WorldWideScience

Sample records for automated content-based image

  1. Localization-based super-resolution imaging meets high-content screening.

    Science.gov (United States)

    Beghin, Anne; Kechkar, Adel; Butler, Corey; Levet, Florian; Cabillic, Marine; Rossier, Olivier; Giannone, Gregory; Galland, Rémi; Choquet, Daniel; Sibarita, Jean-Baptiste

    2017-12-01

    Single-molecule localization microscopy techniques have proven to be essential tools for quantitatively monitoring biological processes at unprecedented spatial resolution. However, these techniques are very low throughput and are not yet compatible with fully automated, multiparametric cellular assays. This shortcoming is primarily due to the huge amount of data generated during imaging and the lack of software for automation and dedicated data mining. We describe an automated quantitative single-molecule-based super-resolution methodology that operates in standard multiwell plates and uses analysis based on high-content screening and data-mining software. The workflow is compatible with fixed- and live-cell imaging and allows extraction of quantitative data like fluorophore photophysics, protein clustering or dynamic behavior of biomolecules. We demonstrate that the method is compatible with high-content screening using 3D dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle tracking.

  2. Bread Water Content Measurement Based on Hyperspectral Imaging

    DEFF Research Database (Denmark)

    Liu, Zhi; Møller, Flemming

    2011-01-01

    Water content is one of the most important properties of the bread for tasting assesment or store monitoring. Traditional bread water content measurement methods mostly are processed manually, which is destructive and time consuming. This paper proposes an automated water content measurement...... for bread quality based on near-infrared hyperspectral imaging against the conventional manual loss-in-weight method. For this purpose, the hyperspectral components unmixing technology is used for measuring the water content quantitatively. And the definition on bread water content index is presented...

  3. Retinal image quality assessment based on image clarity and content

    Science.gov (United States)

    Abdel-Hamid, Lamiaa; El-Rafei, Ahmed; El-Ramly, Salwa; Michelson, Georg; Hornegger, Joachim

    2016-09-01

    Retinal image quality assessment (RIQA) is an essential step in automated screening systems to avoid misdiagnosis caused by processing poor quality retinal images. A no-reference transform-based RIQA algorithm is introduced that assesses images based on five clarity and content quality issues: sharpness, illumination, homogeneity, field definition, and content. Transform-based RIQA algorithms have the advantage of considering retinal structures while being computationally inexpensive. Wavelet-based features are proposed to evaluate the sharpness and overall illumination of the images. A retinal saturation channel is designed and used along with wavelet-based features for homogeneity assessment. The presented sharpness and illumination features are utilized to assure adequate field definition, whereas color information is used to exclude nonretinal images. Several publicly available datasets of varying quality grades are utilized to evaluate the feature sets resulting in area under the receiver operating characteristic curve above 0.99 for each of the individual feature sets. The overall quality is assessed by a classifier that uses the collective features as an input vector. The classification results show superior performance of the algorithm in comparison to other methods from literature. Moreover, the algorithm addresses efficiently and comprehensively various quality issues and is suitable for automatic screening systems.

  4. iScreen: Image-Based High-Content RNAi Screening Analysis Tools.

    Science.gov (United States)

    Zhong, Rui; Dong, Xiaonan; Levine, Beth; Xie, Yang; Xiao, Guanghua

    2015-09-01

    High-throughput RNA interference (RNAi) screening has opened up a path to investigating functional genomics in a genome-wide pattern. However, such studies are often restricted to assays that have a single readout format. Recently, advanced image technologies have been coupled with high-throughput RNAi screening to develop high-content screening, in which one or more cell image(s), instead of a single readout, were generated from each well. This image-based high-content screening technology has led to genome-wide functional annotation in a wider spectrum of biological research studies, as well as in drug and target discovery, so that complex cellular phenotypes can be measured in a multiparametric format. Despite these advances, data analysis and visualization tools are still largely lacking for these types of experiments. Therefore, we developed iScreen (image-Based High-content RNAi Screening Analysis Tool), an R package for the statistical modeling and visualization of image-based high-content RNAi screening. Two case studies were used to demonstrate the capability and efficiency of the iScreen package. iScreen is available for download on CRAN (http://cran.cnr.berkeley.edu/web/packages/iScreen/index.html). The user manual is also available as a supplementary document. © 2014 Society for Laboratory Automation and Screening.

  5. Automated microscopy for high-content RNAi screening

    Science.gov (United States)

    2010-01-01

    Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs. PMID:20176920

  6. Quantification of sterol-specific response in human macrophages using automated imaged-based analysis.

    Science.gov (United States)

    Gater, Deborah L; Widatalla, Namareq; Islam, Kinza; AlRaeesi, Maryam; Teo, Jeremy C M; Pearson, Yanthe E

    2017-12-13

    The transformation of normal macrophage cells into lipid-laden foam cells is an important step in the progression of atherosclerosis. One major contributor to foam cell formation in vivo is the intracellular accumulation of cholesterol. Here, we report the effects of various combinations of low-density lipoprotein, sterols, lipids and other factors on human macrophages, using an automated image analysis program to quantitatively compare single cell properties, such as cell size and lipid content, in different conditions. We observed that the addition of cholesterol caused an increase in average cell lipid content across a range of conditions. All of the sterol-lipid mixtures examined were capable of inducing increases in average cell lipid content, with variations in the distribution of the response, in cytotoxicity and in how the sterol-lipid combination interacted with other activating factors. For example, cholesterol and lipopolysaccharide acted synergistically to increase cell lipid content while also increasing cell survival compared with the addition of lipopolysaccharide alone. Additionally, ergosterol and cholesteryl hemisuccinate caused similar increases in lipid content but also exhibited considerably greater cytotoxicity than cholesterol. The use of automated image analysis enables us to assess not only changes in average cell size and content, but also to rapidly and automatically compare population distributions based on simple fluorescence images. Our observations add to increasing understanding of the complex and multifactorial nature of foam-cell formation and provide a novel approach to assessing the heterogeneity of macrophage response to a variety of factors.

  7. Content-Based Image Retrial Based on Hadoop

    Directory of Open Access Journals (Sweden)

    DongSheng Yin

    2013-01-01

    Full Text Available Generally, time complexity of algorithms for content-based image retrial is extremely high. In order to retrieve images on large-scale databases efficiently, a new way for retrieving based on Hadoop distributed framework is proposed. Firstly, a database of images features is built by using Speeded Up Robust Features algorithm and Locality-Sensitive Hashing and then perform the search on Hadoop platform in a parallel way specially designed. Considerable experimental results show that it is able to retrieve images based on content on large-scale cluster and image sets effectively.

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

    Science.gov (United States)

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

    2014-08-30

    Standardized techniques to detect HIV-neutralizing antibody responses are of great importance in the search for an HIV vaccine. Here, we present a high-throughput, high-content automated plaque reduction (APR) assay based on automated microscopy and image analysis that allows evaluation of neutralization and inhibition of cell-cell fusion within the same assay. Neutralization of virus particles is measured as a reduction in the number of fluorescent plaques, and inhibition of cell-cell fusion as a reduction in plaque area. We found neutralization strength to be a significant factor in the ability of virus to form syncytia. Further, we introduce the inhibitory concentration of plaque area reduction (ICpar) as an additional measure of antiviral activity, i.e. fusion inhibition. We present an automated image based high-throughput, high-content HIV plaque reduction assay. This allows, for the first time, simultaneous evaluation of neutralization and inhibition of cell-cell fusion within the same assay, by quantifying the reduction in number of plaques and mean plaque area, respectively. Inhibition of cell-to-cell fusion requires higher quantities of inhibitory reagent than inhibition of virus neutralization.

  9. Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

    Science.gov (United States)

    Gururaj, C.; Jayadevappa, D.; Tunga, Satish

    2018-06-01

    Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

  10. Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

    Science.gov (United States)

    Gururaj, C.; Jayadevappa, D.; Tunga, Satish

    2018-02-01

    Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

  11. Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement

    Directory of Open Access Journals (Sweden)

    Hakan Kartal

    2018-06-01

    Full Text Available Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs are mainly concentrated in the “Forest” and “Semi Natural Area” LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification.

  12. General Staining and Segmentation Procedures for High Content Imaging and Analysis.

    Science.gov (United States)

    Chambers, Kevin M; Mandavilli, Bhaskar S; Dolman, Nick J; Janes, Michael S

    2018-01-01

    Automated quantitative fluorescence microscopy, also known as high content imaging (HCI), is a rapidly growing analytical approach in cell biology. Because automated image analysis relies heavily on robust demarcation of cells and subcellular regions, reliable methods for labeling cells is a critical component of the HCI workflow. Labeling of cells for image segmentation is typically performed with fluorescent probes that bind DNA for nuclear-based cell demarcation or with those which react with proteins for image analysis based on whole cell staining. These reagents, along with instrument and software settings, play an important role in the successful segmentation of cells in a population for automated and quantitative image analysis. In this chapter, we describe standard procedures for labeling and image segmentation in both live and fixed cell samples. The chapter will also provide troubleshooting guidelines for some of the common problems associated with these aspects of HCI.

  13. Content-Based Image Retrieval Based on Electromagnetism-Like Mechanism

    Directory of Open Access Journals (Sweden)

    Hamid A. Jalab

    2013-01-01

    Full Text Available Recently, many researchers in the field of automatic content-based image retrieval have devoted a remarkable amount of research looking for methods to retrieve the best relevant images to the query image. This paper presents a novel algorithm for increasing the precision in content-based image retrieval based on electromagnetism optimization technique. The electromagnetism optimization is a nature-inspired technique that follows the collective attraction-repulsion mechanism by considering each image as an electrical charge. The algorithm is composed of two phases: fitness function measurement and electromagnetism optimization technique. It is implemented on a database with 8,000 images spread across 80 classes with 100 images in each class. Eight thousand queries are fired on the database, and the overall average precision is computed. Experimental results of the proposed approach have shown significant improvement in the retrieval performance in regard to precision.

  14. Content Based Retrieval System for Magnetic Resonance Images

    International Nuclear Information System (INIS)

    Trojachanets, Katarina

    2010-01-01

    The amount of medical images is continuously increasing as a consequence of the constant growth and development of techniques for digital image acquisition. Manual annotation and description of each image is impractical, expensive and time consuming approach. Moreover, it is an imprecise and insufficient way for describing all information stored in medical images. This induces the necessity for developing efficient image storage, annotation and retrieval systems. Content based image retrieval (CBIR) emerges as an efficient approach for digital image retrieval from large databases. It includes two phases. In the first phase, the visual content of the image is analyzed and the feature extraction process is performed. An appropriate descriptor, namely, feature vector is then associated with each image. These descriptors are used in the second phase, i.e. the retrieval process. With the aim to improve the efficiency and precision of the content based image retrieval systems, feature extraction and automatic image annotation techniques are subject of continuous researches and development. Including the classification techniques in the retrieval process enables automatic image annotation in an existing CBIR system. It contributes to more efficient and easier image organization in the system.Applying content based retrieval in the field of magnetic resonance is a big challenge. Magnetic resonance imaging is an image based diagnostic technique which is widely used in medical environment. According to this, the number of magnetic resonance images is enormously growing. Magnetic resonance images provide plentiful medical information, high resolution and specific nature. Thus, the capability of CBIR systems for image retrieval from large database is of great importance for efficient analysis of this kind of images. The aim of this thesis is to propose content based retrieval system architecture for magnetic resonance images. To provide the system efficiency, feature

  15. Content Based Medical Image Retrieval for Histopathological, CT and MRI Images

    Directory of Open Access Journals (Sweden)

    Swarnambiga AYYACHAMY

    2013-09-01

    Full Text Available A content based approach is followed for medical images. The purpose of this study is to access the stability of these methods for medical image retrieval. The methods used in color based retrieval for histopathological images are color co-occurrence matrix (CCM and histogram with meta features. For texture based retrieval GLCM (gray level co-occurrence matrix and local binary pattern (LBP were used. For shape based retrieval canny edge detection and otsu‘s method with multivariable threshold were used. Texture and shape based retrieval were implemented using MRI (magnetic resonance images. The most remarkable characteristics of the article are its content based approach for each medical imaging modality. Our efforts were focused on the initial visual search. From our experiment, histogram with meta features in color based retrieval for histopathological images shows a precision of 60 % and recall of 30 %. Whereas GLCM in texture based retrieval for MRI images shows a precision of 70 % and recall of 20 %. Shape based retrieval for MRI images shows a precision of 50% and recall of 25 %. The retrieval results shows that this simple approach is successful.

  16. Enhanced Automated Guidance System for Horizontal Auger Boring Based on Image Processing.

    Science.gov (United States)

    Wu, Lingling; Wen, Guojun; Wang, Yudan; Huang, Lei; Zhou, Jiang

    2018-02-15

    Horizontal auger boring (HAB) is a widely used trenchless technology for the high-accuracy installation of gravity or pressure pipelines on line and grade. Differing from other pipeline installations, HAB requires a more precise and automated guidance system for use in a practical project. This paper proposes an economic and enhanced automated optical guidance system, based on optimization research of light-emitting diode (LED) light target and five automated image processing bore-path deviation algorithms. An LED light target was optimized for many qualities, including light color, filter plate color, luminous intensity, and LED layout. The image preprocessing algorithm, direction location algorithm, angle measurement algorithm, deflection detection algorithm, and auto-focus algorithm, compiled in MATLAB, are used to automate image processing for deflection computing and judging. After multiple indoor experiments, this guidance system is applied in a project of hot water pipeline installation, with accuracy controlled within 2 mm in 48-m distance, providing accurate line and grade controls and verifying the feasibility and reliability of the guidance system.

  17. Content-based Image Hiding Method for Secure Network Biometric Verification

    Directory of Open Access Journals (Sweden)

    Xiangjiu Che

    2011-08-01

    Full Text Available For secure biometric verification, most existing methods embed biometric information directly into the cover image, but content correlation analysis between the biometric image and the cover image is often ignored. In this paper, we propose a novel biometric image hiding approach based on the content correlation analysis to protect the network-based transmitted image. By using principal component analysis (PCA, the content correlation between the biometric image and the cover image is firstly analyzed. Then based on particle swarm optimization (PSO algorithm, some regions of the cover image are selected to represent the biometric image, in which the cover image can carry partial content of the biometric image. As a result of the correlation analysis, the unrepresented part of the biometric image is embedded into the cover image by using the discrete wavelet transform (DWT. Combined with human visual system (HVS model, this approach makes the hiding result perceptually invisible. The extensive experimental results demonstrate that the proposed hiding approach is robust against some common frequency and geometric attacks; it also provides an effective protection for the secure biometric verification.

  18. Application of content-based image compression to telepathology

    Science.gov (United States)

    Varga, Margaret J.; Ducksbury, Paul G.; Callagy, Grace

    2002-05-01

    Telepathology is a means of practicing pathology at a distance, viewing images on a computer display rather than directly through a microscope. Without compression, images take too long to transmit to a remote location and are very expensive to store for future examination. However, to date the use of compressed images in pathology remains controversial. This is because commercial image compression algorithms such as JPEG achieve data compression without knowledge of the diagnostic content. Often images are lossily compressed at the expense of corrupting informative content. None of the currently available lossy compression techniques are concerned with what information has been preserved and what data has been discarded. Their sole objective is to compress and transmit the images as fast as possible. By contrast, this paper presents a novel image compression technique, which exploits knowledge of the slide diagnostic content. This 'content based' approach combines visually lossless and lossy compression techniques, judiciously applying each in the appropriate context across an image so as to maintain 'diagnostic' information while still maximising the possible compression. Standard compression algorithms, e.g. wavelets, can still be used, but their use in a context sensitive manner can offer high compression ratios and preservation of diagnostically important information. When compared with lossless compression the novel content-based approach can potentially provide the same degree of information with a smaller amount of data. When compared with lossy compression it can provide more information for a given amount of compression. The precise gain in the compression performance depends on the application (e.g. database archive or second opinion consultation) and the diagnostic content of the images.

  19. Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

    Science.gov (United States)

    Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

    2017-06-28

    High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Content-based quality evaluation of color images: overview and proposals

    Science.gov (United States)

    Tremeau, Alain; Richard, Noel; Colantoni, Philippe; Fernandez-Maloigne, Christine

    2003-12-01

    The automatic prediction of perceived quality from image data in general, and the assessment of particular image characteristics or attributes that may need improvement in particular, becomes an increasingly important part of intelligent imaging systems. The purpose of this paper is to propose to the color imaging community in general to develop a software package available on internet to help the user to select among all these approaches which is better appropriated to a given application. The ultimate goal of this project is to propose, next to implement, an open and unified color imaging system to set up a favourable context for the evaluation and analysis of color imaging processes. Many different methods for measuring the performance of a process have been proposed by different researchers. In this paper, we will discuss the advantages and shortcomings of most of main analysis criteria and performance measures currently used. The aim is not to establish a harsh competition between algorithms or processes, but rather to test and compare the efficiency of methodologies firstly to highlight strengths and weaknesses of a given algorithm or methodology on a given image type and secondly to have these results publicly available. This paper is focused on two important unsolved problems. Why it is so difficult to select a color space which gives better results than another one? Why it is so difficult to select an image quality metric which gives better results than another one, with respect to the judgment of the Human Visual System? Several methods used either in color imaging or in image quality will be thus discussed. Proposals for content-based image measures and means of developing a standard test suite for will be then presented. The above reference advocates for an evaluation protocol based on an automated procedure. This is the ultimate goal of our proposal.

  1. Content-based image retrieval with ontological ranking

    Science.gov (United States)

    Tsai, Shen-Fu; Tsai, Min-Hsuan; Huang, Thomas S.

    2010-02-01

    Images are a much more powerful medium of expression than text, as the adage says: "One picture is worth a thousand words." It is because compared with text consisting of an array of words, an image has more degrees of freedom and therefore a more complicated structure. However, the less limited structure of images presents researchers in the computer vision community a tough task of teaching machines to understand and organize images, especially when a limit number of learning examples and background knowledge are given. The advance of internet and web technology in the past decade has changed the way human gain knowledge. People, hence, can exchange knowledge with others by discussing and contributing information on the web. As a result, the web pages in the internet have become a living and growing source of information. One is therefore tempted to wonder whether machines can learn from the web knowledge base as well. Indeed, it is possible to make computer learn from the internet and provide human with more meaningful knowledge. In this work, we explore this novel possibility on image understanding applied to semantic image search. We exploit web resources to obtain links from images to keywords and a semantic ontology constituting human's general knowledge. The former maps visual content to related text in contrast to the traditional way of associating images with surrounding text; the latter provides relations between concepts for machines to understand to what extent and in what sense an image is close to the image search query. With the aid of these two tools, the resulting image search system is thus content-based and moreover, organized. The returned images are ranked and organized such that semantically similar images are grouped together and given a rank based on the semantic closeness to the input query. The novelty of the system is twofold: first, images are retrieved not only based on text cues but their actual contents as well; second, the grouping

  2. Automated processing of zebrafish imaging data: a survey.

    Science.gov (United States)

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

    2013-09-01

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

  3. Automated Processing of Zebrafish Imaging Data: A Survey

    Science.gov (United States)

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

    2013-01-01

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

  4. Anti-cancer agents in Saudi Arabian herbals revealed by automated high-content imaging

    KAUST Repository

    Hajjar, Dina A.; Kremb, Stephan Georg; Sioud, Salim; Emwas, Abdul-Hamid M.; Voolstra, Christian R.; Ravasi, Timothy

    2017-01-01

    in cancer therapy. Here, we used cell-based phenotypic profiling and image-based high-content screening to study the mode of action and potential cellular targets of plants historically used in Saudi Arabia's traditional medicine. We compared the cytological

  5. Content-based image retrieval: Color-selection exploited

    NARCIS (Netherlands)

    Broek, E.L. van den; Vuurpijl, L.G.; Kisters, P. M. F.; Schmid, J.C.M. von; Moens, M.F.; Busser, R. de; Hiemstra, D.; Kraaij, W.

    2002-01-01

    This research presents a new color selection interface that facilitates query-by-color in Content-Based Image Retrieval (CBIR). Existing CBIR color selection interfaces, are being judged as non-intuitive and difficult to use. Our interface copes with these problems of usability. It is based on 11

  6. Content-Based Image Retrieval: Color-selection exploited

    NARCIS (Netherlands)

    Moens, Marie-Francine; van den Broek, Egon; Vuurpijl, L.G.; de Brusser, Rik; Kisters, P.M.F.; Hiemstra, Djoerd; Kraaij, Wessel; von Schmid, J.C.M.

    2002-01-01

    This research presents a new color selection interface that facilitates query-by-color in Content-Based Image Retrieval (CBIR). Existing CBIR color selection interfaces, are being judged as non-intuitive and difficult to use. Our interface copes with these problems of usability. It is based on 11

  7. Silhouette-based approach of 3D image reconstruction for automated image acquisition using robotic arm

    Science.gov (United States)

    Azhar, N.; Saad, W. H. M.; Manap, N. A.; Saad, N. M.; Syafeeza, A. R.

    2017-06-01

    This study presents the approach of 3D image reconstruction using an autonomous robotic arm for the image acquisition process. A low cost of the automated imaging platform is created using a pair of G15 servo motor connected in series to an Arduino UNO as a main microcontroller. Two sets of sequential images were obtained using different projection angle of the camera. The silhouette-based approach is used in this study for 3D reconstruction from the sequential images captured from several different angles of the object. Other than that, an analysis based on the effect of different number of sequential images on the accuracy of 3D model reconstruction was also carried out with a fixed projection angle of the camera. The effecting elements in the 3D reconstruction are discussed and the overall result of the analysis is concluded according to the prototype of imaging platform.

  8. 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

  9. Automated X-ray image analysis for cargo security: Critical review and future promise.

    Science.gov (United States)

    Rogers, Thomas W; Jaccard, Nicolas; Morton, Edward J; Griffin, Lewis D

    2017-01-01

    We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.

  10. Automating the construction of scene classifiers for content-based video retrieval

    NARCIS (Netherlands)

    Khan, L.; Israël, Menno; Petrushin, V.A.; van den Broek, Egon; van der Putten, Peter

    2004-01-01

    This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a

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

    International Nuclear Information System (INIS)

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

    2007-01-01

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

  12. Automated analysis of high-content microscopy data with deep learning.

    Science.gov (United States)

    Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

    2017-04-18

    Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

  13. Image content authentication based on channel coding

    Science.gov (United States)

    Zhang, Fan; Xu, Lei

    2008-03-01

    The content authentication determines whether an image has been tampered or not, and if necessary, locate malicious alterations made on the image. Authentication on a still image or a video are motivated by recipient's interest, and its principle is that a receiver must be able to identify the source of this document reliably. Several techniques and concepts based on data hiding or steganography designed as a means for the image authentication. This paper presents a color image authentication algorithm based on convolution coding. The high bits of color digital image are coded by the convolution codes for the tamper detection and localization. The authentication messages are hidden in the low bits of image in order to keep the invisibility of authentication. All communications channels are subject to errors introduced because of additive Gaussian noise in their environment. Data perturbations cannot be eliminated but their effect can be minimized by the use of Forward Error Correction (FEC) techniques in the transmitted data stream and decoders in the receiving system that detect and correct bits in error. This paper presents a color image authentication algorithm based on convolution coding. The message of each pixel is convolution encoded with the encoder. After the process of parity check and block interleaving, the redundant bits are embedded in the image offset. The tamper can be detected and restored need not accessing the original image.

  14. Supervised learning of tools for content-based search of image databases

    Science.gov (United States)

    Delanoy, Richard L.

    1996-03-01

    A computer environment, called the Toolkit for Image Mining (TIM), is being developed with the goal of enabling users with diverse interests and varied computer skills to create search tools for content-based image retrieval and other pattern matching tasks. Search tools are generated using a simple paradigm of supervised learning that is based on the user pointing at mistakes of classification made by the current search tool. As mistakes are identified, a learning algorithm uses the identified mistakes to build up a model of the user's intentions, construct a new search tool, apply the search tool to a test image, display the match results as feedback to the user, and accept new inputs from the user. Search tools are constructed in the form of functional templates, which are generalized matched filters capable of knowledge- based image processing. The ability of this system to learn the user's intentions from experience contrasts with other existing approaches to content-based image retrieval that base searches on the characteristics of a single input example or on a predefined and semantically- constrained textual query. Currently, TIM is capable of learning spectral and textural patterns, but should be adaptable to the learning of shapes, as well. Possible applications of TIM include not only content-based image retrieval, but also quantitative image analysis, the generation of metadata for annotating images, data prioritization or data reduction in bandwidth-limited situations, and the construction of components for larger, more complex computer vision algorithms.

  15. Visual analytics for semantic queries of TerraSAR-X image content

    Science.gov (United States)

    Espinoza-Molina, Daniela; Alonso, Kevin; Datcu, Mihai

    2015-10-01

    With the continuous image product acquisition of satellite missions, the size of the image archives is considerably increasing every day as well as the variety and complexity of their content, surpassing the end-user capacity to analyse and exploit them. Advances in the image retrieval field have contributed to the development of tools for interactive exploration and extraction of the images from huge archives using different parameters like metadata, key-words, and basic image descriptors. Even though we count on more powerful tools for automated image retrieval and data analysis, we still face the problem of understanding and analyzing the results. Thus, a systematic computational analysis of these results is required in order to provide to the end-user a summary of the archive content in comprehensible terms. In this context, visual analytics combines automated analysis with interactive visualizations analysis techniques for an effective understanding, reasoning and decision making on the basis of very large and complex datasets. Moreover, currently several researches are focused on associating the content of the images with semantic definitions for describing the data in a format to be easily understood by the end-user. In this paper, we present our approach for computing visual analytics and semantically querying the TerraSAR-X archive. Our approach is mainly composed of four steps: 1) the generation of a data model that explains the information contained in a TerraSAR-X product. The model is formed by primitive descriptors and metadata entries, 2) the storage of this model in a database system, 3) the semantic definition of the image content based on machine learning algorithms and relevance feedback, and 4) querying the image archive using semantic descriptors as query parameters and computing the statistical analysis of the query results. The experimental results shows that with the help of visual analytics and semantic definitions we are able to explain

  16. The Use of QBIC Content-Based Image Retrieval System

    Directory of Open Access Journals (Sweden)

    Ching-Yi Wu

    2004-03-01

    Full Text Available The fast increase in digital images has caught increasing attention on the development of image retrieval technologies. Content-based image retrieval (CBIR has become an important approach in retrieving image data from a large collection. This article reports our results on the use and users study of a CBIR system. Thirty-eight students majored in art and design were invited to use the IBM’s OBIC (Query by Image Content system through the Internet. Data from their information needs, behaviors, and retrieval strategies were collected through an in-depth interview, observation, and self-described think-aloud process. Important conclusions are:(1)There are four types of information needs for image data: implicit, inspirational, ever-changing, and purposive. The types of needs may change during the retrieval process. (2)CBIR is suitable for the example-type query, text retrieval is suitable for the scenario-type query, and image browsing is suitable for the symbolic query. (3)Different from text retrieval, detailed description of the query condition may lead to retrieval failure more easily. (4)CBIR is suitable for the domain-specific image collection, not for the images on the Word-Wide Web.[Article content in Chinese

  17. A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Dhanoa Jaspreet Singh

    2016-01-01

    Full Text Available With the advent of technology and multimedia information, digital images are increasing very quickly. Various techniques are being developed to retrieve/search digital information or data contained in the image. Traditional Text Based Image Retrieval System is not plentiful. Since it is time consuming as it require manual image annotation. Also, the image annotation differs with different peoples. An alternate to this is Content Based Image Retrieval (CBIR system. It retrieves/search for image using its contents rather the text, keywords etc. A lot of exploration has been compassed in the range of Content Based Image Retrieval (CBIR with various feature extraction techniques. Shape is a significant image feature as it reflects the human perception. Moreover, Shape is quite simple to use by the user to define object in an image as compared to other features such as Color, texture etc. Over and above, if applied alone, no descriptor will give fruitful results. Further, by combining it with an improved classifier, one can use the positive features of both the descriptor and classifier. So, a tryout will be made to establish an algorithm for accurate feature (Shape extraction in Content Based Image Retrieval (CBIR. The main objectives of this project are: (a To propose an algorithm for shape feature extraction using CBIR, (b To evaluate the performance of proposed algorithm and (c To compare the proposed algorithm with state of art techniques.

  18. A Novel Optimization-Based Approach for Content-Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Manyu Xiao

    2013-01-01

    Full Text Available Content-based image retrieval is nowadays one of the possible and promising solutions to manage image databases effectively. However, with the large number of images, there still exists a great discrepancy between the users’ expectations (accuracy and efficiency and the real performance in image retrieval. In this work, new optimization strategies are proposed on vocabulary tree building, retrieval, and matching methods. More precisely, a new clustering strategy combining classification and conventional K-Means method is firstly redefined. Then a new matching technique is built to eliminate the error caused by large-scaled scale-invariant feature transform (SIFT. Additionally, a new unit mechanism is proposed to reduce the cost of indexing time. Finally, the numerical results show that excellent performances are obtained in both accuracy and efficiency based on the proposed improvements for image retrieval.

  19. Cardiac imaging: working towards fully-automated machine analysis & interpretation.

    Science.gov (United States)

    Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

    2017-03-01

    Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

  20. FPGA Accelerator for Wavelet-Based Automated Global Image Registration

    Directory of Open Access Journals (Sweden)

    Baofeng Li

    2009-01-01

    Full Text Available Wavelet-based automated global image registration (WAGIR is fundamental for most remote sensing image processing algorithms and extremely computation-intensive. With more and more algorithms migrating from ground computing to onboard computing, an efficient dedicated architecture of WAGIR is desired. In this paper, a BWAGIR architecture is proposed based on a block resampling scheme. BWAGIR achieves a significant performance by pipelining computational logics, parallelizing the resampling process and the calculation of correlation coefficient and parallel memory access. A proof-of-concept implementation with 1 BWAGIR processing unit of the architecture performs at least 7.4X faster than the CL cluster system with 1 node, and at least 3.4X than the MPM massively parallel machine with 1 node. Further speedup can be achieved by parallelizing multiple BWAGIR units. The architecture with 5 units achieves a speedup of about 3X against the CL with 16 nodes and a comparative speed with the MPM with 30 nodes. More importantly, the BWAGIR architecture can be deployed onboard economically.

