images, recent studies used automated microscopy and analysis software to increase speed and to reduce subjective interpretation. In this review, we give a general outline of the workflow for manual and automated image analysis, and we highlight natural products whose bacterial and eucaryotic targets could be identified through such approaches. PMID:26777141

  10. Automated localization of vertebra landmarks in MRI images

    Science.gov (United States)

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

    2011-03-01

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

  11. Automated analysis of small animal PET studies through deformable registration to an atlas

    International Nuclear Information System (INIS)

    This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model. A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed. The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6 mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10 % in most of the organs considered. The proposed automated quantification technique is

  12. Automated analysis of small animal PET studies through deformable registration to an atlas

    Energy Technology Data Exchange (ETDEWEB)

    Gutierrez, Daniel F. [Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva 4 (Switzerland); Zaidi, Habib [Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva 4 (Switzerland); Geneva University, Geneva Neuroscience Center, Geneva (Switzerland); University of Groningen, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen (Netherlands)

    2012-11-15

    This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model. A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed. The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6 mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10 % in most of the organs considered. The proposed automated quantification technique is

  13. Automated Analysis of Security in Networking Systems

    DEFF Research Database (Denmark)

    Buchholtz, Mikael

    2004-01-01

    It has for a long time been a challenge to built secure networking systems. One way to counter this problem is to provide developers of software applications for networking systems with easy-to-use tools that can check security properties before the applications ever reach the marked. These tools...... will both help raise the general level of awareness of the problems and prevent the most basic flaws from occurring. This thesis contributes to the development of such tools. Networking systems typically try to attain secure communication by applying standard cryptographic techniques. In this thesis...... such networking systems are modelled in the process calculus LySa. On top of this programming language based formalism an analysis is developed, which relies on techniques from data and control ow analysis. These are techniques that can be fully automated, which make them an ideal basis for tools targeted at non...

  14. Automated Analysis, Classification, and Display of Waveforms

    Science.gov (United States)

    Kwan, Chiman; Xu, Roger; Mayhew, David; Zhang, Frank; Zide, Alan; Bonggren, Jeff

    2004-01-01

    A computer program partly automates the analysis, classification, and display of waveforms represented by digital samples. In the original application for which the program was developed, the raw waveform data to be analyzed by the program are acquired from space-shuttle auxiliary power units (APUs) at a sampling rate of 100 Hz. The program could also be modified for application to other waveforms -- for example, electrocardiograms. The program begins by performing principal-component analysis (PCA) of 50 normal-mode APU waveforms. Each waveform is segmented. A covariance matrix is formed by use of the segmented waveforms. Three eigenvectors corresponding to three principal components are calculated. To generate features, each waveform is then projected onto the eigenvectors. These features are displayed on a three-dimensional diagram, facilitating the visualization of the trend of APU operations.

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

    Directory of Open Access Journals (Sweden)

    Strandh Christer

    2008-07-01

    Full Text Available Abstract Background Confocal laser scanning microscopy has revolutionized cell biology. However, the technique has major limitations in speed and sensitivity due to the fact that a single laser beam scans the sample, allowing only a few microseconds signal collection for each pixel. This limitation has been overcome by the introduction of parallel beam illumination techniques in combination with cold CCD camera based image capture. Methods Using the combination of microlens enhanced Nipkow spinning disc confocal illumination together with fully automated image capture and large scale in silico image processing we have developed a system allowing the acquisition, presentation and analysis of maximum resolution confocal panorama images of several Gigapixel size. We call the method Extended Field Laser Confocal Microscopy (EFLCM. Results We show using the EFLCM technique that it is possible to create a continuous confocal multi-colour mosaic from thousands of individually captured images. EFLCM can digitize and analyze histological slides, sections of entire rodent organ and full size embryos. It can also record hundreds of thousands cultured cells at multiple wavelength in single event or time-lapse fashion on fixed slides, in live cell imaging chambers or microtiter plates. Conclusion The observer independent image capture of EFLCM allows quantitative measurements of fluorescence intensities and morphological parameters on a large number of cells. EFLCM therefore bridges the gap between the mainly illustrative fluorescence microscopy and purely quantitative flow cytometry. EFLCM can also be used as high content analysis (HCA instrument for automated screening processes.

  16. 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.

  17. Spinal imaging and image analysis

    CERN Document Server

    Yao, Jianhua

    2015-01-01

    This book is instrumental to building a bridge between scientists and clinicians in the field of spine imaging by introducing state-of-the-art computational methods in the context of clinical applications.  Spine imaging via computed tomography, magnetic resonance imaging, and other radiologic imaging modalities, is essential for noninvasively visualizing and assessing spinal pathology. Computational methods support and enhance the physician’s ability to utilize these imaging techniques for diagnosis, non-invasive treatment, and intervention in clinical practice. Chapters cover a broad range of topics encompassing radiological imaging modalities, clinical imaging applications for common spine diseases, image processing, computer-aided diagnosis, quantitative analysis, data reconstruction and visualization, statistical modeling, image-guided spine intervention, and robotic surgery. This volume serves a broad audience as  contributions were written by both clinicians and researchers, which reflects the inte...

  18. Management issues in automated audit analysis

    Energy Technology Data Exchange (ETDEWEB)

    Jackson, K.A.; Hochberg, J.G.; Wilhelmy, S.K.; McClary, J.F.; Christoph, G.G.

    1994-03-01

    This paper discusses management issues associated with the design and implementation of an automated audit analysis system that we use to detect security events. It gives the viewpoint of a team directly responsible for developing and managing such a system. We use Los Alamos National Laboratory`s Network Anomaly Detection and Intrusion Reporter (NADIR) as a case in point. We examine issues encountered at Los Alamos, detail our solutions to them, and where appropriate suggest general solutions. After providing an introduction to NADIR, we explore four general management issues: cost-benefit questions, privacy considerations, legal issues, and system integrity. Our experiences are of general interest both to security professionals and to anyone who may wish to implement a similar system. While NADIR investigates security events, the methods used and the management issues are potentially applicable to a broad range of complex systems. These include those used to audit credit card transactions, medical care payments, and procurement systems.

  19. ASteCA - Automated Stellar Cluster Analysis

    CERN Document Server

    Perren, Gabriel I; Piatti, Andrés E

    2014-01-01

    We present ASteCA (Automated Stellar Cluster Analysis), a suit of tools designed to fully automatize the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster's center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for a cluster's metallicity, age, extinction and distance values along with its unce...

  20. Applications of Automation Methods for Nonlinear Fracture Test Analysis

    Science.gov (United States)

    Allen, Phillip A.; Wells, Douglas N.

    2013-01-01

    Using automated and standardized computer tools to calculate the pertinent test result values has several advantages such as: 1. allowing high-fidelity solutions to complex nonlinear phenomena that would be impractical to express in written equation form, 2. eliminating errors associated with the interpretation and programing of analysis procedures from the text of test standards, 3. lessening the need for expertise in the areas of solid mechanics, fracture mechanics, numerical methods, and/or finite element modeling, to achieve sound results, 4. and providing one computer tool and/or one set of solutions for all users for a more "standardized" answer. In summary, this approach allows a non-expert with rudimentary training to get the best practical solution based on the latest understanding with minimum difficulty.Other existing ASTM standards that cover complicated phenomena use standard computer programs: 1. ASTM C1340/C1340M-10- Standard Practice for Estimation of Heat Gain or Loss Through Ceilings Under Attics Containing Radiant Barriers by Use of a Computer Program 2. ASTM F 2815 - Standard Practice for Chemical Permeation through Protective Clothing Materials: Testing Data Analysis by Use of a Computer Program 3. ASTM E2807 - Standard Specification for 3D Imaging Data Exchange, Version 1.0 The verification, validation, and round-robin processes required of a computer tool closely parallel the methods that are used to ensure the solution validity for equations included in test standard. The use of automated analysis tools allows the creation and practical implementation of advanced fracture mechanics test standards that capture the physics of a nonlinear fracture mechanics problem without adding undue burden or expense to the user. The presented approach forms a bridge between the equation-based fracture testing standards of today and the next generation of standards solving complex problems through analysis automation.

  1. AMDA: an R package for the automated microarray data analysis

    Directory of Open Access Journals (Sweden)

    Foti Maria

    2006-07-01

    Full Text Available Abstract Background Microarrays are routinely used to assess mRNA transcript levels on a genome-wide scale. Large amount of microarray datasets are now available in several databases, and new experiments are constantly being performed. In spite of this fact, few and limited tools exist for quickly and easily analyzing the results. Microarray analysis can be challenging for researchers without the necessary training and it can be time-consuming for service providers with many users. Results To address these problems we have developed an automated microarray data analysis (AMDA software, which provides scientists with an easy and integrated system for the analysis of Affymetrix microarray experiments. AMDA is free and it is available as an R package. It is based on the Bioconductor project that provides a number of powerful bioinformatics and microarray analysis tools. This automated pipeline integrates different functions available in the R and Bioconductor projects with newly developed functions. AMDA covers all of the steps, performing a full data analysis, including image analysis, quality controls, normalization, selection of differentially expressed genes, clustering, correspondence analysis and functional evaluation. Finally a LaTEX document is dynamically generated depending on the performed analysis steps. The generated report contains comments and analysis results as well as the references to several files for a deeper investigation. Conclusion AMDA is freely available as an R package under the GPL license. The package as well as an example analysis report can be downloaded in the Services/Bioinformatics section of the Genopolis http://www.genopolis.it/

  2. Automated segmentation of three-dimensional MR brain images

    Science.gov (United States)

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

    2006-03-01

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

  3. 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.

  4. Advanced biomedical image analysis

    CERN Document Server

    Haidekker, Mark A

    2010-01-01

    "This book covers the four major areas of image processing: Image enhancement and restoration, image segmentation, image quantification and classification, and image visualization. Image registration, storage, and compression are also covered. The text focuses on recently developed image processing and analysis operators and covers topical research"--Provided by publisher.

  5. ASteCA: Automated Stellar Cluster Analysis

    Science.gov (United States)

    Perren, G. I.; Vázquez, R. A.; Piatti, A. E.

    2015-04-01

    We present the Automated Stellar Cluster Analysis package (ASteCA), a suit of tools designed to fully automate the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster's center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for a cluster's metallicity, age, extinction and distance values along with its uncertainties. To validate the code we applied it on a large set of over 400 synthetic MASSCLEAN clusters with varying degrees of field star contamination as well as a smaller set of 20 observed Milky Way open clusters (Berkeley 7, Bochum 11, Czernik 26, Czernik 30, Haffner 11, Haffner 19, NGC 133, NGC 2236, NGC 2264, NGC 2324, NGC 2421, NGC 2627, NGC 6231, NGC 6383, NGC 6705, Ruprecht 1, Tombaugh 1, Trumpler 1, Trumpler 5 and Trumpler 14) studied in the literature. The results show that ASteCA is able to recover cluster parameters with an acceptable precision even for those clusters affected by substantial field star contamination. ASteCA is written in Python and is made available as an open source code which can be downloaded ready to be used from its official site.

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

    Science.gov (United States)

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

    2015-01-01

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

  7. 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

  8. Ecological Automation Design, Extending Work Domain Analysis

    NARCIS (Netherlands)

    Amelink, M.H.J.

    2010-01-01

    In high–risk domains like aviation, medicine and nuclear power plant control, automation has enabled new capabilities, increased the economy of operation and has greatly contributed to safety. However, automation increases the number of couplings in a system, which can inadvertently lead to more com

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

    Science.gov (United States)

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

    2015-01-01

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

  10. Automation literature: A brief review and analysis

    Science.gov (United States)

    Smith, D.; Dieterly, D. L.

    1980-01-01

    Current thought and research positions which may allow for an improved capability to understand the impact of introducing automation to an existing system are established. The orientation was toward the type of studies which may provide some general insight into automation; specifically, the impact of automation in human performance and the resulting system performance. While an extensive number of articles were reviewed, only those that addressed the issue of automation and human performance were selected to be discussed. The literature is organized along two dimensions: time, Pre-1970, Post-1970; and type of approach, Engineering or Behavioral Science. The conclusions reached are not definitive, but do provide the initial stepping stones in an attempt to begin to bridge the concept of automation in a systematic progression.

  11. Analysis of engineering drawings and raster map images

    CERN Document Server

    Henderson, Thomas C

    2013-01-01

    Presents up-to-date methods and algorithms for the automated analysis of engineering drawings and digital cartographic maps Discusses automatic engineering drawing and map analysis techniques Covers detailed accounts of the use of unsupervised segmentation algorithms to map images

  12. 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)

  13. Automated Large-Scale Shoreline Variability Analysis From Video

    Science.gov (United States)

    Pearre, N. S.

    2006-12-01

    Land-based video has been used to quantify changes in nearshore conditions for over twenty years. By combining the ability to track rapid, short-term shoreline change and changes associated with longer term or seasonal processes, video has proved to be a cost effective and versatile tool for coastal science. Previous video-based studies of shoreline change have typically examined the position of the shoreline along a small number of cross-shore lines as a proxy for the continuous coast. The goal of this study is twofold: (1) to further develop automated shoreline extraction algorithms for continuous shorelines, and (2) to track the evolution of a nourishment project at Rehoboth Beach, DE that was concluded in June 2005. Seven cameras are situated approximately 30 meters above mean sea level and 70 meters from the shoreline. Time exposure and variance images are captured hourly during daylight and transferred to a local processing computer. After correcting for lens distortion and geo-rectifying to a shore-normal coordinate system, the images are merged to form a composite planform image of 6 km of coast. Automated extraction algorithms establish shoreline and breaker positions throughout a tidal cycle on a daily basis. Short and long term variability in the daily shoreline will be characterized using empirical orthogonal function (EOF) analysis. Periodic sediment volume information will be extracted by incorporating the results of monthly ground-based LIDAR surveys and by correlating the hourly shorelines to the corresponding tide level under conditions with minimal wave activity. The Delaware coast in the area downdrift of the nourishment site is intermittently interrupted by short groins. An Even/Odd analysis of the shoreline response around these groins will be performed. The impact of groins on the sediment volume transport along the coast during periods of accretive and erosive conditions will be discussed. [This work is being supported by DNREC and the

  14. 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.

  15. Automated analysis and annotation of basketball video

    Science.gov (United States)

    Saur, Drew D.; Tan, Yap-Peng; Kulkarni, Sanjeev R.; Ramadge, Peter J.

    1997-01-01

    Automated analysis and annotation of video sequences are important for digital video libraries, content-based video browsing and data mining projects. A successful video annotation system should provide users with useful video content summary in a reasonable processing time. Given the wide variety of video genres available today, automatically extracting meaningful video content for annotation still remains hard by using current available techniques. However, a wide range video has inherent structure such that some prior knowledge about the video content can be exploited to improve our understanding of the high-level video semantic content. In this paper, we develop tools and techniques for analyzing structured video by using the low-level information available directly from MPEG compressed video. Being able to work directly in the video compressed domain can greatly reduce the processing time and enhance storage efficiency. As a testbed, we have developed a basketball annotation system which combines the low-level information extracted from MPEG stream with the prior knowledge of basketball video structure to provide high level content analysis, annotation and browsing for events such as wide- angle and close-up views, fast breaks, steals, potential shots, number of possessions and possession times. We expect our approach can also be extended to structured video in other domains.

  16. Retinal imaging and image analysis

    NARCIS (Netherlands)

    Abramoff, M.D.; Garvin, Mona K.; Sonka, Milan

    2010-01-01

    Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindne

  17. Clinical validation of semi-automated software for volumetric and dynamic contrast enhancement analysis of soft tissue venous malformations on magnetic resonance imaging examination

    Energy Technology Data Exchange (ETDEWEB)

    Caty, Veronique [Hopital Maisonneuve-Rosemont, Universite de Montreal, Department of Radiology, Montreal, QC (Canada); Kauffmann, Claude; Giroux, Marie-France; Oliva, Vincent; Therasse, Eric [Centre Hospitalier de l' Universite de Montreal (CHUM), Universite de Montreal and Research Centre, CHUM (CRCHUM), Department of Radiology, Montreal, QC (Canada); Dubois, Josee [Centre Hospitalier Universitaire Sainte-Justine et Universite de Montreal, Department of Radiology, Montreal, QC (Canada); Mansour, Asmaa [Institut de Cardiologie de Montreal, Heart Institute Coordinating Centre, Montreal, QC (Canada); Piche, Nicolas [Object Research System, Montreal, QC (Canada); Soulez, Gilles [Centre Hospitalier de l' Universite de Montreal (CHUM), Universite de Montreal and Research Centre, CHUM (CRCHUM), Department of Radiology, Montreal, QC (Canada); CHUM - Hopital Notre-Dame, Department of Radiology, Montreal, Quebec (Canada)

    2014-02-15

    To evaluate venous malformation (VM) volume and contrast-enhancement analysis on magnetic resonance imaging (MRI) compared with diameter evaluation. Baseline MRI was undertaken in 44 patients, 20 of whom were followed by MRI after sclerotherapy. All patients underwent short-tau inversion recovery (STIR) acquisitions and dynamic contrast assessment. VM diameters in three orthogonal directions were measured to obtain the largest and mean diameters. Volumetric reconstruction of VM was generated from two orthogonal STIR sequences and fused with acquisitions after contrast medium injection. Reproducibility (interclass correlation coefficients [ICCs]) of diameter and volume measurements was estimated. VM size variations in diameter and volume after sclerotherapy and contrast enhancement before sclerotherapy were compared in patients with clinical success or failure. Inter-observer ICCs were similar for diameter and volume measurements at baseline and follow-up (range 0.87-0.99). Higher percentages of size reduction after sclerotherapy were observed with volume (32.6 ± 30.7 %) than with diameter measurements (14.4 ± 21.4 %; P = 0.037). Contrast enhancement values were estimated at 65.3 ± 27.5 % and 84 ± 13 % in patients with clinical failure and success respectively (P = 0.056). Venous malformation volume was as reproducible as diameter measurement and more sensitive in detecting therapeutic responses. Patients with better clinical outcome tend to have stronger malformation enhancement. (orig.)

  18. 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

  19. Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

    OpenAIRE

    Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly

    2012-01-01

    High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or...

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

    Science.gov (United States)

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

    2016-02-01

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

  1. Automated Cache Performance Analysis And Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Mohror, Kathryn [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2013-12-23

    While there is no lack of performance counter tools for coarse-grained measurement of cache activity, there is a critical lack of tools for relating data layout to cache behavior to application performance. Generally, any nontrivial optimizations are either not done at all, or are done ”by hand” requiring significant time and expertise. To the best of our knowledge no tool available to users measures the latency of memory reference instructions for partic- ular addresses and makes this information available to users in an easy-to-use and intuitive way. In this project, we worked to enable the Open|SpeedShop performance analysis tool to gather memory reference latency information for specific instructions and memory ad- dresses, and to gather and display this information in an easy-to-use and intuitive way to aid performance analysts in identifying problematic data structures in their codes. This tool was primarily designed for use in the supercomputer domain as well as grid, cluster, cloud-based parallel e-commerce, and engineering systems and middleware. Ultimately, we envision a tool to automate optimization of application cache layout and utilization in the Open|SpeedShop performance analysis tool. To commercialize this soft- ware, we worked to develop core capabilities for gathering enhanced memory usage per- formance data from applications and create and apply novel methods for automatic data structure layout optimizations, tailoring the overall approach to support existing supercom- puter and cluster programming models and constraints. In this Phase I project, we focused on infrastructure necessary to gather performance data and present it in an intuitive way to users. With the advent of enhanced Precise Event-Based Sampling (PEBS) counters on recent Intel processor architectures and equivalent technology on AMD processors, we are now in a position to access memory reference information for particular addresses. Prior to the introduction of PEBS counters

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

    Science.gov (United States)

    Singh, Preetpal

    to detect equipment failure and identify defective products at the assembly line. The research work in this thesis combines machine vision and image processing technology to build a digital imaging and processing system for monitoring and measuring lake ice thickness in real time. An ultra-compact USB camera is programmed to acquire and transmit high resolution imagery for processing with MATLAB Image Processing toolbox. The image acquisition and transmission process is fully automated; image analysis is semi-automated and requires limited user input. Potential design changes to the prototype and ideas on fully automating the imaging and processing procedure are presented to conclude this research work.

  3. Methods of automated cell analysis and their application in radiation biology

    International Nuclear Information System (INIS)

    The present review is concerned with the methods of automated analysis of biological microobjects and covers two groups into which all the systems of automated analysis can be divided-systems of flow ( flow cytometry) and scanning (image analysis systems) type. Particular emphasis has been placed on their use in radiobiological studies, namely, in the micronucleus test, a cytogenetic assay for monitoring the clastogenic action of ionizing radiation commonly used at present. Examples of suing methods described and actual setups in other biomedical researches are given. Analysis of advantages and disadvantages of the methods of automated cell analysis enables to choose more thoroughly between the systems of flow and scanning type to use them in particular research

  4. Automated analysis for scintigraphic evaluation of gastric emptying using invariant moments.

    Science.gov (United States)

    Abutaleb, A; Delalic, Z J; Ech, R; Siegel, J A

    1989-01-01

    This study introduces a method for automated analysis of the standard solid-meal gastric emptying test. The purpose was to develop a diagnostic tool to characterize more reproducibly abnormalities of solid-phase gastric emptying. The processing of gastric emptying is automated using geometrical moments that are invariant to scaling, rotation, and shift. Twenty subjects were studied. The first step was to obtain images of the stomach using a nuclear gamma camera immediately after the subject had eaten a radio-labeled meal. The second step was to process and analyze the images by a recently developed automated gastric emptying analysis (AGEA) method, which determines the gastric contour and the geometrical properties include such parameters as area, centroid, orientation, and moments of inertia. Statistical tests showed that some of the moments were sensitive to the patient's gastric status (normal versus abnormal). The difference between the normal and abnormal patients became noticeable approximately 1 h after meal ingestion. PMID:18230536

  5. Statistical Analysis of Filament Features Based on the H{\\alpha} Solar Images from 1988 to 2013 by Computer Automated Detection Method

    CERN Document Server

    Hao, Q; Cao, W; Chen, P F

    2015-01-01

    We improve our filament automated detection method which was proposed in our previous works. It is then applied to process the full disk H$\\alpha$ data mainly obtained by Big Bear Solar Observatory (BBSO) from 1988 to 2013, spanning nearly 3 solar cycles. The butterfly diagrams of the filaments, showing the information of the filament area, spine length, tilt angle, and the barb number, are obtained. The variations of these features with the calendar year and the latitude band are analyzed. The drift velocities of the filaments in different latitude bands are calculated and studied. We also investigate the north-south (N-S) asymmetries of the filament numbers in total and in each subclass classified according to the filament area, spine length, and tilt angle. The latitudinal distribution of the filament number is found to be bimodal. About 80% of all the filaments have tilt angles within [0{\\deg}, 60{\\deg}]. For the filaments within latitudes lower (higher) than 50{\\deg} the northeast (northwest) direction i...

  6. Automated SIMS Isotopic Analysis Of Small Dust Particles

    Science.gov (United States)

    Nittler, L.; Alexander, C.; Gyngard, F.; Morgand, A.; Zinner, E. K.

    2009-12-01

    The isotopic compositions of sub-μm to μm sized dust grains are of increasing interest in cosmochemistry, nuclear forensics and terrestrial aerosol research. Because of its high sensitivity and spatial resolution, Secondary Ion Mass Spectrometry (SIMS) is the tool of choice for measuring isotopes in such small samples. Indeed, SIMS has enabled an entirely new sub-field of astronomy: presolar grains in meteorites. In recent years, the development of the Cameca NanoSIMS ion probe has extended the reach of isotopic measurements to particles as small as 100 nm in diameter, a regime where isotopic precision is strongly limited by the total number of atoms in the sample. Many applications require obtaining isotopic data on large numbers of particles, necessitating the development of automated techniques. One such method is isotopic imaging, wherein images of multiple isotopes are acquired, each containing multiple dispersed particles, and image processing is used to determine isotopic ratios for individual particles. This method is powerful, but relatively inefficient for raster-based imaging on the NanoSIMS. Modern computerized control of instrumentation has allowed for another approach, analogous to commercial automated SEM-EDS particle analysis systems, in which images are used solely to locate particles followed by fully automated grain-by-grain analysis. The first such system was developed on the Carnegie Institution’s Cameca ims-6f, and was used to generate large databases of presolar grains. We have recently developed a similar system for the NanoSIMS, whose high sensitivity allows for smaller grains to be analyzed with less sample consumption than is possible with the 6f system. The 6f and NanoSIMS systems are functionally identical: an image of dispersed grains is obtained with sufficient statistical precision for an algorithm to identify the positions of individual particles, the primary ion beam is deflected to each particle in turn and rastered in a small

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

    Science.gov (United States)

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

    2016-01-01

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

  8. Functional MRI Preprocessing in Lesioned Brains: Manual Versus Automated Region of Interest Analysis.

    Science.gov (United States)

    Garrison, Kathleen A; Rogalsky, Corianne; Sheng, Tong; Liu, Brent; Damasio, Hanna; Winstein, Carolee J; Aziz-Zadeh, Lisa S

    2015-01-01

    Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant's structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant's non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design.

  9. 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...

  10. 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.

  11. 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

  12. 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

  13. Automated Cache Performance Analysis And Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Mohror, Kathryn [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2013-12-23

    While there is no lack of performance counter tools for coarse-grained measurement of cache activity, there is a critical lack of tools for relating data layout to cache behavior to application performance. Generally, any nontrivial optimizations are either not done at all, or are done ”by hand” requiring significant time and expertise. To the best of our knowledge no tool available to users measures the latency of memory reference instructions for partic- ular addresses and makes this information available to users in an easy-to-use and intuitive way. In this project, we worked to enable the Open|SpeedShop performance analysis tool to gather memory reference latency information for specific instructions and memory ad- dresses, and to gather and display this information in an easy-to-use and intuitive way to aid performance analysts in identifying problematic data structures in their codes. This tool was primarily designed for use in the supercomputer domain as well as grid, cluster, cloud-based parallel e-commerce, and engineering systems and middleware. Ultimately, we envision a tool to automate optimization of application cache layout and utilization in the Open|SpeedShop performance analysis tool. To commercialize this soft- ware, we worked to develop core capabilities for gathering enhanced memory usage per- formance data from applications and create and apply novel methods for automatic data structure layout optimizations, tailoring the overall approach to support existing supercom- puter and cluster programming models and constraints. In this Phase I project, we focused on infrastructure necessary to gather performance data and present it in an intuitive way to users. With the advent of enhanced Precise Event-Based Sampling (PEBS) counters on recent Intel processor architectures and equivalent technology on AMD processors, we are now in a position to access memory reference information for particular addresses. Prior to the introduction of PEBS counters

  14. Automated SEM Modal Analysis Applied to the Diogenites

    Science.gov (United States)

    Bowman, L. E.; Spilde, M. N.; Papike, James J.

    1996-01-01

    Analysis of volume proportions of minerals, or modal analysis, is routinely accomplished by point counting on an optical microscope, but the process, particularly on brecciated samples such as the diogenite meteorites, is tedious and prone to error by misidentification of very small fragments, which may make up a significant volume of the sample. Precise volume percentage data can be gathered on a scanning electron microscope (SEM) utilizing digital imaging and an energy dispersive spectrometer (EDS). This form of automated phase analysis reduces error, and at the same time provides more information than could be gathered using simple point counting alone, such as particle morphology statistics and chemical analyses. We have previously studied major, minor, and trace-element chemistry of orthopyroxene from a suite of diogenites. This abstract describes the method applied to determine the modes on this same suite of meteorites and the results of that research. The modal abundances thus determined add additional information on the petrogenesis of the diogenites. In addition, low-abundance phases such as spinels were located for further analysis by this method.

  15. 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.

  16. 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.

  17. 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)

  18. AUTOMATED VIDEO IMAGE MORPHOMETRY OF THE CORNEAL ENDOTHELIUM

    NARCIS (Netherlands)

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

    1993-01-01

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

  19. Quantification of filamentation by uropathogenic Escherichia coli during experimental bladder cell infection by using semi-automated image analysis

    DEFF Research Database (Denmark)

    Klein, Kasper; Palarasah, Yaseelan; Kolmos, Hans Jørn;

    2015-01-01

    in focus-stacked microscopy images. Used in combination with a flow-chamber based in vitro cystitis model, we study the factors involved in filament formation by uropathogenic E. coli (UPEC) during infection. The influence of substratum surface, intracellular proliferation and flow media on UPEC...... filamentation is evaluated. We show that reversible UPEC filamentation during cystitis is not dependent on intracellular infection, which previous studies have suggested. Instead, we find that filamentation can be induced by contact with surfaces, both biological and artificial. Lastly our data indicate...... that UPEC filamentation is induced by trace-amounts of specific components in urine, rather than being a generic stress-response to high urine salt concentrations. The study shows that the combined methodology is generally useful for investigation of bacterial morphological transitions during cell infection....

  20. Automated face analysis by feature point tracking has high concurrent validity with manual FACS coding.

    Science.gov (United States)

    Cohn, J F; Zlochower, A J; Lien, J; Kanade, T

    1999-01-01

    The face is a rich source of information about human behavior. Available methods for coding facial displays, however, are human-observer dependent, labor intensive, and difficult to standardize. To enable rigorous and efficient quantitative measurement of facial displays, we have developed an automated method of facial display analysis. In this report, we compare the results with this automated system with those of manual FACS (Facial Action Coding System, Ekman & Friesen, 1978a) coding. One hundred university students were videotaped while performing a series of facial displays. The image sequences were coded from videotape by certified FACS coders. Fifteen action units and action unit combinations that occurred a minimum of 25 times were selected for automated analysis. Facial features were automatically tracked in digitized image sequences using a hierarchical algorithm for estimating optical flow. The measurements were normalized for variation in position, orientation, and scale. The image sequences were randomly divided into a training set and a cross-validation set, and discriminant function analyses were conducted on the feature point measurements. In the training set, average agreement with manual FACS coding was 92% or higher for action units in the brow, eye, and mouth regions. In the cross-validation set, average agreement was 91%, 88%, and 81% for action units in the brow, eye, and mouth regions, respectively. Automated face analysis by feature point tracking demonstrated high concurrent validity with manual FACS coding.

  1. 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.

  2. 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.)

  3. 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.)

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

    DEFF Research Database (Denmark)

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

    2013-01-01

    In the basic clinical work-up of infertile couples, a semen analysis is mandatory and the sperm concentration is one of the most essential variables to be determined. Sperm concentration is usually assessed by manual counting using a haemocytometer and is hence labour intensive and may be subjected...... to investigator bias. Here we show that image cytometry can be used to accurately measure the sperm concentration of human semen samples with great ease and reproducibility. The impact of several factors (pipetting, mixing, round cell content, sperm concentration), which can influence the read-out as well....... Moreover, by evaluation of repeated measurements it appeared that image cytometry produced more consistent and accurate measurements than manual counting of human spermatozoa concentration. In conclusion, image cytometry provides an appealing substitute of manual counting by providing reliable, robust...

  5. Automating sensitivity analysis of computer models using computer calculus

    International Nuclear Information System (INIS)

    An automated procedure for performing sensitivity analysis has been developed. The procedure uses a new FORTRAN compiler with computer calculus capabilities to generate the derivatives needed to set up sensitivity equations. The new compiler is called GRESS - Gradient Enhanced Software System. Application of the automated procedure with direct and adjoint sensitivity theory for the analysis of non-linear, iterative systems of equations is discussed. Calculational efficiency consideration and techniques for adjoint sensitivity analysis are emphasized. The new approach is found to preserve the traditional advantages of adjoint theory while removing the tedious human effort previously needed to apply this theoretical methodology. Conclusions are drawn about the applicability of the automated procedure in numerical analysis and large-scale modelling sensitivity studies

  6. Prehospital digital photography and automated image transmission in an emergency medical service – an ancillary retrospective analysis of a prospective controlled trial

    Directory of Open Access Journals (Sweden)

    Bergrath Sebastian

    2013-01-01

    Full Text Available Abstract Background Still picture transmission was performed using a telemedicine system in an Emergency Medical Service (EMS during a prospective, controlled trial. In this ancillary, retrospective study the quality and content of the transmitted pictures and the possible influences of this application on prehospital time requirements were investigated. Methods A digital camera was used with a telemedicine system enabling encrypted audio and data transmission between an ambulance and a remotely located physician. By default, images were compressed (jpeg, 640 x 480 pixels. On occasion, this compression was deactivated (3648 x 2736 pixels. Two independent investigators assessed all transmitted pictures according to predefined criteria. In cases of different ratings, a third investigator had final decision competence. Patient characteristics and time intervals were extracted from the EMS protocol sheets and dispatch centre reports. Results Overall 314 pictures (mean 2.77 ± 2.42 pictures/mission were transmitted during 113 missions (group 1. Pictures were not taken for 151 missions (group 2. Regarding picture quality, the content of 240 (76.4% pictures was clearly identifiable; 45 (14.3% pictures were considered “limited quality” and 29 (9.2% pictures were deemed “not useful” due to not/hardly identifiable content. For pictures with file compression (n = 84 missions and without (n = 17 missions, the content was clearly identifiable in 74% and 97% of the pictures, respectively (p = 0.003. Medical reports (n = 98, 32.8%, medication lists (n = 49, 16.4% and 12-lead ECGs (n = 28, 9.4% were most frequently photographed. The patient characteristics of group 1 vs. 2 were as follows: median age – 72.5 vs. 56.5 years, p = 0.001; frequency of acute coronary syndrome – 24/113 vs. 15/151, p = 0.014. The NACA scores and gender distribution were comparable. Median on-scene times were longer with picture

  7. 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.

  8. Automated identification of retained surgical items in radiological images

    Science.gov (United States)

    Agam, Gady; Gan, Lin; Moric, Mario; Gluncic, Vicko

    2015-03-01

    Retained surgical items (RSIs) in patients is a major operating room (OR) patient safety concern. An RSI is any surgical tool, sponge, needle or other item inadvertently left in a patients body during the course of surgery. If left undetected, RSIs may lead to serious negative health consequences such as sepsis, internal bleeding, and even death. To help physicians efficiently and effectively detect RSIs, we are developing computer-aided detection (CADe) software for X-ray (XR) image analysis, utilizing large amounts of currently available image data to produce a clinically effective RSI detection system. Physician analysis of XRs for the purpose of RSI detection is a relatively lengthy process that may take up to 45 minutes to complete. It is also error prone due to the relatively low acuity of the human eye for RSIs in XR images. The system we are developing is based on computer vision and machine learning algorithms. We address the problem of low incidence by proposing synthesis algorithms. The CADe software we are developing may be integrated into a picture archiving and communication system (PACS), be implemented as a stand-alone software application, or be integrated into portable XR machine software through application programming interfaces. Preliminary experimental results on actual XR images demonstrate the effectiveness of the proposed approach.

  9. 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

  10. 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

  11. 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)

  12. Analysis of Trinity Power Metrics for Automated Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Michalenko, Ashley Christine [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-07-28

    This is a presentation from Los Alamos National Laboraotyr (LANL) about the analysis of trinity power metrics for automated monitoring. The following topics are covered: current monitoring efforts, motivation for analysis, tools used, the methodology, work performed during the summer, and future work planned.

  13. Automating sensitivity analysis of computer models using computer calculus

    International Nuclear Information System (INIS)

    An automated procedure for performing sensitivity analyses has been developed. The procedure uses a new FORTRAN compiler with computer calculus capabilities to generate the derivatives needed to set up sensitivity equations. The new compiler is called GRESS - Gradient Enhanced Software System. Application of the automated procedure with ''direct'' and ''adjoint'' sensitivity theory for the analysis of non-linear, iterative systems of equations is discussed. Calculational efficiency consideration and techniques for adjoint sensitivity analysis are emphasized. The new approach is found to preserve the traditional advantages of adjoint theory while removing the tedious human effort previously needed to apply this theoretical methodology. Conclusions are drawn about the applicability of the automated procedure in numerical analysis and large-scale modelling sensitivity studies. 24 refs., 2 figs

  14. 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

  15. 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

  16. 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.

  17. 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)

  18. 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.

  19. Automated Analysis of Imaging Based Experiments Project

    Data.gov (United States)

    National Aeronautics and Space Administration — For many applications involving liquid injection, the ability to predict the details of the breakup process is often limited due to the complexity of the two-phase...

  20. 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.

  1. Using grid technologies to face medical image analysis challenges

    OpenAIRE

    Montagnat, Johan; Breton, Vincent; Magnin, Isabelle

    2003-01-01

    International audience The availability of digital imagers inside hospitals and their ever growing inspection capabilities have established digital medical images as a key component of many pathologies diagnosis, follow-up and treatment. To face the growing image analysis requirements, automated medical image processing algorithms have been developed over the two past decades. In parallel, medical image databases have been set up in health centers. Some attempts have been made to cross dat...