  1. FPGA Accelerator for Wavelet-Based Automated Global Image Registration

    Directory of Open Access Journals (Sweden)

    Li Baofeng

    2009-01-01

    Full Text Available Abstract Wavelet-based automated global image registration (WAGIR is fundamental for most remote sensing image processing algorithms and extremely computation-intensive. With more and more algorithms migrating from ground computing to onboard computing, an efficient dedicated architecture of WAGIR is desired. In this paper, a BWAGIR architecture is proposed based on a block resampling scheme. BWAGIR achieves a significant performance by pipelining computational logics, parallelizing the resampling process and the calculation of correlation coefficient and parallel memory access. A proof-of-concept implementation with 1 BWAGIR processing unit of the architecture performs at least 7.4X faster than the CL cluster system with 1 node, and at least 3.4X than the MPM massively parallel machine with 1 node. Further speedup can be achieved by parallelizing multiple BWAGIR units. The architecture with 5 units achieves a speedup of about 3X against the CL with 16 nodes and a comparative speed with the MPM with 30 nodes. More importantly, the BWAGIR architecture can be deployed onboard economically.

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

    International Nuclear Information System (INIS)

    Amit, Guy; Purdie, Thomas G.

    2015-01-01

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

  3. Indexing, learning and content-based retrieval for special purpose image databases

    NARCIS (Netherlands)

    M.J. Huiskes (Mark); E.J. Pauwels (Eric)

    2005-01-01

    textabstractThis chapter deals with content-based image retrieval in special purpose image databases. As image data is amassed ever more effortlessly, building efficient systems for searching and browsing of image databases becomes increasingly urgent. We provide an overview of the current

  4. FULLY AUTOMATED IMAGE ORIENTATION IN THE ABSENCE OF TARGETS

    Directory of Open Access Journals (Sweden)

    C. Stamatopoulos

    2012-07-01

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

  5. Parallel content-based sub-image retrieval using hierarchical searching.

    Science.gov (United States)

    Yang, Lin; Qi, Xin; Xing, Fuyong; Kurc, Tahsin; Saltz, Joel; Foran, David J

    2014-04-01

    The capacity to systematically search through large image collections and ensembles and detect regions exhibiting similar morphological characteristics is central to pathology diagnosis. Unfortunately, the primary methods used to search digitized, whole-slide histopathology specimens are slow and prone to inter- and intra-observer variability. The central objective of this research was to design, develop, and evaluate a content-based image retrieval system to assist doctors for quick and reliable content-based comparative search of similar prostate image patches. Given a representative image patch (sub-image), the algorithm will return a ranked ensemble of image patches throughout the entire whole-slide histology section which exhibits the most similar morphologic characteristics. This is accomplished by first performing hierarchical searching based on a newly developed hierarchical annular histogram (HAH). The set of candidates is then further refined in the second stage of processing by computing a color histogram from eight equally divided segments within each square annular bin defined in the original HAH. A demand-driven master-worker parallelization approach is employed to speed up the searching procedure. Using this strategy, the query patch is broadcasted to all worker processes. Each worker process is dynamically assigned an image by the master process to search for and return a ranked list of similar patches in the image. The algorithm was tested using digitized hematoxylin and eosin (H&E) stained prostate cancer specimens. We have achieved an excellent image retrieval performance. The recall rate within the first 40 rank retrieved image patches is ∼90%. Both the testing data and source code can be downloaded from http://pleiad.umdnj.edu/CBII/Bioinformatics/.

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

    Science.gov (United States)

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

    2012-03-01

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

  7. Automated imaging system for single molecules

    Science.gov (United States)

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

    2012-09-18

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

  8. Content-based histopathology image retrieval using CometCloud.

    Science.gov (United States)

    Qi, Xin; Wang, Daihou; Rodero, Ivan; Diaz-Montes, Javier; Gensure, Rebekah H; Xing, Fuyong; Zhong, Hua; Goodell, Lauri; Parashar, Manish; Foran, David J; Yang, Lin

    2014-08-26

    The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance. The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers. In this paper, we present a set of newly developed CBIR algorithms and validate them using two

  9. 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.

  10. Automated image based prominent nucleoli detection.

    Science.gov (United States)

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

    2015-01-01

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

  11. Automated image based prominent nucleoli detection

    Directory of Open Access Journals (Sweden)

    Choon K Yap

    2015-01-01

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

  12. Automated facial acne assessment from smartphone images

    Science.gov (United States)

    Amini, Mohammad; Vasefi, Fartash; Valdebran, Manuel; Huang, Kevin; Zhang, Haomiao; Kemp, William; MacKinnon, Nicholas

    2018-02-01

    A smartphone mobile medical application is presented, that provides analysis of the health of skin on the face using a smartphone image and cloud-based image processing techniques. The mobile application employs the use of the camera to capture a front face image of a subject, after which the captured image is spatially calibrated based on fiducial points such as position of the iris of the eye. A facial recognition algorithm is used to identify features of the human face image, to normalize the image, and to define facial regions of interest (ROI) for acne assessment. We identify acne lesions and classify them into two categories: those that are papules and those that are pustules. Automated facial acne assessment was validated by performing tests on images of 60 digital human models and 10 real human face images. The application was able to identify 92% of acne lesions within five facial ROIs. The classification accuracy for separating papules from pustules was 98%. Combined with in-app documentation of treatment, lifestyle factors, and automated facial acne assessment, the app can be used in both cosmetic and clinical dermatology. It allows users to quantitatively self-measure acne severity and treatment efficacy on an ongoing basis to help them manage their chronic facial acne.

  13. An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.

    Science.gov (United States)

    Lee, Unseok; Chang, Sungyul; Putra, Gian Anantrio; Kim, Hyoungseok; Kim, Dong Hwan

    2018-01-01

    A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

  14. IDAPS (Image Data Automated Processing System) System Description

    Science.gov (United States)

    1988-06-24

    This document describes the physical configuration and components used in the image processing system referred to as IDAPS (Image Data Automated ... Processing System). This system was developed by the Environmental Research Institute of Michigan (ERIM) for Eglin Air Force Base. The system is designed

  15. Machine Learning-Based Content Analysis: Automating the analysis of frames and agendas in political communication research

    NARCIS (Netherlands)

    Burscher, B.

    2016-01-01

    We used machine learning to study policy issues and frames in political messages. With regard to frames, we investigated the automation of two content-analytical tasks: frame coding and frame identification. We found that both tasks can be successfully automated by means of machine learning

  16. Design Guidelines for a Content-Based Image Retrieval Color-Selection Interface

    NARCIS (Netherlands)

    Eggen, Berry; van den Broek, Egon; van der Veer, Gerrit C.; Kisters, Peter M.F.; Willems, Rob; Vuurpijl, Louis G.

    2004-01-01

    In Content-Based Image Retrieval (CBIR) two query-methods exist: query-by-example and query-by-memory. The user either selects an example image or selects image features retrieved from memory (such as color, texture, spatial attributes, and shape) to define his query. Hitherto, research on CBIR

  17. Automated, feature-based image alignment for high-resolution imaging mass spectrometry of large biological samples

    NARCIS (Netherlands)

    Broersen, A.; Liere, van R.; Altelaar, A.F.M.; Heeren, R.M.A.; McDonnell, L.A.

    2008-01-01

    High-resolution imaging mass spectrometry of large biological samples is the goal of several research groups. In mosaic imaging, the most common method, the large sample is divided into a mosaic of small areas that are then analyzed with high resolution. Here we present an automated alignment

  18. Automation of Hessian-Based Tubularity Measure Response Function in 3D Biomedical Images.

    Science.gov (United States)

    Dzyubak, Oleksandr P; Ritman, Erik L

    2011-01-01

    The blood vessels and nerve trees consist of tubular objects interconnected into a complex tree- or web-like structure that has a range of structural scale 5 μm diameter capillaries to 3 cm aorta. This large-scale range presents two major problems; one is just making the measurements, and the other is the exponential increase of component numbers with decreasing scale. With the remarkable increase in the volume imaged by, and resolution of, modern day 3D imagers, it is almost impossible to make manual tracking of the complex multiscale parameters from those large image data sets. In addition, the manual tracking is quite subjective and unreliable. We propose a solution for automation of an adaptive nonsupervised system for tracking tubular objects based on multiscale framework and use of Hessian-based object shape detector incorporating National Library of Medicine Insight Segmentation and Registration Toolkit (ITK) image processing libraries.

  19. A robust computational solution for automated quantification of a specific binding ratio based on [123I]FP-CIT SPECT images

    International Nuclear Information System (INIS)

    Oliveira, F. P. M.; Tavares, J. M. R. S.; Borges, Faria D.; Campos, Costa D.

    2014-01-01

    The purpose of the current paper is to present a computational solution to accurately quantify a specific to a non-specific uptake ratio in [ 123 I]fP-CIT single photon emission computed tomography (SPECT) images and simultaneously measure the spatial dimensions of the basal ganglia, also known as basal nuclei. A statistical analysis based on a reference dataset selected by the user is also automatically performed. The quantification of the specific to non-specific uptake ratio here is based on regions of interest defined after the registration of the image under study with a template image. The computational solution was tested on a dataset of 38 [ 123 I]FP-CIT SPECT images: 28 images were from patients with Parkinson’s disease and the remainder from normal patients, and the results of the automated quantification were compared to the ones obtained by three well-known semi-automated quantification methods. The results revealed a high correlation coefficient between the developed automated method and the three semi-automated methods used for comparison (r ≥0.975). The solution also showed good robustness against different positions of the patient, as an almost perfect agreement between the specific to non-specific uptake ratio was found (ICC=1.000). The mean processing time was around 6 seconds per study using a common notebook PC. The solution developed can be useful for clinicians to evaluate [ 123 I]FP-CIT SPECT images due to its accuracy, robustness and speed. Also, the comparison between case studies and the follow-up of patients can be done more accurately and proficiently since the intra- and inter-observer variability of the semi-automated calculation does not exist in automated solutions. The dimensions of the basal ganglia and their automatic comparison with the values of the population selected as reference are also important for professionals in this area.

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

    International Nuclear Information System (INIS)

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

    2012-01-01

    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 18 F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools. (paper)

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

    Science.gov (United States)

    Hong, Wei

    2008-03-01

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

  2. Ontology of gaps in content-based image retrieval.

    Science.gov (United States)

    Deserno, Thomas M; Antani, Sameer; Long, Rodney

    2009-04-01

    Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however, has not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed (without supporting analysis) to the inability of these applications in overcoming the "semantic gap." The semantic gap divides the high-level scene understanding and interpretation available with human cognitive capabilities from the low-level pixel analysis of computers, based on mathematical processing and artificial intelligence methods. In this paper, we suggest a more systematic and comprehensive view of the concept of "gaps" in medical CBIR research. In particular, we define an ontology of 14 gaps that addresses the image content and features, as well as system performance and usability. In addition to these gaps, we identify seven system characteristics that impact CBIR applicability and performance. The framework we have created can be used a posteriori to compare medical CBIR systems and approaches for specific biomedical image domains and goals and a priori during the design phase of a medical CBIR application, as the systematic analysis of gaps provides detailed insight in system comparison and helps to direct future research.

  3. Tools for automating the imaging of zebrafish larvae.

    Science.gov (United States)

    Pulak, Rock

    2016-03-01

    The VAST BioImager system is a set of tools developed for zebrafish researchers who require the collection of images from a large number of 2-7 dpf zebrafish larvae. The VAST BioImager automates larval handling, positioning and orientation tasks. Color images at about 10 μm resolution are collected from the on-board camera of the system. If images of greater resolution and detail are required, this system is mounted on an upright microscope, such as a confocal or fluorescence microscope, to utilize their capabilities. The system loads a larvae, positions it in view of the camera, determines orientation using pattern recognition analysis, and then more precisely positions to user-defined orientation for optimal imaging of any desired tissue or organ system. Multiple images of the same larva can be collected. The specific part of each larva and the desired orientation and position is identified by the researcher and an experiment defining the settings and a series of steps can be saved and repeated for imaging of subsequent larvae. The system captures images, then ejects and loads another larva from either a bulk reservoir, a well of a 96 well plate using the LP Sampler, or individually targeted larvae from a Petri dish or other container using the VAST Pipettor. Alternative manual protocols for handling larvae for image collection are tedious and time consuming. The VAST BioImager automates these steps to allow for greater throughput of assays and screens requiring high-content image collection of zebrafish larvae such as might be used in drug discovery and toxicology studies. Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.

  4. Automated image quality assessment for chest CT scans.

    Science.gov (United States)

    Reeves, Anthony P; Xie, Yiting; Liu, Shuang

    2018-02-01

    Medical image quality needs to be maintained at standards sufficient for effective clinical reading. Automated computer analytic methods may be applied to medical images for quality assessment. For chest CT scans in a lung cancer screening context, an automated quality assessment method is presented that characterizes image noise and image intensity calibration. This is achieved by image measurements in three automatically segmented homogeneous regions of the scan: external air, trachea lumen air, and descending aorta blood. Profiles of CT scanner behavior are also computed. The method has been evaluated on both phantom and real low-dose chest CT scans and results show that repeatable noise and calibration measures may be realized by automated computer algorithms. Noise and calibration profiles show relevant differences between different scanners and protocols. Automated image quality assessment may be useful for quality control for lung cancer screening and may enable performance improvements to automated computer analysis methods. © 2017 American Association of Physicists in Medicine.

  5. Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images

    Directory of Open Access Journals (Sweden)

    Eva Husson

    2016-09-01

    Full Text Available Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs provides aerial images with sub-decimetre resolution and offers a potential data source for vegetation mapping. In a manual mapping approach, UAS true-colour images with 5-cm-resolution pixels allowed for the identification of non-submerged aquatic vegetation at the species level. However, manual mapping is labour-intensive, and while automated classification methods are available, they have rarely been evaluated for aquatic vegetation, particularly at the scale of individual vegetation stands. We evaluated classification accuracy and time-efficiency for mapping non-submerged aquatic vegetation at three levels of detail at five test sites (100 m × 100 m differing in vegetation complexity. We used object-based image analysis and tested two classification methods (threshold classification and Random Forest using eCognition®. The automated classification results were compared to results from manual mapping. Using threshold classification, overall accuracy at the five test sites ranged from 93% to 99% for the water-versus-vegetation level and from 62% to 90% for the growth-form level. Using Random Forest classification, overall accuracy ranged from 56% to 94% for the growth-form level and from 52% to 75% for the dominant-taxon level. Overall classification accuracy decreased with increasing vegetation complexity. In test sites with more complex vegetation, automated classification was more time-efficient than manual mapping. This study demonstrated that automated classification of non-submerged aquatic vegetation from true-colour UAS images was feasible, indicating good potential for operative mapping of aquatic vegetation. When choosing the preferred mapping method (manual versus automated the desired level of

  6. Image Retargeting by Content-Aware Synthesis

    OpenAIRE

    Dong, Weiming; Wu, Fuzhang; Kong, Yan; Mei, Xing; Lee, Tong-Yee; Zhang, Xiaopeng

    2014-01-01

    Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on content-aware synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategy since they have different...

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

    Directory of Open Access Journals (Sweden)

    Ricardo Andres Pizarro

    2016-12-01

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

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

    Science.gov (United States)

    Pizarro, Ricardo A; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A; Goldman, Aaron L; Xiao, Ena; Luo, Qian; Berman, Karen F; Callicott, Joseph H; Weinberger, Daniel R; Mattay, Venkata S

    2016-01-01

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

  9. Detection of Isoflavones Content in Soybean Based on Hyperspectral Imaging Technology

    Directory of Open Access Journals (Sweden)

    Tan Kezhu

    2014-04-01

    Full Text Available Because of many important biological activities, Soybean isoflavones which has great potential for exploitation is significant to practical applications. Due to the conventional methods for determination of soybean isoflavones having long detection period, used too many reagents, couldn’t be detected on-line, and other issues, we propose hyperspectral imaging technology to detect the contents of soybean isoflavones. Based on the 40 varieties of soybeans produced in Heilongjiang province, we get the spectral reflection datum of soybean samples varied from the soybean’s hyperspectral images which are collected by the hyperspectral imaging system, and apply high performance liquid chromatography (HPLC method to determine the true value of the selected samples of isoflavones. The feature wavelengths for isoflavones content prediction (1516, 1572, 1691, 1716 and 1760 nm were selected based on correlation analysis. The prediction model was established by using the method of BP neural network in order to realize the prediction of soybean isoflavones content analysis. The experimental results show that, the ANN model could predict isoflavones content of soybean samples with of 0.9679, the average relative error is 0.8032 %, and the mean square error (MSE is 0.110328, which indicates the effectiveness of the proposed method and provides a theoretical basis for the applications of hyerspectral imaging in non-destructive detection for interior quality of soybean.

  10. Learning effective color features for content based image retrieval in dermatology

    NARCIS (Netherlands)

    Bunte, Kerstin; Biehl, Michael; Jonkman, Marcel F.; Petkov, Nicolai

    We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn

  11. Adaptive Algorithms for Automated Processing of Document Images

    Science.gov (United States)

    2011-01-01

    ABSTRACT Title of dissertation: ADAPTIVE ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES Mudit Agrawal, Doctor of Philosophy, 2011...2011 4. TITLE AND SUBTITLE Adaptive Algorithms for Automated Processing of Document Images 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES by Mudit Agrawal Dissertation submitted to the Faculty of the Graduate School of the University

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

    Science.gov (United States)

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

    2010-02-01

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

  13. Content Based Radiographic Images Indexing and Retrieval Using Pattern Orientation Histogram

    Directory of Open Access Journals (Sweden)

    Abolfazl Lakdashti

    2008-06-01

    Full Text Available Introduction: Content Based Image Retrieval (CBIR is a method of image searching and retrieval in a  database. In medical applications, CBIR is a tool used by physicians to compare the previous and current  medical images associated with patients pathological conditions. As the volume of pictorial information  stored in medical image databases is in progress, efficient image indexing and retrieval is increasingly  becoming a necessity.  Materials and Methods: This paper presents a new content based radiographic image retrieval approach  based on histogram of pattern orientations, namely pattern orientation histogram (POH. POH represents  the  spatial  distribution  of  five  different  pattern  orientations:  vertical,  horizontal,  diagonal  down/left,  diagonal down/right and non-orientation. In this method, a given image is first divided into image-blocks  and  the  frequency  of  each  type  of  pattern  is  determined  in  each  image-block.  Then,  local  pattern  histograms for each of these image-blocks are computed.   Results: The method was compared to two well known texture-based image retrieval methods: Tamura  and  Edge  Histogram  Descriptors  (EHD  in  MPEG-7  standard.  Experimental  results  based  on  10000  IRMA  radiography  image  dataset,  demonstrate  that  POH  provides  better  precision  and  recall  rates  compared to Tamura and EHD. For some images, the recall and precision rates obtained by POH are,  respectively, 48% and 18% better than the best of the two above mentioned methods.    Discussion and Conclusion: Since we exploit the absolute location of the pattern in the image as well as  its global composition, the proposed matching method can retrieve semantically similar medical images.

  14. 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.

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

    International Nuclear Information System (INIS)

    Linguraru, Marius George; Panjwani, Neil; Fletcher, Joel G.; Summer, Ronald M.

    2011-01-01

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

  16. Ontology of Gaps in Content-Based Image Retrieval

    OpenAIRE

    Deserno, Thomas M.; Antani, Sameer; Long, Rodney

    2008-01-01

    Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however, has not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed (without supporting analysis) to the ina...

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

    Science.gov (United States)

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

    2006-12-01

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

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

    International Nuclear Information System (INIS)

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

    2006-01-01

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

  19. ImageGrouper: a group-oriented user interface for content-based image retrieval and digital image arrangement

    NARCIS (Netherlands)

    Nakazato, Munehiro; Manola, L.; Huang, Thomas S.

    In content-based image retrieval (CBIR), experimental (trial-and-error) query with relevance feedback is essential for successful retrieval. Unfortunately, the traditional user interfaces are not suitable for trying different combinations of query examples. This is because first, these systems

  20. Automated breast segmentation in ultrasound computer tomography SAFT images

    Science.gov (United States)

    Hopp, T.; You, W.; Zapf, M.; Tan, W. Y.; Gemmeke, H.; Ruiter, N. V.

    2017-03-01

    Ultrasound Computer Tomography (USCT) is a promising new imaging system for breast cancer diagnosis. An essential step before further processing is to remove the water background from the reconstructed images. In this paper we present a fully-automated image segmentation method based on three-dimensional active contours. The active contour method is extended by applying gradient vector flow and encoding the USCT aperture characteristics as additional weighting terms. A surface detection algorithm based on a ray model is developed to initialize the active contour, which is iteratively deformed to capture the breast outline in USCT reflection images. The evaluation with synthetic data showed that the method is able to cope with noisy images, and is not influenced by the position of the breast and the presence of scattering objects within the breast. The proposed method was applied to 14 in-vivo images resulting in an average surface deviation from a manual segmentation of 2.7 mm. We conclude that automated segmentation of USCT reflection images is feasible and produces results comparable to a manual segmentation. By applying the proposed method, reproducible segmentation results can be obtained without manual interaction by an expert.

  1. Biased discriminant euclidean embedding for content-based image retrieval.

    Science.gov (United States)

    Bian, Wei; Tao, Dacheng

    2010-02-01

    With many potential multimedia applications, content-based image retrieval (CBIR) has recently gained more attention for image management and web search. A wide variety of relevance feedback (RF) algorithms have been developed in recent years to improve the performance of CBIR systems. These RF algorithms capture user's preferences and bridge the semantic gap. However, there is still a big room to further the RF performance, because the popular RF algorithms ignore the manifold structure of image low-level visual features. In this paper, we propose the biased discriminative Euclidean embedding (BDEE) which parameterises samples in the original high-dimensional ambient space to discover the intrinsic coordinate of image low-level visual features. BDEE precisely models both the intraclass geometry and interclass discrimination and never meets the undersampled problem. To consider unlabelled samples, a manifold regularization-based item is introduced and combined with BDEE to form the semi-supervised BDEE, or semi-BDEE for short. To justify the effectiveness of the proposed BDEE and semi-BDEE, we compare them against the conventional RF algorithms and show a significant improvement in terms of accuracy and stability based on a subset of the Corel image gallery.

  2. Automated otolith image classification with multiple views: an evaluation on Sciaenidae.

    Science.gov (United States)

    Wong, J Y; Chu, C; Chong, V C; Dhillon, S K; Loh, K H

    2016-08-01

    Combined multiple 2D views (proximal, anterior and ventral aspects) of the sagittal otolith are proposed here as a method to capture shape information for fish classification. Classification performance of single view compared with combined 2D views show improved classification accuracy of the latter, for nine species of Sciaenidae. The effects of shape description methods (shape indices, Procrustes analysis and elliptical Fourier analysis) on classification performance were evaluated. Procrustes analysis and elliptical Fourier analysis perform better than shape indices when single view is considered, but all perform equally well with combined views. A generic content-based image retrieval (CBIR) system that ranks dissimilarity (Procrustes distance) of otolith images was built to search query images without the need for detailed information of side (left or right), aspect (proximal or distal) and direction (positive or negative) of the otolith. Methods for the development of this automated classification system are discussed. © 2016 The Fisheries Society of the British Isles.

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

    International Nuclear Information System (INIS)

    Park, Seong Hoon; Seo, Joon Beom; Kim, Nam Kug; Lee, Young Kyung; Kim, Song Soo; Chae, Eun Jin; Lee, June Goo

    2007-01-01

    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

  4. Connecting imaging mass spectrometry and magnetic resonance imaging-based anatomical atlases for automated anatomical interpretation and differential analysis.

    Science.gov (United States)

    Verbeeck, Nico; Spraggins, Jeffrey M; Murphy, Monika J M; Wang, Hui-Dong; Deutch, Ariel Y; Caprioli, Richard M; Van de Plas, Raf

    2017-07-01

    Imaging mass spectrometry (IMS) is a molecular imaging technology that can measure thousands of biomolecules concurrently without prior tagging, making it particularly suitable for exploratory research. However, the data size and dimensionality often makes thorough extraction of relevant information impractical. To help guide and accelerate IMS data analysis, we recently developed a framework that integrates IMS measurements with anatomical atlases, opening up opportunities for anatomy-driven exploration of IMS data. One example is the automated anatomical interpretation of ion images, where empirically measured ion distributions are automatically decomposed into their underlying anatomical structures. While offering significant potential, IMS-atlas integration has thus far been restricted to the Allen Mouse Brain Atlas (AMBA) and mouse brain samples. Here, we expand the applicability of this framework by extending towards new animal species and a new set of anatomical atlases retrieved from the Scalable Brain Atlas (SBA). Furthermore, as many SBA atlases are based on magnetic resonance imaging (MRI) data, a new registration pipeline was developed that enables direct non-rigid IMS-to-MRI registration. These developments are demonstrated on protein-focused FTICR IMS measurements from coronal brain sections of a Parkinson's disease (PD) rat model. The measurements are integrated with an MRI-based rat brain atlas from the SBA. The new rat-focused IMS-atlas integration is used to perform automated anatomical interpretation and to find differential ions between healthy and diseased tissue. IMS-atlas integration can serve as an important accelerator in IMS data exploration, and with these new developments it can now be applied to a wider variety of animal species and modalities. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann. Copyright © 2017. Published by Elsevier B.V.

  5. Automated analysis of angle closure from anterior chamber angle images.

    Science.gov (United States)

    Baskaran, Mani; Cheng, Jun; Perera, Shamira A; Tun, Tin A; Liu, Jiang; Aung, Tin

    2014-10-21

    To evaluate a novel software capable of automatically grading angle closure on EyeCam angle images in comparison with manual grading of images, with gonioscopy as the reference standard. In this hospital-based, prospective study, subjects underwent gonioscopy by a single observer, and EyeCam imaging by a different operator. The anterior chamber angle in a quadrant was classified as closed if the posterior trabecular meshwork could not be seen. An eye was classified as having angle closure if there were two or more quadrants of closure. Automated grading of the angle images was performed using customized software. Agreement between the methods was ascertained by κ statistic and comparison of area under receiver operating characteristic curves (AUC). One hundred forty subjects (140 eyes) were included, most of whom were Chinese (102/140, 72.9%) and women (72/140, 51.5%). Angle closure was detected in 61 eyes (43.6%) with gonioscopy in comparison with 59 eyes (42.1%, P = 0.73) using manual grading, and 67 eyes (47.9%, P = 0.24) with automated grading of EyeCam images. The agreement for angle closure diagnosis between gonioscopy and both manual (κ = 0.88; 95% confidence interval [CI), 0.81-0.96) and automated grading of EyeCam images was good (κ = 0.74; 95% CI, 0.63-0.85). The AUC for detecting eyes with gonioscopic angle closure was comparable for manual and automated grading (AUC 0.974 vs. 0.954, P = 0.31) of EyeCam images. Customized software for automated grading of EyeCam angle images was found to have good agreement with gonioscopy. Human observation of the EyeCam images may still be needed to avoid gross misclassification, especially in eyes with extensive angle closure. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

  6. Phase-image-based content-addressable holographic data storage

    Science.gov (United States)

    John, Renu; Joseph, Joby; Singh, Kehar

    2004-03-01

    We propose and demonstrate the use of phase images for content-addressable holographic data storage. Use of binary phase-based data pages with 0 and π phase changes, produces uniform spectral distribution at the Fourier plane. The absence of strong DC component at the Fourier plane and more intensity of higher order spatial frequencies facilitate better recording of higher spatial frequencies, and improves the discrimination capability of the content-addressable memory. This improves the results of the associative recall in a holographic memory system, and can give low number of false hits even for small search arguments. The phase-modulated pixels also provide an opportunity of subtraction among data pixels leading to better discrimination between similar data pages.

  7. Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle.

    Science.gov (United States)

    Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

    2018-01-10

    We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy.

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

    Science.gov (United States)

    Lahmiri, Salim; Gargour, Christian S; Gabrea, Marcel

    2014-10-01

    An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity.

  9. Information management for high content live cell imaging

    Directory of Open Access Journals (Sweden)

    White Michael RH

    2009-07-01

    Full Text Available Abstract Background High content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. Whilst solutions exist for managing image data, they are primarily concerned with storage and retrieval of the images themselves and not the data derived from the images. There is therefore a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments. Results We have designed and implemented a data model and information management solution for the data gathered through high content live cell imaging experiments. Many of the experiments to be stored measure the translocation of fluorescently labelled proteins from cytoplasm to nucleus in individual cells. The functionality of this database has been enhanced by the addition of an algorithm that automatically annotates results of these experiments with the timings of translocations and periods of any oscillatory translocations as they are uploaded to the repository. Testing has shown the algorithm to perform well with a variety of previously unseen data. Conclusion Our repository is a fully functional example of how high throughput imaging data may be effectively indexed and managed to address the requirements of end users. By implementing the automated analysis of experimental results, we have provided a clear impetus for individuals to ensure that their data forms part of that which is stored in the repository. Although focused on imaging, the solution provided is sufficiently generic to be applied to other functional proteomics and genomics experiments. The software is available from: fhttp://code.google.com/p/livecellim/

  10. Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System.

    Science.gov (United States)

    Poon, Candice C; Ebacher, Vincent; Liu, Katherine; Yong, Voon Wee; Kelly, John James Patrick

    2018-05-03

    Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. Unfortunately, many researchers spend large amounts of time and resources developing and optimizing workflows that are only relevant to their own experiments. In this article, we describe a protocol that can be used by those with access to a widefield high-content analysis system (WHCAS) to image any slide-mounted tissue, with options for customization within pre-built modules found in the associated software. Not originally intended for slide scanning, the steps detailed in this article make it possible to acquire slide scanning images in the WHCAS which can be imported into the associated software. In this example, the automated segmentation of brain tumor slides is demonstrated, but the automated segmentation of any fluorescently-labeled nuclear or cytoplasmic marker is possible. Furthermore, there are a variety of other quantitative software modules including assays for protein localization/translocation, cellular proliferation/viability/apoptosis, and angiogenesis that can be run. This technique will save researchers time and effort and create an automated protocol for slide analysis.