  2. An overview of the contaminant analysis automation program

    International Nuclear Information System (INIS)

    The Department of Energy (DOE) has significant amounts of radioactive and hazardous wastes stored, buried, and still being generated at many sites within the United States. These wastes must be characterized to determine the elemental, isotopic, and compound content before remediation can begin. In this paper, the authors project that sampling requirements will necessitate generating more than 10 million samples by 1995, which will far exceed the capabilities of our current manual chemical analysis laboratories. The Contaminant Analysis Automation effort (CAA), with Los Alamos National Laboratory (LANL) as to the coordinating Laboratory, is designing and fabricating robotic systems that will standardize and automate both the hardware and the software of the most common environmental chemical methods. This will be accomplished by designing and producing several unique analysis systems called Standard Analysis Methods (SAM). Each SAM will automate a specific chemical method, including sample preparation, the analytical analysis, and the data interpretation, by using a building block known as the Standard Laboratory Module (SLM). This concept allows the chemist to assemble an automated environmental method using standardized SLMs easily and without the worry of hardware compatibility or the necessity of generating complicated control programs

  3. Object Type Recognition for Automated Analysis of Protein Subcellular Location

    OpenAIRE

    Zhao, Ting; Velliste, Meel; Boland, Michael V.; Murphy, Robert F.

    2005-01-01

    The new field of location proteomics seeks to provide a comprehensive, objective characterization of the subcellular locations of all proteins expressed in a given cell type. Previous work has demonstrated that automated classifiers can recognize the patterns of all major subcellular organelles and structures in fluorescence microscope images with high accuracy. However, since some proteins may be present in more than one organelle, this paper addresses a more difficult task: recognizing a pa...

  4. ORIGAMI Automator Primer. Automated ORIGEN Source Terms and Spent Fuel Storage Pool Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Wieselquist, William A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Thompson, Adam B. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Bowman, Stephen M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Peterson, Joshua L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2016-04-01

    Source terms and spent nuclear fuel (SNF) storage pool decay heat load analyses for operating nuclear power plants require a large number of Oak Ridge Isotope Generation and Depletion (ORIGEN) calculations. SNF source term calculations also require a significant amount of bookkeeping to track quantities such as core and assembly operating histories, spent fuel pool (SFP) residence times, heavy metal masses, and enrichments. The ORIGEN Assembly Isotopics (ORIGAMI) module in the SCALE code system provides a simple scheme for entering these data. However, given the large scope of the analysis, extensive scripting is necessary to convert formats and process data to create thousands of ORIGAMI input files (one per assembly) and to process the results into formats readily usable by follow-on analysis tools. This primer describes a project within the SCALE Fulcrum graphical user interface (GUI) called ORIGAMI Automator that was developed to automate the scripting and bookkeeping in large-scale source term analyses. The ORIGAMI Automator enables the analyst to (1) easily create, view, and edit the reactor site and assembly information, (2) automatically create and run ORIGAMI inputs, and (3) analyze the results from ORIGAMI. ORIGAMI Automator uses the standard ORIGEN binary concentrations files produced by ORIGAMI, with concentrations available at all time points in each assembly’s life. The GUI plots results such as mass, concentration, activity, and decay heat using a powerful new ORIGEN Post-Processing Utility for SCALE (OPUS) GUI component. This document includes a description and user guide for the GUI, a step-by-step tutorial for a simplified scenario, and appendices that document the file structures used.

  5. An Automated Data Analysis Tool for Livestock Market Data

    Science.gov (United States)

    Williams, Galen S.; Raper, Kellie Curry

    2011-01-01

    This article describes an automated data analysis tool that allows Oklahoma Cooperative Extension Service educators to disseminate results in a timely manner. Primary data collected at Oklahoma Quality Beef Network (OQBN) certified calf auctions across the state results in a large amount of data per sale site. Sale summaries for an individual sale…

  6. 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...

  7. Automated SEM-EDS GSR Analysis for Turkish Ammunitions

    International Nuclear Information System (INIS)

    In this work, Automated Scanning Electron Microscopy with Energy Dispersive X-ray Spectrometry (SEM-EDS) was used to characterize 7.65 and 9mm cartridges Turkish ammunition. All samples were analyzed in a SEM Jeol JSM-5600LV equipped BSE detector and a Link ISIS 300 (EDS). A working distance of 20mm, an accelerating voltage of 20 keV and gunshot residue software was used in all analysis. Automated search resulted in a high number of particles analyzed containing gunshot residues (GSR) unique elements (PbBaSb). The obtained data about the definition of characteristic GSR particles was concordant with other studies on this topic

  8. Automated procedure for performing computer security risk analysis

    International Nuclear Information System (INIS)

    Computers, the invisible backbone of nuclear safeguards, monitor and control plant operations and support many materials accounting systems. Our automated procedure to assess computer security effectiveness differs from traditional risk analysis methods. The system is modeled as an interactive questionnaire, fully automated on a portable microcomputer. A set of modular event trees links the questionnaire to the risk assessment. Qualitative scores are obtained for target vulnerability, and qualitative impact measures are evaluated for a spectrum of threat-target pairs. These are then combined by a linguistic algebra to provide an accurate and meaningful risk measure. 12 references, 7 figures

  9. 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.

  10. 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.

  11. Reproducibility of In Vivo Corneal Confocal Microscopy Using an Automated Analysis Program for Detection of Diabetic Sensorimotor Polyneuropathy.

    Directory of Open Access Journals (Sweden)

    Ilia Ostrovski

    Full Text Available In vivo Corneal Confocal Microscopy (IVCCM is a validated, non-invasive test for diabetic sensorimotor polyneuropathy (DSP detection, but its utility is limited by the image analysis time and expertise required. We aimed to determine the inter- and intra-observer reproducibility of a novel automated analysis program compared to manual analysis.In a cross-sectional diagnostic study, 20 non-diabetes controls (mean age 41.4±17.3y, HbA1c 5.5±0.4% and 26 participants with type 1 diabetes (42.8±16.9y, 8.0±1.9% underwent two separate IVCCM examinations by one observer and a third by an independent observer. Along with nerve density and branch density, corneal nerve fibre length (CNFL was obtained by manual analysis (CNFLMANUAL, a protocol in which images were manually selected for automated analysis (CNFLSEMI-AUTOMATED, and one in which selection and analysis were performed electronically (CNFLFULLY-AUTOMATED. Reproducibility of each protocol was determined using intraclass correlation coefficients (ICC and, as a secondary objective, the method of Bland and Altman was used to explore agreement between protocols.Mean CNFLManual was 16.7±4.0, 13.9±4.2 mm/mm2 for non-diabetes controls and diabetes participants, while CNFLSemi-Automated was 10.2±3.3, 8.6±3.0 mm/mm2 and CNFLFully-Automated was 12.5±2.8, 10.9 ± 2.9 mm/mm2. Inter-observer ICC and 95% confidence intervals (95%CI were 0.73(0.56, 0.84, 0.75(0.59, 0.85, and 0.78(0.63, 0.87, respectively (p = NS for all comparisons. Intra-observer ICC and 95%CI were 0.72(0.55, 0.83, 0.74(0.57, 0.85, and 0.84(0.73, 0.91, respectively (p<0.05 for CNFLFully-Automated compared to others. The other IVCCM parameters had substantially lower ICC compared to those for CNFL. CNFLSemi-Automated and CNFLFully-Automated underestimated CNFLManual by mean and 95%CI of 35.1(-4.5, 67.5% and 21.0(-21.6, 46.1%, respectively.Despite an apparent measurement (underestimation bias in comparison to the manual strategy of image

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. Applications Of Binary Image Analysis Techniques

    Science.gov (United States)

    Tropf, H.; Enderle, E.; Kammerer, H. P.

    1983-10-01

    After discussing the conditions where binary image analysis techniques can be used, three new applications of the fast binary image analysis system S.A.M. (Sensorsystem for Automation and Measurement) are reported: (1) The human view direction is measured at TV frame rate while the subject's head is free movable. (2) Industrial parts hanging on a moving conveyor are classified prior to spray painting by robot. (3) In automotive wheel assembly, the eccentricity of the wheel is minimized by turning the tyre relative to the rim in order to balance the eccentricity of the components.

  17. 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.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

    An algorithm for image matching of multi-sensor and multi-temporal satellite images is developed. The method is based on the SIFT feature detector proposed by Lowe in (Lowe, 1999). First, SIFT feature points are detected independently in two images (reference and sensed image). The features detec...... of each feature set for each image are computed. The isomorphism of the Delaunay triangulations is determined to guarantee the quality of the image matching. The algorithm is implemented in Matlab and tested on World-View 2, SPOT6 and TerraSAR-X image patches....

  19. 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 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.

  20. 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

  1. Fully automated apparatus for the proximate analysis of coals

    Energy Technology Data Exchange (ETDEWEB)

    Fukumoto, K.; Ishibashi, Y.; Ishii, T.; Maeda, K.; Ogawa, A.; Gotoh, K.

    1985-01-01

    The authors report the development of fully-automated equipment for the proximate analysis of coals, a development undertaken with the twin aims of labour-saving and developing robot applications technology. This system comprises a balance, electric furnaces, a sulfur analyzer, etc., arranged concentrically around a multi-jointed robot which automatically performs all the necessary operations, such as sampling and weighing the materials for analysis, and inserting and removing them from the furnaces. 2 references.

  2. Automated haematology analysis to diagnose malaria

    Directory of Open Access Journals (Sweden)

    Grobusch Martin P

    2010-11-01

    Full Text Available Abstract For more than a decade, flow cytometry-based automated haematology analysers have been studied for malaria diagnosis. Although current haematology analysers are not specifically designed to detect malaria-related abnormalities, most studies have found sensitivities that comply with WHO malaria-diagnostic guidelines, i.e. ≥ 95% in samples with > 100 parasites/μl. Establishing a correct and early malaria diagnosis is a prerequisite for an adequate treatment and to minimizing adverse outcomes. Expert light microscopy remains the 'gold standard' for malaria diagnosis in most clinical settings. However, it requires an explicit request from clinicians and has variable accuracy. Malaria diagnosis with flow cytometry-based haematology analysers could become an important adjuvant diagnostic tool in the routine laboratory work-up of febrile patients in or returning from malaria-endemic regions. Haematology analysers so far studied for malaria diagnosis are the Cell-Dyn®, Coulter® GEN·S and LH 750, and the Sysmex XE-2100® analysers. For Cell-Dyn analysers, abnormal depolarization events mainly in the lobularity/granularity and other scatter-plots, and various reticulocyte abnormalities have shown overall sensitivities and specificities of 49% to 97% and 61% to 100%, respectively. For the Coulter analysers, a 'malaria factor' using the monocyte and lymphocyte size standard deviations obtained by impedance detection has shown overall sensitivities and specificities of 82% to 98% and 72% to 94%, respectively. For the XE-2100, abnormal patterns in the DIFF, WBC/BASO, and RET-EXT scatter-plots, and pseudoeosinophilia and other abnormal haematological variables have been described, and multivariate diagnostic models have been designed with overall sensitivities and specificities of 86% to 97% and 81% to 98%, respectively. The accuracy for malaria diagnosis may vary according to species, parasite load, immunity and clinical context where the

  3. An Automated Video Object Extraction System Based on Spatiotemporal Independent Component Analysis and Multiscale Segmentation

    Directory of Open Access Journals (Sweden)

    Zhang Xiao-Ping

    2006-01-01

    Full Text Available Video content analysis is essential for efficient and intelligent utilizations of vast multimedia databases over the Internet. In video sequences, object-based extraction techniques are important for content-based video processing in many applications. In this paper, a novel technique is developed to extract objects from video sequences based on spatiotemporal independent component analysis (stICA and multiscale analysis. The stICA is used to extract the preliminary source images containing moving objects in video sequences. The source image data obtained after stICA analysis are further processed using wavelet-based multiscale image segmentation and region detection techniques to improve the accuracy of the extracted object. An automated video object extraction system is developed based on these new techniques. Preliminary results demonstrate great potential for the new stICA and multiscale-segmentation-based object extraction system in content-based video processing applications.

  4. CRITICAL ASSESSMENT OF AUTOMATED FLOW CYTOMETRY DATA ANALYSIS TECHNIQUES

    Science.gov (United States)

    Aghaeepour, Nima; Finak, Greg; Hoos, Holger; Mosmann, Tim R.; Gottardo, Raphael; Brinkman, Ryan; Scheuermann, Richard H.

    2013-01-01

    Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks – mammalian cell population identification to determine if automated algorithms can reproduce expert manual gating, and sample classification to determine if analysis pipelines can identify characteristics that correlate with external variables (e.g., clinical outcome). This analysis presents the results of the first of these challenges. Several methods performed well compared to manual gating or external variables using statistical performance measures, suggesting that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis. PMID:23396282

  5. Towards Automated Design, Analysis and Optimization of Declarative Curation Workflows

    Directory of Open Access Journals (Sweden)

    Tianhong Song

    2014-10-01

    Full Text Available Data curation is increasingly important. Our previous work on a Kepler curation package has demonstrated advantages that come from automating data curation pipelines by using workflow systems. However, manually designed curation workflows can be error-prone and inefficient due to a lack of user understanding of the workflow system, misuse of actors, or human error. Correcting problematic workflows is often very time-consuming. A more proactive workflow system can help users avoid such pitfalls. For example, static analysis before execution can be used to detect the potential problems in a workflow and help the user to improve workflow design. In this paper, we propose a declarative workflow approach that supports semi-automated workflow design, analysis and optimization. We show how the workflow design engine helps users to construct data curation workflows, how the workflow analysis engine detects different design problems of workflows and how workflows can be optimized by exploiting parallelism.

  6. Microscopic images dataset for automation of RBCs counting.

    Science.gov (United States)

    Abbas, Sherif

    2015-12-01

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

  7. 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...

  8. 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.)

  9. 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.)

  10. Automating with ROBOCOM. An expert system for complex engineering analysis

    International Nuclear Information System (INIS)

    Nuclear engineering analysis is automated with the help of preprocessors and postprocessors. All the analysis and processing steps are recorded in a form that is reportable and replayable. These recordings serve both as documentations and as robots, for they are capable of performing the analyses they document. Since the processors and robots in ROBOCOM interface the users in a way independent of the analysis program being used, it is now possible to unify input modeling for programs with similar functionality. ROBOCOM will eventually evolve into an encyclopedia of how every nuclear engineering analysis is performed

  11. 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)

  12. The automation of analysis of technological process effectiveness

    Directory of Open Access Journals (Sweden)

    B. Krupińska

    2007-10-01

    Full Text Available Purpose: Improvement of technological processes by the use of technological efficiency analysis can create basis of their optimization. Informatization and computerization of wider and wider scope of activity is one of the most important current development trends of an enterprise.Design/methodology/approach: Indicators appointment makes it possible to evaluate the process efficiency, which can constitute an optimization basis of particular operation. Model of technological efficiency analysis is based on particular efficiency indicators that characterize operation, taking into account following criteria: operation – material, operation – machine, operation – human, operation – technological parameters.Findings: From the qualitative and correctness of choose of technology point of view comprehensive technological processes assessment makes up the basis of technological efficiency analysis. Results of technological efficiency analysis of technological process of prove that the chosen model of technological efficiency analysis makes it possible to improve the process continuously by the technological analysis, and application of computer assistance makes it possible to automate the process of efficiency analysis, and finally controlled improvement of technological processes.Practical implications: For the sake of complexity of technological efficiency analysis one has created an AEPT computer analysis from which result: operation efficiency indicators with distinguished indicators with minimal acceptable values, values of efficiency of the applied samples, value of technological process efficiency.Originality/value: The created computer analysis of ef technological process efficiency (AEPT makes it possible to automate the process of analysis and optimization.

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

    Science.gov (United States)

    Mouton, Andre; Breckon, Toby P

    2015-01-01

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

  14. Tank Farm Operations Surveillance Automation Analysis

    Energy Technology Data Exchange (ETDEWEB)

    MARQUEZ, D.L.

    2000-12-21

    The Nuclear Operations Project Services identified the need to improve manual tank farm surveillance data collection, review, distribution and storage practices often referred to as Operator Rounds. This document provides the analysis in terms of feasibility to improve the manual data collection methods by using handheld computer units, barcode technology, a database for storage and acquisitions, associated software, and operational procedures to increase the efficiency of Operator Rounds associated with surveillance activities.

  15. Tank Farm Operations Surveillance Automation Analysis

    International Nuclear Information System (INIS)

    The Nuclear Operations Project Services identified the need to improve manual tank farm surveillance data collection, review, distribution and storage practices often referred to as Operator Rounds. This document provides the analysis in terms of feasibility to improve the manual data collection methods by using handheld computer units, barcode technology, a database for storage and acquisitions, associated software, and operational procedures to increase the efficiency of Operator Rounds associated with surveillance activities

  16. Micro photometer's automation for quantitative spectrograph analysis

    International Nuclear Information System (INIS)

    A Microphotometer is used to increase the sharpness of dark spectral lines. Analyzing these lines one sample content and its concentration could be determined and the analysis is known as Quantitative Spectrographic Analysis. The Quantitative Spectrographic Analysis is carried out in 3 steps, as follows. 1. Emulsion calibration. This consists of gauging a photographic emulsion, to determine the intensity variations in terms of the incident radiation. For the procedure of emulsion calibration an adjustment with square minimum to the data obtained is applied to obtain a graph. It is possible to determine the density of dark spectral line against the incident light intensity shown by the microphotometer. 2. Working curves. The values of known concentration of an element against incident light intensity are plotted. Since the sample contains several elements, it is necessary to find a work curve for each one of them. 3. Analytical results. The calibration curve and working curves are compared and the concentration of the studied element is determined. The automatic data acquisition, calculation and obtaining of resulting, is done by means of a computer (PC) and a computer program. The conditioning signal circuits have the function of delivering TTL levels (Transistor Transistor Logic) to make the communication between the microphotometer and the computer possible. Data calculation is done using a computer programm

  17. 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

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

    DEFF Research Database (Denmark)

    Carl, Jesper; Nielsen, Henning; Nielsen, Jane;

    2006-01-01

    of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is based on the Ni-Ti stent having a cylindrical shape with a fixed diameter, which was used as the basis for an automated detection...... algorithm. The automated method uses enhancement of lines combined with a grayscale morphology operation that looks for enhanced pixels separated with a distance similar to the diameter of the stent. The images in this study are all from prostate cancer patients treated with radiotherapy in a previous study....... Images of a stent inserted in a humanoid phantom demonstrated a localization accuracy of 0.4-0.7  mm which equals the pixel size in the image. The automated detection of the stent was compared to manual detection in 71 pairs of orthogonal images taken in nine patients. The algorithm was successful in 67...

  19. Automated Asteroseismic Analysis of Solar-type Stars

    DEFF Research Database (Denmark)

    Karoff, Christoffer; Campante, T.L.; Chaplin, W.J.

    2010-01-01

    , radius, luminosity, effective temperature, surface gravity and age based on grid modeling. All the tools take into account the window function of the observations which means that they work equally well for space-based photometry observations from e.g. the NASA Kepler satellite and ground-based velocity......The rapidly increasing volume of asteroseismic observations on solar-type stars has revealed a need for automated analysis tools. The reason for this is not only that individual analyses of single stars are rather time consuming, but more importantly that these large volumes of observations open...... are calculated in a consistent way. Here we present a set of automated asterosesimic analysis tools. The main engine of these set of tools is an algorithm for modelling the autocovariance spectra of the stellar acoustic spectra allowing us to measure not only the frequency of maximum power and the large...

  20. RFI detection by automated feature extraction and statistical analysis

    OpenAIRE

    Winkel, Benjamin; Kerp, Juergen; Stanko, Stephan

    2006-01-01

    In this paper we present an interference detection toolbox consisting of a high dynamic range Digital Fast-Fourier-Transform spectrometer (DFFT, based on FPGA-technology) and data analysis software for automated radio frequency interference (RFI) detection. The DFFT spectrometer allows high speed data storage of spectra on time scales of less than a second. The high dynamic range of the device assures constant calibration even during extremely powerful RFI events. The software uses an algorit...

  1. A Method of Automated Nonparametric Content Analysis for Social Science

    OpenAIRE

    Hopkins, Daniel J.; King, Gary

    2010-01-01

    The increasing availability of digitized text presents enormous opportunities for social scientists. Yet hand coding many blogs, speeches, government records, newspapers, or other sources of unstructured text is infeasible. Although computer scientists have methods for automated content analysis, most are optimized to classify individual documents, whereas social scientists instead want generalizations about the population of documents, such as the proportion in a given category. Unfortunatel...

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  4. An optimized method for automated analysis of algal pigments by HPLC

    NARCIS (Netherlands)

    van Leeuwe, M. A.; Villerius, L. A.; Roggeveld, J.; Visser, R. J. W.; Stefels, J.

    2006-01-01

    A recent development in algal pigment analysis by high-performance liquid chromatography (HPLC) is the application of automation. An optimization of a complete sampling and analysis protocol applied specifically in automation has not yet been performed. In this paper we show that automation can only

  5. Comparison of the automated evaluation of phantom mama in digital and digitalized images; Comparacao da avaliacao automatizada do phantom mama em imagens digitais e digitalizadas

    Energy Technology Data Exchange (ETDEWEB)

    Santana, Priscila do Carmo, E-mail: pcs@cdtn.b [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Dept. de Engenharia Nuclear. Programa de Pos-Graduacao em Ciencias e Tecnicas Nucleares; Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Fac. de Medicina. Dept. de Propedeutica Complementar; Gomes, Danielle Soares; Oliveira, Marcio Alves; Nogueira, Maria do Socorro, E-mail: mnogue@cdtn.b [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

    2011-07-01

    Mammography is an essential tool for diagnosis and early detection of breast cancer if it is provided as a very good quality service. The process of evaluating the quality of radiographic images in general, and mammography in particular, can be much more accurate, practical and fast with the help of computer analysis tools. This work compare the automated methodology for the evaluation of scanned digital images the phantom mama. By applied the DIP method techniques was possible determine geometrical and radiometric images evaluated. The evaluated parameters include circular details of low contrast, contrast ratio, spatial resolution, tumor masses, optical density and background in Phantom Mama scanned and digitized images. The both results of images were evaluated. Through this comparison was possible to demonstrate that this automated methodology is presented as a promising alternative for the reduction or elimination of subjectivity in both types of images, but the Phantom Mama present insufficient parameters for spatial resolution evaluation. (author)

  6. 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

  7. Prevalence of discordant microscopic changes with automated CBC analysis

    Directory of Open Access Journals (Sweden)

    Fabiano de Jesus Santos

    2014-12-01

    Full Text Available Introduction:The most common cause of diagnostic error is related to errors in laboratory tests as well as errors of results interpretation. In order to reduce them, the laboratory currently has modern equipment which provides accurate and reliable results. The development of automation has revolutionized the laboratory procedures in Brazil and worldwide.Objective:To determine the prevalence of microscopic changes present in blood slides concordant and discordant with results obtained using fully automated procedures.Materials and method:From January to July 2013, 1,000 hematological parameters slides were analyzed. Automated analysis was performed on last generation equipment, which methodology is based on electrical impedance, and is able to quantify all the figurative elements of the blood in a universe of 22 parameters. The microscopy was performed by two experts in microscopy simultaneously.Results:The data showed that only 42.70% were concordant, comparing with 57.30% discordant. The main findings among discordant were: Changes in red blood cells 43.70% (n = 250, white blood cells 38.46% (n = 220, and number of platelet 17.80% (n = 102.Discussion:The data show that some results are not consistent with clinical or physiological state of an individual, and cannot be explained because they have not been investigated, which may compromise the final diagnosis.Conclusion:It was observed that it is of fundamental importance that the microscopy qualitative analysis must be performed in parallel with automated analysis in order to obtain reliable results, causing a positive impact on the prevention, diagnosis, prognosis, and therapeutic follow-up.

  8. Some selected quantitative methods of thermal image analysis in Matlab.

    Science.gov (United States)

    Koprowski, Robert

    2016-05-01

    The paper presents a new algorithm based on some selected automatic quantitative methods for analysing thermal images. It shows the practical implementation of these image analysis methods in Matlab. It enables to perform fully automated and reproducible measurements of selected parameters in thermal images. The paper also shows two examples of the use of the proposed image analysis methods for the area of ​​the skin of a human foot and face. The full source code of the developed application is also provided as an attachment. The main window of the program during dynamic analysis of the foot thermal image.

  9. 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 ($\

  10. 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

  11. 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

  12. 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

  13. Digital image analysis

    DEFF Research Database (Denmark)

    Riber-Hansen, Rikke; Vainer, Ben; Steiniche, Torben

    2012-01-01

    Digital image analysis (DIA) is increasingly implemented in histopathological research to facilitate truly quantitative measurements, decrease inter-observer variation and reduce hands-on time. Originally, efforts were made to enable DIA to reproduce manually obtained results on histological slides...... reproducibility, application of stereology-based quantitative measurements, time consumption, optimization of histological slides, regions of interest selection and recent developments in staining and imaging techniques....

  14. Automation of Large-scale Computer Cluster Monitoring Information Analysis

    Science.gov (United States)

    Magradze, Erekle; Nadal, Jordi; Quadt, Arnulf; Kawamura, Gen; Musheghyan, Haykuhi

    2015-12-01

    High-throughput computing platforms consist of a complex infrastructure and provide a number of services apt to failures. To mitigate the impact of failures on the quality of the provided services, a constant monitoring and in time reaction is required, which is impossible without automation of the system administration processes. This paper introduces a way of automation of the process of monitoring information analysis to provide the long and short term predictions of the service response time (SRT) for a mass storage and batch systems and to identify the status of a service at a given time. The approach for the SRT predictions is based on Adaptive Neuro Fuzzy Inference System (ANFIS). An evaluation of the approaches is performed on real monitoring data from the WLCG Tier 2 center GoeGrid. Ten fold cross validation results demonstrate high efficiency of both approaches in comparison to known methods.

  15. 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

  16. 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.

  17. ASTRiDE: Automated Streak Detection for Astronomical Images

    Science.gov (United States)

    Kim, Dae-Won

    2016-05-01

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

  18. 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...

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

    Science.gov (United States)

    Joutsijoki, Henry; Haponen, Markus; Rasku, Jyrki; Aalto-Setälä, Katriina; Juhola, Martti

    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

  20. 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

  1. 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.

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

    Science.gov (United States)

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

    2006-01-01

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

  3. Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling.

    Directory of Open Access Journals (Sweden)

    Daniel P Riordan

    Full Text Available Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the

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

    Institute of Scientific and Technical Information of China (English)

    DONG Hai-yan; WANG Hui-nan

    2006-01-01

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

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

    Science.gov (United States)

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

    2014-03-01

    The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non-invasive determination of these characteristics. We present an image-processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source-code for MATLAB and ImageJ is freely available under a permissive open-source license. PMID:24037521

  6. 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.

  7. Using historical wafermap data for automated yield analysis

    International Nuclear Information System (INIS)

    To be productive and profitable in a modern semiconductor fabrication environment, large amounts of manufacturing data must be collected, analyzed, and maintained. This includes data collected from in- and off-line wafer inspection systems and from the process equipment itself. This data is increasingly being used to design new processes, control and maintain tools, and to provide the information needed for rapid yield learning and prediction. Because of increasing device complexity, the amount of data being generated is outstripping the yield engineer close-quote s ability to effectively monitor and correct unexpected trends and excursions. The 1997 SIA National Technology Roadmap for Semiconductors highlights a need to address these issues through open-quotes automated data reduction algorithms to source defects from multiple data sources and to reduce defect sourcing time.close quotes SEMATECH and the Oak Ridge National Laboratory have been developing new strategies and technologies for providing the yield engineer with higher levels of assisted data reduction for the purpose of automated yield analysis. In this article, we will discuss the current state of the art and trends in yield management automation. copyright 1999 American Vacuum Society

  8. 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.

  9. 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.

  10. Cost Analysis of an Automated and Manual Cataloging and Book Processing System.

    Science.gov (United States)

    Druschel, Joselyn

    1981-01-01

    Cost analysis of an automated network system and a manual system of cataloging and book processing indicates a 20 percent savings using automation. Per unit costs based on the average monthly automation rate are used for comparison. Higher manual system costs are attributed to staff costs. (RAA)

  11. Basic research planning in mathematical pattern recognition and image analysis

    Science.gov (United States)

    Bryant, J.; Guseman, L. F., Jr.

    1981-01-01

    Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis.

  12. A new web-based method for automated analysis of muscle histology

    Directory of Open Access Journals (Sweden)

    Pertl Cordula

    2013-01-01

    Full Text Available Abstract Background Duchenne Muscular Dystrophy is an inherited degenerative neuromuscular disease characterised by rapidly progressive muscle weakness. Currently, curative treatment is not available. Approaches for new treatments that improve muscle strength and quality of life depend on preclinical testing in animal models. The mdx mouse model is the most frequently used animal model for preclinical studies in muscular dystrophy research. Standardised pathology-relevant parameters of dystrophic muscle in mdx mice for histological analysis have been developed in international, collaborative efforts, but automation has not been accessible to most research groups. A standardised and mainly automated quantitative assessment of histopathological parameters in the mdx mouse model is desirable to allow an objective comparison between laboratories. Methods Immunological and histochemical reactions were used to obtain a double staining for fast and slow myosin. Additionally, fluorescence staining of the myofibre membranes allows defining the minimal Feret’s diameter. The staining of myonuclei with the fluorescence dye bisbenzimide H was utilised to identify nuclei located internally within myofibres. Relevant structures were extracted from the image as single objects and assigned to different object classes using web-based image analysis (MyoScan. Quantitative and morphometric data were analysed, e.g. the number of nuclei per fibre and minimal Feret’s diameter in 6 month old wild-type C57BL/10 mice and mdx mice. Results In the current version of the module “MyoScan”, essential parameters for histologic analysis of muscle sections were implemented including the minimal Feret’s diameter of the myofibres and the automated calculation of the percentage of internally nucleated myofibres. Morphometric data obtained in the present study were in good agreement with previously reported data in the literature and with data obtained from manual

  13. Hyperspectral image analysis. A tutorial

    DEFF Research Database (Denmark)

    Amigo Rubio, Jose Manuel; Babamoradi, Hamid; Elcoroaristizabal Martin, Saioa

    2015-01-01

    This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processi...... to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares - Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case....

  14. Computer-assisted tree taxonomy by automated image recognition

    NARCIS (Netherlands)

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

    2009-01-01

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

  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

    Time gain compensation (TGC) is essential to ensure the optimal image quality of the clinical ultrasound scans. When large fluid collections are present within the scan plane, the attenuation distribution is changed drastically and TGC compensation becomes challenging. This paper presents...

  16. 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.

  17. 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.

  18. 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.

  19. 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...

  20. Solar Image Analysis and Visualization

    CERN Document Server

    Ireland, J

    2009-01-01

    This volume presents a selection of papers on the state of the art of image enhancement, automated feature detection, machine learning, and visualization tools in support of solar physics that focus on the challenges presented by new ground-based and space-based instrumentation. The articles and topics were inspired by the Third Solar Image Processing Workshop, held at Trinity College Dublin, Ireland but contributions from other experts have been included as well. This book is mainly aimed at researchers and graduate students working on image processing and compter vision in astronomy and solar physics.

  1. 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)

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

    DEFF Research Database (Denmark)

    Gutte, Henrik; Jakobsson, David; Olofsson, Fredrik;

    2007-01-01

    cancer. METHODS: A total of 87 patients who underwent PET/CT examinations due to suspected lung cancer comprised the training group. The test group consisted of PET/CT images from 49 patients suspected with lung cancer. The consensus interpretations by two experienced physicians were used as the 'gold...... for localization of lesions in the PET images in the feature extraction process. Eight features from each examination were used as inputs to artificial neural networks trained to classify the images. Thereafter, the performance of the network was evaluated in the test set. RESULTS: The performance of the automated...

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

    DEFF Research Database (Denmark)

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

    2001-01-01

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

  4. 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.

  5. Analysis and simulation of a torque assist automated manual transmission

    Science.gov (United States)

    Galvagno, E.; Velardocchia, M.; Vigliani, A.

    2011-08-01

    The paper presents the kinematic and dynamic analysis of a power-shift automated manual transmission (AMT) characterised by a wet clutch, called assist clutch (ACL), replacing the fifth gear synchroniser. This torque assist mechanism becomes a torque transfer path during gearshifts, in order to overcome a typical dynamic problem of the AMTs, that is the driving force interruption. The mean power contributions during gearshifts are computed for different engine and ACL interventions, thus allowing to draw considerations useful for developing the control algorithms. The simulation results prove the advantages in terms of gearshift quality and ride comfort of the analysed transmission.

  6. A Robust Actin Filaments Image Analysis Framework.

    Science.gov (United States)

    Alioscha-Perez, Mitchel; Benadiba, Carine; Goossens, Katty; Kasas, Sandor; Dietler, Giovanni; Willaert, Ronnie; Sahli, Hichem

    2016-08-01

    The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a 'cartoon' part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the 'cartoon' image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts grown in

  7. A Robust Actin Filaments Image Analysis Framework

    Science.gov (United States)

    Alioscha-Perez, Mitchel; Benadiba, Carine; Goossens, Katty; Kasas, Sandor; Dietler, Giovanni; Willaert, Ronnie; Sahli, Hichem

    2016-01-01

    The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a ‘cartoon’ part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the ‘cartoon’ image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts

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

    Science.gov (United States)

    Strom, K. B.

    2009-12-01

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

  9. A Multi-Wavelength Analysis of Active Regions and Sunspots by Comparison of Automated Detection Algorithms

    CERN Document Server

    Verbeeck, Cis; Colak, Tufan; Watson, Fraser T; Delouille, Veronique; Mampaey, Benjamin; Qahwaji, Rami

    2011-01-01

    Since the Solar Dynamics Observatory (SDO) began recording ~ 1 TB of data per day, there has been an increased need to automatically extract features and events for further analysis. Here we compare the overall detection performance, correlations between extracted properties, and usability for feature tracking of four solar feature-detection algorithms: the Solar Monitor Active Region Tracker (SMART) detects active regions in line-of-sight magnetograms; the Automated Solar Activity Prediction code (ASAP) detects sunspots and pores in white-light continuum images; the Sunspot Tracking And Recognition Algorithm (STARA) detects sunspots in white-light continuum images; the Spatial Possibilistic Clustering Algorithm (SPoCA) automatically segments solar EUV images into active regions (AR), coronal holes (CH) and quiet Sun (QS). One month of data from the SOHO/MDI and SOHO/EIT instruments during 12 May - 23 June 2003 is analysed. The overall detection performance of each algorithm is benchmarked against National Oc...

  10. 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.

  11. Spreadsheet-like image analysis

    Science.gov (United States)

    Wilson, Paul

    1992-08-01

    This report describes the design of a new software system being built by the Army to support and augment automated nondestructive inspection (NDI) on-line equipment implemented by the Army for detection of defective manufactured items. The new system recalls and post-processes (off-line) the NDI data sets archived by the on-line equipment for the purpose of verifying the correctness of the inspection analysis paradigms, of developing better analysis paradigms and to gather statistics on the defects of the items inspected. The design of the system is similar to that of a spreadsheet, i.e., an array of cells which may be programmed to contain functions with arguments being data from other cells and whose resultant is the output of that cell's function. Unlike a spreadsheet, the arguments and the resultants of a cell may be a matrix such as a two-dimensional matrix of picture elements (pixels). Functions include matrix mathematics, neural networks and image processing as well as those ordinarily found in spreadsheets. The system employs all of the common environmental supports of the Macintosh computer, which is the hardware platform. The system allows the resultant of a cell to be displayed in any of multiple formats such as a matrix of numbers, text, an image, or a chart. Each cell is a window onto the resultant. Like a spreadsheet if the input value of any cell is changed its effect is cascaded into the resultants of all cells whose functions use that value directly or indirectly. The system encourages the user to play what-of games, as ordinary spreadsheets do.