  11. Content Based Image Matching for Planetary Science

    Science.gov (United States)

    Deans, M. C.; Meyer, C.

    2006-12-01

    Planetary missions generate large volumes of data. With the MER rovers still functioning on Mars, PDS contains over 7200 released images from the Microscopic Imagers alone. These data products are only searchable by keys such as the Sol, spacecraft clock, or rover motion counter index, with little connection to the semantic content of the images. We have developed a method for matching images based on the visual textures in images. For every image in a database, a series of filters compute the image response to localized frequencies and orientations. Filter responses are turned into a low dimensional descriptor vector, generating a 37 dimensional fingerprint. For images such as the MER MI, this represents a compression ratio of 99.9965% (the fingerprint is approximately 0.0035% the size of the original image). At query time, fingerprints are quickly matched to find images with similar appearance. Image databases containing several thousand images are preprocessed offline in a matter of hours. Image matches from the database are found in a matter of seconds. We have demonstrated this image matching technique using three sources of data. The first database consists of 7200 images from the MER Microscopic Imager. The second database consists of 3500 images from the Narrow Angle Mars Orbital Camera (MOC-NA), which were cropped into 1024×1024 sub-images for consistency. The third database consists of 7500 scanned archival photos from the Apollo Metric Camera. Example query results from all three data sources are shown. We have also carried out user tests to evaluate matching performance by hand labeling results. User tests verify approximately 20% false positive rate for the top 14 results for MOC NA and MER MI data. This means typically 10 to 12 results out of 14 match the query image sufficiently. This represents a powerful search tool for databases of thousands of images where the a priori match probability for an image might be less than 1%. Qualitatively, correct

  12. An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes

    Directory of Open Access Journals (Sweden)

    Marco Gaiani

    2016-02-01

    Full Text Available Automated image-based 3D reconstruction methods are more and more flooding our 3D modeling applications. Fully automated solutions give the impression that from a sample of randomly acquired images we can derive quite impressive visual 3D models. Although the level of automation is reaching very high standards, image quality is a fundamental pre-requisite to produce successful and photo-realistic 3D products, in particular when dealing with large datasets of images. This article presents an efficient pipeline based on color enhancement, image denoising, color-to-gray conversion and image content enrichment. The pipeline stems from an analysis of various state-of-the-art algorithms and aims to adjust the most promising methods, giving solutions to typical failure causes. The assessment evaluation proves how an effective image pre-processing, which considers the entire image dataset, can improve the automated orientation procedure and dense 3D point cloud reconstruction, even in the case of poor texture scenarios.

  13. Multiscale Distance Coherence Vector Algorithm for Content-Based Image Retrieval

    Science.gov (United States)

    Jiexian, Zeng; Xiupeng, Liu

    2014-01-01

    Multiscale distance coherence vector algorithm for content-based image retrieval (CBIR) is proposed due to the same descriptor with different shapes and the shortcomings of antinoise performance of the distance coherence vector algorithm. By this algorithm, the image contour curve is evolved by Gaussian function first, and then the distance coherence vector is, respectively, extracted from the contour of the original image and evolved images. Multiscale distance coherence vector was obtained by reasonable weight distribution of the distance coherence vectors of evolved images contour. This algorithm not only is invariable to translation, rotation, and scaling transformation but also has good performance of antinoise. The experiment results show us that the algorithm has a higher recall rate and precision rate for the retrieval of images polluted by noise. PMID:24883416

  14. Retrieval Architecture with Classified Query for Content Based Image Recognition

    Directory of Open Access Journals (Sweden)

    Rik Das

    2016-01-01

    Full Text Available The consumer behavior has been observed to be largely influenced by image data with increasing familiarity of smart phones and World Wide Web. Traditional technique of browsing through product varieties in the Internet with text keywords has been gradually replaced by the easy accessible image data. The importance of image data has portrayed a steady growth in application orientation for business domain with the advent of different image capturing devices and social media. The paper has described a methodology of feature extraction by image binarization technique for enhancing identification and retrieval of information using content based image recognition. The proposed algorithm was tested on two public datasets, namely, Wang dataset and Oliva and Torralba (OT-Scene dataset with 3688 images on the whole. It has outclassed the state-of-the-art techniques in performance measure and has shown statistical significance.

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

    OpenAIRE

    Zeder, M; Van den Wyngaert, S; Köster, O; Felder, K M; Pernthaler, J

    2010-01-01

    Quantification and sizing of filamentous cyanobacteria in environmental samples or cultures are time-consuming and are often performed by using manual or semiautomated microscopic analysis. Automation of conventional image analysis is difficult because filaments may exhibit great variations in length and patchy autofluorescence. Moreover, individual filaments frequently cross each other in microscopic preparations, as deduced by modeling. This paper describes a novel approach based on object-...

  16. Towards a framework for agent-based image analysis of remote-sensing data.

    Science.gov (United States)

    Hofmann, Peter; Lettmayer, Paul; Blaschke, Thomas; Belgiu, Mariana; Wegenkittl, Stefan; Graf, Roland; Lampoltshammer, Thomas Josef; Andrejchenko, Vera

    2015-04-03

    Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects' properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).

  17. Automated flow quantification in valvular heart disease based on backscattered Doppler power analysis: implementation on matrix-array ultrasound imaging systems.

    Science.gov (United States)

    Buck, Thomas; Hwang, Shawn M; Plicht, Björn; Mucci, Ronald A; Hunold, Peter; Erbel, Raimund; Levine, Robert A

    2008-06-01

    Cardiac ultrasound imaging systems are limited in the noninvasive quantification of valvular regurgitation due to indirect measurements and inaccurate hemodynamic assumptions. We recently demonstrated that the principle of integration of backscattered acoustic Doppler power times velocity can be used for flow quantification in valvular regurgitation directly at the vena contracta of a regurgitant flow jet. We now aimed to accomplish implementation of automated Doppler power flow analysis software on a standard cardiac ultrasound system utilizing novel matrix-array transducer technology with detailed description of system requirements, components and software contributing to the system. This system based on a 3.5 MHz, matrix-array cardiac ultrasound scanner (Sonos 5500, Philips Medical Systems) was validated by means of comprehensive experimental signal generator trials, in vitro flow phantom trials and in vivo testing in 48 patients with mitral regurgitation of different severity and etiology using magnetic resonance imaging (MRI) for reference. All measurements displayed good correlation to the reference values, indicating successful implementation of automated Doppler power flow analysis on a matrix-array ultrasound imaging system. Systematic underestimation of effective regurgitant orifice areas >0.65 cm(2) and volumes >40 ml was found due to currently limited Doppler beam width that could be readily overcome by the use of new generation 2D matrix-array technology. Automated flow quantification in valvular heart disease based on backscattered Doppler power can be fully implemented on board a routinely used matrix-array ultrasound imaging systems. Such automated Doppler power flow analysis of valvular regurgitant flow directly, noninvasively, and user independent overcomes the practical limitations of current techniques.

  18. A review of content-based image retrieval systems in medical applications-clinical benefits and future directions.

    Science.gov (United States)

    Müller, Henning; Michoux, Nicolas; Bandon, David; Geissbuhler, Antoine

    2004-02-01

    Content-based visual information retrieval (CBVIR) or content-based image retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. The availability of large and steadily growing amounts of visual and multimedia data, and the development of the Internet underline the need to create thematic access methods that offer more than simple text-based queries or requests based on matching exact database fields. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of differing sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered. In the medical field, images, and especially digital images, are produced in ever-increasing quantities and used for diagnostics and therapy. The Radiology Department of the University Hospital of Geneva alone produced more than 12,000 images a day in 2002. The cardiology is currently the second largest producer of digital images, especially with videos of cardiac catheterization ( approximately 1800 exams per year containing almost 2000 images each). The total amount of cardiologic image data produced in the Geneva University Hospital was around 1 TB in 2002. Endoscopic videos can equally produce enormous amounts of data. With digital imaging and communications in medicine (DICOM), a standard for image communication has been set and patient information can be stored with the actual image(s), although still a few problems prevail with respect to the standardization. In several articles, content-based access to medical images for supporting clinical decision-making has been proposed that would ease the management of clinical data and scenarios for the integration of

  19. Automated Archiving of Archaeological Aerial Images

    Directory of Open Access Journals (Sweden)

    Michael Doneus

    2016-03-01

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

  20. 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.

  1. 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

  2. The utilization of human color categorization for content-based image retrieval

    NARCIS (Netherlands)

    van den Broek, Egon; Rogowitz, Bernice E.; Kisters, Peter M.F.; Pappas, Thrasyvoulos N.; Vuurpijl, Louis G.

    2004-01-01

    We present the concept of intelligent Content-Based Image Retrieval (iCBIR), which incorporates knowledge concerning human cognition in system development. The present research focuses on the utilization of color categories (or focal colors) for CBIR purposes, in particularly considered to be useful

  3. 3-D image pre-processing algorithms for improved automated tracing of neuronal arbors.

    Science.gov (United States)

    Narayanaswamy, Arunachalam; Wang, Yu; Roysam, Badrinath

    2011-09-01

    The accuracy and reliability of automated neurite tracing systems is ultimately limited by image quality as reflected in the signal-to-noise ratio, contrast, and image variability. This paper describes a novel combination of image processing methods that operate on images of neurites captured by confocal and widefield microscopy, and produce synthetic images that are better suited to automated tracing. The algorithms are based on the curvelet transform (for denoising curvilinear structures and local orientation estimation), perceptual grouping by scalar voting (for elimination of non-tubular structures and improvement of neurite continuity while preserving branch points), adaptive focus detection, and depth estimation (for handling widefield images without deconvolution). The proposed methods are fast, and capable of handling large images. Their ability to handle images of unlimited size derives from automated tiling of large images along the lateral dimension, and processing of 3-D images one optical slice at a time. Their speed derives in part from the fact that the core computations are formulated in terms of the Fast Fourier Transform (FFT), and in part from parallel computation on multi-core computers. The methods are simple to apply to new images since they require very few adjustable parameters, all of which are intuitive. Examples of pre-processing DIADEM Challenge images are used to illustrate improved automated tracing resulting from our pre-processing methods.

  4. ARTIP: Automated Radio Telescope Image Processing Pipeline

    Science.gov (United States)

    Sharma, Ravi; Gyanchandani, Dolly; Kulkarni, Sarang; Gupta, Neeraj; Pathak, Vineet; Pande, Arti; Joshi, Unmesh

    2018-02-01

    The Automated Radio Telescope Image Processing Pipeline (ARTIP) automates the entire process of flagging, calibrating, and imaging for radio-interferometric data. ARTIP starts with raw data, i.e. a measurement set and goes through multiple stages, such as flux calibration, bandpass calibration, phase calibration, and imaging to generate continuum and spectral line images. Each stage can also be run independently. The pipeline provides continuous feedback to the user through various messages, charts and logs. It is written using standard python libraries and the CASA package. The pipeline can deal with datasets with multiple spectral windows and also multiple target sources which may have arbitrary combinations of flux/bandpass/phase calibrators.

  5. Automated medical image segmentation techniques

    Directory of Open Access Journals (Sweden)

    Sharma Neeraj

    2010-01-01

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

  6. AUTOMATED CELL SEGMENTATION WITH 3D FLUORESCENCE MICROSCOPY IMAGES.

    Science.gov (United States)

    Kong, Jun; Wang, Fusheng; Teodoro, George; Liang, Yanhui; Zhu, Yangyang; Tucker-Burden, Carol; Brat, Daniel J

    2015-04-01

    A large number of cell-oriented cancer investigations require an effective and reliable cell segmentation method on three dimensional (3D) fluorescence microscopic images for quantitative analysis of cell biological properties. In this paper, we present a fully automated cell segmentation method that can detect cells from 3D fluorescence microscopic images. Enlightened by fluorescence imaging techniques, we regulated the image gradient field by gradient vector flow (GVF) with interpolated and smoothed data volume, and grouped voxels based on gradient modes identified by tracking GVF field. Adaptive thresholding was then applied to voxels associated with the same gradient mode where voxel intensities were enhanced by a multiscale cell filter. We applied the method to a large volume of 3D fluorescence imaging data of human brain tumor cells with (1) small cell false detection and missing rates for individual cells; and (2) trivial over and under segmentation incidences for clustered cells. Additionally, the concordance of cell morphometry structure between automated and manual segmentation was encouraging. These results suggest a promising 3D cell segmentation method applicable to cancer studies.

  7. Content-Based Image Retrieval Benchmarking: Utilizing color categories and color distributions

    NARCIS (Netherlands)

    van den Broek, Egon; Kisters, Peter M.F.; Vuurpijl, Louis G.

    From a human centered perspective three ingredients for Content-Based Image Retrieval (CBIR) were developed. First, with their existence confirmed by experimental data, 11 color categories were utilized for CBIR and used as input for a new color space segmentation technique. The complete HSI color

  8. Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

    Directory of Open Access Journals (Sweden)

    Li Yizheng

    2007-09-01

    Full Text Available Abstract Background High content screening (HCS is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells. Results Cases of poor cell body segmentation occurred frequently for the SK-BR-3 cell line. We trained classifiers to identify SK-BR-3 cells that were well segmented. On an independent test set created by human review of cell images, our optimal support-vector machine classifier identified well-segmented cells with 81% accuracy. The dose responses of morphological features were measurably different in well- and poorly-segmented populations. Elimination of the poorly-segmented cell population increased the purity of DNA content distributions, while appropriately retaining biological heterogeneity, and simultaneously increasing our ability to resolve specific morphological changes in perturbed cells. Conclusion Image segmentation has a measurable impact on HCS data. The application of a multivariate shape-based filter to identify well-segmented cells improved HCS data quality for an HCS-unfriendly cell line, and could be a valuable post-processing step for some HCS datasets.

  9. Automated image segmentation using information theory

    International Nuclear Information System (INIS)

    Hibbard, L.S.

    2001-01-01

    Full text: Our development of automated contouring of CT images for RT planning is based on maximum a posteriori (MAP) analyses of region textures, edges, and prior shapes, and assumes stationary Gaussian distributions for voxel textures and contour shapes. Since models may not accurately represent image data, it would be advantageous to compute inferences without relying on models. The relative entropy (RE) from information theory can generate inferences based solely on the similarity of probability distributions. The entropy of a distribution of a random variable X is defined as -Σ x p(x)log 2 p(x) for all the values x which X may assume. The RE (Kullback-Liebler divergence) of two distributions p(X), q(X), over X is Σ x p(x)log 2 {p(x)/q(x)}. The RE is a kind of 'distance' between p,q, equaling zero when p=q and increasing as p,q are more different. Minimum-error MAP and likelihood ratio decision rules have RE equivalents: minimum error decisions obtain with functions of the differences between REs of compared distributions. One applied result is the contour ideally separating two regions is that which maximizes the relative entropy of the two regions' intensities. A program was developed that automatically contours the outlines of patients in stereotactic headframes, a situation most often requiring manual drawing. The relative entropy of intensities inside the contour (patient) versus outside (background) was maximized by conjugate gradient descent over the space of parameters of a deformable contour. shows the computed segmentation of a patient from headframe backgrounds. This program is particularly useful for preparing images for multimodal image fusion. Relative entropy and allied measures of distribution similarity provide automated contouring criteria that do not depend on statistical models of image data. This approach should have wide utility in medical image segmentation applications. Copyright (2001) Australasian College of Physical Scientists and

  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. Mosaicing of single plane illumination microscopy images using groupwise registration and fast content-based image fusion

    Science.gov (United States)

    Preibisch, Stephan; Rohlfing, Torsten; Hasak, Michael P.; Tomancak, Pavel

    2008-03-01

    Single Plane Illumination Microscopy (SPIM; Huisken et al., Nature 305(5686):1007-1009, 2004) is an emerging microscopic technique that enables live imaging of large biological specimens in their entirety. By imaging the living biological sample from multiple angles SPIM has the potential to achieve isotropic resolution throughout even relatively large biological specimens. For every angle, however, only a relatively shallow section of the specimen is imaged with high resolution, whereas deeper regions appear increasingly blurred. In order to produce a single, uniformly high resolution image, we propose here an image mosaicing algorithm that combines state of the art groupwise image registration for alignment with content-based image fusion to prevent degrading of the fused image due to regional blurring of the input images. For the registration stage, we introduce an application-specific groupwise transformation model that incorporates per-image as well as groupwise transformation parameters. We also propose a new fusion algorithm based on Gaussian filters, which is substantially faster than fusion based on local image entropy. We demonstrate the performance of our mosaicing method on data acquired from living embryos of the fruit fly, Drosophila, using four and eight angle acquisitions.

  12. Step-by-step guide to building an inexpensive 3D printed motorized positioning stage for automated high-content screening microscopy.

    Science.gov (United States)

    Schneidereit, Dominik; Kraus, Larissa; Meier, Jochen C; Friedrich, Oliver; Gilbert, Daniel F

    2017-06-15

    High-content screening microscopy relies on automation infrastructure that is typically proprietary, non-customizable, costly and requires a high level of skill to use and maintain. The increasing availability of rapid prototyping technology makes it possible to quickly engineer alternatives to conventional automation infrastructure that are low-cost and user-friendly. Here, we describe a 3D printed inexpensive open source and scalable motorized positioning stage for automated high-content screening microscopy and provide detailed step-by-step instructions to re-building the device, including a comprehensive parts list, 3D design files in STEP (Standard for the Exchange of Product model data) and STL (Standard Tessellation Language) format, electronic circuits and wiring diagrams as well as software code. System assembly including 3D printing requires approx. 30h. The fully assembled device is light-weight (1.1kg), small (33×20×8cm) and extremely low-cost (approx. EUR 250). We describe positioning characteristics of the stage, including spatial resolution, accuracy and repeatability, compare imaging data generated with our device to data obtained using a commercially available microplate reader, demonstrate its suitability to high-content microscopy in 96-well high-throughput screening format and validate its applicability to automated functional Cl - - and Ca 2+ -imaging with recombinant HEK293 cells as a model system. A time-lapse video of the stage during operation and as part of a custom assembled screening robot can be found at https://vimeo.com/158813199. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  13. Automated discrimination of lower and higher grade gliomas based on histopathological image analysis

    Directory of Open Access Journals (Sweden)

    Hojjat Seyed Mousavi

    2015-01-01

    Full Text Available Introduction: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images. To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG and high-grade glioma (HGG which represent a more advanced stage of the disease. Results: We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1 A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2 a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP. In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1 Successful detection rates of pseudopalisading necrosis

  14. Next frontier in agent-based complex automated negotiation

    CERN Document Server

    Ito, Takayuki; Zhang, Minjie; Robu, Valentin

    2015-01-01

    This book focuses on automated negotiations based on multi-agent systems. It is intended for researchers and students in various fields involving autonomous agents and multi-agent systems, such as e-commerce tools, decision-making and negotiation support systems, and collaboration tools. The contents will help them to understand the concept of automated negotiations, negotiation protocols, negotiating agents’ strategies, and the applications of those strategies. In this book, some negotiation protocols focusing on the multiple interdependent issues in negotiations are presented, making it possible to find high-quality solutions for the complex agents’ utility functions. This book is a compilation of the extended versions of the very best papers selected from the many that were presented at the International Workshop on Agent-Based Complex Automated Negotiations.

  15. Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

    Science.gov (United States)

    Li, Qingli; Zhou, Mei; Liu, Hongying; Wang, Yiting; Guo, Fangmin

    2015-12-01

    Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.

  16. Automation in high-content flow cytometry screening.

    Science.gov (United States)

    Naumann, U; Wand, M P

    2009-09-01

    High-content flow cytometric screening (FC-HCS) is a 21st Century technology that combines robotic fluid handling, flow cytometric instrumentation, and bioinformatics software, so that relatively large numbers of flow cytometric samples can be processed and analysed in a short period of time. We revisit a recent application of FC-HCS to the problem of cellular signature definition for acute graft-versus-host-disease. Our focus is on automation of the data processing steps using recent advances in statistical methodology. We demonstrate that effective results, on par with those obtained via manual processing, can be achieved using our automatic techniques. Such automation of FC-HCS has the potential to drastically improve diagnosis and biomarker identification.

  17. 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.

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

    International Nuclear Information System (INIS)

    Flaberg, Emilie; Sabelström, Per; Strandh, Christer; Szekely, Laszlo

    2008-01-01

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

  19. Automated magnification calibration in transmission electron microscopy using Fourier analysis of replica images

    International Nuclear Information System (INIS)

    Laak, Jeroen A.W.M. van der; Dijkman, Henry B.P.M.; Pahlplatz, Martin M.M.

    2006-01-01

    The magnification factor in transmission electron microscopy is not very precise, hampering for instance quantitative analysis of specimens. Calibration of the magnification is usually performed interactively using replica specimens, containing line or grating patterns with known spacing. In the present study, a procedure is described for automated magnification calibration using digital images of a line replica. This procedure is based on analysis of the power spectrum of Fourier transformed replica images, and is compared to interactive measurement in the same images. Images were used with magnification ranging from 1,000x to 200,000x. The automated procedure deviated on average 0.10% from interactive measurements. Especially for catalase replicas, the coefficient of variation of automated measurement was considerably smaller (average 0.28%) compared to that of interactive measurement (average 3.5%). In conclusion, calibration of the magnification in digital images from transmission electron microscopy may be performed automatically, using the procedure presented here, with high precision and accuracy

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

    International Nuclear Information System (INIS)

    Shahidi, Shoaleh; Bahrampour, Ehsan; Soltanimehr, Elham; Zamani, Ali; Oshagh, Morteza; Moattari, Marzieh; Mehdizadeh, Alireza

    2014-01-01

    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

  1. Content Progressive Coding of Limited Bits/pixel Images

    DEFF Research Database (Denmark)

    Jensen, Ole Riis; Forchhammer, Søren

    1999-01-01

    A new lossless context based method for content progressive coding of limited bits/pixel images is proposed. Progressive coding is achieved by separating the image into contelnt layers. Digital maps are compressed up to 3 times better than GIF.......A new lossless context based method for content progressive coding of limited bits/pixel images is proposed. Progressive coding is achieved by separating the image into contelnt layers. Digital maps are compressed up to 3 times better than GIF....

  2. System for accessing a collection of histology images using content-based strategies

    International Nuclear Information System (INIS)

    Gonzalez F; Caicedo J C; Cruz Roa A; Camargo, J; Spinel, C

    2010-01-01

    Histology images are an important resource for research, education and medical practice. The availability of image collections with reference purposes is limited to printed formats such as books and specialized journals. When histology image sets are published in digital formats, they are composed of some tens of images that do not represent the wide diversity of biological structures that can be found in fundamental tissues; making a complete histology image collection available to the general public having a great impact on research and education in different areas such as medicine, biology and natural sciences. This work presents the acquisition process of a histology image collection with 20,000 samples in digital format, from tissue processing to digital image capturing. The main purpose of collecting these images is to make them available as reference material to the academic community. In addition, this paper presents the design and architecture of a system to query and explore the image collection, using content-based image retrieval tools and text-based search on the annotations provided by experts. The system also offers novel image visualization methods to allow easy identification of interesting images among hundreds of possible pictures. The system has been developed using a service-oriented architecture and allows web-based access in http://www.informed.unal.edu.co

  3. 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.

  4. Microscope image based fully automated stomata detection and pore measurement method for grapevines

    Directory of Open Access Journals (Sweden)

    Hiranya Jayakody

    2017-11-01

    Full Text Available Abstract Background Stomatal behavior in grapevines has been identified as a good indicator of the water stress level and overall health of the plant. Microscope images are often used to analyze stomatal behavior in plants. However, most of the current approaches involve manual measurement of stomatal features. The main aim of this research is to develop a fully automated stomata detection and pore measurement method for grapevines, taking microscope images as the input. The proposed approach, which employs machine learning and image processing techniques, can outperform available manual and semi-automatic methods used to identify and estimate stomatal morphological features. Results First, a cascade object detection learning algorithm is developed to correctly identify multiple stomata in a large microscopic image. Once the regions of interest which contain stomata are identified and extracted, a combination of image processing techniques are applied to estimate the pore dimensions of the stomata. The stomata detection approach was compared with an existing fully automated template matching technique and a semi-automatic maximum stable extremal regions approach, with the proposed method clearly surpassing the performance of the existing techniques with a precision of 91.68% and an F1-score of 0.85. Next, the morphological features of the detected stomata were measured. Contrary to existing approaches, the proposed image segmentation and skeletonization method allows us to estimate the pore dimensions even in cases where the stomatal pore boundary is only partially visible in the microscope image. A test conducted using 1267 images of stomata showed that the segmentation and skeletonization approach was able to correctly identify the stoma opening 86.27% of the time. Further comparisons made with manually traced stoma openings indicated that the proposed method is able to estimate stomata morphological features with accuracies of 89.03% for area

  5. 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.

  6. Anti-cancer agents in Saudi Arabian herbals revealed by automated high-content imaging

    KAUST Repository

    Hajjar, Dina

    2017-06-13

    Natural products have been used for medical applications since ancient times. Commonly, natural products are structurally complex chemical compounds that efficiently interact with their biological targets, making them useful drug candidates in cancer therapy. Here, we used cell-based phenotypic profiling and image-based high-content screening to study the mode of action and potential cellular targets of plants historically used in Saudi Arabia\\'s traditional medicine. We compared the cytological profiles of fractions taken from Juniperus phoenicea (Arar), Anastatica hierochuntica (Kaff Maryam), and Citrullus colocynthis (Hanzal) with a set of reference compounds with established modes of action. Cluster analyses of the cytological profiles of the tested compounds suggested that these plants contain possible topoisomerase inhibitors that could be effective in cancer treatment. Using histone H2AX phosphorylation as a marker for DNA damage, we discovered that some of the compounds induced double-strand DNA breaks. Furthermore, chemical analysis of the active fraction isolated from Juniperus phoenicea revealed possible anti-cancer compounds. Our results demonstrate the usefulness of cell-based phenotypic screening of natural products to reveal their biological activities.

  7. Relevance Feedback in Content Based Image Retrieval: A Review

    Directory of Open Access Journals (Sweden)

    Manesh B. Kokare

    2011-01-01

    Full Text Available This paper provides an overview of the technical achievements in the research area of relevance feedback (RF in content-based image retrieval (CBIR. Relevance feedback is a powerful technique in CBIR systems, in order to improve the performance of CBIR effectively. It is an open research area to the researcher to reduce the semantic gap between low-level features and high level concepts. The paper covers the current state of art of the research in relevance feedback in CBIR, various relevance feedback techniques and issues in relevance feedback are discussed in detail.

  8. AUTOMATION OF IMAGE DATA PROCESSING

    Directory of Open Access Journals (Sweden)

    Preuss Ryszard

    2014-12-01

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

  9. PET-MR image fusion in soft tissue sarcoma: accuracy, reliability and practicality of interactive point-based and automated mutual information techniques

    International Nuclear Information System (INIS)

    Somer, Edward J.R.; Marsden, Paul K.; Benatar, Nigel A.; O'Doherty, Michael J.; Goodey, Joanne; Smith, Michael A.

    2003-01-01

    The fusion of functional positron emission tomography (PET) data with anatomical magnetic resonance (MR) or computed tomography images, using a variety of interactive and automated techniques, is becoming commonplace, with the technique of choice dependent on the specific application. The case of PET-MR image fusion in soft tissue is complicated by a lack of conspicuous anatomical features and deviation from the rigid-body model. Here we compare a point-based external marker technique with an automated mutual information algorithm and discuss the practicality, reliability and accuracy of each when applied to the study of soft tissue sarcoma. Ten subjects with suspected sarcoma in the knee, thigh, groin, flank or back underwent MR and PET scanning after the attachment of nine external fiducial markers. In the assessment of the point-based technique, three error measures were considered: fiducial localisation error (FLE), fiducial registration error (FRE) and target registration error (TRE). FLE, which represents the accuracy with which the fiducial points can be located, is related to the FRE minimised by the registration algorithm. The registration accuracy is best characterised by the TRE, which is the distance between corresponding points in each image space after registration. In the absence of salient features within the target volume, the TRE can be measured at fiducials excluded from the registration process. To assess the mutual information technique, PET data, acquired after physically removing the markers, were reconstructed in a variety of ways and registered with MR. Having applied the transform suggested by the algorithm to the PET scan acquired before the markers were removed, the residual distance between PET and MR marker-pairs could be measured. The manual point-based technique yielded the best results (RMS TRE =8.3 mm, max =22.4 mm, min =1.7 mm), performing better than the automated algorithm (RMS TRE =20.0 mm, max =30.5 mm, min =7.7 mm) when

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

    International Nuclear Information System (INIS)

    Mizuta, Shinobu; Urayama, Shin-ichi; Zoroofi, R.A.; Uyama, Chikao

    1998-01-01

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

  11. 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.

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

    International Nuclear Information System (INIS)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang; Chen, Ken Chung; Shen, Steve G. F.; Yan, Jin; Lee, Philip K. M.; Chow, Ben; Liu, Nancy X.; Xia, James J.; Shen, Dinggang

    2014-01-01

    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

  14. Automated image enhancement using power law transformations

    Indian Academy of Sciences (India)

    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 ...

  15. HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening.

    Science.gov (United States)

    Charoenkwan, Phasit; Hwang, Eric; Cutler, Robert W; Lee, Hua-Chin; Ko, Li-Wei; Huang, Hui-Ling; Ho, Shinn-Ying

    2013-01-01

    High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images. We propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e.g., neurite length, and generic feature descriptors (GFDs), e.g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable

  16. Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy system.

    Science.gov (United States)

    Liu, Bo; Yue, Yue-Min; Li, Ru; Shen, Wen-Jing; Wang, Ke-Lin

    2014-10-23

    A field imaging spectrometer system (FISS; 380-870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%-35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector.

  17. 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

    2015-01-07

    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.

  18. Automated landmark-guided deformable image registration

    International Nuclear Information System (INIS)

    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. (paper)

  19. 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...... method measured as the area under the receiver operating characteristic curve, was 0.97 in the test group, with an accuracy of 92%. The sensitivity was 86% at a specificity of 100%. CONCLUSIONS: A completely automated method using artificial neural networks can be used to detect lung cancer......PURPOSE: To develop a completely automated method based on image processing techniques and artificial neural networks for the interpretation of combined [(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images for the diagnosis and staging of lung...

  20. Content-adaptive Image Enhancement, Based on Sky and Grass Segmentation

    NARCIS (Netherlands)

    Zafarifar, B.; With, de P.H.N.

    2009-01-01

    Current TV image enhancement functions employ globally controlled settings. A more flexible system can be achieved if the global control is extended to incorporate semantic-level image content information. In this paper, we present a system that extends existing TV image enhancement functions with

  1. Comparison of manual vs. automated multimodality (CT-MRI) image registration for brain tumors

    International Nuclear Information System (INIS)

    Sarkar, Abhirup; Santiago, Roberto J.; Smith, Ryan; Kassaee, Alireza

    2005-01-01

    Computed tomgoraphy-magnetic resonance imaging (CT-MRI) registrations are routinely used for target-volume delineation of brain tumors. We clinically use 2 software packages based on manual operation and 1 automated package with 2 different algorithms: chamfer matching using bony structures, and mutual information using intensity patterns. In all registration algorithms, a minimum of 3 pairs of identical anatomical and preferably noncoplanar landmarks is used on each of the 2 image sets. In manual registration, the program registers these points and links the image sets using a 3-dimensional (3D) transformation. In automated registration, the 3 landmarks are used as an initial starting point and further processing is done to complete the registration. Using our registration packages, registration of CT and MRI was performed on 10 patients. We scored the results of each registration set based on the amount of time spent, the accuracy reported by the software, and a final evaluation. We evaluated each software program by measuring the residual error between 'matched' points on the right and left globes and the posterior fossa for fused image slices. In general, manual registration showed higher misalignment between corresponding points compared to automated registration using intensity matching. This error had no directional dependence and was, most of the time, larger for a larger structure in both registration techniques. Automated algorithm based on intensity matching also gave the best results in terms of registration accuracy, irrespective of whether or not the initial landmarks were chosen carefully, when compared to that done using bone matching algorithm. Intensity-matching algorithm required the least amount of user-time and provided better accuracy

  2. Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network

    Energy Technology Data Exchange (ETDEWEB)

    Aykac, Deniz [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK); Fox, Karen [Delta Health Alliance; Garg, Seema [University of North Carolina; Giancardo, Luca [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Nichols, Trent L [ORNL; Tobin Jr, Kenneth William [ORNL

    2011-01-01

    A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion/anomaly detection is a low-cost way of achieving broad-based screening for diabetic retinopathy (DR) and other eye diseases. In the process of a routine eye-screening examination, other non-image data is often available which may be useful in automated diagnosis of disease. In this work, we report on the results of combining this non-image data with image data, using the protocol and processing steps of a prototype system for automated disease diagnosis of retina examinations from a telemedicine network. The system includes quality assessments, automated physiology detection, and automated lesion detection to create an archive of known cases. Non-image data such as diabetes onset date and hemoglobin A1c (HgA1c) for each patient examination are included as well, and the system is used to create a content-based image retrieval engine capable of automated diagnosis of disease into 'normal' and 'abnormal' categories. The system achieves a sensitivity and specificity of 91.2% and 71.6% using hold-one-out validation testing.

  3. 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.

  4. How automated image analysis techniques help scientists in species identification and classification?

    Science.gov (United States)

    Yousef Kalafi, Elham; Town, Christopher; Kaur Dhillon, Sarinder

    2017-09-04

    Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification increased over the last two decades. Automation of data classification is primarily focussed on images, incorporating and analysing image data has recently become easier due to developments in computational technology. Research efforts in identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, categorizing and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies.

  5. An automated approach for segmentation of intravascular ultrasound images based on parametric active contour models

    International Nuclear Information System (INIS)

    Vard, Alireza; Jamshidi, Kamal; Movahhedinia, Naser

    2012-01-01

    This paper presents a fully automated approach to detect the intima and media-adventitia borders in intravascular ultrasound images based on parametric active contour models. To detect the intima border, we compute a new image feature applying a combination of short-term autocorrelations calculated for the contour pixels. These feature values are employed to define an energy function of the active contour called normalized cumulative short-term autocorrelation. Exploiting this energy function, the intima border is separated accurately from the blood region contaminated by high speckle noise. To extract media-adventitia boundary, we define a new form of energy function based on edge, texture and spring forces for the active contour. Utilizing this active contour, the media-adventitia border is identified correctly even in presence of branch openings and calcifications. Experimental results indicate accuracy of the proposed methods. In addition, statistical analysis demonstrates high conformity between manual tracing and the results obtained by the proposed approaches.

  6. An Automated, Image Processing System for Concrete Evaluation

    International Nuclear Information System (INIS)

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

    1998-01-01

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

  7. A rapid automatic analyzer and its methodology for effective bentonite content based on image recognition technology

    Directory of Open Access Journals (Sweden)

    Wei Long

    2016-09-01

    Full Text Available Fast and accurate determination of effective bentonite content in used clay bonded sand is very important for selecting the correct mixing ratio and mixing process to obtain high-performance molding sand. Currently, the effective bentonite content is determined by testing the ethylene blue absorbed in used clay bonded sand, which is usually a manual operation with some disadvantages including complicated process, long testing time and low accuracy. A rapid automatic analyzer of the effective bentonite content in used clay bonded sand was developed based on image recognition technology. The instrument consists of auto stirring, auto liquid removal, auto titration, step-rotation and image acquisition components, and processor. The principle of the image recognition method is first to decompose the color images into three-channel gray images based on the photosensitive degree difference of the light blue and dark blue in the three channels of red, green and blue, then to make the gray values subtraction calculation and gray level transformation of the gray images, and finally, to extract the outer circle light blue halo and the inner circle blue spot and calculate their area ratio. The titration process can be judged to reach the end-point while the area ratio is higher than the setting value.

  8. An automated image processing method for classification of diabetic retinopathy stages from conjunctival microvasculature images

    Science.gov (United States)

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

    2017-03-01

    The conjunctiva is a densely vascularized tissue of the eye that provides an opportunity for imaging of human microcirculation. In the current study, automated fine structure analysis of conjunctival microvasculature images was performed to discriminate stages of diabetic retinopathy (DR). The study population consisted of one group of nondiabetic control subjects (NC) and 3 groups of diabetic subjects, with no clinical DR (NDR), non-proliferative DR (NPDR), or proliferative DR (PDR). Ordinary least square regression and Fisher linear discriminant analyses were performed to automatically discriminate images between group pairs of subjects. Human observers who were masked to the grouping of subjects performed image discrimination between group pairs. Over 80% and 70% of images of subjects with clinical and non-clinical DR were correctly discriminated by the automated method, respectively. The discrimination rates of the automated method were higher than human observers. The fine structure analysis of conjunctival microvasculature images provided discrimination of DR stages and can be potentially useful for DR screening and monitoring.

  9. Wavelet optimization for content-based image retrieval in medical databases.

    Science.gov (United States)

    Quellec, G; Lamard, M; Cazuguel, G; Cochener, B; Roux, C

    2010-04-01

    We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system. Copyright 2009 Elsevier B.V. All rights reserved.

  10. Automated tracking of the vascular tree on DSA images

    International Nuclear Information System (INIS)

    Alperin, N.; Hoffmann, K.R.; Doi, K.

    1990-01-01

    Determination of the vascular tree structure is important for reconstruction of three-dimensional vascular tree from biplane images, for assessment of the significance of a lesion, and for planning treatment for arteriovenous malformation. To automate these analyses, the authors of this paper are developing a method to determine the vascular tree structure from digital subtraction angiography (DSA) images. The authors have previously described a vessel tracking method, based on the double-square-box technique. To improve the tracking accuracy, they have developed and integrated with the previous method a connectivity test and guided-sector-search technique. The connectivity test, based on region growing techniques, eliminates tracking across nonvessel regions. The guided sector-search method incorporates information from a larger are of the image to guide the search for the next tracking point

  11. Automated Image Analysis Corrosion Working Group Update: February 1, 2018

    Energy Technology Data Exchange (ETDEWEB)

    Wendelberger, James G. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2018-02-01

    These are slides for the automated image analysis corrosion working group update. The overall goals were: automate the detection and quantification of features in images (faster, more accurate), how to do this (obtain data, analyze data), focus on Laser Scanning Confocal Microscope (LCM) data (laser intensity, laser height/depth, optical RGB, optical plus laser RGB).

  12. Identifying and Quantifying Cultural Factors That Matter to the IT Workforce: An Approach Based on Automated Content Analysis

    DEFF Research Database (Denmark)

    Schmiedel, Theresa; Müller, Oliver; Debortoli, Stefan

    2016-01-01

    builds on 112,610 online reviews of Fortune 500 IT companies collected from Glassdoor, an online platform on which current and former employees can anonymously review companies and their management. We perform an automated content analysis to identify cultural factors that employees emphasize...

  13. Automating the radiographic NDT process

    International Nuclear Information System (INIS)

    Aman, J.K.

    1986-01-01

    Automation, the removal of the human element in inspection, has not been generally applied to film radiographic NDT. The justication for automating is not only productivity but also reliability of results. Film remains in the automated system of the future because of its extremely high image content, approximately 8 x 10 9 bits per 14 x 17. The equivalent to 2200 computer floppy discs. Parts handling systems and robotics applied for manufacturing and some NDT modalities, should now be applied to film radiographic NDT systems. Automatic film handling can be achieved with the daylight NDT film handling system. Automatic film processing is becoming the standard in industry and can be coupled to the daylight system. Robots offer the opportunity to automate fully the exposure step. Finally, computer aided interpretation appears on the horizon. A unit which laser scans a 14 x 17 (inch) film in 6 - 8 seconds can digitize film information for further manipulation and possible automatic interrogations (computer aided interpretation). The system called FDRS (for Film Digital Radiography System) is moving toward 50 micron (*approx* 16 lines/mm) resolution. This is believed to meet the need of the majority of image content needs. We expect the automated system to appear first in parts (modules) as certain operations are automated. The future will see it all come together in an automated film radiographic NDT system (author) [pt

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

    Directory of Open Access Journals (Sweden)

    Ronald van 't Klooster

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

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

    International Nuclear Information System (INIS)

    Ben Abdallah, M.; Malek, J.; Tourki, R.; Krissian, K.

    2011-01-01

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

  16. IMAGE DESCRIPTIONS FOR SKETCH BASED IMAGE RETRIEVAL

    OpenAIRE

    SAAVEDRA RONDO, JOSE MANUEL; SAAVEDRA RONDO, JOSE MANUEL

    2008-01-01

    Due to the massive use of Internet together with the proliferation of media devices, content based image retrieval has become an active discipline in computer science. A common content based image retrieval approach requires that the user gives a regular image (e.g, a photo) as a query. However, having a regular image as query may be a serious problem. Indeed, people commonly use an image retrieval system because they do not count on the desired image. An easy alternative way t...

  17. Automated curved planar reformation of 3D spine images

    International Nuclear Information System (INIS)

    Vrtovec, Tomaz; Likar, Bostjan; Pernus, Franjo

    2005-01-01

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

  18. Comparison of known food weights with image-based portion-size automated estimation and adolescents' self-reported portion size.

    Science.gov (United States)

    Lee, Christina D; Chae, Junghoon; Schap, TusaRebecca E; Kerr, Deborah A; Delp, Edward J; Ebert, David S; Boushey, Carol J

    2012-03-01

    Diet is a critical element of diabetes self-management. An emerging area of research is the use of images for dietary records using mobile telephones with embedded cameras. These tools are being designed to reduce user burden and to improve accuracy of portion-size estimation through automation. The objectives of this study were to (1) assess the error of automatically determined portion weights compared to known portion weights of foods and (2) to compare the error between automation and human. Adolescents (n = 15) captured images of their eating occasions over a 24 h period. All foods and beverages served were weighed. Adolescents self-reported portion sizes for one meal. Image analysis was used to estimate portion weights. Data analysis compared known weights, automated weights, and self-reported portions. For the 19 foods, the mean ratio of automated weight estimate to known weight ranged from 0.89 to 4.61, and 9 foods were within 0.80 to 1.20. The largest error was for lettuce and the most accurate was strawberry jam. The children were fairly accurate with portion estimates for two foods (sausage links, toast) using one type of estimation aid and two foods (sausage links, scrambled eggs) using another aid. The automated method was fairly accurate for two foods (sausage links, jam); however, the 95% confidence intervals for the automated estimates were consistently narrower than human estimates. The ability of humans to estimate portion sizes of foods remains a problem and a perceived burden. Errors in automated portion-size estimation can be systematically addressed while minimizing the burden on people. Future applications that take over the burden of these processes may translate to better diabetes self-management. © 2012 Diabetes Technology Society.

  19. Morphological images analysis and chromosomic aberrations classification based on fuzzy logic

    International Nuclear Information System (INIS)

    Souza, Leonardo Peres

    2011-01-01

    This work has implemented a methodology for automation of images analysis of chromosomes of human cells irradiated at IEA-R1 nuclear reactor (located at IPEN, Sao Paulo, Brazil), and therefore subject to morphological aberrations. This methodology intends to be a tool for helping cytogeneticists on identification, characterization and classification of chromosomal metaphasic analysis. The methodology development has included the creation of a software application based on artificial intelligence techniques using Fuzzy Logic combined with image processing techniques. The developed application was named CHRIMAN and is composed of modules that contain the methodological steps which are important requirements in order to achieve an automated analysis. The first step is the standardization of the bi-dimensional digital image acquisition procedure through coupling a simple digital camera to the ocular of the conventional metaphasic analysis microscope. Second step is related to the image treatment achieved through digital filters application; storing and organization of information obtained both from image content itself, and from selected extracted features, for further use on pattern recognition algorithms. The third step consists on characterizing, counting and classification of stored digital images and extracted features information. The accuracy in the recognition of chromosome images is 93.9%. This classification is based on classical standards obtained at Buckton [1973], and enables support to geneticist on chromosomic analysis procedure, decreasing analysis time, and creating conditions to include this method on a broader evaluation system on human cell damage due to ionizing radiation exposure. (author)

  20. Content dependent selection of image enhancement parameters for mobile displays

    Science.gov (United States)

    Lee, Yoon-Gyoo; Kang, Yoo-Jin; Kim, Han-Eol; Kim, Ka-Hee; Kim, Choon-Woo

    2011-01-01

    Mobile devices such as cellular phones and portable multimedia player with capability of playing terrestrial digital multimedia broadcasting (T-DMB) contents have been introduced into consumer market. In this paper, content dependent image quality enhancement method for sharpness and colorfulness and noise reduction is presented to improve perceived image quality on mobile displays. Human visual experiments are performed to analyze viewers' preference. Relationship between the objective measures and the optimal values of image control parameters are modeled by simple lookup tables based on the results of human visual experiments. Content dependent values of image control parameters are determined based on the calculated measures and predetermined lookup tables. Experimental results indicate that dynamic selection of image control parameters yields better image quality.

  1. Combining semantic technologies with a content-based image retrieval system - Preliminary considerations

    Science.gov (United States)

    Chmiel, P.; Ganzha, M.; Jaworska, T.; Paprzycki, M.

    2017-10-01

    Nowadays, as a part of systematic growth of volume, and variety, of information that can be found on the Internet, we observe also dramatic increase in sizes of available image collections. There are many ways to help users browsing / selecting images of interest. One of popular approaches are Content-Based Image Retrieval (CBIR) systems, which allow users to search for images that match their interests, expressed in the form of images (query by example). However, we believe that image search and retrieval could take advantage of semantic technologies. We have decided to test this hypothesis. Specifically, on the basis of knowledge captured in the CBIR, we have developed a domain ontology of residential real estate (detached houses, in particular). This allows us to semantically represent each image (and its constitutive architectural elements) represented within the CBIR. The proposed ontology was extended to capture not only the elements resulting from image segmentation, but also "spatial relations" between them. As a result, a new approach to querying the image database (semantic querying) has materialized, thus extending capabilities of the developed system.

  2. Implementation and evaluation of a medical image management system with content-based retrieval support

    International Nuclear Information System (INIS)

    Carita, Edilson Carlos; Seraphim, Enzo; Honda, Marcelo Ossamu; Azevedo-Marques, Paulo Mazzoncini de

    2008-01-01

    Objective: the present paper describes the implementation and evaluation of a medical images management system with content-based retrieval support (PACS-CBIR) integrating modules focused on images acquisition, storage and distribution, and text retrieval by keyword and images retrieval by similarity. Materials and methods: internet-compatible technologies were utilized for the system implementation with free ware, and C ++ , PHP and Java languages on a Linux platform. There is a DICOM-compatible image management module and two query modules, one of them based on text and the other on similarity of image texture attributes. Results: results demonstrate an appropriate images management and storage, and that the images retrieval time, always < 15 sec, was found to be good by users. The evaluation of retrieval by similarity has demonstrated that the selected images extractor allowed the sorting of images according to anatomical areas. Conclusion: based on these results, one can conclude that the PACS-CBIR implementation is feasible. The system has demonstrated to be DICOM-compatible, and that it can be integrated with the local information system. The similar images retrieval functionality can be enhanced by the introduction of further descriptors. (author)

  3. Radiographic examination takes on an automated image

    International Nuclear Information System (INIS)

    Aman, J.

    1988-01-01

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

  4. Semantics-based Automated Web Testing

    Directory of Open Access Journals (Sweden)

    Hai-Feng Guo

    2015-08-01

    Full Text Available We present TAO, a software testing tool performing automated test and oracle generation based on a semantic approach. TAO entangles grammar-based test generation with automated semantics evaluation using a denotational semantics framework. We show how TAO can be incorporated with the Selenium automation tool for automated web testing, and how TAO can be further extended to support automated delta debugging, where a failing web test script can be systematically reduced based on grammar-directed strategies. A real-life parking website is adopted throughout the paper to demonstrate the effectivity of our semantics-based web testing approach.

  5. Content-based image retrieval applied to bone age assessment

    Science.gov (United States)

    Fischer, Benedikt; Brosig, André; Welter, Petra; Grouls, Christoph; Günther, Rolf W.; Deserno, Thomas M.

    2010-03-01

    Radiological bone age assessment is based on local image regions of interest (ROI), such as the epiphysis or the area of carpal bones. These are compared to a standardized reference and scores determining the skeletal maturity are calculated. For computer-aided diagnosis, automatic ROI extraction and analysis is done so far mainly by heuristic approaches. Due to high variations in the imaged biological material and differences in age, gender and ethnic origin, automatic analysis is difficult and frequently requires manual interactions. On the contrary, epiphyseal regions (eROIs) can be compared to previous cases with known age by content-based image retrieval (CBIR). This requires a sufficient number of cases with reliable positioning of the eROI centers. In this first approach to bone age assessment by CBIR, we conduct leaving-oneout experiments on 1,102 left hand radiographs and 15,428 metacarpal and phalangeal eROIs from the USC hand atlas. The similarity of the eROIs is assessed by cross-correlation of 16x16 scaled eROIs. The effects of the number of eROIs, two age computation methods as well as the number of considered CBIR references are analyzed. The best results yield an error rate of 1.16 years and a standard deviation of 0.85 years. As the appearance of the hand varies naturally by up to two years, these results clearly demonstrate the applicability of the CBIR approach for bone age estimation.

  6. An efficient similarity measure for content based image retrieval using memetic algorithm

    Directory of Open Access Journals (Sweden)

    Mutasem K. Alsmadi

    2017-06-01

    Full Text Available Content based image retrieval (CBIR systems work by retrieving images which are related to the query image (QI from huge databases. The available CBIR systems extract limited feature sets which confine the retrieval efficacy. In this work, extensive robust and important features were extracted from the images database and then stored in the feature repository. This feature set is composed of color signature with the shape and color texture features. Where, features are extracted from the given QI in the similar fashion. Consequently, a novel similarity evaluation using a meta-heuristic algorithm called a memetic algorithm (genetic algorithm with great deluge is achieved between the features of the QI and the features of the database images. Our proposed CBIR system is assessed by inquiring number of images (from the test dataset and the efficiency of the system is evaluated by calculating precision-recall value for the results. The results were superior to other state-of-the-art CBIR systems in regard to precision.

  7. Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging.

    Directory of Open Access Journals (Sweden)

    Matthias C Roethke

    Full Text Available To evaluate the diagnostic performance of an automated analysis tool for the assessment of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI of the prostate.A fully automated analysis tool was used for a retrospective analysis of mpMRI sets (T2-weighted, T1-weighted dynamic contrast-enhanced, and diffusion-weighted sequences. The software provided a malignancy prediction value for each image pixel, defined as Malignancy Attention Index (MAI that can be depicted as a colour map overlay on the original images. The malignancy maps were compared to histopathology derived from a combination of MRI-targeted and systematic transperineal MRI/TRUS-fusion biopsies.In total, mpMRI data of 45 patients were evaluated. With a sensitivity of 85.7% (with 95% CI of 65.4-95.0, a specificity of 87.5% (with 95% CI of 69.0-95.7 and a diagnostic accuracy of 86.7% (with 95% CI of 73.8-93.8 for detection of prostate cancer, the automated analysis results corresponded well with the reported diagnostic accuracies by human readers based on the PI-RADS system in the current literature.The study revealed comparable diagnostic accuracies for the detection of prostate cancer of a user-independent MAI-based automated analysis tool and PI-RADS-scoring-based human reader analysis of mpMRI. Thus, the analysis tool could serve as a detection support system for less experienced readers. The results of the study also suggest the potential of MAI-based analysis for advanced lesion assessments, such as cancer extent and staging prediction.

  8. AN ENSEMBLE TEMPLATE MATCHING AND CONTENT-BASED IMAGE RETRIEVAL SCHEME TOWARDS EARLY STAGE DETECTION OF MELANOMA

    Directory of Open Access Journals (Sweden)

    Spiros Kostopoulos

    2016-12-01

    Full Text Available Malignant melanoma represents the most dangerous type of skin cancer. In this study we present an ensemble classification scheme, employing the mutual information, the cross-correlation and the clustering based on proximity of image features methods, for early stage assessment of melanomas on plain photography images. The proposed scheme performs two main operations. First, it retrieves the most similar, to the unknown case, image samples from an available image database with verified benign moles and malignant melanoma cases. Second, it provides an automated estimation regarding the nature of the unknown image sample based on the majority of the most similar images retrieved from the available database. Clinical material comprised 75 melanoma and 75 benign plain photography images collected from publicly available dermatological atlases. Results showed that the ensemble scheme outperformed all other methods tested in terms of accuracy with 94.9±1.5%, following an external cross-validation evaluation methodology. The proposed scheme may benefit patients by providing a second opinion consultation during the self-skin examination process and the physician by providing a second opinion estimation regarding the nature of suspicious moles that may assist towards decision making especially for ambiguous cases, safeguarding, in this way from potential diagnostic misinterpretations.

  9. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

    Science.gov (United States)

    Bray, Mark-Anthony; Singh, Shantanu; Han, Han; Davis, Chadwick T.; Borgeson, Blake; Hartland, Cathy; Kost-Alimova, Maria; Gustafsdottir, Sigrun M.; Gibson, Christopher C.; Carpenter, Anne E.

    2016-01-01

    In morphological profiling, quantitative data are extracted from microscopy images of cells to identify biologically relevant similarities and differences among samples based on these profiles. This protocol describes the design and execution of experiments using Cell Painting, a morphological profiling assay multiplexing six fluorescent dyes imaged in five channels, to reveal eight broadly relevant cellular components or organelles. Cells are plated in multi-well plates, perturbed with the treatments to be tested, stained, fixed, and imaged on a high-throughput microscope. Then, automated image analysis software identifies individual cells and measures ~1,500 morphological features (various measures of size, shape, texture, intensity, etc.) to produce a rich profile suitable for detecting subtle phenotypes. Profiles of cell populations treated with different experimental perturbations can be compared to suit many goals, such as identifying the phenotypic impact of chemical or genetic perturbations, grouping compounds and/or genes into functional pathways, and identifying signatures of disease. Cell culture and image acquisition takes two weeks; feature extraction and data analysis take an additional 1-2 weeks. PMID:27560178

  10. Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting.

    Science.gov (United States)

    Liu, Jianfei; Jung, HaeWon; Dubra, Alfredo; Tam, Johnny

    2017-09-01

    Adaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images. Algorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data. There was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test). MSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics.

  11. A Sensitive Measurement for Estimating Impressions of Image-Contents

    Science.gov (United States)

    Sato, Mie; Matouge, Shingo; Mori, Toshifumi; Suzuki, Noboru; Kasuga, Masao

    We have investigated Kansei Content that appeals maker's intention to viewer's kansei. An SD method is a very good way to evaluate subjective impression of image-contents. However, because the SD method is performed after subjects view the image-contents, it is difficult to examine impression of detailed scenes of the image-contents in real time. To measure viewer's impression of the image-contents in real time, we have developed a Taikan sensor. With the Taikan sensor, we investigate relations among the image-contents, the grip strength and the body temperature. We also explore the interface of the Taikan sensor to use it easily. In our experiment, a horror movie is used that largely affects emotion of the subjects. Our results show that there is a possibility that the grip strength increases when the subjects view a strained scene and that it is easy to use the Taikan sensor without its circle base that is originally installed.

  12. Automated quality control in a file-based broadcasting workflow

    Science.gov (United States)

    Zhang, Lina

    2014-04-01

    Benefit from the development of information and internet technologies, television broadcasting is transforming from inefficient tape-based production and distribution to integrated file-based workflows. However, no matter how many changes have took place, successful broadcasting still depends on the ability to deliver a consistent high quality signal to the audiences. After the transition from tape to file, traditional methods of manual quality control (QC) become inadequate, subjective, and inefficient. Based on China Central Television's full file-based workflow in the new site, this paper introduces an automated quality control test system for accurate detection of hidden troubles in media contents. It discusses the system framework and workflow control when the automated QC is added. It puts forward a QC criterion and brings forth a QC software followed this criterion. It also does some experiments on QC speed by adopting parallel processing and distributed computing. The performance of the test system shows that the adoption of automated QC can make the production effective and efficient, and help the station to achieve a competitive advantage in the media market.

  13. The Effect of Information Analysis Automation Display Content on Human Judgment Performance in Noisy Environments

    Science.gov (United States)

    Bass, Ellen J.; Baumgart, Leigh A.; Shepley, Kathryn Klein

    2014-01-01

    Displaying both the strategy that information analysis automation employs to makes its judgments and variability in the task environment may improve human judgment performance, especially in cases where this variability impacts the judgment performance of the information analysis automation. This work investigated the contribution of providing either information analysis automation strategy information, task environment information, or both, on human judgment performance in a domain where noisy sensor data are used by both the human and the information analysis automation to make judgments. In a simplified air traffic conflict prediction experiment, 32 participants made probability of horizontal conflict judgments under different display content conditions. After being exposed to the information analysis automation, judgment achievement significantly improved for all participants as compared to judgments without any of the automation's information. Participants provided with additional display content pertaining to cue variability in the task environment had significantly higher aided judgment achievement compared to those provided with only the automation's judgment of a probability of conflict. When designing information analysis automation for environments where the automation's judgment achievement is impacted by noisy environmental data, it may be beneficial to show additional task environment information to the human judge in order to improve judgment performance. PMID:24847184

  14. The Effect of Information Analysis Automation Display Content on Human Judgment Performance in Noisy Environments.

    Science.gov (United States)

    Bass, Ellen J; Baumgart, Leigh A; Shepley, Kathryn Klein

    2013-03-01

    Displaying both the strategy that information analysis automation employs to makes its judgments and variability in the task environment may improve human judgment performance, especially in cases where this variability impacts the judgment performance of the information analysis automation. This work investigated the contribution of providing either information analysis automation strategy information, task environment information, or both, on human judgment performance in a domain where noisy sensor data are used by both the human and the information analysis automation to make judgments. In a simplified air traffic conflict prediction experiment, 32 participants made probability of horizontal conflict judgments under different display content conditions. After being exposed to the information analysis automation, judgment achievement significantly improved for all participants as compared to judgments without any of the automation's information. Participants provided with additional display content pertaining to cue variability in the task environment had significantly higher aided judgment achievement compared to those provided with only the automation's judgment of a probability of conflict. When designing information analysis automation for environments where the automation's judgment achievement is impacted by noisy environmental data, it may be beneficial to show additional task environment information to the human judge in order to improve judgment performance.

  15. SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials

    International Nuclear Information System (INIS)

    Wahi-Anwar, M; Lo, P; Kim, H; Brown, M; McNitt-Gray, M

    2016-01-01

    Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifies the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel

  16. SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials

    Energy Technology Data Exchange (ETDEWEB)

    Wahi-Anwar, M; Lo, P; Kim, H; Brown, M; McNitt-Gray, M [UCLA Radiological Sciences, Los Angeles, CA (United States)

    2016-06-15

    Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifies the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel

  17. Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images.