  12. IHC Profiler: An Open Source Plugin for the Quantitative Evaluation and Automated Scoring of Immunohistochemistry Images of Human Tissue Samples

    Science.gov (United States)

    Malhotra, Renu; De, Abhijit

    2014-01-01

    In anatomic pathology, immunohistochemistry (IHC) serves as a diagnostic and prognostic method for identification of disease markers in tissue samples that directly influences classification and grading the disease, influencing patient management. However, till today over most of the world, pathological analysis of tissue samples remained a time-consuming and subjective procedure, wherein the intensity of antibody staining is manually judged and thus scoring decision is directly influenced by visual bias. This instigated us to design a simple method of automated digital IHC image analysis algorithm for an unbiased, quantitative assessment of antibody staining intensity in tissue sections. As a first step, we adopted the spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. Then the DAB stained image is displayed in a new window wherein it undergoes pixel-by-pixel analysis, and displays the full profile along with its scoring decision. Based on the mathematical formula conceptualized, the algorithm is thoroughly tested by analyzing scores assigned to thousands (n = 1703) of DAB stained IHC images including sample images taken from human protein atlas web resource. The IHC Profiler plugin developed is compatible with the open resource digital image analysis software, ImageJ, which creates a pixel-by-pixel analysis profile of a digital IHC image and further assigns a score in a four tier system. A comparison study between manual pathological analysis and IHC Profiler resolved in a match of 88.6% (P<0.0001, CI = 95%). This new tool developed for clinical histopathological sample analysis can be adopted globally for scoring most protein targets where the marker protein expression is of cytoplasmic and/or nuclear type. We foresee that this method will minimize the problem of inter-observer variations across labs and further help in

  13. 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

  14. Analysis of automated highway system risks and uncertainties. Volume 5

    Energy Technology Data Exchange (ETDEWEB)

    Sicherman, A.

    1994-10-01

    This volume describes a risk analysis performed to help identify important Automated Highway System (AHS) deployment uncertainties and quantify their effect on costs and benefits for a range of AHS deployment scenarios. The analysis identified a suite of key factors affecting vehicle and roadway costs, capacities and market penetrations for alternative AHS deployment scenarios. A systematic protocol was utilized for obtaining expert judgments of key factor uncertainties in the form of subjective probability percentile assessments. Based on these assessments, probability distributions on vehicle and roadway costs, capacity and market penetration were developed for the different scenarios. The cost/benefit risk methodology and analysis provide insights by showing how uncertainties in key factors translate into uncertainties in summary cost/benefit indices.

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

    Science.gov (United States)

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

    1998-03-01

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

  16. Trends in biomedical informatics: automated topic analysis of JAMIA articles.

    Science.gov (United States)

    Han, Dong; Wang, Shuang; Jiang, Chao; Jiang, Xiaoqian; Kim, Hyeon-Eui; Sun, Jimeng; Ohno-Machado, Lucila

    2015-11-01

    Biomedical Informatics is a growing interdisciplinary field in which research topics and citation trends have been evolving rapidly in recent years. To analyze these data in a fast, reproducible manner, automation of certain processes is needed. JAMIA is a "generalist" journal for biomedical informatics. Its articles reflect the wide range of topics in informatics. In this study, we retrieved Medical Subject Headings (MeSH) terms and citations of JAMIA articles published between 2009 and 2014. We use tensors (i.e., multidimensional arrays) to represent the interaction among topics, time and citations, and applied tensor decomposition to automate the analysis. The trends represented by tensors were then carefully interpreted and the results were compared with previous findings based on manual topic analysis. A list of most cited JAMIA articles, their topics, and publication trends over recent years is presented. The analyses confirmed previous studies and showed that, from 2012 to 2014, the number of articles related to MeSH terms Methods, Organization & Administration, and Algorithms increased significantly both in number of publications and citations. Citation trends varied widely by topic, with Natural Language Processing having a large number of citations in particular years, and Medical Record Systems, Computerized remaining a very popular topic in all years.

  17. Automated Image-Based Procedures for Adaptive Radiotherapy

    DEFF Research Database (Denmark)

    Bjerre, Troels

    Fractionated radiotherapy for cancer treatment is a field of constant innovation. Developments in dose delivery techniques have made it possible to precisely direct ionizing radiation at complicated targets. In order to further increase tumour control probability (TCP) and decrease normal...... to encourage bone rigidity and local tissue volume change only in the gross tumour volume and the lungs. This is highly relevant in adaptive radiotherapy when modelling significant tumour volume changes. - It is described how cone beam CT reconstruction can be modelled as a deformation of a planning CT scan...... be employed for contour propagation in adaptive radiotherapy. - MRI-radiotherapy devices have the potential to offer near real-time intrafraction imaging without any additional ionising radiation. It is detailed how the use of multiple, orthogonal slices can form the basis for reliable 3D soft tissue tracking....

  18. Medical Image Analysis Facility

    Science.gov (United States)

    1978-01-01

    To improve the quality of photos sent to Earth by unmanned spacecraft. NASA's Jet Propulsion Laboratory (JPL) developed a computerized image enhancement process that brings out detail not visible in the basic photo. JPL is now applying this technology to biomedical research in its Medical lrnage Analysis Facility, which employs computer enhancement techniques to analyze x-ray films of internal organs, such as the heart and lung. A major objective is study of the effects of I stress on persons with heart disease. In animal tests, computerized image processing is being used to study coronary artery lesions and the degree to which they reduce arterial blood flow when stress is applied. The photos illustrate the enhancement process. The upper picture is an x-ray photo in which the artery (dotted line) is barely discernible; in the post-enhancement photo at right, the whole artery and the lesions along its wall are clearly visible. The Medical lrnage Analysis Facility offers a faster means of studying the effects of complex coronary lesions in humans, and the research now being conducted on animals is expected to have important application to diagnosis and treatment of human coronary disease. Other uses of the facility's image processing capability include analysis of muscle biopsy and pap smear specimens, and study of the microscopic structure of fibroprotein in the human lung. Working with JPL on experiments are NASA's Ames Research Center, the University of Southern California School of Medicine, and Rancho Los Amigos Hospital, Downey, California.

  19. 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

  20. 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.

  1. 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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-03-15

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

  3. 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.

  4. Radar image analysis utilizing junctive image metamorphosis

    Science.gov (United States)

    Krueger, Peter G.; Gouge, Sally B.; Gouge, Jim O.

    1998-09-01

    A feasibility study was initiated to investigate the ability of algorithms developed for medical sonogram image analysis, to be trained for extraction of cartographic information from synthetic aperture radar imagery. BioComputer Research Inc. has applied proprietary `junctive image metamorphosis' algorithms to cancer cell recognition and identification in ultrasound prostate images. These algorithms have been shown to support automatic radar image feature detection and identification. Training set images were used to develop determinants for representative point, line and area features, which were used on test images to identify and localize the features of interest. The software is computationally conservative; operating on a PC platform in real time. The algorithms are robust; having applicability to be trained for feature recognition on any digital imagery, not just those formed from reflected energy, such as sonograms and radar images. Applications include land mass characterization, feature identification, target recognition, and change detection.

  5. 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.

  6. Gabor Analysis for Imaging

    DEFF Research Database (Denmark)

    Christensen, Ole; Feichtinger, Hans G.; Paukner, Stephan

    2015-01-01

    In contrast to classical Fourier analysis, time–frequency analysis is concerned with localized Fourier transforms. Gabor analysis is an important branch of time–frequency analysis. Although significantly different, it shares with the wavelet transform methods the ability to describe the smoothness...... of a given function in a location-dependent way. The main tool is the sliding window Fourier transform or short-time Fourier transform (STFT) in the context of audio signals. It describes the correlation of a signal with the time–frequency shifted copies of a fixed function (or window or atom). Thus......, it characterizes a function by its transform over phase space, which is the time–frequency plane (TF-plane) in a musical context or the location–wave-number domain in the context of image processing. Since the transition from the signal domain to the phase space domain introduces an enormous amount of data...

  7. 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

  8. Automated drawing of network plots in network meta-analysis.

    Science.gov (United States)

    Rücker, Gerta; Schwarzer, Guido

    2016-03-01

    In systematic reviews based on network meta-analysis, the network structure should be visualized. Network plots often have been drawn by hand using generic graphical software. A typical way of drawing networks, also implemented in statistical software for network meta-analysis, is a circular representation, often with many crossing lines. We use methods from graph theory in order to generate network plots in an automated way. We give a number of requirements for graph drawing and present an algorithm that fits prespecified ideal distances between the nodes representing the treatments. The method was implemented in the function netgraph of the R package netmeta and applied to a number of networks from the literature. We show that graph representations with a small number of crossing lines are often preferable to circular representations. PMID:26060934

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

    Science.gov (United States)

    Wojciechowski, Wadim; Molka, Adrian; Tabor, Zbisław

    2016-03-01

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

  10. AutoGate: automating analysis of flow cytometry data.

    Science.gov (United States)

    Meehan, Stephen; Walther, Guenther; Moore, Wayne; Orlova, Darya; Meehan, Connor; Parks, David; Ghosn, Eliver; Philips, Megan; Mitsunaga, Erin; Waters, Jeffrey; Kantor, Aaron; Okamura, Ross; Owumi, Solomon; Yang, Yang; Herzenberg, Leonard A; Herzenberg, Leonore A

    2014-05-01

    Nowadays, one can hardly imagine biology and medicine without flow cytometry to measure CD4 T cell counts in HIV, follow bone marrow transplant patients, characterize leukemias, etc. Similarly, without flow cytometry, there would be a bleak future for stem cell deployment, HIV drug development and full characterization of the cells and cell interactions in the immune system. But while flow instruments have improved markedly, the development of automated tools for processing and analyzing flow data has lagged sorely behind. To address this deficit, we have developed automated flow analysis software technology, provisionally named AutoComp and AutoGate. AutoComp acquires sample and reagent labels from users or flow data files, and uses this information to complete the flow data compensation task. AutoGate replaces the manual subsetting capabilities provided by current analysis packages with newly defined statistical algorithms that automatically and accurately detect, display and delineate subsets in well-labeled and well-recognized formats (histograms, contour and dot plots). Users guide analyses by successively specifying axes (flow parameters) for data subset displays and selecting statistically defined subsets to be used for the next analysis round. Ultimately, this process generates analysis "trees" that can be applied to automatically guide analyses for similar samples. The first AutoComp/AutoGate version is currently in the hands of a small group of users at Stanford, Emory and NIH. When this "early adopter" phase is complete, the authors expect to distribute the software free of charge to .edu, .org and .gov users.

  11. Image sequence analysis

    CERN Document Server

    1981-01-01

    The processing of image sequences has a broad spectrum of important applica­ tions including target tracking, robot navigation, bandwidth compression of TV conferencing video signals, studying the motion of biological cells using microcinematography, cloud tracking, and highway traffic monitoring. Image sequence processing involves a large amount of data. However, because of the progress in computer, LSI, and VLSI technologies, we have now reached a stage when many useful processing tasks can be done in a reasonable amount of time. As a result, research and development activities in image sequence analysis have recently been growing at a rapid pace. An IEEE Computer Society Workshop on Computer Analysis of Time-Varying Imagery was held in Philadelphia, April 5-6, 1979. A related special issue of the IEEE Transactions on Pattern Anal­ ysis and Machine Intelligence was published in November 1980. The IEEE Com­ puter magazine has also published a special issue on the subject in 1981. The purpose of this book ...

  12. 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.

  13. 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. 

  14. Software fault tree analysis of an automated control system device written in Ada

    OpenAIRE

    Winter, Mathias William.

    1995-01-01

    Software Fault Tree Analysis (SFTA) is a technique used to analyze software for faults that could lead to hazardous conditions in systems which contain software components. Previous thesis works have developed three Ada-based, semi-automated software analysis tools, the Automated Code Translation Tool (ACm) an Ada statement template generator, the Fault Tree Editor (Fm) a graphical fault tree editor, and the Fault Isolator (Fl) an automated software fault tree isolator. These previous works d...

  15. Detection of DNA Aneuploidy in Exfoliated Airway Epithelia Cells of Sputum Specimens by the Automated Image Cytometry and Its Clinical Value in the Identification of Lung Cancer

    Institute of Scientific and Technical Information of China (English)

    杨健; 周宜开

    2004-01-01

    To evaluate the value of detecton of DNA aneuploidy in exfoliated airway epithelia cells of sputum specimens by the automated image cytometry for the identification of lung cancer, 100patients were divided into patient group (50 patients with lung cancer)and control group (30 patients with tuberculosis and 20 healthy people). Sputum was obtained for the quantitative analysis of DNA content of exfoliated airway epithelial cells with the automated image cytometry, together with the examinations of brush cytology and conventional sputum cytology. Our results showed that DNA aneuploidy (DI>2.5 or 5c) was found in 20 out of 50 sputum samples of lung cancer, 1 out of 30 sputum samples from tuberculosis patients, and none of 20 sputum samples from healthy people. The positive rates of conventional sputum cytology and brush cytology were 16 % and 32 %,which was lower than that of DNA aneuploidy detection by the automated image cytometry (P<0.01 ,P>0.05). Our study showed that automated image cytometry, which uses DNA aneuploidy as a marker for tumor, can detect the malignant cells in sputum samples of lung cancer and it is a sensitive and specific method serving as a complement for the diagnosis of lung cancer.

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

    Science.gov (United States)

    Miller, Linzey; Trier, Caroline; Ben-Zikri, Yehuda K.; Linte, Cristian A.

    2016-03-01

    Due to the digital X-ray imaging system's limited field of view, several individual sector images are required to capture the posture of an individual in standing position. These images are then "stitched together" to reconstruct the standing posture. We have created an image processing application that automates the stitching, therefore minimizing user input, optimizing workflow, and reducing human error. The application begins with pre-processing the input images by removing artifacts, filtering out isolated noisy regions, and amplifying a seamless bone edge. The resulting binary images are then registered together using a rigid-body intensity based registration algorithm. The identified registration transformations are then used to map the original sector images into the panorama image. Our method focuses primarily on the use of the anatomical content of the images to generate the panoramas as opposed to using external markers employed to aid with the alignment process. Currently, results show robust edge detection prior to registration and we have tested our approach by comparing the resulting automatically-stitched panoramas to the manually stitched panoramas in terms of registration parameters, target registration error of homologous markers, and the homogeneity of the digitally subtracted automatically- and manually-stitched images using 26 patient datasets.

  17. Image analysis software and sample preparation demands

    Science.gov (United States)

    Roth, Karl n.; Wenzelides, Knut; Wolf, Guenter; Hufnagl, Peter

    1990-11-01

    Image analysis offers the opportunity to analyse many processes in medicine, biology and engeneering in a quantitative manner. Experience shows that it is only by awareness of preparation methods and attention to software design that full benefit can be reaped from a picture processing system in the fields of cytology and histology. Some examples of special stains for automated analysis are given here and the effectiveness of commercially available software packages is investigated. The application of picture processing and development of related special hardware and software has been increasing within the last years. As PC-based picture processing systems can be purchased at reasonable costs more and more users are confronted with these problems. Experience shows that the quality of commercially available software packages differ and the requirements on the sample preparation needed for successful problem solutions are often underestimated. But as always, sample preparation is still the key to success in automated image analysis for cells and tissues. Hence, a problem solution requires the permanent interaction between sample preparation methods and algorithm development.

  18. 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

  19. 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.

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

    Science.gov (United States)

    Bhikha, Charita; Andreasen, Arne; Christensen, Erik I; Letts, Robyn F R; Pantanowitz, Adam; Rubin, David M; Thomsen, Jesper S; Zhai, Xiao-Yue

    2015-01-01

    An automated approach for tracking individual nephrons through three-dimensional histological image sets of mouse and rat kidneys is presented. In a previous study, the available images were tracked manually through the image sets in order to explore renal microarchitecture. The purpose of the current research is to reduce the time and effort required to manually trace nephrons by creating an automated, intelligent system as a standard tool for such datasets. The algorithm is robust enough to isolate closely packed nephrons and track their convoluted paths despite a number of nonideal, interfering conditions such as local image distortions, artefacts, and interstitial tissue interference. The system comprises image preprocessing, feature extraction, and a custom graph-based tracking algorithm, which is validated by a rule base and a machine learning algorithm. A study of a selection of automatically tracked nephrons, when compared with manual tracking, yields a 95% tracking accuracy for structures in the cortex, while those in the medulla have lower accuracy due to narrower diameter and higher density. Limited manual intervention is introduced to improve tracking, enabling full nephron paths to be obtained with an average of 17 manual corrections per mouse nephron and 58 manual corrections per rat nephron.

  1. 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.

  2. Malaria: the value of the automated depolarization analysis.

    Science.gov (United States)

    Josephine, F P; Nissapatorn, V

    2005-01-01

    This retrospective and descriptive study was carried out in the University of Malaya Medical Center (UMMC) from January to September, 2004. This study aimed to evaluate the diagnostic utility of the Cell-Dyn 4000 hematology analyzer's depolarization analysis and to determine the sensitivity and specificity of this technique in the context of malaria diagnosis. A total of 889 cases presenting with pyrexia of unknown origin or clinically suspected of malaria were examined. Sixteen of these blood samples were found to be positive; 12 for P. vivax, 3 for P. malariae, and 1 for P. falciparum by peripheral blood smear as the standard technique for parasite detection and species identification. Demographic characteristics showed that the majority of patients were in the age range of 20-57 with a mean of 35.9 (+/- SD) 11.4 years, and male foreign workers. Of these, 16 positive blood samples were also processed by Cell-Dyne 4000 analyzer in the normal complete blood count (CBC) operational mode. Malaria parasites produce hemozoin, which depolarizes light and this allows the automated detection of malaria during routine complete blood count analysis with the Abbot Cell-Dyn CD4000 instrument. The white blood cell (WBC) differential plots of all malaria positive samples showed abnormal depolarization events in the NEU-EOS and EOS I plots. This was not seen in the negative samples. In 12 patients with P. vivax infection, a cluster pattern in the Neu-EOS and EOS I plots was observed, and appeared color-coded green or black. In 3 patients with P. malariae infection, few random depolarization events in the NEU-EOS and EOS I plots were seen, and appeared color-coded green, black or blue. While in the patient with P. falciparum infection, the sample was color-coded green with a few random purple depolarizing events in the NEU-EOS and EOS I plots. This study confirms that automated depolarization analysis is a highly sensitive and specific method to diagnose whether or not a patient

  3. Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine

    Science.gov (United States)

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

    2010-01-01

    We propose a completely automated algorithm for the detection of the spinal centreline and the centres of vertebral bodies and intervertebral discs in images acquired by computed tomography (CT) and magnetic resonance (MR) imaging. The developed methods are based on the analysis of the geometry of spinal structures and the characteristics of CT and MR images and were evaluated on 29 CT and 13 MR images of lumbar spine. The overall mean distance between the obtained and the ground truth spinal centrelines and centres of vertebral bodies and intervertebral discs were 1.8 ± 1.1 mm and 2.8 ± 1.9 mm, respectively, and no considerable differences were detected among the results for CT, T1-weighted MR and T2-weighted MR images. The knowledge of the location of the spinal centreline and the centres of vertebral bodies and intervertebral discs is valuable for the analysis of the spine. The proposed method may therefore be used to initialize the techniques for labelling and segmentation of vertebrae.