    Science.gov (United States)

    Wang, Kang; Jayadev, Chaitra; Nittala, Muneeswar G; Velaga, Swetha B; Ramachandra, Chaithanya A; Bhaskaranand, Malavika; Bhat, Sandeep; Solanki, Kaushal; Sadda, SriniVas R

    2018-03-01

    We examined the sensitivity and specificity of an automated algorithm for detecting referral-warranted diabetic retinopathy (DR) on Optos ultrawidefield (UWF) pseudocolour images. Patients with diabetes were recruited for UWF imaging. A total of 383 subjects (754 eyes) were enrolled. Nonproliferative DR graded to be moderate or higher on the 5-level International Clinical Diabetic Retinopathy (ICDR) severity scale was considered as grounds for referral. The software automatically detected DR lesions using the previously trained classifiers and classified each image in the test set as referral-warranted or not warranted. Sensitivity, specificity and the area under the receiver operating curve (AUROC) of the algorithm were computed. The automated algorithm achieved a 91.7%/90.3% sensitivity (95% CI 90.1-93.9/80.4-89.4) with a 50.0%/53.6% specificity (95% CI 31.7-72.8/36.5-71.4) for detecting referral-warranted retinopathy at the patient/eye levels, respectively; the AUROC was 0.873/0.851 (95% CI 0.819-0.922/0.804-0.894). Diabetic retinopathy (DR) lesions were detected from Optos pseudocolour UWF images using an automated algorithm. Images were classified as referral-warranted DR with a high degree of sensitivity and moderate specificity. Automated analysis of UWF images could be of value in DR screening programmes and could allow for more complete and accurate disease staging. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

  18. DNA index determination with Automated Cellular Imaging System (ACIS in Barrett's esophagus: Comparison with CAS 200

    Directory of Open Access Journals (Sweden)

    Klein Michael

    2005-08-01

    Full Text Available Abstract Background For solid tumors, image cytometry has been shown to be more sensitive for diagnosing DNA content abnormalities (aneuploidy than flow cytometry. Image cytometry has often been performed using the semi-automated CAS 200 system. Recently, an Automated Cellular Imaging System (ACIS was introduced to determine DNA content (DNA index, but it has not been validated. Methods Using the CAS 200 system and ACIS, we compared the DNA index (DI obtained from the same archived formalin-fixed and paraffin embedded tissue samples from Barrett's esophagus related lesions, including samples with specialized intestinal metaplasia without dysplasia, low-grade dysplasia, high-grade dysplasia and adenocarcinoma. Results Although there was a very good correlation between the DI values determined by ACIS and CAS 200, the former was 25% more sensitive in detecting aneuploidy. ACIS yielded a mean DI value 18% higher than that obtained by CAS 200 (p t test. In addition, the average time required to perform a DNA ploidy analysis was shorter with the ACIS (30–40 min than with the CAS 200 (40–70 min. Results obtained by ACIS gave excellent inter-and intra-observer variability (coefficient of correlation >0.9 for both, p Conclusion Compared with the CAS 200, the ACIS is a more sensitive and less time consuming technique for determining DNA ploidy. Results obtained by ACIS are also highly reproducible.

  19. AUTOMATED INSPECTION OF POWER LINE CORRIDORS TO MEASURE VEGETATION UNDERCUT USING UAV-BASED IMAGES

    Directory of Open Access Journals (Sweden)

    M. Maurer

    2017-08-01

    Full Text Available Power line corridor inspection is a time consuming task that is performed mostly manually. As the development of UAVs made huge progress in recent years, and photogrammetric computer vision systems became well established, it is time to further automate inspection tasks. In this paper we present an automated processing pipeline to inspect vegetation undercuts of power line corridors. For this, the area of inspection is reconstructed, geo-referenced, semantically segmented and inter class distance measurements are calculated. The presented pipeline performs an automated selection of the proper 3D reconstruction method for on the one hand wiry (power line, and on the other hand solid objects (surrounding. The automated selection is realized by performing pixel-wise semantic segmentation of the input images using a Fully Convolutional Neural Network. Due to the geo-referenced semantic 3D reconstructions a documentation of areas where maintenance work has to be performed is inherently included in the distance measurements and can be extracted easily. We evaluate the influence of the semantic segmentation according to the 3D reconstruction and show that the automated semantic separation in wiry and dense objects of the 3D reconstruction routine improves the quality of the vegetation undercut inspection. We show the generalization of the semantic segmentation to datasets acquired using different acquisition routines and to varied seasons in time.

  20. Automated Inspection of Power Line Corridors to Measure Vegetation Undercut Using Uav-Based Images

    Science.gov (United States)

    Maurer, M.; Hofer, M.; Fraundorfer, F.; Bischof, H.

    2017-08-01

    Power line corridor inspection is a time consuming task that is performed mostly manually. As the development of UAVs made huge progress in recent years, and photogrammetric computer vision systems became well established, it is time to further automate inspection tasks. In this paper we present an automated processing pipeline to inspect vegetation undercuts of power line corridors. For this, the area of inspection is reconstructed, geo-referenced, semantically segmented and inter class distance measurements are calculated. The presented pipeline performs an automated selection of the proper 3D reconstruction method for on the one hand wiry (power line), and on the other hand solid objects (surrounding). The automated selection is realized by performing pixel-wise semantic segmentation of the input images using a Fully Convolutional Neural Network. Due to the geo-referenced semantic 3D reconstructions a documentation of areas where maintenance work has to be performed is inherently included in the distance measurements and can be extracted easily. We evaluate the influence of the semantic segmentation according to the 3D reconstruction and show that the automated semantic separation in wiry and dense objects of the 3D reconstruction routine improves the quality of the vegetation undercut inspection. We show the generalization of the semantic segmentation to datasets acquired using different acquisition routines and to varied seasons in time.

  1. A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

    Science.gov (United States)

    Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

    2018-01-01

    The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

  2. Automated detection of acute haemorrhagic stroke in non-contrasted CT images

    International Nuclear Information System (INIS)

    Meetz, K.; Buelow, T.

    2007-01-01

    An efficient treatment of stroke patients implies a profound differential diagnosis that includes the detection of acute haematoma. The proposed approach provides an automated detection of acute haematoma, assisting the non-stroke expert in interpreting non-contrasted CT images. It consists of two steps: First, haematoma candidates are detected applying multilevel region growing approach based on a typical grey value characteristic. Second, true haematomas are differentiated from partial volume artefacts, relying on spatial features derived from distance-based histograms. This approach achieves a specificity of 77% and a sensitivity of 89.7% in detecting acute haematoma in non-contrasted CT images when applied to a set of 25 non-contrasted CT images. (orig.)

  3. Fluorescence In Situ Hybridization (FISH Signal Analysis Using Automated Generated Projection Images

    Directory of Open Access Journals (Sweden)

    Xingwei Wang

    2012-01-01

    Full Text Available Fluorescence in situ hybridization (FISH tests provide promising molecular imaging biomarkers to more accurately and reliably detect and diagnose cancers and genetic disorders. Since current manual FISH signal analysis is low-efficient and inconsistent, which limits its clinical utility, developing automated FISH image scanning systems and computer-aided detection (CAD schemes has been attracting research interests. To acquire high-resolution FISH images in a multi-spectral scanning mode, a huge amount of image data with the stack of the multiple three-dimensional (3-D image slices is generated from a single specimen. Automated preprocessing these scanned images to eliminate the non-useful and redundant data is important to make the automated FISH tests acceptable in clinical applications. In this study, a dual-detector fluorescence image scanning system was applied to scan four specimen slides with FISH-probed chromosome X. A CAD scheme was developed to detect analyzable interphase cells and map the multiple imaging slices recorded FISH-probed signals into the 2-D projection images. CAD scheme was then applied to each projection image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm, identify FISH-probed signals using a top-hat transform, and compute the ratios between the normal and abnormal cells. To assess CAD performance, the FISH-probed signals were also independently visually detected by an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots in four testing samples. The study demonstrated the feasibility of automated FISH signal analysis that applying a CAD scheme to the automated generated 2-D projection images.

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

    Science.gov (United States)

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

    2010-12-15

    A complementary metal oxide semiconductor (CMOS) image sensor was applied to high-content analysis of single cells which were assembled closely or directly onto the CMOS sensor surface. The direct assembling of cell groups on CMOS sensor surface allows large-field (6.66 mm×5.32 mm in entire active area of CMOS sensor) imaging within a second. Trypan blue-stained and non-stained cells in the same field area on the CMOS sensor were successfully distinguished as white- and blue-colored images under white LED light irradiation. Furthermore, the chemiluminescent signals of each cell were successfully visualized as blue-colored images on CMOS sensor only when HeLa cells were placed directly on the micro-lens array of the CMOS sensor. Our proposed approach will be a promising technique for real-time and high-content analysis of single cells in a large-field area based on color imaging. Copyright © 2010 Elsevier B.V. All rights reserved.

  5. Shedding Light on Filovirus Infection with High-Content Imaging

    Directory of Open Access Journals (Sweden)

    Rekha G. Panchal

    2012-08-01

    Full Text Available Microscopy has been instrumental in the discovery and characterization of microorganisms. Major advances in high-throughput fluorescence microscopy and automated, high-content image analysis tools are paving the way to the systematic and quantitative study of the molecular properties of cellular systems, both at the population and at the single-cell level. High-Content Imaging (HCI has been used to characterize host-virus interactions in genome-wide reverse genetic screens and to identify novel cellular factors implicated in the binding, entry, replication and egress of several pathogenic viruses. Here we present an overview of the most significant applications of HCI in the context of the cell biology of filovirus infection. HCI assays have been recently implemented to quantitatively study filoviruses in cell culture, employing either infectious viruses in a BSL-4 environment or surrogate genetic systems in a BSL-2 environment. These assays are becoming instrumental for small molecule and siRNA screens aimed at the discovery of both cellular therapeutic targets and of compounds with anti-viral properties. We discuss the current practical constraints limiting the implementation of high-throughput biology in a BSL-4 environment, and propose possible solutions to safely perform high-content, high-throughput filovirus infection assays. Finally, we discuss possible novel applications of HCI in the context of filovirus research with particular emphasis on the identification of possible cellular biomarkers of virus infection.

  6. 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.

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

    Science.gov (United States)

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

    2012-01-01

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

  8. 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

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

    Science.gov (United States)

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

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

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

    Science.gov (United States)

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

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi. PMID:21461364

  11. 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...... slides stained with Van Gieson (VG). PATIENTS AND METHODS: A training set consisting of ten biopsies diagnosed as CC, CCi, and normal colon mucosa was used to develop the automated image analysis (VG app) to match the assessment by a pathologist. The study set consisted of biopsies from 75 patients...

  12. 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.

  13. Automated image analysis for quantification of filamentous bacteria

    DEFF Research Database (Denmark)

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

    2015-01-01

    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...

  14. Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

    Science.gov (United States)

    Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Gao, Yang; Chen, Yang; Feng, Qianjin; Chen, Wufan; Lu, Zhentai

    2014-01-01

    This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.

  15. Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

    Directory of Open Access Journals (Sweden)

    Meiyan Huang

    Full Text Available This study aims to develop content-based image retrieval (CBIR system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor. Using the BoVW model with partition learning, the mean average precision (mAP of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.

  16. 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.

  17. Automated synovium segmentation in doppler ultrasound images for rheumatoid arthritis assessment

    Science.gov (United States)

    Yeung, Pak-Hei; Tan, York-Kiat; Xu, Shuoyu

    2018-02-01

    We need better clinical tools to improve monitoring of synovitis, synovial inflammation in the joints, in rheumatoid arthritis (RA) assessment. Given its economical, safe and fast characteristics, ultrasound (US) especially Doppler ultrasound is frequently used. However, manual scoring of synovitis in US images is subjective and prone to observer variations. In this study, we propose a new and robust method for automated synovium segmentation in the commonly affected joints, i.e. metacarpophalangeal (MCP) and metatarsophalangeal (MTP) joints, which would facilitate automation in quantitative RA assessment. The bone contour in the US image is firstly detected based on a modified dynamic programming method, incorporating angular information for detecting curved bone surface and using image fuzzification to identify missing bone structure. K-means clustering is then performed to initialize potential synovium areas by utilizing the identified bone contour as boundary reference. After excluding invalid candidate regions, the final segmented synovium is identified by reconnecting remaining candidate regions using level set evolution. 15 MCP and 15 MTP US images were analyzed in this study. For each image, segmentations by our proposed method as well as two sets of annotations performed by an experienced clinician at different time-points were acquired. Dice's coefficient is 0.77+/-0.12 between the two sets of annotations. Similar Dice's coefficients are achieved between automated segmentation and either the first set of annotations (0.76+/-0.12) or the second set of annotations (0.75+/-0.11), with no significant difference (P = 0.77). These results verify that the accuracy of segmentation by our proposed method and by clinician is comparable. Therefore, reliable synovium identification can be made by our proposed method.

  18. Advances in Automated Plankton Imaging: Enhanced Throughput, Automated Staining, and Extended Deployment Modes for Imaging FlowCytobot

    Science.gov (United States)

    Sosik, H. M.; Olson, R. J.; Brownlee, E.; Brosnahan, M.; Crockford, E. T.; Peacock, E.; Shalapyonok, A.

    2016-12-01

    Imaging FlowCytobot (IFCB) was developed to fill a need for automated identification and monitoring of nano- and microplankton, especially phytoplankton in the size range 10 200 micrometer, which are important in coastal blooms (including harmful algal blooms). IFCB uses a combination of flow cytometric and video technology to capture high resolution (1 micrometer) images of suspended particles. This proven, now commercially available, submersible instrument technology has been deployed in fixed time series locations for extended periods (months to years) and in shipboard laboratories where underway water is automatically analyzed during surveys. Building from these successes, we have now constructed and evaluated three new prototype IFCB designs that extend measurement and deployment capabilities. To improve cell counting statistics without degrading image quality, a high throughput version (IFCB-HT) incorporates in-flow acoustic focusing to non-disruptively pre-concentrate cells before the measurement area of the flow cell. To extend imaging to all heterotrophic cells (even those that do not exhibit chlorophyll fluorescence), Staining IFCB (IFCB-S) incorporates automated addition of a live-cell fluorescent stain (fluorescein diacetate) to samples before analysis. A horizontally-oriented IFCB-AV design addresses the need for spatial surveying from surface autonomous vehicles, including design features that reliably eliminate air bubbles and mitigate wave motion impacts. Laboratory evaluation and test deployments in waters near Woods Hole show the efficacy of each of these enhanced IFCB designs.

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

    Science.gov (United States)

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

    2015-04-06

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

  20. A Semi-automated Approach to Improve the Efficiency of Medical Imaging Segmentation for Haptic Rendering.

    Science.gov (United States)

    Banerjee, Pat; Hu, Mengqi; Kannan, Rahul; Krishnaswamy, Srinivasan

    2017-08-01

    The Sensimmer platform represents our ongoing research on simultaneous haptics and graphics rendering of 3D models. For simulation of medical and surgical procedures using Sensimmer, 3D models must be obtained from medical imaging data, such as magnetic resonance imaging (MRI) or computed tomography (CT). Image segmentation techniques are used to determine the anatomies of interest from the images. 3D models are obtained from segmentation and their triangle reduction is required for graphics and haptics rendering. This paper focuses on creating 3D models by automating the segmentation of CT images based on the pixel contrast for integrating the interface between Sensimmer and medical imaging devices, using the volumetric approach, Hough transform method, and manual centering method. Hence, automating the process has reduced the segmentation time by 56.35% while maintaining the same accuracy of the output at ±2 voxels.

  1. 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.

  2. Automated estimation of choroidal thickness distribution and volume based on OCT images of posterior visual section.

    Science.gov (United States)

    Vupparaboina, Kiran Kumar; Nizampatnam, Srinath; Chhablani, Jay; Richhariya, Ashutosh; Jana, Soumya

    2015-12-01

    A variety of vision ailments are indicated by anomalies in the choroid layer of the posterior visual section. Consequently, choroidal thickness and volume measurements, usually performed by experts based on optical coherence tomography (OCT) images, have assumed diagnostic significance. Now, to save precious expert time, it has become imperative to develop automated methods. To this end, one requires choroid outer boundary (COB) detection as a crucial step, where difficulty arises as the COB divides the choroidal granularity and the scleral uniformity only notionally, without marked brightness variation. In this backdrop, we measure the structural dissimilarity between choroid and sclera by structural similarity (SSIM) index, and hence estimate the COB by thresholding. Subsequently, smooth COB estimates, mimicking manual delineation, are obtained using tensor voting. On five datasets, each consisting of 97 adult OCT B-scans, automated and manual segmentation results agree visually. We also demonstrate close statistical match (greater than 99.6% correlation) between choroidal thickness distributions obtained algorithmically and manually. Further, quantitative superiority of our method is established over existing results by respective factors of 27.67% and 76.04% in two quotient measures defined relative to observer repeatability. Finally, automated choroidal volume estimation, being attempted for the first time, also yields results in close agreement with that of manual methods. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Automated rice leaf disease detection using color image analysis

    Science.gov (United States)

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

    2011-06-01

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

  4. 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...

  5. An Ibm PC/AT-Based Image Acquisition And Processing System For Quantitative Image Analysis

    Science.gov (United States)

    Kim, Yongmin; Alexander, Thomas

    1986-06-01

    In recent years, a large number of applications have been developed for image processing systems in the area of biological imaging. We have already finished the development of a dedicated microcomputer-based image processing and analysis system for quantitative microscopy. The system's primary function has been to facilitate and ultimately automate quantitative image analysis tasks such as the measurement of cellular DNA contents. We have recognized from this development experience, and interaction with system users, biologists and technicians, that the increasingly widespread use of image processing systems, and the development and application of new techniques for utilizing the capabilities of such systems, would generate a need for some kind of inexpensive general purpose image acquisition and processing system specially tailored for the needs of the medical community. We are currently engaged in the development and testing of hardware and software for a fairly high-performance image processing computer system based on a popular personal computer. In this paper, we describe the design and development of this system. Biological image processing computer systems have now reached a level of hardware and software refinement where they could become convenient image analysis tools for biologists. The development of a general purpose image processing system for quantitative image analysis that is inexpensive, flexible, and easy-to-use represents a significant step towards making the microscopic digital image processing techniques more widely applicable not only in a research environment as a biologist's workstation, but also in clinical environments as a diagnostic tool.

  6. CEST ANALYSIS: AUTOMATED CHANGE DETECTION FROM VERY-HIGH-RESOLUTION REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    M. Ehlers

    2012-08-01

    Full Text Available A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST of the change algorithms is applied to calculate the probability of change for a particular location. CEST

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

    International Nuclear Information System (INIS)

    Ahmadian, Alireza; Ay, Mohammad R.; Sarkar, Saeed; Bidgoli, Javad H.; Zaidi, Habib

    2008-01-01

    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 (μmap), 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 μmaps 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 in

  8. Automating the radiographic NDT process

    International Nuclear Information System (INIS)

    Aman, J.K.

    1988-01-01

    Automation, the removal of the human element in inspection has not been generally applied to film radiographic NDT. The justification for automation is not only productivity but also reliability of results. Film remains in the automated system of the future because of its extremely high image content, approximately 3x10 (to the power of nine) bits per 14x17. This is equivalent to 2200 computer floppy disks parts handling systems and robotics applied for manufacturing and some NDT modalities, should now be applied to film radiographic NDT systems. Automatic film handling can be achieved with the daylight NDT film handling system. Automatic film processing is becoming the standard in industry and can be coupled to the daylight system. Robots offer the opportunity to automate fully the exposure step. Finally, a computer aided interpretation appears on the horizon. A unit which laser scans a 14x27 (inch) film in 6-8 seconds can digitize film in information for further manipulation and possible automatic interrogations (computer aided interpretation). The system called FDRS (for film digital radiography system) is moving toward 50 micron (16 lines/mm) resolution. This is believed to meet the need of the majority of image content needs. (Author). 4 refs.; 21 figs

  9. 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.

  10. Automated processing for proton spectroscopic imaging using water reference deconvolution.

    Science.gov (United States)

    Maudsley, A A; Wu, Z; Meyerhoff, D J; Weiner, M W

    1994-06-01

    Automated formation of MR spectroscopic images (MRSI) is necessary before routine application of these methods is possible for in vivo studies; however, this task is complicated by the presence of spatially dependent instrumental distortions and the complex nature of the MR spectrum. A data processing method is presented for completely automated formation of in vivo proton spectroscopic images, and applied for analysis of human brain metabolites. This procedure uses the water reference deconvolution method (G. A. Morris, J. Magn. Reson. 80, 547(1988)) to correct for line shape distortions caused by instrumental and sample characteristics, followed by parametric spectral analysis. Results for automated image formation were found to compare favorably with operator dependent spectral integration methods. While the water reference deconvolution processing was found to provide good correction of spatially dependent resonance frequency shifts, it was found to be susceptible to errors for correction of line shape distortions. These occur due to differences between the water reference and the metabolite distributions.

  11. High content live cell imaging for the discovery of new antimalarial marine natural products

    Directory of Open Access Journals (Sweden)

    Cervantes Serena

    2012-01-01

    Full Text Available Abstract Background The human malaria parasite remains a burden in developing nations. It is responsible for up to one million deaths a year, a number that could rise due to increasing multi-drug resistance to all antimalarial drugs currently available. Therefore, there is an urgent need for the discovery of new drug therapies. Recently, our laboratory developed a simple one-step fluorescence-based live cell-imaging assay to integrate the complex biology of the human malaria parasite into drug discovery. Here we used our newly developed live cell-imaging platform to discover novel marine natural products and their cellular phenotypic effects against the most lethal malaria parasite, Plasmodium falciparum. Methods A high content live cell imaging platform was used to screen marine extracts effects on malaria. Parasites were grown in vitro in the presence of extracts, stained with RNA sensitive dye, and imaged at timed intervals with the BD Pathway HT automated confocal microscope. Results Image analysis validated our new methodology at a larger scale level and revealed potential antimalarial activity of selected extracts with a minimal cytotoxic effect on host red blood cells. To further validate our assay, we investigated parasite's phenotypes when incubated with the purified bioactive natural product bromophycolide A. We show that bromophycolide A has a strong and specific morphological effect on parasites, similar to the ones observed from the initial extracts. Conclusion Collectively, our results show that high-content live cell-imaging (HCLCI can be used to screen chemical libraries and identify parasite specific inhibitors with limited host cytotoxic effects. All together we provide new leads for the discovery of novel antimalarials.

  12. High content live cell imaging for the discovery of new antimalarial marine natural products.

    Science.gov (United States)

    Cervantes, Serena; Stout, Paige E; Prudhomme, Jacques; Engel, Sebastian; Bruton, Matthew; Cervantes, Michael; Carter, David; Tae-Chang, Young; Hay, Mark E; Aalbersberg, William; Kubanek, Julia; Le Roch, Karine G

    2012-01-03

    The human malaria parasite remains a burden in developing nations. It is responsible for up to one million deaths a year, a number that could rise due to increasing multi-drug resistance to all antimalarial drugs currently available. Therefore, there is an urgent need for the discovery of new drug therapies. Recently, our laboratory developed a simple one-step fluorescence-based live cell-imaging assay to integrate the complex biology of the human malaria parasite into drug discovery. Here we used our newly developed live cell-imaging platform to discover novel marine natural products and their cellular phenotypic effects against the most lethal malaria parasite, Plasmodium falciparum. A high content live cell imaging platform was used to screen marine extracts effects on malaria. Parasites were grown in vitro in the presence of extracts, stained with RNA sensitive dye, and imaged at timed intervals with the BD Pathway HT automated confocal microscope. Image analysis validated our new methodology at a larger scale level and revealed potential antimalarial activity of selected extracts with a minimal cytotoxic effect on host red blood cells. To further validate our assay, we investigated parasite's phenotypes when incubated with the purified bioactive natural product bromophycolide A. We show that bromophycolide A has a strong and specific morphological effect on parasites, similar to the ones observed from the initial extracts. Collectively, our results show that high-content live cell-imaging (HCLCI) can be used to screen chemical libraries and identify parasite specific inhibitors with limited host cytotoxic effects. All together we provide new leads for the discovery of novel antimalarials. © 2011 Cervantes et al; licensee BioMed Central Ltd.

  13. Automated daily quality control analysis for mammography in a multi-unit imaging center.

    Science.gov (United States)

    Sundell, Veli-Matti; Mäkelä, Teemu; Meaney, Alexander; Kaasalainen, Touko; Savolainen, Sauli

    2018-01-01

    Background The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users. Purpose To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center. Material and Methods An automated image quality analysis software using the discrete wavelet transform and multiresolution analysis was developed for the American College of Radiology accreditation phantom. The software was validated by analyzing 60 randomly selected phantom images from six mammography systems and 20 phantom images with different dose levels from one mammography system. The results were compared to a visual analysis made by four reviewers. Additionally, long-term image quality trends of a full-field digital mammography system and a computed radiography mammography system were investigated. Results The automated software produced feature detection levels comparable to visual analysis. The agreement was good in the case of fibers, while the software detected somewhat more microcalcifications and characteristic masses. Long-term follow-up via a quality assurance web portal demonstrated the feasibility of using the software for monitoring the performance of mammography systems in a multi-unit imaging center. Conclusion Automated image quality analysis enables monitoring the performance of digital mammography systems in an efficient, centralized manner.

  14. 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

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

    International Nuclear Information System (INIS)

    Yamakawa, Junki; Matsubara, Hiroaki; Kimura, Shouta; Hasegawa, Junichi; Shinozaki, Kenji; Nawano, Shigeru

    2010-01-01

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

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

    International Nuclear Information System (INIS)

    Toth, P.; Farrer, J.K.; Palotas, A.B.; Lighty, J.S.; Eddings, E.G.

    2013-01-01

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

  17. 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 R.

    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.

  18. Application of an Automated Discharge Imaging System and LSPIV during Typhoon Events in Taiwan

    OpenAIRE

    Wei-Che Huang; Chih-Chieh Young; Wen-Cheng Liu

    2018-01-01

    An automated discharge imaging system (ADIS), which is a non-intrusive and safe approach, was developed for measuring river flows during flash flood events. ADIS consists of dual cameras to capture complete surface images in the near and far fields. Surface velocities are accurately measured using the Large Scale Particle Image Velocimetry (LSPIV) technique. The stream discharges are then obtained from the depth-averaged velocity (based upon an empirical velocity-index relationship) and cross...

  19. An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

    Science.gov (United States)

    Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

    2017-08-01

    Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

  20. Computer-aided diagnostics of screening mammography using content-based image retrieval

    Science.gov (United States)

    Deserno, Thomas M.; Soiron, Michael; de Oliveira, Júlia E. E.; de A. Araújo, Arnaldo

    2012-03-01

    Breast cancer is one of the main causes of death among women in occidental countries. In the last years, screening mammography has been established worldwide for early detection of breast cancer, and computer-aided diagnostics (CAD) is being developed to assist physicians reading mammograms. A promising method for CAD is content-based image retrieval (CBIR). Recently, we have developed a classification scheme of suspicious tissue pattern based on the support vector machine (SVM). In this paper, we continue moving towards automatic CAD of screening mammography. The experiments are based on in total 10,509 radiographs that have been collected from different sources. From this, 3,375 images are provided with one and 430 radiographs with more than one chain code annotation of cancerous regions. In different experiments, this data is divided into 12 and 20 classes, distinguishing between four categories of tissue density, three categories of pathology and in the 20 class problem two categories of different types of lesions. Balancing the number of images in each class yields 233 and 45 images remaining in each of the 12 and 20 classes, respectively. Using a two-dimensional principal component analysis, features are extracted from small patches of 128 x 128 pixels and classified by means of a SVM. Overall, the accuracy of the raw classification was 61.6 % and 52.1 % for the 12 and the 20 class problem, respectively. The confusion matrices are assessed for detailed analysis. Furthermore, an implementation of a SVM-based CBIR system for CADx in screening mammography is presented. In conclusion, with a smarter patch extraction, the CBIR approach might reach precision rates that are helpful for the physicians. This, however, needs more comprehensive evaluation on clinical data.

  1. An automatic fuzzy-based multi-temporal brain digital subtraction angiography image fusion algorithm using curvelet transform and content selection strategy.

    Science.gov (United States)

    Momeni, Saba; Pourghassem, Hossein

    2014-08-01

    Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.

  2. TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

    Science.gov (United States)

    Li, Shimiao; Gong, Tianxia; Wang, Jie; Liu, Ruizhe; Tan, Chew Lim; Leong, Tze Yun; Pang, Boon Chuan; Lim, C. C. Tchoyoson; Lee, Cheng Kiang; Tian, Qi; Zhang, Zhuo

    2010-03-01

    Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.

  3. Automated image-matching technique for comparative diagnosis of the liver on CT examination

    International Nuclear Information System (INIS)

    Okumura, Eiichiro; Sanada, Shigeru; Suzuki, Masayuki; Tsushima, Yoshito; Matsui, Osamu

    2005-01-01

    When interpreting enhanced computer tomography (CT) images of the upper abdomen, radiologists visually select a set of images of the same anatomical positions from two or more CT image series (i.e., non-enhanced and contrast-enhanced CT images at arterial and delayed phase) to depict and to characterize any abnormalities. The same process is also necessary to create subtraction images by computer. We have developed an automated image selection system using a template-matching technique that allows the recognition of image sets at the same anatomical position from two CT image series. Using the template-matching technique, we compared several anatomical structures in each CT image at the same anatomical position. As the position of the liver may shift according to respiratory movement, not only the shape of the liver but also the gallbladder and other prominent structures included in the CT images were compared to allow appropriate selection of a set of CT images. This novel technique was applied in 11 upper abdominal CT examinations. In CT images with a slice thickness of 7.0 or 7.5 mm, the percentage of image sets selected correctly by the automated procedure was 86.6±15.3% per case. In CT images with a slice thickness of 1.25 mm, the percentages of correct selection of image sets by the automated procedure were 79.4±12.4% (non-enhanced and arterial-phase CT images) and 86.4±10.1% (arterial- and delayed-phase CT images). This automated method is useful for assisting in interpreting CT images and in creating digital subtraction images. (author)

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

    International Nuclear Information System (INIS)

    Gratama van Andel, Hugo A.F.; Meijering, Erik; Vrooman, Henri A.; Stokking, Rik; Lugt, Aad van der; Monye, Cecile de

    2006-01-01

    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.)

  5. Automated Registration of Multimodal Optic Disc Images: Clinical Assessment of Alignment Accuracy.

    Science.gov (United States)

    Ng, Wai Siene; Legg, Phil; Avadhanam, Venkat; Aye, Kyaw; Evans, Steffan H P; North, Rachel V; Marshall, Andrew D; Rosin, Paul; Morgan, James E

    2016-04-01

    To determine the accuracy of automated alignment algorithms for the registration of optic disc images obtained by 2 different modalities: fundus photography and scanning laser tomography. Images obtained with the Heidelberg Retina Tomograph II and paired photographic optic disc images of 135 eyes were analyzed. Three state-of-the-art automated registration techniques Regional Mutual Information, rigid Feature Neighbourhood Mutual Information (FNMI), and nonrigid FNMI (NRFNMI) were used to align these image pairs. Alignment of each composite picture was assessed on a 5-point grading scale: "Fail" (no alignment of vessels with no vessel contact), "Weak" (vessels have slight contact), "Good" (vessels with 50% contact), and "Excellent" (complete alignment). Custom software generated an image mosaic in which the modalities were interleaved as a series of alternate 5×5-pixel blocks. These were graded independently by 3 clinically experienced observers. A total of 810 image pairs were assessed. All 3 registration techniques achieved a score of "Good" or better in >95% of the image sets. NRFNMI had the highest percentage of "Excellent" (mean: 99.6%; range, 95.2% to 99.6%), followed by Regional Mutual Information (mean: 81.6%; range, 86.3% to 78.5%) and FNMI (mean: 73.1%; range, 85.2% to 54.4%). Automated registration of optic disc images by different modalities is a feasible option for clinical application. All 3 methods provided useful levels of alignment, but the NRFNMI technique consistently outperformed the others and is recommended as a practical approach to the automated registration of multimodal disc images.

  6. Automated delineation of stroke lesions using brain CT images

    Directory of Open Access Journals (Sweden)

    Céline R. Gillebert

    2014-01-01

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

  7. Automated classification of Acid Rock Drainage potential from Corescan drill core imagery

    Science.gov (United States)

    Cracknell, M. J.; Jackson, L.; Parbhakar-Fox, A.; Savinova, K.

    2017-12-01

    Classification of the acid forming potential of waste rock is important for managing environmental hazards associated with mining operations. Current methods for the classification of acid rock drainage (ARD) potential usually involve labour intensive and subjective assessment of drill core and/or hand specimens. Manual methods are subject to operator bias, human error and the amount of material that can be assessed within a given time frame is limited. The automated classification of ARD potential documented here is based on the ARD Index developed by Parbhakar-Fox et al. (2011). This ARD Index involves the combination of five indicators: A - sulphide content; B - sulphide alteration; C - sulphide morphology; D - primary neutraliser content; and E - sulphide mineral association. Several components of the ARD Index require accurate identification of sulphide minerals. This is achieved by classifying Corescan Red-Green-Blue true colour images into the presence or absence of sulphide minerals using supervised classification. Subsequently, sulphide classification images are processed and combined with Corescan SWIR-based mineral classifications to obtain information on sulphide content, indices representing sulphide textures (disseminated versus massive and degree of veining), and spatially associated minerals. This information is combined to calculate ARD Index indicator values that feed into the classification of ARD potential. Automated ARD potential classifications of drill core samples associated with a porphyry Cu-Au deposit are compared to manually derived classifications and those obtained by standard static geochemical testing and X-ray diffractometry analyses. Results indicate a high degree of similarity between automated and manual ARD potential classifications. Major differences between approaches are observed in sulphide and neutraliser mineral percentages, likely due to the subjective nature of manual estimates of mineral content. The automated approach

  8. Study of automated segmentation of the cerebellum and brainstem on brain MR images

    International Nuclear Information System (INIS)

    Hayashi, Norio; Matsuura, Yukihiro; Sanada, Shigeru; Suzuki, Masayuki

    2005-01-01

    MR imaging is an important method for diagnosing abnormalities of the brain. This paper presents an automated method to segment the cerebellum and brainstem for brain MR images. MR images were obtained from 10 normal subjects (male 4, female 6; 22-75 years old, average 31.0 years) and 15 patients with brain atrophy (male 3, female 12; 62-85 years of age, average 76.0 years). The automated method consisted of the following four steps: segmentation of the brain on original images, detection of an upper plane of the cerebellum using the Hough transform, correction of the plane using three-dimensional (3D) information, and segmentation of the cerebellum and brainstem using the plane. The results indicated that the regions obtained by the automated method were visually similar to those obtained by a manual method. The average rates of coincidence between the automated method and manual method were 83.0±9.0% in normal subjects and 86.4±3.6% in patients. (author)

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

    Science.gov (United States)

    Moraguez, Mathew; Patankar, Kunal; Fitz-Coy, Norman; Liou, J.-C.; Sorge, Marlon; Cowardin, Heather; Opiela, John; Krisko, Paula H.

    2015-01-01

    The debris fragments generated by DebriSat's hypervelocity impact test are currently being processed and characterized through an effort of NASA and USAF. The debris characteristics will be used to update satellite breakup models. In particular, the physical dimensions of the debris fragments must be measured to provide characteristic lengths for use in these models. Calipers and commercial 3D scanners were considered as measurement options, but an automated imaging system was ultimately developed to measure debris fragments. By automating the entire process, the measurement results are made repeatable and the human factor associated with calipers and 3D scanning is eliminated. Unlike using calipers to measure, the imaging system obtains non-contact measurements to avoid damaging delicate fragments. Furthermore, this fully automated measurement system minimizes fragment handling, which reduces the potential for fragment damage during the characterization process. In addition, the imaging system reduces the time required to determine the characteristic length of the debris fragment. In this way, the imaging system can measure the tens of thousands of DebriSat fragments at a rate of about six minutes per fragment, compared to hours per fragment in NASA's current 3D scanning measurement approach. The imaging system utilizes a space carving algorithm to generate a 3D point cloud of the article being measured and a custom developed algorithm then extracts the characteristic length from the point cloud. This paper describes the measurement process, results, challenges, and future work of the imaging system used for automated characteristic length measurement of DebriSat fragments.

  10. An automated patient recognition method based on an image-matching technique using previous chest radiographs in the picture archiving and communication system environment

    International Nuclear Information System (INIS)

    Morishita, Junji; Katsuragawa, Shigehiko; Kondo, Keisuke; Doi, Kunio

    2001-01-01

    An automated patient recognition method for correcting 'wrong' chest radiographs being stored in a picture archiving and communication system (PACS) environment has been developed. The method is based on an image-matching technique that uses previous chest radiographs. For identification of a 'wrong' patient, the correlation value was determined for a previous image of a patient and a new, current image of the presumed corresponding patient. The current image was shifted horizontally and vertically and rotated, so that we could determine the best match between the two images. The results indicated that the correlation values between the current and previous images for the same, 'correct' patients were generally greater than those for different, 'wrong' patients. Although the two histograms for the same patient and for different patients overlapped at correlation values greater than 0.80, most parts of the histograms were separated. The correlation value was compared with a threshold value that was determined based on an analysis of the histograms of correlation values obtained for the same patient and for different patients. If the current image is considered potentially to belong to a 'wrong' patient, then a warning sign with the probability for a 'wrong' patient is provided to alert radiology personnel. Our results indicate that at least half of the 'wrong' images in our database can be identified correctly with the method described in this study. The overall performance in terms of a receiver operating characteristic curve showed a high performance of the system. The results also indicate that some readings of 'wrong' images for a given patient in the PACS environment can be prevented by use of the method we developed. Therefore an automated warning system for patient recognition would be useful in correcting 'wrong' images being stored in the PACS environment

  11. WE-G-BRD-07: Automated MR Image Standardization and Auto-Contouring Strategy for MRI-Based Adaptive Brachytherapy for Cervix Cancer

    International Nuclear Information System (INIS)

    Saleh, H Al; Erickson, B; Paulson, E

    2015-01-01

    Purpose: MRI-based adaptive brachytherapy (ABT) is an emerging treatment modality for patients with gynecological tumors. However, MR image intensity non-uniformities (IINU) can vary from fraction to fraction, complicating image interpretation and auto-contouring accuracy. We demonstrate here an automated MR image standardization and auto-contouring strategy for MRI-based ABT of cervix cancer. Methods: MR image standardization consisted of: 1) IINU correction using the MNI N3 algorithm, 2) noise filtering using anisotropic diffusion, and 3) signal intensity normalization using the volumetric median. This post-processing chain was implemented as a series of custom Matlab and Java extensions in MIM (v6.4.5, MIM Software) and was applied to 3D T2 SPACE images of six patients undergoing MRI-based ABT at 3T. Coefficients of variation (CV=σ/µ) were calculated for both original and standardized images and compared using Mann-Whitney tests. Patient-specific cumulative MR atlases of bladder, rectum, and sigmoid contours were constructed throughout ABT, using original and standardized MR images from all previous ABT fractions. Auto-contouring was performed in MIM two ways: 1) best-match of one atlas image to the daily MR image, 2) multi-match of all previous fraction atlas images to the daily MR image. Dice’s Similarity Coefficients (DSCs) were calculated for auto-generated contours relative to reference contours for both original and standardized MR images and compared using Mann-Whitney tests. Results: Significant improvements in CV were detected following MR image standardization (p=0.0043), demonstrating an improvement in MR image uniformity. DSCs consistently increased for auto-contoured bladder, rectum, and sigmoid following MR image standardization, with the highest DSCs detected when the combination of MR image standardization and multi-match cumulative atlas-based auto-contouring was utilized. Conclusion: MR image standardization significantly improves MR image

  12. WE-G-BRD-07: Automated MR Image Standardization and Auto-Contouring Strategy for MRI-Based Adaptive Brachytherapy for Cervix Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Saleh, H Al; Erickson, B; Paulson, E [Medical College of Wisconsin, Milwaukee, WI (United States)

    2015-06-15

    Purpose: MRI-based adaptive brachytherapy (ABT) is an emerging treatment modality for patients with gynecological tumors. However, MR image intensity non-uniformities (IINU) can vary from fraction to fraction, complicating image interpretation and auto-contouring accuracy. We demonstrate here an automated MR image standardization and auto-contouring strategy for MRI-based ABT of cervix cancer. Methods: MR image standardization consisted of: 1) IINU correction using the MNI N3 algorithm, 2) noise filtering using anisotropic diffusion, and 3) signal intensity normalization using the volumetric median. This post-processing chain was implemented as a series of custom Matlab and Java extensions in MIM (v6.4.5, MIM Software) and was applied to 3D T2 SPACE images of six patients undergoing MRI-based ABT at 3T. Coefficients of variation (CV=σ/µ) were calculated for both original and standardized images and compared using Mann-Whitney tests. Patient-specific cumulative MR atlases of bladder, rectum, and sigmoid contours were constructed throughout ABT, using original and standardized MR images from all previous ABT fractions. Auto-contouring was performed in MIM two ways: 1) best-match of one atlas image to the daily MR image, 2) multi-match of all previous fraction atlas images to the daily MR image. Dice’s Similarity Coefficients (DSCs) were calculated for auto-generated contours relative to reference contours for both original and standardized MR images and compared using Mann-Whitney tests. Results: Significant improvements in CV were detected following MR image standardization (p=0.0043), demonstrating an improvement in MR image uniformity. DSCs consistently increased for auto-contoured bladder, rectum, and sigmoid following MR image standardization, with the highest DSCs detected when the combination of MR image standardization and multi-match cumulative atlas-based auto-contouring was utilized. Conclusion: MR image standardization significantly improves MR image

  13. Automated processing of X-ray images in medicine

    International Nuclear Information System (INIS)

    Babij, Ya.S.; B'yalyuk, Ya.O.; Yanovich, I.A.; Lysenko, A.V.

    1991-01-01

    Theoretical and practical achievements in application of computing technology means for processing of X-ray images in medicine were generalized. The scheme of the main directions and tasks of processing of X-ray images was given and analyzed. The principal problems appeared in automated processing of X-ray images were distinguished. It is shown that for interpretation of X-ray images it is expedient to introduce a notion of relative operating characteristic (ROC) of a roentgenologist. Every point on ROC curve determines the individual criteria of roentgenologist to put a positive diagnosis for definite situation

  14. INTEGRATION OF SPATIAL INFORMATION WITH COLOR FOR CONTENT RETRIEVAL OF REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    Bikesh Kumar Singh

    2010-08-01

    Full Text Available There is rapid increase in image databases of remote sensing images due to image satellites with high resolution, commercial applications of remote sensing & high available bandwidth in last few years. The problem of content-based image retrieval (CBIR of remotely sensed images presents a major challenge not only because of the surprisingly increasing volume of images acquired from a wide range of sensors but also because of the complexity of images themselves. In this paper, a software system for content-based retrieval of remote sensing images using RGB and HSV color spaces is presented. Further, we also compare our results with spatiogram based content retrieval which integrates spatial information along with color histogram. Experimental results show that the integration of spatial information in color improves the image analysis of remote sensing data. In general, retrievals in HSV color space showed better performance than in RGB color space.

  15. Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining.

    Science.gov (United States)

    Zhang, Ling; Kong, Hui; Ting Chin, Chien; Liu, Shaoxiong; Fan, Xinmin; Wang, Tianfu; Chen, Siping

    2014-03-01

    Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a cost-efficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 μm. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals. © 2013 International Society for Advancement of Cytometry.

  16. Reconfigurable pipelined sensing for image-based control

    NARCIS (Netherlands)

    Medina, R.; Stuijk, S.; Goswami, D.; Basten, T.

    2016-01-01

    Image-based control systems are becoming common in domains such as robotics, healthcare and industrial automation. Coping with a long sample period because of the latency of the image processing algorithm is an open challenge. Modern multi-core platforms allow to address this challenge by pipelining

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

    International Nuclear Information System (INIS)

    Mahnken, A.H.; Kohnen, M.; Steinberg, S.; Wein, B.B.; Guenther, R.W.

    2001-01-01

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

  18. Automated Defect Recognition as a Critical Element of a Three Dimensional X-ray Computed Tomography Imaging-Based Smart Non-Destructive Testing Technique in Additive Manufacturing of Near Net-Shape Parts

    Directory of Open Access Journals (Sweden)

    Istvan Szabo

    2017-11-01

    Full Text Available In this paper, a state of the art automated defect recognition (ADR system is presented that was developed specifically for Non-Destructive Testing (NDT of powder metallurgy (PM parts using three dimensional X-ray Computed Tomography (CT imaging, towards enabling online quality assurance and enhanced integrity confidence. PM parts exhibit typical defects such as microscopic cracks, porosity, and voids, internal to components that without an effective detection system, limit the growth of industrial applications. Compared to typical testing methods (e.g., destructive such as metallography that is based on sampling, cutting, and polishing of parts, CT provides full coverage of defect detection. This paper establishes the importance and advantages of an automated NDT system for the PM industry applications with particular emphasis on image processing procedures for defect recognition. Moreover, the article describes how to establish a reference library based on real 3D X-ray CT images of net-shape parts. The paper follows the development of the ADR system from processing 2D image slices of a measured 3D X-ray image to processing the complete 3D X-ray image as a whole. The introduced technique is successfully integrated into an automated in-line quality control system highly sought by major industry sectors in Oil and Gas, Automotive, and Aerospace.

  19. Automated image alignment and segmentation to follow progression of geographic atrophy in age-related macular degeneration.

    Science.gov (United States)

    Ramsey, David J; Sunness, Janet S; Malviya, Poorva; Applegate, Carol; Hager, Gregory D; Handa, James T

    2014-07-01

    To develop a computer-based image segmentation method for standardizing the quantification of geographic atrophy (GA). The authors present an automated image segmentation method based on the fuzzy c-means clustering algorithm for the detection of GA lesions. The method is evaluated by comparing computerized segmentation against outlines of GA drawn by an expert grader for a longitudinal series of fundus autofluorescence images with paired 30° color fundus photographs for 10 patients. The automated segmentation method showed excellent agreement with an expert grader for fundus autofluorescence images, achieving a performance level of 94 ± 5% sensitivity and 98 ± 2% specificity on a per-pixel basis for the detection of GA area, but performed less well on color fundus photographs with a sensitivity of 47 ± 26% and specificity of 98 ± 2%. The segmentation algorithm identified 75 ± 16% of the GA border correctly in fundus autofluorescence images compared with just 42 ± 25% for color fundus photographs. The results of this study demonstrate a promising computerized segmentation method that may enhance the reproducibility of GA measurement and provide an objective strategy to assist an expert in the grading of images.

  20. The influence of image setting on intracranial translucency measurement by manual and semi-automated system.

    Science.gov (United States)

    Zhen, Li; Yang, Xin; Ting, Yuen Ha; Chen, Min; Leung, Tak Yeung

    2013-09-01

    To investigate the agreement between manual and semi-automated system and the effect of different image settings on intracranial translucency (IT) measurement. A prospective study was conducted on 55 women carrying singleton pregnancy who attended first trimester Down syndrome screening. IT was measured both manually and by semi-automated system at the same default image setting. The IT measurements were then repeated with the post-processing changes in the image setting one at a time. The difference in IT measurements between the altered and the original images were assessed. Intracranial translucency was successfully measured on 55 images both manually and by semi-automated method. There was strong agreement in IT measurements between the two methods with a mean difference (manual minus semi-automated) of 0.011 mm (95% confidence interval--0.052 mm-0.094 mm). There were statistically significant variations in both manual and semi-automated IT measurement after changing the Gain and the Contrast. The greatest changes occurred when the Contrast was reduced to 1 (IT reduced by 0.591 mm in semi-automated; 0.565 mm in manual), followed by when the Gain was increased to 15 (IT reduced by 0.424 mm in semi-automated; 0.524 mm in manual). The image settings may affect IT identification and measurement. Increased Gain and reduced Contrast are the most influential factors and may cause under-measurement of IT. © 2013 John Wiley & Sons, Ltd.

  1. Extraction of the number of peroxisomes in yeast cells by automated image analysis.

    Science.gov (United States)

    Niemistö, Antti; Selinummi, Jyrki; Saleem, Ramsey; Shmulevich, Ilya; Aitchison, John; Yli-Harja, Olli

    2006-01-01

    An automated image analysis method for extracting the number of peroxisomes in yeast cells is presented. Two images of the cell population are required for the method: a bright field microscope image from which the yeast cells are detected and the respective fluorescent image from which the number of peroxisomes in each cell is found. The segmentation of the cells is based on clustering the local mean-variance space. The watershed transformation is thereafter employed to separate cells that are clustered together. The peroxisomes are detected by thresholding the fluorescent image. The method is tested with several images of a budding yeast Saccharomyces cerevisiae population, and the results are compared with manually obtained results.

  2. 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.

  3. Orthogonally Based Digital Content Management Applicable to Projects-bases

    Directory of Open Access Journals (Sweden)

    Daniel MILODIN

    2009-01-01

    Full Text Available There is defined the concept of digital content. The requirements of an efficient management of the digital content are established. There are listed the quality characteristics of digital content. Orthogonality indicators of digital content are built up. They are meant to measure the image, the sound as well as the text orthogonality as well. Projects-base concept is introduced. There is presented the model of structuring the content in order to maximize orthogonality via a convergent iterative process. The model is instantiated for the digital content of a projects-base. It is introduced the application used to test the model. The paper ends with conclusions.

  4. 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

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

    International Nuclear Information System (INIS)

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

    2013-01-01

    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

  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

    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...... 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...

  7. ImageSURF: An ImageJ Plugin for Batch Pixel-Based Image Segmentation Using Random Forests

    Directory of Open Access Journals (Sweden)

    Aidan O'Mara

    2017-11-01

    Full Text Available Image segmentation is a necessary step in automated quantitative imaging. ImageSURF is a macro-compatible ImageJ2/FIJI plugin for pixel-based image segmentation that considers a range of image derivatives to train pixel classifiers which are then applied to image sets of any size to produce segmentations without bias in a consistent, transparent and reproducible manner. The plugin is available from ImageJ update site http://sites.imagej.net/ImageSURF/ and source code from https://github.com/omaraa/ImageSURF. Funding statement: This research was supported by an Australian Government Research Training Program Scholarship.

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

    International Nuclear Information System (INIS)

    Xia, Ying; Fripp, Jurgen; Chandra, Shekhar S; Schwarz, Raphael; Engstrom, Craig; Crozier, Stuart

    2013-01-01

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

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

    Science.gov (United States)

    Xia, Ying; Fripp, Jurgen; Chandra, Shekhar S.; Schwarz, Raphael; Engstrom, Craig; Crozier, Stuart

    2013-10-01

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

  10. Automating PACS Quality Control with the Vanderbilt Image Processing Enterprise Resource.

    Science.gov (United States)

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

    2012-02-12

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

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

    Science.gov (United States)

    Chevreau, Grégoire; Troccaz, Jocelyne; Conort, Pierre; Renard-Penna, Raphaëlle; Mallet, Alain; Daudon, Michel; Mozer, Pierre

    2009-10-01

    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 eliminates manual intervention on the images by the radiologist while providing identical performances including for low-dose protocols.

  12. Automated extraction of radiation dose information from CT dose report images.

    Science.gov (United States)

    Li, Xinhua; Zhang, Da; Liu, Bob

    2011-06-01

    The purpose of this article is to describe the development of an automated tool for retrieving texts from CT dose report images. Optical character recognition was adopted to perform text recognitions of CT dose report images. The developed tool is able to automate the process of analyzing multiple CT examinations, including text recognition, parsing, error correction, and exporting data to spreadsheets. The results were precise for total dose-length product (DLP) and were about 95% accurate for CT dose index and DLP of scanned series.

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

    International Nuclear Information System (INIS)

    Lee, Jung Hyung; Rhee, Chang Soo; Lee, Sung Moon; Kim, Hong; Woo, Sung Ku; Suh, Soo Jhi

    1994-01-01

    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

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

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jung Hyung; Rhee, Chang Soo; Lee, Sung Moon; Kim, Hong; Woo, Sung Ku; Suh, Soo Jhi [School of Medicine, Keimyung University, Daegu (Korea, Republic of)

    1994-12-15

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

  15. Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content.

    Science.gov (United States)

    Sun, Ye; Wang, Yihang; Xiao, Hui; Gu, Xinzhe; Pan, Leiqing; Tu, Kang

    2017-11-15

    Honey peach is a very common but highly perishable market fruit. When pathogens infect fruit, chlorophyll as one of the important components related to fruit quality, decreased significantly. Here, the feasibility of hyperspectral imaging to determine the chlorophyll content thus distinguishing diseased peaches was investigated. Three optimal wavelengths (617nm, 675nm, and 818nm) were selected according to chlorophyll content via successive projections algorithm. Partial least square regression models were established to determine chlorophyll content. Three band ratios were obtained using these optimal wavelengths, which improved spatial details, but also integrates the information of chemical composition from spectral characteristics. The band ratio values were suitable to classify the diseased peaches with 98.75% accuracy and clearly show the spatial distribution of diseased parts. This study provides a new perspective for the selection of optimal wavelengths of hyperspectral imaging via chlorophyll content, thus enabling the detection of fungal diseases in peaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Digital image analysis applied to industrial nondestructive evaluation and automated parts assembly

    International Nuclear Information System (INIS)

    Janney, D.H.; Kruger, R.P.

    1979-01-01

    Many ideas of image enhancement and analysis are relevant to the needs of the nondestructive testing engineer. These ideas not only aid the engineer in the performance of his current responsibilities, they also open to him new areas of industrial development and automation which are logical extensions of classical testing problems. The paper begins with a tutorial on the fundamentals of computerized image enhancement as applied to nondestructive testing, then progresses through pattern recognition and automated inspection to automated, or robotic, assembly procedures. It is believed that such procedures are cost-effective in many instances, and are but the logical extension of those techniques now commonly used, but often limited to analysis of data from quality-assurance images. Many references are given in order to help the reader who wishes to pursue a given idea further

  17. Optimizing top precision performance measure of content-based image retrieval by learning similarity function

    KAUST Repository

    Liang, Ru-Ze

    2017-04-24

    In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

  18. Optimizing top precision performance measure of content-based image retrieval by learning similarity function

    KAUST Repository

    Liang, Ru-Ze; Shi, Lihui; Wang, Haoxiang; Meng, Jiandong; Wang, Jim Jing-Yan; Sun, Qingquan; Gu, Yi

    2017-01-01

    In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

  19. Adaptive platform for fluorescence microscopy-based high-content screening

    Science.gov (United States)

    Geisbauer, Matthias; Röder, Thorsten; Chen, Yang; Knoll, Alois; Uhl, Rainer

    2010-04-01

    Fluorescence microscopy has become a widely used tool for the study of medically relevant intra- and intercellular processes. Extracting meaningful information out of a bulk of acquired images is usually performed during a separate post-processing task. Thus capturing raw data results in an unnecessary huge number of images, whereas usually only a few images really show the particular information that is searched for. Here we propose a novel automated high-content microscope system, which enables experiments to be carried out with only a minimum of human interaction. It facilitates a huge speed-increase for cell biology research and its applications compared to the widely performed workflows. Our fluorescence microscopy system can automatically execute application-dependent data processing algorithms during the actual experiment. They are used for image contrast enhancement, cell segmentation and/or cell property evaluation. On-the-fly retrieved information is used to reduce data and concomitantly control the experiment process in real-time. Resulting in a closed loop of perception and action the system can greatly decrease the amount of stored data on one hand and increases the relative valuable data content on the other hand. We demonstrate our approach by addressing the problem of automatically finding cells with a particular combination of labeled receptors and then selectively stimulate them with antagonists or agonists. The results are then compared against the results of traditional, static systems.

  20. Simple and robust image-based autofocusing for digital microscopy.

    Science.gov (United States)

    Yazdanfar, Siavash; Kenny, Kevin B; Tasimi, Krenar; Corwin, Alex D; Dixon, Elizabeth L; Filkins, Robert J

    2008-06-09

    A simple image-based autofocusing scheme for digital microscopy is demonstrated that uses as few as two intermediate images to bring the sample into focus. The algorithm is adapted to a commercial inverted microscope and used to automate brightfield and fluorescence imaging of histopathology tissue sections.

  1. Optical Coherence Tomography in the UK Biobank Study - Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies.

    Directory of Open Access Journals (Sweden)

    Pearse A Keane

    Full Text Available To describe an approach to the use of optical coherence tomography (OCT imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness.In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available "spectral domain" OCT device (3D OCT-1000, Topcon. Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL. This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion.67,321 participants (134,642 eyes in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days.We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging.

  2. Investigating the link between radiologists’ gaze, diagnostic decision, and image content

    Science.gov (United States)

    Tourassi, Georgia; Voisin, Sophie; Paquit, Vincent; Krupinski, Elizabeth

    2013-01-01

    Objective To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods Gaze data and diagnostic decisions were collected from three breast imaging radiologists and three radiology residents who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Image analysis was performed in mammographic regions that attracted radiologists’ attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results By pooling the data from all readers, machine learning produced highly accurate predictive models linking image content, gaze, and cognition. Potential linking of those with diagnostic error was also supported to some extent. Merging readers’ gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the readers’ diagnostic errors while confirming 97.3% of their correct diagnoses. The readers’ individual perceptual and cognitive behaviors could be adequately predicted by modeling the behavior of others. However, personalized tuning was in many cases beneficial for capturing more accurately individual behavior. Conclusions There is clearly an interaction between radiologists’ gaze, diagnostic decision, and image content which can be modeled with machine learning algorithms. PMID:23788627

  3. A content analysis of thinspiration images and text posts on Tumblr.

    Science.gov (United States)

    Wick, Madeline R; Harriger, Jennifer A

    2018-03-01

    Thinspiration is content advocating extreme weight loss by means of images and/or text posts. While past content analyses have examined thinspiration content on social media and other websites, no research to date has examined thinspiration content on Tumblr. Over the course of a week, 222 images and text posts were collected after entering the keyword 'thinspiration' into the Tumblr search bar. These images were then rated on a variety of characteristics. The majority of thinspiration images included a thin woman adhering to culturally based beauty, often posing in a manner that accentuated her thinness or sexuality. The most common themes for thinspiration text posts included dieting/restraint, weight loss, food guilt, and body guilt. The thinspiration content on Tumblr appears to be consistent with that on other mediums. Future research should utilize experimental methods to examine the potential effects of consuming thinspiration content on Tumblr. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2015-11-01

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

  5. Automated extraction of pleural effusion in three-dimensional thoracic CT images

    Science.gov (United States)

    Kido, Shoji; Tsunomori, Akinori

    2009-02-01

    It is important for diagnosis of pulmonary diseases to measure volume of accumulating pleural effusion in threedimensional thoracic CT images quantitatively. However, automated extraction of pulmonary effusion correctly is difficult. Conventional extraction algorithm using a gray-level based threshold can not extract pleural effusion from thoracic wall or mediastinum correctly, because density of pleural effusion in CT images is similar to those of thoracic wall or mediastinum. So, we have developed an automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion. Our method used a template of lung obtained from a normal lung for segmentation of lungs with pleural effusions. Registration process consisted of two steps. First step was a global matching processing between normal and abnormal lungs of organs such as bronchi, bones (ribs, sternum and vertebrae) and upper surfaces of livers which were extracted using a region-growing algorithm. Second step was a local matching processing between normal and abnormal lungs which were deformed by the parameter obtained from the global matching processing. Finally, we segmented a lung with pleural effusion by use of the template which was deformed by two parameters obtained from the global matching processing and the local matching processing. We compared our method with a conventional extraction method using a gray-level based threshold and two published methods. The extraction rates of pleural effusions obtained from our method were much higher than those obtained from other methods. Automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion is promising for diagnosis of pulmonary diseases by providing quantitative volume of accumulating pleural effusion.

  6. Automated intraretinal layer segmentation of optical coherence tomography images using graph-theoretical methods

    Science.gov (United States)

    Roy, Priyanka; Gholami, Peyman; Kuppuswamy Parthasarathy, Mohana; Zelek, John; Lakshminarayanan, Vasudevan

    2018-02-01

    Segmentation of spectral-domain Optical Coherence Tomography (SD-OCT) images facilitates visualization and quantification of sub-retinal layers for diagnosis of retinal pathologies. However, manual segmentation is subjective, expertise dependent, and time-consuming, which limits applicability of SD-OCT. Efforts are therefore being made to implement active-contours, artificial intelligence, and graph-search to automatically segment retinal layers with accuracy comparable to that of manual segmentation, to ease clinical decision-making. Although, low optical contrast, heavy speckle noise, and pathologies pose challenges to automated segmentation. Graph-based image segmentation approach stands out from the rest because of its ability to minimize the cost function while maximising the flow. This study has developed and implemented a shortest-path based graph-search algorithm for automated intraretinal layer segmentation of SD-OCT images. The algorithm estimates the minimal-weight path between two graph-nodes based on their gradients. Boundary position indices (BPI) are computed from the transition between pixel intensities. The mean difference between BPIs of two consecutive layers quantify individual layer thicknesses, which shows statistically insignificant differences when compared to a previous study [for overall retina: p = 0.17, for individual layers: p > 0.05 (except one layer: p = 0.04)]. These results substantiate the accurate delineation of seven intraretinal boundaries in SD-OCT images by this algorithm, with a mean computation time of 0.93 seconds (64-bit Windows10, core i5, 8GB RAM). Besides being self-reliant for denoising, the algorithm is further computationally optimized to restrict segmentation within the user defined region-of-interest. The efficiency and reliability of this algorithm, even in noisy image conditions, makes it clinically applicable.