  4. Automated quantitative gait analysis in animal models of movement disorders

    Directory of Open Access Journals (Sweden)

    Vandeputte Caroline

    2010-08-01

    Full Text Available Abstract Background Accurate and reproducible behavioral tests in animal models are of major importance in the development and evaluation of new therapies for central nervous system disease. In this study we investigated for the first time gait parameters of rat models for Parkinson's disease (PD, Huntington's disease (HD and stroke using the Catwalk method, a novel automated gait analysis test. Static and dynamic gait parameters were measured in all animal models, and these data were compared to readouts of established behavioral tests, such as the cylinder test in the PD and stroke rats and the rotarod tests for the HD group. Results Hemiparkinsonian rats were generated by unilateral injection of the neurotoxin 6-hydroxydopamine in the striatum or in the medial forebrain bundle. For Huntington's disease, a transgenic rat model expressing a truncated huntingtin fragment with multiple CAG repeats was used. Thirdly, a stroke model was generated by a photothrombotic induced infarct in the right sensorimotor cortex. We found that multiple gait parameters were significantly altered in all three disease models compared to their respective controls. Behavioural deficits could be efficiently measured using the cylinder test in the PD and stroke animals, and in the case of the PD model, the deficits in gait essentially confirmed results obtained by the cylinder test. However, in the HD model and the stroke model the Catwalk analysis proved more sensitive than the rotarod test and also added new and more detailed information on specific gait parameters. Conclusion The automated quantitative gait analysis test may be a useful tool to study both motor impairment and recovery associated with various neurological motor disorders.

  5. Curvelet based offline analysis of SEM images.

    Directory of Open Access Journals (Sweden)

    Syed Hamad Shirazi

    Full Text Available Manual offline analysis, of a scanning electron microscopy (SEM image, is a time consuming process and requires continuous human intervention and efforts. This paper presents an image processing based method for automated offline analyses of SEM images. To this end, our strategy relies on a two-stage process, viz. texture analysis and quantification. The method involves a preprocessing step, aimed at the noise removal, in order to avoid false edges. For texture analysis, the proposed method employs a state of the art Curvelet transform followed by segmentation through a combination of entropy filtering, thresholding and mathematical morphology (MM. The quantification is carried out by the application of a box-counting algorithm, for fractal dimension (FD calculations, with the ultimate goal of measuring the parameters, like surface area and perimeter. The perimeter is estimated indirectly by counting the boundary boxes of the filled shapes. The proposed method, when applied to a representative set of SEM images, not only showed better results in image segmentation but also exhibited a good accuracy in the calculation of surface area and perimeter. The proposed method outperforms the well-known Watershed segmentation algorithm.

  6. Clinical application of automated digital image analysis for morphology review of peripheral blood leukocyte%全自动数字图像分析在外周血白细胞形态学复检中的临床应用

    Institute of Scientific and Technical Information of China (English)

    邢莹; 闫晓华; 普程伟; 尚柯; 董宁; 汪润; 王建中

    2016-01-01

    检查,对血细胞分析仪复检规则进行优化.结论 全自动血细胞形态学数字图像分析对外周血异常白细胞的分类计数和形态学异常检测具有较高的敏感度和特异度,可用于触发血细胞分析仪复检规则时异常白细胞的形态学复检.%Objective To explore the clinical application of automated digital image analysis in leukocyte morphology examination when review criteria of hematology analyzer are triggered.Methods The reference range of leukocyte differentiation by automated digital image analysis was established by analyzing 304 healthy blood samples from Peking University First Hospital.Six hundred and ninty-seven blood samples from Peking University First Hospital were randomly collected from November 2013 to April 2014,complete blood cells were counted on hematology analyzer,blood smears were made and stained at the same time.Blood smears were detected by automated digital image analyzer and the results were checked (reclassification) by a staff with abundant morphology experience.The same smear was examined manually by microscope.The results by manual microscopic differentiation were used as "golden standard",and diagnostic efficiency of abnormal specimens by automated digital image analysis was calculated,including sensitivity,specificity and accuracy.The difference of abnormal leukocytes detected by two different methods was analyzed in 30 samples of hematological and infectious diseases.Results Specificity of identifying abnormalities of white blood cells by automated digital image analysis was more than 90% except monocyte.Sensitivity of neutrophil toxic abnormities (including D(o)hle body,toxic granulate and vacuolization) was 100%;sensitivity of blast cells,immature granulates and atypical lymphocytes were 91.7%,60% to 81.5% and 61.5%,respectively.Sensitivity of leukocyte differential count was 91.8% for neutrophils,88.5% for lymphocytes,69.1% for monocytes,78.9% for eosinophils

  7. Automated generation of burnup chain for reactor analysis applications

    International Nuclear Information System (INIS)

    This paper presents the development of an automated generation of a new burnup chain for reactor analysis applications. The JENDL FP Decay Data File 2011 and Fission Yields Data File 2011 were used as the data sources. The nuclides in the new chain are determined by restrictions of the half-life and cumulative yield of fission products or from a given list. Then, decay modes, branching ratios and fission yields are recalculated taking into account intermediate reactions. The new burnup chain is output according to the format for the SRAC code system. Verification was performed to evaluate the accuracy of the new burnup chain. The results show that the new burnup chain reproduces well the results of a reference one with 193 fission products used in SRAC. Further development and applications are being planned with the burnup chain code. (author)

  8. galaxieEST: addressing EST identity through automated phylogenetic analysis

    Directory of Open Access Journals (Sweden)

    Larsson Karl-Henrik

    2004-07-01

    Full Text Available Abstract Background Research involving expressed sequence tags (ESTs is intricately coupled to the existence of large, well-annotated sequence repositories. Comparatively complete and satisfactory annotated public sequence libraries are, however, available only for a limited range of organisms, rendering the absence of sequences and gene structure information a tangible problem for those working with taxa lacking an EST or genome sequencing project. Paralogous genes belonging to the same gene family but distinguished by derived characteristics are particularly prone to misidentification and erroneous annotation; high but incomplete levels of sequence similarity are typically difficult to interpret and have formed the basis of many unsubstantiated assumptions of orthology. In these cases, a phylogenetic study of the query sequence together with the most similar sequences in the database may be of great value to the identification process. In order to facilitate this laborious procedure, a project to employ automated phylogenetic analysis in the identification of ESTs was initiated. Results galaxieEST is an open source Perl-CGI script package designed to complement traditional similarity-based identification of EST sequences through employment of automated phylogenetic analysis. It uses a series of BLAST runs as a sieve to retrieve nucleotide and protein sequences for inclusion in neighbour joining and parsimony analyses; the output includes the BLAST output, the results of the phylogenetic analyses, and the corresponding multiple alignments. galaxieEST is available as an on-line web service for identification of fungal ESTs and for download / local installation for use with any organism group at http://galaxie.cgb.ki.se/galaxieEST.html. Conclusions By addressing sequence relatedness in addition to similarity, galaxieEST provides an integrative view on EST origin and identity, which may prove particularly useful in cases where similarity searches

  9. 14 CFR 1261.413 - Analysis of costs; automation; prevention of overpayments, delinquencies, or defaults.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Analysis of costs; automation; prevention of overpayments, delinquencies, or defaults. 1261.413 Section 1261.413 Aeronautics and Space NATIONAL...) § 1261.413 Analysis of costs; automation; prevention of overpayments, delinquencies, or defaults....

  10. 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

  11. 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...

  12. Pattern recognition software and techniques for biological image analysis.

    Directory of Open Access Journals (Sweden)

    Lior Shamir

    Full Text Available The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

  13. 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

  14. 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.

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

    Science.gov (United States)

    Jiang, Luan; Ling, Shan; Li, Qiang

    2016-03-01

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

  16. 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

  17. Automated seismic event location by waveform coherence analysis

    OpenAIRE

    Grigoli, Francesco

    2014-01-01

    Automated location of seismic events is a very important task in microseismic monitoring operations as well for local and regional seismic monitoring. Since microseismic records are generally characterised by low signal-to-noise ratio, such methods are requested to be noise robust and sufficiently accurate. Most of the standard automated location routines are based on the automated picking, identification and association of the first arrivals of P and S waves and on the minimization of the re...

  18. Automated analysis of damages for radiation in plastics surfaces; Analisis automatizado de danos por radiacion en superficies plasticas

    Energy Technology Data Exchange (ETDEWEB)

    Andrade, C.; Camacho M, E.; Tavera, L.; Balcazar, M. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)

    1990-02-15

    Analysis of damages done by the radiation in a polymer characterized by optic properties of polished surfaces, of uniformity and chemical resistance that the acrylic; resistant until the 150 centigrade grades of temperature, and with an approximate weight of half of the glass. An objective of this work is the development of a method that analyze in automated form the superficial damages induced by radiation in plastic materials means an images analyst. (Author)

  19. 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.

  20. 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.

  1. Computer-assisted scheme for automated determination of imaging planes in cervical spinal cord MRI

    Science.gov (United States)

    Tsurumaki, Masaki; Tsai, Du-Yih; Lee, Yongbum; Sekiya, Masaru; Kazama, Kiyoko

    2009-02-01

    This paper presents a computerized scheme to assist MRI operators in accurate and rapid determination of sagittal sections for MRI exam of cervical spinal cord. The algorithm of the proposed scheme consisted of 6 steps: (1) extraction of a cervical vertebra containing spinal cord from an axial localizer image; (2) extraction of spinal cord with sagittal image from the extracted vertebra; (3) selection of a series of coronal localizer images corresponding to various, involved portions of the extracted spinal cord with sagittal image; (4) generation of a composite coronal-plane image from the obtained coronal images; (5) extraction of spinal cord from the obtained composite image; (6) determination of oblique sagittal sections from the detected location and gradient of the extracted spinal cord. Cervical spine images obtained from 25 healthy volunteers were used for the study. A perceptual evaluation was performed by five experienced MRI operators. Good agreement between the automated and manual determinations was achieved. By use of the proposed scheme, average execution time was reduced from 39 seconds/case to 1 second/case. The results demonstrate that the proposed scheme can assist MRI operators in performing cervical spinal cord MRI exam accurately and rapidly.

  2. Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification

    Science.gov (United States)

    Ding, Yifu; Tavolara, Thomas; Cheng, Keith

    2016-03-01

    Our group is developing a method to examine biological specimens in cellular detail using synchrotron microCT. The method can acquire 3D images of tissue at micrometer-scale resolutions, allowing for individual cell types to be visualized in the context of the entire specimen. For model organism research, this tool will enable the rapid characterization of tissue architecture and cellular morphology from every organ system. This characterization is critical for proposed and ongoing "phenome" projects that aim to phenotype whole-organism mutants and diseased tissues from different organisms including humans. With the envisioned collection of hundreds to thousands of images for a phenome project, it is important to develop quantitative image analysis tools for the automated scoring of organism phenotypes across organ systems. Here we present a first step towards that goal, demonstrating the use of support vector machines (SVM) in detecting retinal cell nuclei in 3D images of wild-type zebrafish. In addition, we apply the SVM classifier on a mutant zebrafish to examine whether SVMs can be used to capture phenotypic differences in these images. The longterm goal of this work is to allow cellular and tissue morphology to be characterized quantitatively for many organ systems, at the level of the whole-organism.

  3. 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

  4. A Semi-Automated Functional Test Data Analysis Tool

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Peng; Haves, Philip; Kim, Moosung

    2005-05-01

    The growing interest in commissioning is creating a demand that will increasingly be met by mechanical contractors and less experienced commissioning agents. They will need tools to help them perform commissioning effectively and efficiently. The widespread availability of standardized procedures, accessible in the field, will allow commissioning to be specified with greater certainty as to what will be delivered, enhancing the acceptance and credibility of commissioning. In response, a functional test data analysis tool is being developed to analyze the data collected during functional tests for air-handling units. The functional test data analysis tool is designed to analyze test data, assess performance of the unit under test and identify the likely causes of the failure. The tool has a convenient user interface to facilitate manual entry of measurements made during a test. A graphical display shows the measured performance versus the expected performance, highlighting significant differences that indicate the unit is not able to pass the test. The tool is described as semiautomated because the measured data need to be entered manually, instead of being passed from the building control system automatically. However, the data analysis and visualization are fully automated. The tool is designed to be used by commissioning providers conducting functional tests as part of either new building commissioning or retro-commissioning, as well as building owners and operators interested in conducting routine tests periodically to check the performance of their HVAC systems.

  5. Intelligent Control in Automation Based on Wireless Traffic Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kurt Derr; Milos Manic

    2007-08-01

    Wireless technology is a central component of many factory automation infrastructures in both the commercial and government sectors, providing connectivity among various components in industrial realms (distributed sensors, machines, mobile process controllers). However wireless technologies provide more threats to computer security than wired environments. The advantageous features of Bluetooth technology resulted in Bluetooth units shipments climbing to five million per week at the end of 2005 [1, 2]. This is why the real-time interpretation and understanding of Bluetooth traffic behavior is critical in both maintaining the integrity of computer systems and increasing the efficient use of this technology in control type applications. Although neuro-fuzzy approaches have been applied to wireless 802.11 behavior analysis in the past, a significantly different Bluetooth protocol framework has not been extensively explored using this technology. This paper presents a new neurofuzzy traffic analysis algorithm of this still new territory of Bluetooth traffic. Further enhancements of this algorithm are presented along with the comparison against the traditional, numerical approach. Through test examples, interesting Bluetooth traffic behavior characteristics were captured, and the comparative elegance of this computationally inexpensive approach was demonstrated. This analysis can be used to provide directions for future development and use of this prevailing technology in various control type applications, as well as making the use of it more secure.

  6. Intelligent Control in Automation Based on Wireless Traffic Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kurt Derr; Milos Manic

    2007-09-01

    Wireless technology is a central component of many factory automation infrastructures in both the commercial and government sectors, providing connectivity among various components in industrial realms (distributed sensors, machines, mobile process controllers). However wireless technologies provide more threats to computer security than wired environments. The advantageous features of Bluetooth technology resulted in Bluetooth units shipments climbing to five million per week at the end of 2005 [1, 2]. This is why the real-time interpretation and understanding of Bluetooth traffic behavior is critical in both maintaining the integrity of computer systems and increasing the efficient use of this technology in control type applications. Although neuro-fuzzy approaches have been applied to wireless 802.11 behavior analysis in the past, a significantly different Bluetooth protocol framework has not been extensively explored using this technology. This paper presents a new neurofuzzy traffic analysis algorithm of this still new territory of Bluetooth traffic. Further enhancements of this algorithm are presented along with the comparison against the traditional, numerical approach. Through test examples, interesting Bluetooth traffic behavior characteristics were captured, and the comparative elegance of this computationally inexpensive approach was demonstrated. This analysis can be used to provide directions for future development and use of this prevailing technology in various control type applications, as well as making the use of it more secure.

  7. 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)

  8. 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

  9. Automated analysis of three-dimensional stress echocardiography

    NARCIS (Netherlands)

    K.Y.E. Leung (Esther); M. van Stralen (Marijn); M.G. Danilouchkine (Mikhail); G. van Burken (Gerard); M.L. Geleijnse (Marcel); J.H.C. Reiber (Johan); N. de Jong (Nico); A.F.W. van der Steen (Ton); J.G. Bosch (Johan)

    2011-01-01

    textabstractReal-time three-dimensional (3D) ultrasound imaging has been proposed as an alternative for two-dimensional stress echocardiography for assessing myocardial dysfunction and underlying coronary artery disease. Analysis of 3D stress echocardiography is no simple task and requires considera

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

    Science.gov (United States)

    Zifan, A; Liatsis, P; Chapman, B E

    2013-06-01

    In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging.

  11. 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)

  12. Microbleed detection using automated segmentation (MIDAS: a new method applicable to standard clinical MR images.

    Directory of Open Access Journals (Sweden)

    Mohamed L Seghier

    Full Text Available BACKGROUND: Cerebral microbleeds, visible on gradient-recalled echo (GRE T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction. METHODOLOGY/PRINCIPAL FINDINGS: Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient's GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS identified cerebral microbleeds by explicitly incorporating an "extra" tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts. Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87. MIDAS successfully detected all patients with multiple (≥2 lobar microbleeds. CONCLUSIONS/SIGNIFICANCE: MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds.

  13. 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

  14. 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

  15. 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)

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

    Science.gov (United States)

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

    2014-03-01

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

  17. 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.

  18. 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.

  19. Automated determination of the centers of vertebral bodies and intervertebral discs in CT and MR lumbar spine images

    Science.gov (United States)

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

    2010-03-01

    The knowledge of the location of the centers of vertebral bodies and intervertebral discs is valuable for the analysis of the spine. Existing methods for the detection and segmentation of vertebrae in images acquired by computed tomography (CT) and magnetic resonance (MR) imaging are usually applicable only to a specific image modality and require prior knowledge of the location of vertebrae, usually obtained by manual identification or statistical modeling. We propose a completely automated framework for the detection of the centers of vertebral bodies and intervertebral discs in CT and MR images. The image intensity and gradient magnitude profiles are first extracted in each image along the already obtained spinal centerline and therefore contain a repeating pattern representing the vertebral bodies and intervertebral discs. Based on the period of the repeating pattern and by using a function that approximates the shape of the vertebral body, a model of the vertebral body is generated. The centers of vertebral bodies and intervertebral discs are detected by measuring the similarity between the generated model and the extracted profiles. The method was evaluated on 29 CT and 13 MR images of lumbar spine with varying number of vertebrae. The overall mean distance between the obtained and the ground truth centers was 2.8 +/- 1.9 mm, and no considerable differences were detected between the results for CT, T1-weighted MR or T2-weighted MR images, or among different vertebrae. The proposed method may therefore be valuable for initializing the techniques for the detection and segmentation of vertebrae.

  20. An application of image processing techniques in computed tomography image analysis

    DEFF Research Database (Denmark)

    McEvoy, Fintan

    2007-01-01

    An estimate of the thickness of subcutaneous adipose tissue at differing positions around the body was required in a study examining body composition. To eliminate human error associated with the manual placement of markers for measurements and to facilitate the collection of data from a large...... number of animals and image slices, automation of the process was desirable. The open-source and free image analysis program ImageJ was used. A macro procedure was created that provided the required functionality. The macro performs a number of basic image processing procedures. These include an initial...... process designed to remove the scanning table from the image and to center the animal in the image. This is followed by placement of a vertical line segment from the mid point of the upper border of the image to the image center. Measurements are made between automatically detected outer and inner...