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

    Science.gov (United States)

    Singh, Preetpal

    Canada is home to thousands of freshwater lakes and rivers. Apart from being sources of infinite natural beauty, rivers and lakes are an important source of water, food and transportation. The northern hemisphere of Canada experiences extreme cold temperatures in the winter resulting in a freeze up of regional lakes and rivers. Frozen lakes and rivers tend to offer unique opportunities in terms of wildlife harvesting and winter transportation. Ice roads built on frozen rivers and lakes are vital supply lines for industrial operations in the remote north. Monitoring the ice freeze-up and break-up dates annually can help predict regional climatic changes. Lake ice impacts a variety of physical, ecological and economic processes. The construction and maintenance of a winter road can cost millions of dollars annually. A good understanding of ice mechanics is required to build and deem an ice road safe. A crucial factor in calculating load bearing capacity of ice sheets is the thickness of ice. Construction costs are mainly attributed to producing and maintaining a specific thickness and density of ice that can support different loads. Climate change is leading to warmer temperatures causing the ice to thin faster. At a certain point, a winter road may not be thick enough to support travel and transportation. There is considerable interest in monitoring winter road conditions given the high construction and maintenance costs involved. Remote sensing technologies such as Synthetic Aperture Radar have been successfully utilized to study the extent of ice covers and record freeze-up and break-up dates of ice on lakes and rivers across the north. Ice road builders often used Ultrasound equipment to measure ice thickness. However, an automated monitoring system, based on machine vision and image processing technology, which can measure ice thickness on lakes has not been thought of. Machine vision and image processing techniques have successfully been used in manufacturing

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

    Science.gov (United States)

    Loh, K B; Ramli, N; Tan, L K; Roziah, M; Rahmat, K; Ariffin, H

    2012-07-01

    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. Diffusion tensor imaging outperforms conventional MRI in depicting white matter maturation. • DTI will become an important clinical tool for diagnosing paediatric neurological diseases. • DTI appears especially helpful for developmental abnormalities, tumours and white matter disease. • An automated processing pipeline assists quantitative analysis of high throughput DTI data.

  9. FluidCam 1&2 - UAV-based Fluid Lensing Instruments for High-Resolution 3D Subaqueous Imaging and Automated Remote Biosphere Assessment of Reef Ecosystems

    Science.gov (United States)

    Chirayath, V.; Instrella, R.

    2016-02-01

    We present NASA ESTO FluidCam 1 & 2, Visible and NIR Fluid-Lensing-enabled imaging payloads for Unmanned Aerial Vehicles (UAVs). Developed as part of a focused 2014 earth science technology grant, FluidCam 1&2 are Fluid-Lensing-based computational optical imagers designed for automated 3D mapping and remote sensing of underwater coastal targets from airborne platforms. Fluid Lensing has been used to map underwater reefs in 3D in American Samoa and Hamelin Pool, Australia from UAV platforms at sub-cm scale, which has proven a valuable tool in modern marine research for marine biosphere assessment and conservation. We share FluidCam 1&2 instrument validation and testing results as well as preliminary processed data from field campaigns. Petabyte-scale aerial survey efforts using Fluid Lensing to image at-risk reefs demonstrate broad applicability to large-scale automated species identification, morphology studies and reef ecosystem characterization for shallow marine environments and terrestrial biospheres, of crucial importance to improving bathymetry data for physical oceanographic models and understanding climate change's impact on coastal zones, global oxygen production, carbon sequestration.

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

    International Nuclear Information System (INIS)

    Loh, K.B.; Ramli, N.; Tan, L.K.; Roziah, M.; Rahmat, K.; Ariffin, H.

    2012-01-01

    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.)

  11. 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.)

  12. Content-Based Information Retrieval from Forensic Databases

    NARCIS (Netherlands)

    Geradts, Z.J.M.H.

    2002-01-01

    In forensic science, the number of image databases is growing rapidly. For this reason, it is necessary to have a proper procedure for searching in these images databases based on content. The use of image databases results in more solved crimes; furthermore, statistical information can be obtained

  13. Automated voxel-based analysis of brain perfusion SPECT for vasospasm after subarachnoid haemorrhage

    International Nuclear Information System (INIS)

    Iwabuchi, S.; Yokouchi, T.; Hayashi, M.; Kimura, H.; Tomiyama, A.; Hirata, Y.; Saito, N.; Harashina, J.; Nakayama, H.; Sato, K.; Aoki, K.; Samejima, H.; Ueda, M.; Terada, H.; Hamazaki, K.

    2008-01-01

    We evaluated regional cerebral blood flow (rCBF) during vasospasm after subarachnoid haemorrhage ISAH) using automated voxel-based analysis of brain perfusion single-photon emission computed tomography (SPELT). Brain perfusion SPECT was performed 7 to 10 days after onset of SAH. Automated voxel-based analysis of SPECT used a Z-score map that was calculated by comparing the patients data with a control database. In cases where computed tomography (CT) scans detected an ischemic region due to vasospasm, automated voxel-based analysis of brain perfusion SPECT revealed dramatically reduced rCBF (Z-score ≤ -4). No patients with mildly or moderately diminished rCBF (Z-score > -3) progressed to cerebral infarction. Some patients with a Z-score < -4 did not progress to cerebral infarction after active treatment with a angioplasty. Three-dimensional images provided detailed anatomical information and helped us to distinguish surgical sequelae from vasospasm. In conclusion, automated voxel-based analysis of brain perfusion SPECT using a Z-score map is helpful in evaluating decreased rCBF due to vasospasm. (author)

  14. Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor.

    Science.gov (United States)

    Niu, Sijie; de Sisternes, Luis; Chen, Qiang; Leng, Theodore; Rubin, Daniel L

    2016-02-01

    Age-related macular degeneration (AMD) is the leading cause of blindness among elderly individuals. Geographic atrophy (GA) is a phenotypic manifestation of the advanced stages of non-exudative AMD. Determination of GA extent in SD-OCT scans allows the quantification of GA-related features, such as radius or area, which could be of important value to monitor AMD progression and possibly identify regions of future GA involvement. The purpose of this work is to develop an automated algorithm to segment GA regions in SD-OCT images. An en face GA fundus image is generated by averaging the axial intensity within an automatically detected sub-volume of the three dimensional SD-OCT data, where an initial coarse GA region is estimated by an iterative threshold segmentation method and an intensity profile set, and subsequently refined by a region-based Chan-Vese model with a local similarity factor. Two image data sets, consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, respectively, were utilized to quantitatively evaluate the automated segmentation algorithm. We compared results obtained by the proposed algorithm, manual segmentation by graders, a previously proposed method, and experimental commercial software. When compared to a manually determined gold standard, our algorithm presented a mean overlap ratio (OR) of 81.86% and 70% for the first and second data sets, respectively, while the previously proposed method OR was 72.60% and 65.88% for the first and second data sets, respectively, and the experimental commercial software OR was 62.40% for the second data set.

  15. A novel tool for automated evaluation of radiographic weld images

    International Nuclear Information System (INIS)

    Rajagopalan, C.; Venkatraman, B.; Jayakumar, T.; Kalyanasundaram, P.; Raj, B.

    2004-01-01

    Radiography is one of the oldest and the most widely used NDT method for the detection of volumetric defects in welds and castings. Once a radiograph of a weld or a casting or an assembly is taken, the radiographer examines the same. The task of the radiographer consists of identifying the defects and quantitatively evaluating the same based on codes and specifications. Radiographic interpretation primarily depends on the expertise of the individual radiographer. To overcome the subjectivity involved in human interpretation, it is thus desirable to develop a computer based automated system to aid in the interpretation of radiographs. Towards this goal, the authors have developed a flowchart chalking out the various stages involved. Typical weld images of tube to tubesheet weld joints were digitised using high resolution digitiser. The images were segmented and 52 invariant moments were computed to be used as features. The results of these are presented in this paper. Once the features (invariant moments) are extracted and ranked, a neural network classifier based on error back-propagation has to classify the (top ranking) features and evaluate the image for acceptance or rejection. (author)

  16. A content-based digital image watermarking scheme resistant to local geometric distortions

    International Nuclear Information System (INIS)

    Yang, Hong-ying; Chen, Li-li; Wang, Xiang-yang

    2011-01-01

    Geometric distortion is known as one of the most difficult attacks to resist, as it can desynchronize the location of the watermark and hence cause incorrect watermark detection. Geometric distortion can be decomposed into two classes: global affine transforms and local geometric distortions. Most countermeasures proposed in the literature only address the problem of global affine transforms. It is a challenging problem to design a robust image watermarking scheme against local geometric distortions. In this paper, we propose a new content-based digital image watermarking scheme with good visual quality and reasonable resistance against local geometric distortions. Firstly, the robust feature points, which can survive various common image processing and global affine transforms, are extracted by using a multi-scale SIFT (scale invariant feature transform) detector. Then, the affine covariant local feature regions (LFRs) are constructed adaptively according to the feature scale and local invariant centroid. Finally, the digital watermark is embedded into the affine covariant LFRs by modulating the magnitudes of discrete Fourier transform (DFT) coefficients. By binding the watermark with the affine covariant LFRs, the watermark detection can be done without synchronization error. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as sharpening, noise addition, and JPEG compression, etc, but also robust against global affine transforms and local geometric distortions

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

    International Nuclear Information System (INIS)

    Chang Icheng; Lue Kunhan; Hsieh Hungjen; Liu Shuhsin; Kao, Chinhao K.

    2011-01-01

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

  18. Automated detection of analyzable metaphase chromosome cells depicted on scanned digital microscopic images

    Science.gov (United States)

    Qiu, Yuchen; Wang, Xingwei; Chen, Xiaodong; Li, Yuhua; Liu, Hong; Li, Shibo; Zheng, Bin

    2010-02-01

    Visually searching for analyzable metaphase chromosome cells under microscopes is quite time-consuming and difficult. To improve detection efficiency, consistency, and diagnostic accuracy, an automated microscopic image scanning system was developed and tested to directly acquire digital images with sufficient spatial resolution for clinical diagnosis. A computer-aided detection (CAD) scheme was also developed and integrated into the image scanning system to search for and detect the regions of interest (ROI) that contain analyzable metaphase chromosome cells in the large volume of scanned images acquired from one specimen. Thus, the cytogeneticists only need to observe and interpret the limited number of ROIs. In this study, the high-resolution microscopic image scanning and CAD performance was investigated and evaluated using nine sets of images scanned from either bone marrow (three) or blood (six) specimens for diagnosis of leukemia. The automated CAD-selection results were compared with the visual selection. In the experiment, the cytogeneticists first visually searched for the analyzable metaphase chromosome cells from specimens under microscopes. The specimens were also automated scanned and followed by applying the CAD scheme to detect and save ROIs containing analyzable cells while deleting the others. The automated selected ROIs were then examined by a panel of three cytogeneticists. From the scanned images, CAD selected more analyzable cells than initially visual examinations of the cytogeneticists in both blood and bone marrow specimens. In general, CAD had higher performance in analyzing blood specimens. Even in three bone marrow specimens, CAD selected 50, 22, 9 ROIs, respectively. Except matching with the initially visual selection of 9, 7, and 5 analyzable cells in these three specimens, the cytogeneticists also selected 41, 15 and 4 new analyzable cells, which were missed in initially visual searching. This experiment showed the feasibility of

  19. Automated high-content live animal drug screening using C. elegans expressing the aggregation prone serpin α1-antitrypsin Z.

    Directory of Open Access Journals (Sweden)

    Sager J Gosai

    2010-11-01

    Full Text Available The development of preclinical models amenable to live animal bioactive compound screening is an attractive approach to discovering effective pharmacological therapies for disorders caused by misfolded and aggregation-prone proteins. In general, however, live animal drug screening is labor and resource intensive, and has been hampered by the lack of robust assay designs and high throughput work-flows. Based on their small size, tissue transparency and ease of cultivation, the use of C. elegans should obviate many of the technical impediments associated with live animal drug screening. Moreover, their genetic tractability and accomplished record for providing insights into the molecular and cellular basis of human disease, should make C. elegans an ideal model system for in vivo drug discovery campaigns. The goal of this study was to determine whether C. elegans could be adapted to high-throughput and high-content drug screening strategies analogous to those developed for cell-based systems. Using transgenic animals expressing fluorescently-tagged proteins, we first developed a high-quality, high-throughput work-flow utilizing an automated fluorescence microscopy platform with integrated image acquisition and data analysis modules to qualitatively assess different biological processes including, growth, tissue development, cell viability and autophagy. We next adapted this technology to conduct a small molecule screen and identified compounds that altered the intracellular accumulation of the human aggregation prone mutant that causes liver disease in α1-antitrypsin deficiency. This study provides powerful validation for advancement in preclinical drug discovery campaigns by screening live C. elegans modeling α1-antitrypsin deficiency and other complex disease phenotypes on high-content imaging platforms.

  20. Automated multiscale morphometry of muscle disease from second harmonic generation microscopy using tensor-based image processing.

    Science.gov (United States)

    Garbe, Christoph S; Buttgereit, Andreas; Schürmann, Sebastian; Friedrich, Oliver

    2012-01-01

    Practically, all chronic diseases are characterized by tissue remodeling that alters organ and cellular function through changes to normal organ architecture. Some morphometric alterations become irreversible and account for disease progression even on cellular levels. Early diagnostics to categorize tissue alterations, as well as monitoring progression or remission of disturbed cytoarchitecture upon treatment in the same individual, are a new emerging field. They strongly challenge spatial resolution and require advanced imaging techniques and strategies for detecting morphological changes. We use a combined second harmonic generation (SHG) microscopy and automated image processing approach to quantify morphology in an animal model of inherited Duchenne muscular dystrophy (mdx mouse) with age. Multiphoton XYZ image stacks from tissue slices reveal vast morphological deviation in muscles from old mdx mice at different scales of cytoskeleton architecture: cell calibers are irregular, myofibrils within cells are twisted, and sarcomere lattice disruptions (detected as "verniers") are larger in number compared to samples from healthy mice. In young mdx mice, such alterations are only minor. The boundary-tensor approach, adapted and optimized for SHG data, is a suitable approach to allow quick quantitative morphometry in whole tissue slices. The overall detection performance of the automated algorithm compares very well with manual "by eye" detection, the latter being time consuming and prone to subjective errors. Our algorithm outperfoms manual detection by time with similar reliability. This approach will be an important prerequisite for the implementation of a clinical image databases to diagnose and monitor specific morphological alterations in chronic (muscle) diseases. © 2011 IEEE

  1. A Container Horizontal Positioning Method with Image Sensors for Cranes in Automated Container Terminals

    Directory of Open Access Journals (Sweden)

    FU Yonghua

    2014-03-01

    Full Text Available Automation is a trend for large container terminals nowadays, and container positioning techniques are key factor in the automating process. Vision based positioning techniques are inexpensive and rather accurate in nature, while the effect with insufficient illumination is left in question. This paper proposed a vision-based procedure with image sensors to determine the position of one container in the horizontal plane. The points found by the edge detection operator are clustered, and only the peak points in the parameter space of the Hough transformation is selected, in order that the effect of noises could be much decreased. The effectiveness of our procedure is verified in experiments, in which the efficiency of the procedure is also investigated.

  2. Investigating the Link Between Radiologists Gaze, Diagnostic Decision, and Image Content

    Energy Technology Data Exchange (ETDEWEB)

    Tourassi, Georgia [ORNL; Voisin, Sophie [ORNL; Paquit, Vincent C [ORNL; Krupinski, Elizabeth [University of Arizona

    2013-01-01

    Objective: To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods: Gaze data and diagnostic decisions were collected from six radiologists who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Texture analysis was performed in mammographic regions that attracted radiologists attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results: By pooling the data from all radiologists machine learning produced highly accurate predictive models linking image content, gaze, cognition, and error. Merging radiologists gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the radiologists diagnostic errors while confirming 96.2% of their correct diagnoses. The radiologists individual errors could be adequately predicted by modeling the behavior of their peers. However, personalized tuning appears to be beneficial in many cases to capture more accurately individual behavior. Conclusions: Machine learning algorithms combining image features with radiologists gaze data and diagnostic decisions can be effectively developed to recognize cognitive and perceptual errors associated with the diagnostic interpretation of mammograms.

  3. Toward standardized quantitative image quality (IQ) assessment in computed tomography (CT): A comprehensive framework for automated and comparative IQ analysis based on ICRU Report 87.

    Science.gov (United States)

    Pahn, Gregor; Skornitzke, Stephan; Schlemmer, Hans-Peter; Kauczor, Hans-Ulrich; Stiller, Wolfram

    2016-01-01

    Based on the guidelines from "Report 87: Radiation Dose and Image-quality Assessment in Computed Tomography" of the International Commission on Radiation Units and Measurements (ICRU), a software framework for automated quantitative image quality analysis was developed and its usability for a variety of scientific questions demonstrated. The extendable framework currently implements the calculation of the recommended Fourier image quality (IQ) metrics modulation transfer function (MTF) and noise-power spectrum (NPS), and additional IQ quantities such as noise magnitude, CT number accuracy, uniformity across the field-of-view, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of simulated lesions for a commercially available cone-beam phantom. Sample image data were acquired with different scan and reconstruction settings on CT systems from different manufacturers. Spatial resolution is analyzed in terms of edge-spread function, line-spread-function, and MTF. 3D NPS is calculated according to ICRU Report 87, and condensed to 2D and radially averaged 1D representations. Noise magnitude, CT numbers, and uniformity of these quantities are assessed on large samples of ROIs. Low-contrast resolution (CNR, SNR) is quantitatively evaluated as a function of lesion contrast and diameter. Simultaneous automated processing of several image datasets allows for straightforward comparative assessment. The presented framework enables systematic, reproducible, automated and time-efficient quantitative IQ analysis. Consistent application of the ICRU guidelines facilitates standardization of quantitative assessment not only for routine quality assurance, but for a number of research questions, e.g. the comparison of different scanner models or acquisition protocols, and the evaluation of new technology or reconstruction methods. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  4. A SVD Based Image Complexity Measure

    DEFF Research Database (Denmark)

    Gustafsson, David Karl John; Pedersen, Kim Steenstrup; Nielsen, Mads

    2009-01-01

    Images are composed of geometric structures and texture, and different image processing tools - such as denoising, segmentation and registration - are suitable for different types of image contents. Characterization of the image content in terms of geometric structure and texture is an important...... problem that one is often faced with. We propose a patch based complexity measure, based on how well the patch can be approximated using singular value decomposition. As such the image complexity is determined by the complexity of the patches. The concept is demonstrated on sequences from the newly...... collected DIKU Multi-Scale image database....

  5. Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline.

    Science.gov (United States)

    Dowsey, Andrew W; Dunn, Michael J; Yang, Guang-Zhong

    2008-04-01

    The quest for high-throughput proteomics has revealed a number of challenges in recent years. Whilst substantial improvements in automated protein separation with liquid chromatography and mass spectrometry (LC/MS), aka 'shotgun' proteomics, have been achieved, large-scale open initiatives such as the Human Proteome Organization (HUPO) Brain Proteome Project have shown that maximal proteome coverage is only possible when LC/MS is complemented by 2D gel electrophoresis (2-DE) studies. Moreover, both separation methods require automated alignment and differential analysis to relieve the bioinformatics bottleneck and so make high-throughput protein biomarker discovery a reality. The purpose of this article is to describe a fully automatic image alignment framework for the integration of 2-DE into a high-throughput differential expression proteomics pipeline. The proposed method is based on robust automated image normalization (RAIN) to circumvent the drawbacks of traditional approaches. These use symbolic representation at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in modelling and alignment. In RAIN, a third-order volume-invariant B-spline model is incorporated into a multi-resolution schema to correct for geometric and expression inhomogeneity at multiple scales. The normalized images can then be compared directly in the image domain for quantitative differential analysis. Through evaluation against an existing state-of-the-art method on real and synthetically warped 2D gels, the proposed analysis framework demonstrates substantial improvements in matching accuracy and differential sensitivity. High-throughput analysis is established through an accelerated GPGPU (general purpose computation on graphics cards) implementation. Supplementary material, software and images used in the validation are available at http://www.proteomegrid.org/rain/.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Science.gov (United States)

    Barat, Christian; Phlypo, Ronald

    2010-12-01

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

  8. 78 FR 53466 - Modification of Two National Customs Automation Program (NCAP) Tests Concerning Automated...

    Science.gov (United States)

    2013-08-29

    ... Customs Automation Program (NCAP) Tests Concerning Automated Commercial Environment (ACE) Document Image... National Customs Automation Program (NCAP) tests concerning document imaging, known as the Document Image... the National Customs Automation Program (NCAP) tests concerning document imaging, known as the...

  9. Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network.

    Science.gov (United States)

    Wang, Zhiwei; Liu, Chaoyue; Cheng, Danpeng; Wang, Liang; Yang, Xin; Cheng, Kwang-Ting

    2018-05-01

    Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

  10. [The segmentation of urinary cells--a first step in the automated processing in urine cytology (author's transl)].

    Science.gov (United States)

    Liedtke, C E; Aeikens, B

    1980-01-01

    By segmentation of cell images we understand the automated decomposition of microscopic cell scenes into nucleus, plasma and background. A segmentation is achieved by using information from the microscope image and prior knowledge about the content of the scene. Different algorithms have been investigated and applied to samples of urothelial cells. A particular algorithm based on a histogram approach which can be easily implemented in hardware is discussed in more detail.

  11. Dynamic CT myocardial perfusion imaging: performance of 3D semi-automated evaluation software

    Energy Technology Data Exchange (ETDEWEB)

    Ebersberger, Ullrich [Medical University of South Carolina, Heart and Vascular Center, Charleston, SC (United States); Heart Center Munich-Bogenhausen, Department of Cardiology and Intensive Care Medicine, Munich (Germany); Marcus, Roy P.; Nikolaou, Konstantin; Bamberg, Fabian [University of Munich, Institute of Clinical Radiology, Munich (Germany); Schoepf, U.J.; Gray, J.C.; McQuiston, Andrew D. [Medical University of South Carolina, Heart and Vascular Center, Charleston, SC (United States); Lo, Gladys G. [Hong Kong Sanatorium and Hospital, Department of Diagnostic and Interventional Radiology, Hong Kong (China); Wang, Yining [Medical University of South Carolina, Heart and Vascular Center, Charleston, SC (United States); Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Department of Radiology, Beijing (China); Blanke, Philipp [Medical University of South Carolina, Heart and Vascular Center, Charleston, SC (United States); University Hospital Freiburg, Department of Diagnostic Radiology, Freiburg (Germany); Geyer, Lucas L. [Medical University of South Carolina, Heart and Vascular Center, Charleston, SC (United States); University of Munich, Institute of Clinical Radiology, Munich (Germany); Cho, Young Jun [Medical University of South Carolina, Heart and Vascular Center, Charleston, SC (United States); Konyang University College of Medicine, Department of Radiology, Daejeon (Korea, Republic of); Scheuering, Michael; Canstein, Christian [Siemens Healthcare, CT Division, Forchheim (Germany); Hoffmann, Ellen [Heart Center Munich-Bogenhausen, Department of Cardiology and Intensive Care Medicine, Munich (Germany)

    2014-01-15

    To evaluate the performance of three-dimensional semi-automated evaluation software for the assessment of myocardial blood flow (MBF) and blood volume (MBV) at dynamic myocardial perfusion computed tomography (CT). Volume-based software relying on marginal space learning and probabilistic boosting tree-based contour fitting was applied to CT myocardial perfusion imaging data of 37 subjects. In addition, all image data were analysed manually and both approaches were compared with SPECT findings. Study endpoints included time of analysis and conventional measures of diagnostic accuracy. Of 592 analysable segments, 42 showed perfusion defects on SPECT. Average analysis times for the manual and software-based approaches were 49.1 ± 11.2 and 16.5 ± 3.7 min respectively (P < 0.01). There was strong agreement between the two measures of interest (MBF, ICC = 0.91, and MBV, ICC = 0.88, both P < 0.01) and no significant difference in MBF/MBV with respect to diagnostic accuracy between the two approaches for both MBF and MBV for manual versus software-based approach; respectively; all comparisons P > 0.05. Three-dimensional semi-automated evaluation of dynamic myocardial perfusion CT data provides similar measures and diagnostic accuracy to manual evaluation, albeit with substantially reduced analysis times. This capability may aid the integration of this test into clinical workflows. (orig.)

  12. Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.

    Science.gov (United States)

    de Groot, Reinoud; Lüthi, Joel; Lindsay, Helen; Holtackers, René; Pelkmans, Lucas

    2018-01-23

    High-content imaging using automated microscopy and computer vision allows multivariate profiling of single-cell phenotypes. Here, we present methods for the application of the CISPR-Cas9 system in large-scale, image-based, gene perturbation experiments. We show that CRISPR-Cas9-mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image-based phenotyping. We developed a pipeline to construct a large-scale arrayed library of 2,281 sequence-verified CRISPR-Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine-learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in-depth characterization of gene perturbation effects. This approach enables genome-scale image-based multivariate gene perturbation profiling using CRISPR-Cas9. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

  13. Detecting content adaptive scaling of images for forensic applications

    Science.gov (United States)

    Fillion, Claude; Sharma, Gaurav

    2010-01-01

    Content-aware resizing methods have recently been developed, among which, seam-carving has achieved the most widespread use. Seam-carving's versatility enables deliberate object removal and benign image resizing, in which perceptually important content is preserved. Both types of modifications compromise the utility and validity of the modified images as evidence in legal and journalistic applications. It is therefore desirable that image forensic techniques detect the presence of seam-carving. In this paper we address detection of seam-carving for forensic purposes. As in other forensic applications, we pose the problem of seam-carving detection as the problem of classifying a test image in either of two classes: a) seam-carved or b) non-seam-carved. We adopt a pattern recognition approach in which a set of features is extracted from the test image and then a Support Vector Machine based classifier, trained over a set of images, is utilized to estimate which of the two classes the test image lies in. Based on our study of the seam-carving algorithm, we propose a set of intuitively motivated features for the detection of seam-carving. Our methodology for detection of seam-carving is then evaluated over a test database of images. We demonstrate that the proposed method provides the capability for detecting seam-carving with high accuracy. For images which have been reduced 30% by benign seam-carving, our method provides a classification accuracy of 91%.

  14. Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation

    Directory of Open Access Journals (Sweden)

    Shuiping Gou, PhD

    2016-07-01

    Conclusions: Our study demonstrated potential feasibility of automated segmentation of the pancreas on MRI scans with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization.

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

    Directory of Open Access Journals (Sweden)

    Konsti Juho

    2012-03-01

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

  16. Evolution of a Benthic Imaging System From a Towed Camera to an Automated Habitat Characterization System

    Science.gov (United States)

    2008-09-01

    automated processing of images for color correction, segmentation of foreground targets from sediment and classification of targets to taxonomic category...element in the development of HabCam as a tool for habitat characterization is the automated processing of images for color correction, segmentation of

  17. Comparison of semi-automated center-dot and fully automated endothelial cell analyses from specular microscopy images.

    Science.gov (United States)

    Maruoka, Sachiko; Nakakura, Shunsuke; Matsuo, Naoko; Yoshitomi, Kayo; Katakami, Chikako; Tabuchi, Hitoshi; Chikama, Taiichiro; Kiuchi, Yoshiaki

    2017-10-30

    To evaluate two specular microscopy analysis methods across different endothelial cell densities (ECDs). Endothelial images of one eye from each of 45 patients were taken by using three different specular microscopes (three replicates each). To determine the consistency of the center-dot method, we compared SP-6000 and SP-2000P images. CME-530 and SP-6000 images were compared to assess the consistency of the fully automated method. The SP-6000 images from the two methods were compared. Intraclass correlation coefficients (ICCs) for the three measurements were calculated, and parametric multiple comparisons tests and Bland-Altman analysis were performed. The ECD mean value was 2425 ± 883 (range 516-3707) cells/mm 2 . ICC values were > 0.9 for all three microscopes for ECD, but the coefficients of variation (CVs) were 0.3-0.6. For ECD measurements, Bland-Altman analysis revealed that the mean difference was 42 cells/mm 2 between the SP-2000P and SP-6000 for the center-dot method; 57 cells/mm 2 between the SP-6000 measurements from both methods; and -5 cells/mm 2 between the SP-6000 and CME-530 for the fully automated method (95% limits of agreement: - 201 to 284 cell/mm 2 , - 410 to 522 cells/mm 2 , and - 327 to 318 cells/mm 2 , respectively). For CV measurements, the mean differences were - 3, - 12, and 13% (95% limits of agreement - 18 to 11, - 26 to 2, and - 5 to 32%, respectively). Despite using three replicate measurements, the precision of the center-dot method with the SP-2000P and SP-6000 software was only ± 10% for ECD data and was even worse for the fully automated method. Japan Clinical Trials Register ( http://www.umin.ac.jp/ctr/index/htm9 ) number UMIN 000015236.

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

    International Nuclear Information System (INIS)

    Venkataraman, S.; Allison, D.P.; Qi, H.; Morrell-Falvey, J.L.; Kallewaard, N.L.; Crowe, J.E.; Doktycz, M.J.

    2006-01-01

    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

  19. 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.

  20. Semi-automated relative quantification of cell culture contamination with mycoplasma by Photoshop-based image analysis on immunofluorescence preparations.

    Science.gov (United States)

    Kumar, Ashok; Yerneni, Lakshmana K

    2009-01-01

    Mycoplasma contamination in cell culture is a serious setback for the cell-culturist. The experiments undertaken using contaminated cell cultures are known to yield unreliable or false results due to various morphological, biochemical and genetic effects. Earlier surveys revealed incidences of mycoplasma contamination in cell cultures to range from 15 to 80%. Out of a vast array of methods for detecting mycoplasma in cell culture, the cytological methods directly demonstrate the contaminating organism present in association with the cultured cells. In this investigation, we report the adoption of a cytological immunofluorescence assay (IFA), in an attempt to obtain a semi-automated relative quantification of contamination by employing the user-friendly Photoshop-based image analysis. The study performed on 77 cell cultures randomly collected from various laboratories revealed mycoplasma contamination in 18 cell cultures simultaneously by IFA and Hoechst DNA fluorochrome staining methods. It was observed that the Photoshop-based image analysis on IFA stained slides was very valuable as a sensitive tool in providing quantitative assessment on the extent of contamination both per se and in comparison to cellularity of cell cultures. The technique could be useful in estimating the efficacy of anti-mycoplasma agents during decontaminating measures.