  1. Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis.

    Science.gov (United States)

    Kammen, Alexandra; Law, Meng; Tjan, Bosco S; Toga, Arthur W; Shi, Yonggang

    2016-01-15

    Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone-air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer's Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic

  2. Development of an Automated Technique for Failure Modes and Effect Analysis

    DEFF Research Database (Denmark)

    Blanke, M.; Borch, Ole; Allasia, G.;

    1999-01-01

    Advances in automation have provided integration of monitoring and control functions to enhance the operator's overview and ability to take remedy actions when faults occur. Automation in plant supervision is technically possible with integrated automation systems as platforms, but new design...... implementing an automated technique for Failure Modes and Effects Analysis (FMEA). This technique is based on the matrix formulation of FMEA for the investigation of failure propagation through a system. As main result, this technique will provide the design engineer with decision tables for fault handling...

  3. Development of an automated technique for failure modes and effect analysis

    DEFF Research Database (Denmark)

    Blanke, M.; Borch, Ole; Bagnoli, F.;

    Advances in automation have provided integration of monitoring and control functions to enhance the operator's overview and ability to take remedy actions when faults occur. Automation in plant supervision is technically possible with integrated automation systems as platforms, but new design...... implementing an automated technique for Failure Modes and Effects Analysis (FMEA). This technique is based on the matrix formulation of FMEA for the investigation of failure propagation through a system. As main result, this technique will provide the design engineer with decision tables for fault handling...

  4. 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.

  5. Reflections on ultrasound image analysis.

    Science.gov (United States)

    Alison Noble, J

    2016-10-01

    Ultrasound (US) image analysis has advanced considerably in twenty years. Progress in ultrasound image analysis has always been fundamental to the advancement of image-guided interventions research due to the real-time acquisition capability of ultrasound and this has remained true over the two decades. But in quantitative ultrasound image analysis - which takes US images and turns them into more meaningful clinical information - thinking has perhaps more fundamentally changed. From roots as a poor cousin to Computed Tomography (CT) and Magnetic Resonance (MR) image analysis, both of which have richer anatomical definition and thus were better suited to the earlier eras of medical image analysis which were dominated by model-based methods, ultrasound image analysis has now entered an exciting new era, assisted by advances in machine learning and the growing clinical and commercial interest in employing low-cost portable ultrasound devices outside traditional hospital-based clinical settings. This short article provides a perspective on this change, and highlights some challenges ahead and potential opportunities in ultrasound image analysis which may both have high impact on healthcare delivery worldwide in the future but may also, perhaps, take the subject further away from CT and MR image analysis research with time. PMID:27503078

  6. Reflections on ultrasound image analysis.

    Science.gov (United States)

    Alison Noble, J

    2016-10-01

    Ultrasound (US) image analysis has advanced considerably in twenty years. Progress in ultrasound image analysis has always been fundamental to the advancement of image-guided interventions research due to the real-time acquisition capability of ultrasound and this has remained true over the two decades. But in quantitative ultrasound image analysis - which takes US images and turns them into more meaningful clinical information - thinking has perhaps more fundamentally changed. From roots as a poor cousin to Computed Tomography (CT) and Magnetic Resonance (MR) image analysis, both of which have richer anatomical definition and thus were better suited to the earlier eras of medical image analysis which were dominated by model-based methods, ultrasound image analysis has now entered an exciting new era, assisted by advances in machine learning and the growing clinical and commercial interest in employing low-cost portable ultrasound devices outside traditional hospital-based clinical settings. This short article provides a perspective on this change, and highlights some challenges ahead and potential opportunities in ultrasound image analysis which may both have high impact on healthcare delivery worldwide in the future but may also, perhaps, take the subject further away from CT and MR image analysis research with time.

  7. ANALYSIS OF FUNDUS IMAGES

    DEFF Research Database (Denmark)

    2000-01-01

    A method classifying objects man image as respective arterial or venous vessels comprising: identifying pixels of the said modified image which are located on a line object, determining which of the said image points is associated with crossing point or a bifurcation of the respective line object......; (e) vessel length. Furthermore methods for detecting size and positions of features of eye fundus images.......A method classifying objects man image as respective arterial or venous vessels comprising: identifying pixels of the said modified image which are located on a line object, determining which of the said image points is associated with crossing point or a bifurcation of the respective line object......, wherein a crossing point is represented by an image point which is the intersection of four line segments, performing a matching operation on pairs of said line segments for each said crossing point, to determine the path of blood vessels in the image, thereby classifying the line objects in the original...

  8. A comprehensive and precise quantification of the calanoid copepod Acartia tonsa (Dana) for intensive live feed cultures using an automated ZooImage system

    DEFF Research Database (Denmark)

    Vu, Minh Thi Thuy; Jepsen, Per Meyer; Hansen, Benni Winding

    2014-01-01

    ignored. In this study, we propose a novel method for highly precise classification of development stages and biomass of A. tonsa, in intensive live feed cultures, using an automated ZooImage system, a freeware image analysis. We successfully created a training set of 13 categories, including 7 copepod...... and 6 non-copepod (debris) groups. ZooImage used this training set for automatic discrimination through a random forest algorithm with the general accuracy of 92.8%. The ZooImage showed no significant difference in classifying solitary eggs, or mixed nauplii stages and copepodites compared to personal...... microscope observation. Furthermore, ZooImage was also adapted for automatic estimation of A. tonsa biomass. This is the first study that has successfully applied ZooImage software which enables fast and reliable quantification of the development stages and the biomass of A. tonsa. As a result, relevant...

  9. Automated analysis for detecting beams in laser wakefield simulations

    Energy Technology Data Exchange (ETDEWEB)

    Ushizima, Daniela M.; Rubel, Oliver; Prabhat, Mr.; Weber, Gunther H.; Bethel, E. Wes; Aragon, Cecilia R.; Geddes, Cameron G.R.; Cormier-Michel, Estelle; Hamann, Bernd; Messmer, Peter; Hagen, Hans

    2008-07-03

    Laser wakefield particle accelerators have shown the potential to generate electric fields thousands of times higher than those of conventional accelerators. The resulting extremely short particle acceleration distance could yield a potential new compact source of energetic electrons and radiation, with wide applications from medicine to physics. Physicists investigate laser-plasma internal dynamics by running particle-in-cell simulations; however, this generates a large dataset that requires time-consuming, manual inspection by experts in order to detect key features such as beam formation. This paper describes a framework to automate the data analysis and classification of simulation data. First, we propose a new method to identify locations with high density of particles in the space-time domain, based on maximum extremum point detection on the particle distribution. We analyze high density electron regions using a lifetime diagram by organizing and pruning the maximum extrema as nodes in a minimum spanning tree. Second, we partition the multivariate data using fuzzy clustering to detect time steps in a experiment that may contain a high quality electron beam. Finally, we combine results from fuzzy clustering and bunch lifetime analysis to estimate spatially confined beams. We demonstrate our algorithms successfully on four different simulation datasets.

  10. Progress on automated data analysis algorithms for ultrasonic inspection of composites

    Science.gov (United States)

    Aldrin, John C.; Forsyth, David S.; Welter, John T.

    2015-03-01

    Progress is presented on the development and demonstration of automated data analysis (ADA) software to address the burden in interpreting ultrasonic inspection data for large composite structures. The automated data analysis algorithm is presented in detail, which follows standard procedures for analyzing signals for time-of-flight indications and backwall amplitude dropout. New algorithms have been implemented to reliably identify indications in time-of-flight images near the front and back walls of composite panels. Adaptive call criteria have also been applied to address sensitivity to variation in backwall signal level, panel thickness variation, and internal signal noise. ADA processing results are presented for a variety of test specimens that include inserted materials and discontinuities produced under poor manufacturing conditions. Software tools have been developed to support both ADA algorithm design and certification, producing a statistical evaluation of indication results and false calls using a matching process with predefined truth tables. Parametric studies were performed to evaluate detection and false call results with respect to varying algorithm settings.

  11. 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.

  12. 40 CFR 13.19 - Analysis of costs; automation; prevention of overpayments, delinquencies or defaults.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 1 2010-07-01 2010-07-01 false Analysis of costs; automation; prevention of overpayments, delinquencies or defaults. 13.19 Section 13.19 Protection of Environment...; automation; prevention of overpayments, delinquencies or defaults. (a) The Administrator may...

  13. 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

  14. Image Analysis in CT Angiography

    NARCIS (Netherlands)

    Manniesing, R.

    2006-01-01

    In this thesis we develop and validate novel image processing techniques for the analysis of vascular structures in medical images. First a new type of filter is proposed which is capable of enhancing vascular structures while suppressing noise in the remainder of the image. This filter is based on

  15. SU-E-J-252: Reproducibility of Radiogenomic Image Features: Comparison of Two Semi-Automated Segmentation Methods

    Energy Technology Data Exchange (ETDEWEB)

    Lee, M; Woo, B; Kim, J [Seoul National University, Seoul (Korea, Republic of); Jamshidi, N; Kuo, M [UCLA School of Medicine, Los Angeles, CA (United States)

    2015-06-15

    Purpose: Objective and reliable quantification of imaging phenotype is an essential part of radiogenomic studies. We compared the reproducibility of two semi-automatic segmentation methods for quantitative image phenotyping in magnetic resonance imaging (MRI) of glioblastoma multiforme (GBM). Methods: MRI examinations with T1 post-gadolinium and FLAIR sequences of 10 GBM patients were downloaded from the Cancer Image Archive site. Two semi-automatic segmentation tools with different algorithms (deformable model and grow cut method) were used to segment contrast enhancement, necrosis and edema regions by two independent observers. A total of 21 imaging features consisting of area and edge groups were extracted automatically from the segmented tumor. The inter-observer variability and coefficient of variation (COV) were calculated to evaluate the reproducibility. Results: Inter-observer correlations and coefficient of variation of imaging features with the deformable model ranged from 0.953 to 0.999 and 2.1% to 9.2%, respectively, and the grow cut method ranged from 0.799 to 0.976 and 3.5% to 26.6%, respectively. Coefficient of variation for especially important features which were previously reported as predictive of patient survival were: 3.4% with deformable model and 7.4% with grow cut method for the proportion of contrast enhanced tumor region; 5.5% with deformable model and 25.7% with grow cut method for the proportion of necrosis; and 2.1% with deformable model and 4.4% with grow cut method for edge sharpness of tumor on CE-T1W1. Conclusion: Comparison of two semi-automated tumor segmentation techniques shows reliable image feature extraction for radiogenomic analysis of GBM patients with multiparametric Brain MRI.

  16. Digital Images Analysis

    OpenAIRE

    Roman-Gonzalez, Avid

    2012-01-01

    International audience; A specific field of image processing focuses on the evaluation of image quality and assessment of their authenticity. A loss of image quality may be due to the various processes by which it passes. In assessing the authenticity of the image we detect forgeries, detection of hidden messages, etc. In this work, we present an overview of these areas; these areas have in common the need to develop theories and techniques to detect changes in the image that it is not detect...

  17. Spectral analysis for automated exploration and sample acquisition

    Science.gov (United States)

    Eberlein, Susan; Yates, Gigi

    1992-05-01

    Future space exploration missions will rely heavily on the use of complex instrument data for determining the geologic, chemical, and elemental character of planetary surfaces. One important instrument is the imaging spectrometer, which collects complete images in multiple discrete wavelengths in the visible and infrared regions of the spectrum. Extensive computational effort is required to extract information from such high-dimensional data. A hierarchical classification scheme allows multispectral data to be analyzed for purposes of mineral classification while limiting the overall computational requirements. The hierarchical classifier exploits the tunability of a new type of imaging spectrometer which is based on an acousto-optic tunable filter. This spectrometer collects a complete image in each wavelength passband without spatial scanning. It may be programmed to scan through a range of wavelengths or to collect only specific bands for data analysis. Spectral classification activities employ artificial neural networks, trained to recognize a number of mineral classes. Analysis of the trained networks has proven useful in determining which subsets of spectral bands should be employed at each step of the hierarchical classifier. The network classifiers are capable of recognizing all mineral types which were included in the training set. In addition, the major components of many mineral mixtures can also be recognized. This capability may prove useful for a system designed to evaluate data in a strange environment where details of the mineral composition are not known in advance.

  18. Automated absolute activation analysis with californium-252 sources

    Energy Technology Data Exchange (ETDEWEB)

    MacMurdo, K.W.; Bowman, W.W.

    1978-09-01

    A 100-mg /sup 252/Cf neutron activation analysis facility is used routinely at the Savannah River Laboratory for multielement analysis of many solid and liquid samples. An absolute analysis technique converts counting data directly to elemental concentration without the use of classical comparative standards and flux monitors. With the totally automated pneumatic sample transfer system, cyclic irradiation-decay-count regimes can be pre-selected for up to 40 samples, and samples can be analyzed with the facility unattended. An automatic data control system starts and stops a high-resolution gamma-ray spectrometer and/or a delayed-neutron detector; the system also stores data and controls output modes. Gamma ray data are reduced by three main programs in the IBM 360/195 computer: the 4096-channel spectrum and pertinent experimental timing, counting, and sample data are stored on magnetic tape; the spectrum is then reduced to a list of significant photopeak energies, integrated areas, and their associated statistical errors; and the third program assigns gamma ray photopeaks to the appropriate neutron activation product(s) by comparing photopeak energies to tabulated gamma ray energies. Photopeak areas are then converted to elemental concentration by using experimental timing and sample data, calculated elemental neutron capture rates, absolute detector efficiencies, and absolute spectroscopic decay data. Calculational procedures have been developed so that fissile material can be analyzed by cyclic neutron activation and delayed-neutron counting procedures. These calculations are based on a 6 half-life group model of delayed neutron emission; calculations include corrections for delayed neutron interference from /sup 17/O. Detection sensitivities of < or = 400 ppB for natural uranium and 8 ppB (< or = 0.5 (nCi/g)) for /sup 239/Pu were demonstrated with 15-g samples at a throughput of up to 140 per day. Over 40 elements can be detected at the sub-ppM level.

  19. MORPHY, a program for an automated "atoms in molecules" analysis

    Science.gov (United States)

    Popelier, Paul L. A.

    1996-02-01

    The operating manual for a structured FORTAN 77 program called MORPHY is presented. This code performs an automated topological analysis of a molecular electron density and its Laplacian. The program is written in a stylistically homogeneous, transparant and modular manner. The input is compact but flexible and allows for multiple jobs in one deck. The output is detailed and has an attractive lay-out. Critical points in the charge density and its Laplacian can be located in a robust and economic way and are displayed via an external on-line visualisation package. The gradient vector field of the charge density can be traced with great accuracy, planar contour, relief and one-dimensional line plots of many scalar properties can be generated. Non-bonded radii are calculated and analytical expressions for interatomic surfaces are computed (with error estimates) and plotted. MORPHY is interfaced with the AIMPAC suite of programs. The capabilities of the program are illustrated with two test runs and five selected figures.

  20. 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

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

    Science.gov (United States)

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

    2007-01-01

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

  2. Fully automated fluorescent in situ hybridization (FISH staining and digital analysis of HER2 in breast cancer: a validation study.

    Directory of Open Access Journals (Sweden)

    Elise M J van der Logt

    Full Text Available HER2 assessment is routinely used to select patients with invasive breast cancer that might benefit from HER2-targeted therapy. The aim of this study was to validate a fully automated in situ hybridization (ISH procedure that combines the automated Leica HER2 fluorescent ISH system for Bond with supervised automated analysis with the Visia imaging D-Sight digital imaging platform. HER2 assessment was performed on 328 formalin-fixed/paraffin-embedded invasive breast cancer tumors on tissue microarrays (TMA and 100 (50 selected IHC 2+ and 50 random IHC scores full-sized slides of resections/biopsies obtained for diagnostic purposes previously. For digital analysis slides were pre-screened at 20x and 100x magnification for all fluorescent signals and supervised-automated scoring was performed on at least two pictures (in total at least 20 nuclei were counted with the D-Sight HER2 FISH analysis module by two observers independently. Results were compared to data obtained previously with the manual Abbott FISH test. The overall agreement with Abbott FISH data among TMA samples and 50 selected IHC 2+ cases was 98.8% (κ = 0.94 and 93.8% (κ = 0.88, respectively. The results of 50 additionally tested unselected IHC cases were concordant with previously obtained IHC and/or FISH data. The combination of the Leica FISH system with the D-Sight digital imaging platform is a feasible method for HER2 assessment in routine clinical practice for patients with invasive breast cancer.

  3. 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.

  4. Linking Automated Data Analysis and Visualization with Applications in Developmental Biology and High-Energy Physics

    Energy Technology Data Exchange (ETDEWEB)

    Ruebel, Oliver [Technical Univ. of Darmstadt (Germany)

    2009-11-20

    Knowledge discovery from large and complex collections of today's scientific datasets is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the increasing number of data dimensions and data objects is presenting tremendous challenges for data analysis and effective data exploration methods and tools. Researchers are overwhelmed with data and standard tools are often insufficient to enable effective data analysis and knowledge discovery. The main objective of this thesis is to provide important new capabilities to accelerate scientific knowledge discovery form large, complex, and multivariate scientific data. The research covered in this thesis addresses these scientific challenges using a combination of scientific visualization, information visualization, automated data analysis, and other enabling technologies, such as efficient data management. The effectiveness of the proposed analysis methods is demonstrated via applications in two distinct scientific research fields, namely developmental biology and high-energy physics.Advances in microscopy, image analysis, and embryo registration enable for the first time measurement of gene expression at cellular resolution for entire organisms. Analysis of high-dimensional spatial gene expression datasets is a challenging task. By integrating data clustering and visualization, analysis of complex, time-varying, spatial gene expression patterns and their formation becomes possible. The analysis framework MATLAB and the visualization have been integrated, making advanced analysis tools accessible to biologist and enabling bioinformatic researchers to directly integrate their analysis with the visualization. Laser wakefield particle accelerators (LWFAs) promise to be a new compact source of high-energy particles and radiation, with wide applications ranging from medicine to physics. To gain insight into the complex physical processes of particle

  5. Linking Automated Data Analysis and Visualization with Applications in Developmental Biology and High-Energy Physics

    Energy Technology Data Exchange (ETDEWEB)

    Ruebel, Oliver

    2009-12-01

    Knowledge discovery from large and complex collections of today's scientific datasets is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the increasing number of data dimensions and data objects is presenting tremendous challenges for data analysis and effective data exploration methods and tools. Researchers are overwhelmed with data and standard tools are often insufficient to enable effective data analysis and knowledge discovery. The main objective of this thesis is to provide important new capabilities to accelerate scientific knowledge discovery form large, complex, and multivariate scientific data. The research covered in this thesis addresses these scientific challenges using a combination of scientific visualization, information visualization, automated data analysis, and other enabling technologies, such as efficient data management. The effectiveness of the proposed analysis methods is demonstrated via applications in two distinct scientific research fields, namely developmental biology and high-energy physics.Advances in microscopy, image analysis, and embryo registration enable for the first time measurement of gene expression at cellular resolution for entire organisms. Analysis of high-dimensional spatial gene expression datasets is a challenging task. By integrating data clustering and visualization, analysis of complex, time-varying, spatial gene expression patterns and their formation becomes possible. The analysis framework MATLAB and the visualization have been integrated, making advanced analysis tools accessible to biologist and enabling bioinformatic researchers to directly integrate their analysis with the visualization. Laser wakefield particle accelerators (LWFAs) promise to be a new compact source of high-energy particles and radiation, with wide applications ranging from medicine to physics. To gain insight into the complex physical processes of particle

  6. Linking Automated Data Analysis and Visualization with Applications in Developmental Biology and High-Energy Physics

    International Nuclear Information System (INIS)

    Knowledge discovery from large and complex collections of today's scientific datasets is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the increasing number of data dimensions and data objects is presenting tremendous challenges for data analysis and effective data exploration methods and tools. Researchers are overwhelmed with data and standard tools are often insufficient to enable effective data analysis and knowledge discovery. The main objective of this thesis is to provide important new capabilities to accelerate scientific knowledge discovery form large, complex, and multivariate scientific data. The research covered in this thesis addresses these scientific challenges using a combination of scientific visualization, information visualization, automated data analysis, and other enabling technologies, such as efficient data management. The effectiveness of the proposed analysis methods is demonstrated via applications in two distinct scientific research fields, namely developmental biology and high-energy physics.Advances in microscopy, image analysis, and embryo registration enable for the first time measurement of gene expression at cellular resolution for entire organisms. Analysis of high-dimensional spatial gene expression datasets is a challenging task. By integrating data clustering and visualization, analysis of complex, time-varying, spatial gene expression patterns and their formation becomes possible. The analysis framework MATLAB and the visualization have been integrated, making advanced analysis tools accessible to biologist and enabling bioinformatic researchers to directly integrate their analysis with the visualization. Laser wakefield particle accelerators (LWFAs) promise to be a new compact source of high-energy particles and radiation, with wide applications ranging from medicine to physics. To gain insight into the complex physical processes of particle

  7. Automated high-speed video analysis of the bubble dynamics in subcooled flow boiling

    Energy Technology Data Exchange (ETDEWEB)

    Maurus, Reinhold; Ilchenko, Volodymyr; Sattelmayer, Thomas [Technische Univ. Muenchen, Lehrstuhl fuer Thermodynamik, Garching (Germany)

    2004-04-01

    Subcooled flow boiling is a commonly applied technique for achieving efficient heat transfer. In the study, an experimental investigation in the nucleate boiling regime was performed for water circulating in a closed loop at atmospheric pressure. The test-section consists of a rectangular channel with a one side heated copper strip and a very good optical access. For the optical observation of the bubble behaviour the high-speed cinematography is used. Automated image processing and analysis algorithms developed by the authors were applied for a wide range of mass flow rates and heat fluxes in order to extract characteristic length and time scales of the bubbly layer during the boiling process. Using this methodology, a huge number of bubble cycles could be analysed. The structure of the developed algorithms for the detection of the bubble diameter, the bubble lifetime, the lifetime after the detachment process and the waiting time between two bubble cycles is described. Subsequently, the results from using these automated procedures are presented. A remarkable novelty is the presentation of all results as distribution functions. This is of physical importance because the commonly applied spatial and temporal averaging leads to a loss of information and, moreover, to an unjustified deterministic view of the boiling process, which exhibits in reality a very wide spread of bubble sizes and characteristic times. The results show that the mass flux dominates the temporal bubble behaviour. An increase of the liquid mass flux reveals a strong decrease of the bubble life - and waiting time. In contrast, the variation of the heat flux has a much smaller impact. It is shown in addition that the investigation of the bubble history using automated algorithms delivers novel information with respect to the bubble lift-off probability. (Author)

  8. Interobserver and Intraobserver Variability in pH-Impedance Analysis between 10 Experts and Automated Analysis

    DEFF Research Database (Denmark)

    Loots, Clara M; van Wijk, Michiel P; Blondeau, Kathleen;

    2011-01-01

    OBJECTIVE: To determine interobserver and intraobserver variability in pH-impedance interpretation between experts and accuracy of automated analysis (AA). STUDY DESIGN: Ten pediatric 24-hour pH-impedance tracings were analyzed by 10 observers from 7 world groups and with AA. Detection of gastroe......OBJECTIVE: To determine interobserver and intraobserver variability in pH-impedance interpretation between experts and accuracy of automated analysis (AA). STUDY DESIGN: Ten pediatric 24-hour pH-impedance tracings were analyzed by 10 observers from 7 world groups and with AA. Detection....... CONCLUSION: Interobserver agreement in combined pH-multichannel intraluminal impedance analysis in experts is moderate; only 42% of GER episodes were detected by the majority of observers. Detection of total GER numbers is more consistent. Considering these poor outcomes, AA seems favorable compared...

  9. Blind Analysis of CT Image Noise Using Residual Denoised Images

    CERN Document Server

    Roychowdhury, Sohini; Alessio, Adam

    2016-01-01

    CT protocol design and quality control would benefit from automated tools to estimate the quality of generated CT images. These tools could be used to identify erroneous CT acquisitions or refine protocols to achieve certain signal to noise characteristics. This paper investigates blind estimation methods to determine global signal strength and noise levels in chest CT images. Methods: We propose novel performance metrics corresponding to the accuracy of noise and signal estimation. We implement and evaluate the noise estimation performance of six spatial- and frequency- based methods, derived from conventional image filtering algorithms. Algorithms were tested on patient data sets from whole-body repeat CT acquisitions performed with a higher and lower dose technique over the same scan region. Results: The proposed performance metrics can evaluate the relative tradeoff of filter parameters and noise estimation performance. The proposed automated methods tend to underestimate CT image noise at low-flux levels...

  10. Automated Design and Analysis Tool for CEV Structural and TPS Components Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The innovation of the proposed effort is a unique automated process for the analysis, design, and sizing of CEV structures and TPS. This developed process will...

  11. Automated Design and Analysis Tool for CLV/CEV Composite and Metallic Structural Components Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The innovation of the proposed effort is a unique automated process for the analysis, design, and sizing of CLV/CEV composite and metallic structures. This...

  12. 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

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

    Science.gov (United States)

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

    2016-10-01

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

  14. 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

  15. Blind Analysis of CT Image Noise Using Residual Denoised Images

    OpenAIRE

    Roychowdhury, Sohini; Hollraft, Nathan; Alessio, Adam

    2016-01-01

    CT protocol design and quality control would benefit from automated tools to estimate the quality of generated CT images. These tools could be used to identify erroneous CT acquisitions or refine protocols to achieve certain signal to noise characteristics. This paper investigates blind estimation methods to determine global signal strength and noise levels in chest CT images. Methods: We propose novel performance metrics corresponding to the accuracy of noise and signal estimation. We implem...

  16. Contrast-enhanced magnetic resonance angiography in carotid artery disease: does automated image registration improve image quality?

    Energy Technology Data Exchange (ETDEWEB)

    Menke, Jan [University Hospital, Department of Diagnostic Radiology, Goettingen (Germany); Larsen, Joerg [Braunschweig Teaching Hospitals, Institute for Roentgendiagnostics, Braunschweig (Germany)

    2009-05-15

    Contrast-enhanced magnetic resonance angiography (MRA) is a noninvasive imaging alternative to digital subtraction angiography (DSA) for patients with carotid artery disease. In DSA, image quality can be improved by shifting the mask image if the patient has moved during angiography. This study investigated whether such image registration may also help to improve the image quality of carotid MRA. Data from 370 carotid MRA examinations of patients likely to have carotid artery disease were prospectively collected. The standard nonregistered MRAs were compared to automatically linear, affine and warp registered MRA by using three image quality parameters: the vessel detection probability (VDP) in maximum intensity projection (MIP) images, contrast-to-noise ratio (CNR) in MIP images, and contrast-to-noise ratio in three-dimensional image volumes. A body shift of less than 1 mm occurred in 96.2% of cases. Analysis of variance revealed no significant influence of image registration and body shift on image quality (p > 0.05). In conclusion, standard contrast-enhanced carotid MRA usually requires no image registration to improve image quality and is generally robust against any naturally occurring body shift. (orig.)

  17. Stochastic geometry for image analysis

    CERN Document Server

    Descombes, Xavier

    2013-01-01

    This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are  described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed.  Numerous applications, covering remote sensing images, biological and medical imaging, are detailed.  This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling.

  18. Automated Production Flow Line Failure Rate Mathematical Analysis with Probability Theory

    Directory of Open Access Journals (Sweden)

    Tan Chan Sin

    2014-12-01

    Full Text Available Automated lines have been widely used in the industries especially for mass production and to customize product. Productivity of automated line is a crucial indicator to show the output and performance of the production. Failure or breakdown of station or mechanisms is commonly occurs in the automated line in real condition due to the technological and technical problem which is highly affect the productivity. The failure rates of automated line are not express or analyse in terms of mathematic form. This paper presents the mathematic analysis by using probability theory towards the failure condition in automated line. The mathematic express for failure rates can produce and forecast the output of productivity accurately

  19. 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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-05-15

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

  1. Automated analysis of eclipsing binary lightcurves. I. EBAS -- a new Eclipsing Binary Automated Solver with EBOP

    CERN Document Server

    Tamuz, O; North, P; Mazeh, Tsevi; North, Pierre; Tamuz, Omer

    2006-01-01

    We present a new algorithm -- Eclipsing Binary Automated Solver (EBAS), to analyse lightcurves of eclipsing binaries. The algorithm is designed to analyse large numbers of lightcurves, and is therefore based on the relatively fast EBOP code. To facilitate the search for the best solution, EBAS uses two parameter transformations. Instead of the radii of the two stellar components, EBAS uses the sum of radii and their ratio, while the inclination is transformed into the impact parameter. To replace human visual assessment, we introduce a new 'alarm' goodness-of-fit statistic that takes into account correlation between neighbouring residuals. We perform extensive tests and simulations that show that our algorithm converges well, finds a good set of parameters and provides reasonable error estimation.

  2. Correction of oral contrast artifacts in CT-based attenuation correction of PET images using an automated segmentation algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Ahmadian, Alireza; Ay, Mohammad R.; Sarkar, Saeed [Medical Sciences/University of Tehran, Research Center for Science and Technology in Medicine, Tehran (Iran); Medical Sciences/University of Tehran, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran (Iran); Bidgoli, Javad H. [Medical Sciences/University of Tehran, Research Center for Science and Technology in Medicine, Tehran (Iran); East Tehran Azad University, Department of Electrical and Computer Engineering, Tehran (Iran); Zaidi, Habib [Geneva University Hospital, Division of Nuclear Medicine, Geneva (Switzerland)

    2008-10-15

    Oral contrast is usually administered in most X-ray computed tomography (CT) examinations of the abdomen and the pelvis as it allows more accurate identification of the bowel and facilitates the interpretation of abdominal and pelvic CT studies. However, the misclassification of contrast medium with high-density bone in CT-based attenuation correction (CTAC) is known to generate artifacts in the attenuation map ({mu}map), thus resulting in overcorrection for attenuation of positron emission tomography (PET) images. In this study, we developed an automated algorithm for segmentation and classification of regions containing oral contrast medium to correct for artifacts in CT-attenuation-corrected PET images using the segmented contrast correction (SCC) algorithm. The proposed algorithm consists of two steps: first, high CT number object segmentation using combined region- and boundary-based segmentation and second, object classification to bone and contrast agent using a knowledge-based nonlinear fuzzy classifier. Thereafter, the CT numbers of pixels belonging to the region classified as contrast medium are substituted with their equivalent effective bone CT numbers using the SCC algorithm. The generated CT images are then down-sampled followed by Gaussian smoothing to match the resolution of PET images. A piecewise calibration curve was then used to convert CT pixel values to linear attenuation coefficients at 511 keV. The visual assessment of segmented regions performed by an experienced radiologist confirmed the accuracy of the segmentation and classification algorithms for delineation of contrast-enhanced regions in clinical CT images. The quantitative analysis of generated {mu}maps of 21 clinical CT colonoscopy datasets showed an overestimation ranging between 24.4% and 37.3% in the 3D-classified regions depending on their volume and the concentration of contrast medium. Two PET/CT studies known to be problematic demonstrated the applicability of the technique

  3. Automated Spatio-Temporal Analysis of Remotely Sensed Imagery for Water Resources Management

    Science.gov (United States)

    Bahr, Thomas

    2016-04-01

    Since 2012, the state of California faces an extreme drought, which impacts water supply in many ways. Advanced remote sensing is an important technology to better assess water resources, monitor drought conditions and water supplies, plan for drought response and mitigation, and measure drought impacts. In the present case study latest time series analysis capabilities are used to examine surface water in reservoirs located along the western flank of the Sierra Nevada region of California. This case study was performed using the COTS software package ENVI 5.3. Integration of custom processes and automation is supported by IDL (Interactive Data Language). Thus, ENVI analytics is running via the object-oriented and IDL-based ENVITask API. A time series from Landsat images (L-5 TM, L-7 ETM+, L-8 OLI) of the AOI was obtained for 1999 to 2015 (October acquisitions). Downloaded from the USGS EarthExplorer web site, they already were georeferenced to a UTM Zone 10N (WGS-84) coordinate system. ENVITasks were used to pre-process the Landsat images as follows: • Triangulation based gap-filling for the SLC-off Landsat-7 ETM+ images. • Spatial subsetting to the same geographic extent. • Radiometric correction to top-of-atmosphere (TOA) reflectance. • Atmospheric correction using QUAC®, which determines atmospheric correction parameters directly from the observed pixel spectra in a scene, without ancillary information. Spatio-temporal analysis was executed with the following tasks: • Creation of Modified Normalized Difference Water Index images (MNDWI, Xu 2006) to enhance open water features while suppressing noise from built-up land, vegetation, and soil. • Threshold based classification of the water index images to extract the water features. • Classification aggregation as a post-classification cleanup process. • Export of the respective water classes to vector layers for further evaluation in a GIS. • Animation of the classification series and export to

  4. 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.

  5. Topological analysis of weather images

    OpenAIRE

    MUBI, GREGOR

    2015-01-01

    In this thesis computational topology is used to analyse weather data. The goal is to analyse weather images and discern, if it is possible to expose certain parameters, which could improve humid mass classification. For this reason we were observing how those masses appear and merge on sequences of images. We used topological method named persistence. For data analysis we obtained weather radar images. We also wrote around 20 scripts in computing environment MATLAB, which served for data co...

  6. An algorithm for automated ROI definition in water or epoxy-filled NEMA NU-2 image quality phantoms.

    Science.gov (United States)

    Pierce Ii, Larry A; Byrd, Darrin W; Elston, Brian F; Karp, Joel S; Sunderland, John J; Kinahan, Paul E

    2016-01-08

    Drawing regions of interest (ROIs) in positron emission tomography/computed tomography (PET/CT) scans of the National Electrical Manufacturers Association (NEMA) NU-2 Image Quality (IQ) phantom is a time-consuming process that allows for interuser variability in the measurements. In order to reduce operator effort and allow batch processing of IQ phantom images, we propose a fast, robust, automated algorithm for performing IQ phantom sphere localization and analysis. The algorithm is easily altered to accommodate different configurations of the IQ phantom. The proposed algorithm uses information from both the PET and CT image volumes in order to overcome the challenges of detecting the smallest spheres in the PET volume. This algorithm has been released as an open-source plug-in to the Osirix medical image viewing software package. We test the algorithm under various noise conditions, positions within the scanner, air bubbles in the phantom spheres, and scanner misalignment conditions. The proposed algorithm shows run-times between 3 and 4 min and has proven to be robust under all tested conditions, with expected sphere localization deviations of less than 0.2 mm and variations of PET ROI mean and maximum values on the order of 0.5% and 2%, respectively, over multiple PET acquisitions. We conclude that the proposed algorithm is stable when challenged with a variety of physical and imaging anomalies, and that the algorithm can be a valuable tool for those who use the NEMA NU-2 IQ phantom for PET/CT scanner acceptance testing and QA/QC.

  7. HEIDI: An Automated Process for the Identification and Extraction of Photometric Light Curves from Astronomical Images

    CERN Document Server

    Todd, M; Tanga, P; Coward, D M; Zadnik, M G

    2014-01-01

    The production of photometric light curves from astronomical images is a very time-consuming task. Larger data sets improve the resolution of the light curve, however, the time requirement scales with data volume. The data analysis is often made more difficult by factors such as a lack of suitable calibration sources and the need to correct for variations in observing conditions from one image to another. Often these variations are unpredictable and corrections are based on experience and intuition. The High Efficiency Image Detection & Identification (HEIDI) pipeline software rapidly processes sets of astronomical images. HEIDI automatically selects multiple sources for calibrating the images using an algorithm that provides a reliable means of correcting for variations between images in a time series. The algorithm takes into account that some sources may intrinsically vary on short time scales and excludes these from being used as calibration sources. HEIDI processes a set of images from an entire nigh...

  8. PIZZARO: Forensic analysis and restoration of image and video data.

    Science.gov (United States)

    Kamenicky, Jan; Bartos, Michal; Flusser, Jan; Mahdian, Babak; Kotera, Jan; Novozamsky, Adam; Saic, Stanislav; Sroubek, Filip; Sorel, Michal; Zita, Ales; Zitova, Barbara; Sima, Zdenek; Svarc, Petr; Horinek, Jan

    2016-07-01

    This paper introduces a set of methods for image and video forensic analysis. They were designed to help to assess image and video credibility and origin and to restore and increase image quality by diminishing unwanted blur, noise, and other possible artifacts. The motivation came from the best practices used in the criminal investigation utilizing images and/or videos. The determination of the image source, the verification of the image content, and image restoration were identified as the most important issues of which automation can facilitate criminalists work. Novel