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

    Science.gov (United States)

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

    2013-09-01

    Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability of high-resolution infrared thermal imaging for noninvasive automated detection of signs of diabetic foot disease. The plantar foot surfaces of 15 diabetes patients were imaged with an infrared camera (resolution, 1.2 mm/pixel): 5 patients had no visible signs of foot complications, 5 patients had local complications (e.g., abundant callus or neuropathic ulcer), and 5 patients had diffuse complications (e.g., Charcot foot, infected ulcer, or critical ischemia). Foot temperature was calculated as mean temperature across pixels for the whole foot and for specified regions of interest (ROIs). No differences in mean temperature >1.5 °C between the ipsilateral and the contralateral foot were found in patients without complications. In patients with local complications, mean temperatures of the ipsilateral and the contralateral foot were similar, but temperature at the ROI was >2 °C higher compared with the corresponding region in the contralateral foot and to the mean of the whole ipsilateral foot. In patients with diffuse complications, mean temperature differences of >3 °C between ipsilateral and contralateral foot were found. With an algorithm based on parameters that can be captured and analyzed with a high-resolution infrared camera and a computer, it is possible to detect signs of diabetic foot disease and to discriminate between no, local, or diffuse diabetic foot complications. As such, an intelligent telemedicine monitoring system for noninvasive automated detection of signs of diabetic foot disease is one step closer. Future studies are essential to confirm and extend these promising early findings. © 2013 Diabetes Technology Society.

  2. Automated Snow Extent Mapping Based on Orthophoto Images from Unmanned Aerial Vehicles

    Science.gov (United States)

    Niedzielski, Tomasz; Spallek, Waldemar; Witek-Kasprzak, Matylda

    2018-04-01

    The paper presents the application of the k-means clustering in the process of automated snow extent mapping using orthophoto images generated using the Structure-from-Motion (SfM) algorithm from oblique aerial photographs taken by unmanned aerial vehicle (UAV). A simple classification approach has been implemented to discriminate between snow-free and snow-covered terrain. The procedure uses the k-means clustering and classifies orthophoto images based on the three-dimensional space of red-green-blue (RGB) or near-infrared-red-green (NIRRG) or near-infrared-green-blue (NIRGB) bands. To test the method, several field experiments have been carried out, both in situations when snow cover was continuous and when it was patchy. The experiments have been conducted using three fixed-wing UAVs (swinglet CAM by senseFly, eBee by senseFly, and Birdie by FlyTech UAV) on 10/04/2015, 23/03/2016, and 16/03/2017 within three test sites in the Izerskie Mountains in southwestern Poland. The resulting snow extent maps, produced automatically using the classification method, have been validated against real snow extents delineated through a visual analysis and interpretation offered by human analysts. For the simplest classification setup, which assumes two classes in the k-means clustering, the extent of snow patches was estimated accurately, with areal underestimation of 4.6% (RGB) and overestimation of 5.5% (NIRGB). For continuous snow cover with sparse discontinuities at places where trees or bushes protruded from snow, the agreement between automatically produced snow extent maps and observations was better, i.e. 1.5% (underestimation with RGB) and 0.7-0.9% (overestimation, either with RGB or with NIRRG). Shadows on snow were found to be mainly responsible for the misclassification.

  3. Development of an automated asbestos counting software based on fluorescence microscopy.

    Science.gov (United States)

    Alexandrov, Maxym; Ichida, Etsuko; Nishimura, Tomoki; Aoki, Kousuke; Ishida, Takenori; Hirota, Ryuichi; Ikeda, Takeshi; Kawasaki, Tetsuo; Kuroda, Akio

    2015-01-01

    An emerging alternative to the commonly used analytical methods for asbestos analysis is fluorescence microscopy (FM), which relies on highly specific asbestos-binding probes to distinguish asbestos from interfering non-asbestos fibers. However, all types of microscopic asbestos analysis require laborious examination of large number of fields of view and are prone to subjective errors and large variability between asbestos counts by different analysts and laboratories. A possible solution to these problems is automated counting of asbestos fibers by image analysis software, which would lower the cost and increase the reliability of asbestos testing. This study seeks to develop a fiber recognition and counting software for FM-based asbestos analysis. We discuss the main features of the developed software and the results of its testing. Software testing showed good correlation between automated and manual counts for the samples with medium and high fiber concentrations. At low fiber concentrations, the automated counts were less accurate, leading us to implement correction mode for automated counts. While the full automation of asbestos analysis would require further improvements in accuracy of fiber identification, the developed software could already assist professional asbestos analysts and record detailed fiber dimensions for the use in epidemiological research.

  4. Study radiolabeling of urea-based PSMA inhibitor with 68-Galliu: Comparative evaluation of automated and not automated methods

    International Nuclear Information System (INIS)

    Alcarde, Lais Fernanda

    2016-01-01

    The methods for clinical diagnosis of prostate cancer include rectal examination and the dosage of the prostatic specific antigen (PSA). However, the PSA level is elevated in about 20 to 30% of cases related to benign pathologies, resulting in false positives and leading patients to unnecessary biopsies. The prostate specific membrane antigen (PSMA), in contrast, is over expressed in prostate cancer and founded at low levels in healthy organs. As a result, it stimulated the development of small molecule inhibitors of PSMA, which carry imaging agents to the tumor and are not affected by their microvasculature. Recent studies suggest that the HBED-CC chelator intrinsically contributes to the binding of the PSMA inhibitor peptide based on urea (Glu-urea-Lys) to the pharmacophore group. This work describes the optimization of radiolabeling conditions of PSMA-HBED-CC with "6"8Ga, using automated system (synthesis module) and no automated method, seeking to establish an appropriate condition to prepare this new radiopharmaceutical, with emphasis on the labeling yield and radiochemical purity of the product. It also aimed to evaluate the stability of the radiolabeled peptide in transport conditions and study the biological distribution of the radiopharmaceutical in healthy mice. The study of radiolabeling parameters enabled to define a non-automated method which resulted in high radiochemical purity (> 95 %) without the need for purification of the labeled peptide. The automated method has been adapted, using a module of synthesis and software already available at IPEN, and also resulted in high synthetic yield (≥ 90%) specially when compared with those described in the literature, with the associated benefit of greater control of the production process in compliance with Good Manufacturing Practices. The study of radiolabeling parameters afforded the PSMA-HBED-CC-"6"8Ga with higher specific activity than observed in published clinical studies (≥ 140,0 GBq

  5. Design and development of a content-based medical image retrieval system for spine vertebrae irregularity.

    Science.gov (United States)

    Mustapha, Aouache; Hussain, Aini; Samad, Salina Abdul; Zulkifley, Mohd Asyraf; Diyana Wan Zaki, Wan Mimi; Hamid, Hamzaini Abdul

    2015-01-16

    Content-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities. In this paper, a more robust CBMIR system that deals with both cervical and lumbar vertebrae irregularity is afforded. It comprises three main phases, namely modelling, indexing and retrieval of the vertebrae image. The main tasks in the modelling phase are to improve and enhance the visibility of the x-ray image for better segmentation results using active shape model (ASM). The segmented vertebral fractures are then characterized in the indexing phase using region-based fracture characterization (RB-FC) and contour-based fracture characterization (CB-FC). Upon a query, the characterized features are compared to the query image. Effectiveness of the retrieval phase is determined by its retrieval, thus, we propose an integration of the predictor model based cross validation neural network (PMCVNN) and similarity matching (SM) in this stage. The PMCVNN task is to identify the correct vertebral irregularity class through classification allowing the SM process to be more efficient. Retrieval performance between the proposed and the standard retrieval architectures are then compared using retrieval precision (Pr@M) and average group score (AGS) measures. Experimental results show that the new integrated retrieval architecture performs better than those of the standard CBMIR architecture with retrieval results of cervical (AGS > 87%) and lumbar (AGS > 82%) datasets. The proposed CBMIR architecture shows encouraging results with high Pr@M accuracy. As a result, images from the same visualization class are returned for further used by the medical personnel.

  6. Integrating high-content imaging and chemical genetics to probe host cellular pathways critical for Yersinia pestis infection.

    Directory of Open Access Journals (Sweden)

    Krishna P Kota

    Full Text Available The molecular machinery that regulates the entry and survival of Yersinia pestis in host macrophages is poorly understood. Here, we report the development of automated high-content imaging assays to quantitate the internalization of virulent Y. pestis CO92 by macrophages and the subsequent activation of host NF-κB. Implementation of these assays in a focused chemical screen identified kinase inhibitors that inhibited both of these processes. Rac-2-ethoxy-3 octadecanamido-1-propylphosphocholine (a protein Kinase C inhibitor, wortmannin (a PI3K inhibitor, and parthenolide (an IκB kinase inhibitor, inhibited pathogen-induced NF-κB activation and reduced bacterial entry and survival within macrophages. Parthenolide inhibited NF-κB activation in response to stimulation with Pam3CSK4 (a TLR2 agonist, E. coli LPS (a TLR4 agonist or Y. pestis infection, while the PI3K and PKC inhibitors were selective only for Y. pestis infection. Together, our results suggest that phagocytosis is the major stimulus for NF-κB activation in response to Y. pestis infection, and that Y. pestis entry into macrophages may involve the participation of protein kinases such as PI3K and PKC. More importantly, the automated image-based screening platform described here can be applied to the study of other bacteria in general and, in combination with chemical genetic screening, can be used to identify host cell functions facilitating the identification of novel antibacterial therapeutics.

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

    Science.gov (United States)

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

    2014-03-01

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

  8. Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging.

    Science.gov (United States)

    Patel, Tapan P; Man, Karen; Firestein, Bonnie L; Meaney, David F

    2015-03-30

    Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s-1000+neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. Copyright © 2015. Published by Elsevier B.V.

  9. Quantification of lung fibrosis and emphysema in mice using automated micro-computed tomography.

    Directory of Open Access Journals (Sweden)

    Ellen De Langhe

    Full Text Available BACKGROUND: In vivo high-resolution micro-computed tomography allows for longitudinal image-based measurements in animal models of lung disease. The combination of repetitive high resolution imaging with fully automated quantitative image analysis in mouse models of lung fibrosis lung benefits preclinical research. This study aimed to develop and validate such an automated micro-computed tomography analysis algorithm for quantification of aerated lung volume in mice; an indicator of pulmonary fibrosis and emphysema severity. METHODOLOGY: Mice received an intratracheal instillation of bleomycin (n = 8, elastase (0.25 U elastase n = 9, 0.5 U elastase n = 8 or saline control (n = 6 for fibrosis, n = 5 for emphysema. A subset of mice was scanned without intervention, to evaluate potential radiation-induced toxicity (n = 4. Some bleomycin-instilled mice were treated with imatinib for proof of concept (n = 8. Mice were scanned weekly, until four weeks after induction, when they underwent pulmonary function testing, lung histology and collagen quantification. Aerated lung volumes were calculated with our automated algorithm. PRINCIPAL FINDINGS: Our automated image-based aerated lung volume quantification method is reproducible with low intra-subject variability. Bleomycin-treated mice had significantly lower scan-derived aerated lung volumes, compared to controls. Aerated lung volume correlated with the histopathological fibrosis score and total lung collagen content. Inversely, a dose-dependent increase in lung volume was observed in elastase-treated mice. Serial scanning of individual mice is feasible and visualized dynamic disease progression. No radiation-induced toxicity was observed. Three-dimensional images provided critical topographical information. CONCLUSIONS: We report on a high resolution in vivo micro-computed tomography image analysis algorithm that runs fully automated and allows quantification of aerated lung volume in mice. This

  10. Relative Panoramic Camera Position Estimation for Image-Based Virtual Reality Networks in Indoor Environments

    Science.gov (United States)

    Nakagawa, M.; Akano, K.; Kobayashi, T.; Sekiguchi, Y.

    2017-09-01

    Image-based virtual reality (VR) is a virtual space generated with panoramic images projected onto a primitive model. In imagebased VR, realistic VR scenes can be generated with lower rendering cost, and network data can be described as relationships among VR scenes. The camera network data are generated manually or by an automated procedure using camera position and rotation data. When panoramic images are acquired in indoor environments, network data should be generated without Global Navigation Satellite Systems (GNSS) positioning data. Thus, we focused on image-based VR generation using a panoramic camera in indoor environments. We propose a methodology to automate network data generation using panoramic images for an image-based VR space. We verified and evaluated our methodology through five experiments in indoor environments, including a corridor, elevator hall, room, and stairs. We confirmed that our methodology can automatically reconstruct network data using panoramic images for image-based VR in indoor environments without GNSS position data.

  11. RELATIVE PANORAMIC CAMERA POSITION ESTIMATION FOR IMAGE-BASED VIRTUAL REALITY NETWORKS IN INDOOR ENVIRONMENTS

    Directory of Open Access Journals (Sweden)

    M. Nakagawa

    2017-09-01

    Full Text Available Image-based virtual reality (VR is a virtual space generated with panoramic images projected onto a primitive model. In imagebased VR, realistic VR scenes can be generated with lower rendering cost, and network data can be described as relationships among VR scenes. The camera network data are generated manually or by an automated procedure using camera position and rotation data. When panoramic images are acquired in indoor environments, network data should be generated without Global Navigation Satellite Systems (GNSS positioning data. Thus, we focused on image-based VR generation using a panoramic camera in indoor environments. We propose a methodology to automate network data generation using panoramic images for an image-based VR space. We verified and evaluated our methodology through five experiments in indoor environments, including a corridor, elevator hall, room, and stairs. We confirmed that our methodology can automatically reconstruct network data using panoramic images for image-based VR in indoor environments without GNSS position data.

  12. Automated, parallel mass spectrometry imaging and structural identification of lipids

    DEFF Research Database (Denmark)

    Ellis, Shane R.; Paine, Martin R.L.; Eijkel, Gert B.

    2018-01-01

    We report a method that enables automated data-dependent acquisition of lipid tandem mass spectrometry data in parallel with a high-resolution mass spectrometry imaging experiment. The method does not increase the total image acquisition time and is combined with automatic structural assignments....... This lipidome-per-pixel approach automatically identified and validated 104 unique molecular lipids and their spatial locations from rat cerebellar tissue....

  13. 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.

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

    International Nuclear Information System (INIS)

    Wisotzky, E.; Fast, M.F.; Nill, S.

    2015-01-01

    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 registered beacons were embedded to achieve better contrast during MV radiation. For a single beacon, image acquisition and automated marker detection typically took around 76±6 ms. The success rate was found to be highly dependent on imaging dose and gantry angle. To eliminate possible false detections, the authors implemented a training phase prior to treatment beam irradiation and also introduced speed limits for motion between subsequent images.

  15. Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching

    Directory of Open Access Journals (Sweden)

    Tianyang Cao

    2017-01-01

    Full Text Available Self-localization and mapping are important for indoor mobile robot. We report a robust algorithm for map building and subsequent localization especially suited for indoor floor-cleaning robots. Common methods, for example, SLAM, can easily be kidnapped by colliding or disturbed by similar objects. Therefore, keyframes global map establishing method for robot localization in multiple rooms and corridors is needed. Content-based image matching is the core of this method. It is designed for the situation, by establishing keyframes containing both floor and distorted wall images. Image distortion, caused by robot view angle and movement, is analyzed and deduced. And an image matching solution is presented, consisting of extraction of overlap regions of keyframes extraction and overlap region rebuild through subblocks matching. For improving accuracy, ceiling points detecting and mismatching subblocks checking methods are incorporated. This matching method can process environment video effectively. In experiments, less than 5% frames are extracted as keyframes to build global map, which have large space distance and overlap each other. Through this method, robot can localize itself by matching its real-time vision frames with our keyframes map. Even with many similar objects/background in the environment or kidnapping robot, robot localization is achieved with position RMSE <0.5 m.

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

    Science.gov (United States)

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

    2013-09-01

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

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

    OpenAIRE

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

    2006-01-01

      Udgivelsesdato: DEC  Planning target volumes (PTV) in fractionated radiotherapy still have to be outlined with wide margins to the clinical target volume due to uncertainties arising from daily shift of the prostate position. A recently proposed new method of visualization of the prostate is based on insertion of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is ba...

  18. Reproducibility of myelin content-based human habenula segmentation at 3 Tesla.

    Science.gov (United States)

    Kim, Joo-Won; Naidich, Thomas P; Joseph, Joshmi; Nair, Divya; Glasser, Matthew F; O'halloran, Rafael; Doucet, Gaelle E; Lee, Won Hee; Krinsky, Hannah; Paulino, Alejandro; Glahn, David C; Anticevic, Alan; Frangou, Sophia; Xu, Junqian

    2018-03-26

    In vivo morphological study of the human habenula, a pair of small epithalamic nuclei adjacent to the dorsomedial thalamus, has recently gained significant interest for its role in reward and aversion processing. However, segmenting the habenula from in vivo magnetic resonance imaging (MRI) is challenging due to the habenula's small size and low anatomical contrast. Although manual and semi-automated habenula segmentation methods have been reported, the test-retest reproducibility of the segmented habenula volume and the consistency of the boundaries of habenula segmentation have not been investigated. In this study, we evaluated the intra- and inter-site reproducibility of in vivo human habenula segmentation from 3T MRI (0.7-0.8 mm isotropic resolution) using our previously proposed semi-automated myelin contrast-based method and its fully-automated version, as well as a previously published manual geometry-based method. The habenula segmentation using our semi-automated method showed consistent boundary definition (high Dice coefficient, low mean distance, and moderate Hausdorff distance) and reproducible volume measurement (low coefficient of variation). Furthermore, the habenula boundary in our semi-automated segmentation from 3T MRI agreed well with that in the manual segmentation from 7T MRI (0.5 mm isotropic resolution) of the same subjects. Overall, our proposed semi-automated habenula segmentation showed reliable and reproducible habenula localization, while its fully-automated version offers an efficient way for large sample analysis. © 2018 Wiley Periodicals, Inc.

  19. Automated processing of label-free Raman microscope images of macrophage cells with standardized regression for high-throughput analysis.

    Science.gov (United States)

    Milewski, Robert J; Kumagai, Yutaro; Fujita, Katsumasa; Standley, Daron M; Smith, Nicholas I

    2010-11-19

    Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively. We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest. The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without

  20. Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis

    Science.gov (United States)

    Itoh, Hayato; Mori, Yuichi; Misawa, Masashi; Oda, Masahiro; Kudo, Shin-ei; Mori, Kensaku

    2018-02-01

    This paper presents a new classification method for endocytoscopic images. Endocytoscopy is a new endoscope that enables us to perform conventional endoscopic observation and ultramagnified observation of cell level. This ultramagnified views (endocytoscopic images) make possible to perform pathological diagnosis only on endo-scopic views of polyps during colonoscopy. However, endocytoscopic image diagnosis requires higher experiences for physicians. An automated pathological diagnosis system is required to prevent the overlooking of neoplastic lesions in endocytoscopy. For this purpose, we propose a new automated endocytoscopic image classification method that classifies neoplastic and non-neoplastic endocytoscopic images. This method consists of two classification steps. At the first step, we classify an input image by support vector machine. We forward the image to the second step if the confidence of the first classification is low. At the second step, we classify the forwarded image by convolutional neural network. We reject the input image if the confidence of the second classification is also low. We experimentally evaluate the classification performance of the proposed method. In this experiment, we use about 16,000 and 4,000 colorectal endocytoscopic images as training and test data, respectively. The results show that the proposed method achieves high sensitivity 93.4% with small rejection rate 9.3% even for difficult test data.

  1. Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment

    International Nuclear Information System (INIS)

    Wengert, G.J.; Helbich, T.H.; Woitek, R.; Kapetas, P.; Clauser, P.; Baltzer, P.A.; Vogl, W.D.; Weber, M.; Meyer-Baese, A.; Pinker, Katja

    2016-01-01

    To evaluate the inter-/intra-observer agreement of BI-RADS-based subjective visual estimation of the amount of fibroglandular tissue (FGT) with magnetic resonance imaging (MRI), and to investigate whether FGT assessment benefits from an automated, observer-independent, quantitative MRI measurement by comparing both approaches. Eighty women with no imaging abnormalities (BI-RADS 1 and 2) were included in this institutional review board (IRB)-approved prospective study. All women underwent un-enhanced breast MRI. Four radiologists independently assessed FGT with MRI by subjective visual estimation according to BI-RADS. Automated observer-independent quantitative measurement of FGT with MRI was performed using a previously described measurement system. Inter-/intra-observer agreements of qualitative and quantitative FGT measurements were assessed using Cohen's kappa (k). Inexperienced readers achieved moderate inter-/intra-observer agreement and experienced readers a substantial inter- and perfect intra-observer agreement for subjective visual estimation of FGT. Practice and experience reduced observer-dependency. Automated observer-independent quantitative measurement of FGT was successfully performed and revealed only fair to moderate agreement (k = 0.209-0.497) with subjective visual estimations of FGT. Subjective visual estimation of FGT with MRI shows moderate intra-/inter-observer agreement, which can be improved by practice and experience. Automated observer-independent quantitative measurements of FGT are necessary to allow a standardized risk evaluation. (orig.)

  2. Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment

    Energy Technology Data Exchange (ETDEWEB)

    Wengert, G.J.; Helbich, T.H.; Woitek, R.; Kapetas, P.; Clauser, P.; Baltzer, P.A. [Medical University of Vienna/ Vienna General Hospital, Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Vienna (Austria); Vogl, W.D. [Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Wien (Austria); Weber, M. [Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Wien (Austria); Meyer-Baese, A. [State University of Florida, Department of Scientific Computing in Medicine, Tallahassee, FL (United States); Pinker, Katja [Medical University of Vienna/ Vienna General Hospital, Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Vienna (Austria); State University of Florida, Department of Scientific Computing in Medicine, Tallahassee, FL (United States); Memorial Sloan-Kettering Cancer Center, Department of Radiology, Molecular Imaging and Therapy Services, New York City, NY (United States)

    2016-11-15

    To evaluate the inter-/intra-observer agreement of BI-RADS-based subjective visual estimation of the amount of fibroglandular tissue (FGT) with magnetic resonance imaging (MRI), and to investigate whether FGT assessment benefits from an automated, observer-independent, quantitative MRI measurement by comparing both approaches. Eighty women with no imaging abnormalities (BI-RADS 1 and 2) were included in this institutional review board (IRB)-approved prospective study. All women underwent un-enhanced breast MRI. Four radiologists independently assessed FGT with MRI by subjective visual estimation according to BI-RADS. Automated observer-independent quantitative measurement of FGT with MRI was performed using a previously described measurement system. Inter-/intra-observer agreements of qualitative and quantitative FGT measurements were assessed using Cohen's kappa (k). Inexperienced readers achieved moderate inter-/intra-observer agreement and experienced readers a substantial inter- and perfect intra-observer agreement for subjective visual estimation of FGT. Practice and experience reduced observer-dependency. Automated observer-independent quantitative measurement of FGT was successfully performed and revealed only fair to moderate agreement (k = 0.209-0.497) with subjective visual estimations of FGT. Subjective visual estimation of FGT with MRI shows moderate intra-/inter-observer agreement, which can be improved by practice and experience. Automated observer-independent quantitative measurements of FGT are necessary to allow a standardized risk evaluation. (orig.)

  3. LAIR: A Language for Automated Semantics-Aware Text Sanitization based on Frame Semantics

    DEFF Research Database (Denmark)

    Hedegaard, Steffen; Houen, Søren; Simonsen, Jakob Grue

    2009-01-01

    We present \\lair{}: A domain-specific language that enables users to specify actions to be taken upon meeting specific semantic frames in a text, in particular to rephrase and redact the textual content. While \\lair{} presupposes superficial knowledge of frames and frame semantics, it requires on...... with automated redaction of web pages for subjectively undesirable content; initial experiments suggest that using a small language based on semantic recognition of undesirable terms can be highly useful as a supplement to traditional methods of text sanitization.......We present \\lair{}: A domain-specific language that enables users to specify actions to be taken upon meeting specific semantic frames in a text, in particular to rephrase and redact the textual content. While \\lair{} presupposes superficial knowledge of frames and frame semantics, it requires only...... limited prior programming experience. It neither contain scripting or I/O primitives, nor does it contain general loop constructions and is not Turing-complete. We have implemented a \\lair{} compiler and integrated it in a pipeline for automated redaction of web pages. We detail our experience...

  4. MR efficiency using automated MRI-desktop eProtocol

    Science.gov (United States)

    Gao, Fei; Xu, Yanzhe; Panda, Anshuman; Zhang, Min; Hanson, James; Su, Congzhe; Wu, Teresa; Pavlicek, William; James, Judy R.

    2017-03-01

    MRI protocols are instruction sheets that radiology technologists use in routine clinical practice for guidance (e.g., slice position, acquisition parameters etc.). In Mayo Clinic Arizona (MCA), there are over 900 MR protocols (ranging across neuro, body, cardiac, breast etc.) which makes maintaining and updating the protocol instructions a labor intensive effort. The task is even more challenging given different vendors (Siemens, GE etc.). This is a universal problem faced by all the hospitals and/or medical research institutions. To increase the efficiency of the MR practice, we designed and implemented a web-based platform (eProtocol) to automate the management of MRI protocols. It is built upon a database that automatically extracts protocol information from DICOM compliant images and provides a user-friendly interface to the technologists to create, edit and update the protocols. Advanced operations such as protocol migrations from scanner to scanner and capability to upload Multimedia content were also implemented. To the best of our knowledge, eProtocol is the first MR protocol automated management tool used clinically. It is expected that this platform will significantly improve the radiology operations efficiency including better image quality and exam consistency, fewer repeat examinations and less acquisition errors. These protocols instructions will be readily available to the technologists during scans. In addition, this web-based platform can be extended to other imaging modalities such as CT, Mammography, and Interventional Radiology and different vendors for imaging protocol management.

  5. Evaluation of automated image analysis software for the detection of diabetic retinopathy to reduce the ophthalmologists' workload.

    Science.gov (United States)

    Soto-Pedre, Enrique; Navea, Amparo; Millan, Saray; Hernaez-Ortega, Maria C; Morales, Jesús; Desco, Maria C; Pérez, Pablo

    2015-02-01

    To assess the safety and workload reduction of an automated 'disease/no disease' grading system for diabetic retinopathy (DR) within a systematic screening programme. Single 45° macular field image per eye was obtained from consecutive patients attending a regional primary care based DR screening programme in Valencia (Spain). The sensitivity and specificity of automated system operating as 'one or more than one microaneurysm detection for disease presence' grader were determined relative to a manual grading as gold standard. Data on age, gender and diabetes mellitus were also recorded. A total of 5278 patients with diabetes were screened. The median age and duration of diabetes was 69 years and 6.9 years, respectively. Estimated prevalence of DR was 15.6%. The software classified 43.9% of the patients as having no DR and 26.1% as having ungradable images. Detection of DR was achieved with 94.5% sensitivity (95% CI 92.6- 96.5) and 68.8% specificity (95%CI 67.2-70.4). The overall accuracy of the automated system was 72.5% (95%CI 71.1-73.9). The present retinal image processing algorithm that can act as prefilter to flag out images with pathological lesions can be implemented in practice. Our results suggest that it could be considered when implementing DR screening programmes. © 2014 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

  6. Automated prescription of oblique brain 3D magnetic resonance spectroscopic imaging.

    Science.gov (United States)

    Ozhinsky, Eugene; Vigneron, Daniel B; Chang, Susan M; Nelson, Sarah J

    2013-04-01

    Two major difficulties encountered in implementing Magnetic Resonance Spectroscopic Imaging (MRSI) in a clinical setting are limited coverage and difficulty in prescription. The goal of this project was to automate completely the process of 3D PRESS MRSI prescription, including placement of the selection box, saturation bands and shim volume, while maximizing the coverage of the brain. The automated prescription technique included acquisition of an anatomical MRI image, optimization of the oblique selection box parameters, optimization of the placement of outer-volume suppression saturation bands, and loading of the calculated parameters into a customized 3D MRSI pulse sequence. To validate the technique and compare its performance with existing protocols, 3D MRSI data were acquired from six exams from three healthy volunteers. To assess the performance of the automated 3D MRSI prescription for patients with brain tumors, the data were collected from 16 exams from 8 subjects with gliomas. This technique demonstrated robust coverage of the tumor, high consistency of prescription and very good data quality within the T2 lesion. Copyright © 2012 Wiley Periodicals, Inc.

  7. Interactive classification and content-based retrieval of tissue images

    Science.gov (United States)

    Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

    2002-11-01

    We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

  8. Automated Segmentation of High-Resolution Photospheric Images of Active Regions

    Science.gov (United States)

    Yang, Meng; Tian, Yu; Rao, Changhui

    2018-02-01

    Due to the development of ground-based, large-aperture solar telescopes with adaptive optics (AO) resulting in increasing resolving ability, more accurate sunspot identifications and characterizations are required. In this article, we have developed a set of automated segmentation methods for high-resolution solar photospheric images. Firstly, a local-intensity-clustering level-set method is applied to roughly separate solar granulation and sunspots. Then reinitialization-free level-set evolution is adopted to adjust the boundaries of the photospheric patch; an adaptive intensity threshold is used to discriminate between umbra and penumbra; light bridges are selected according to their regional properties from candidates produced by morphological operations. The proposed method is applied to the solar high-resolution TiO 705.7-nm images taken by the 151-element AO system and Ground-Layer Adaptive Optics prototype system at the 1-m New Vacuum Solar Telescope of the Yunnan Observatory. Experimental results show that the method achieves satisfactory robustness and efficiency with low computational cost on high-resolution images. The method could also be applied to full-disk images, and the calculated sunspot areas correlate well with the data given by the National Oceanic and Atmospheric Administration (NOAA).

  9. Comparison of the automated evaluation of phantom mama in digital and digitalized images

    International Nuclear Information System (INIS)

    Santana, Priscila do Carmo

    2011-01-01

    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)

  10. Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.

    Science.gov (United States)

    Yang, Yu Xin; Chong, Mei Sian; Tay, Laura; Yew, Suzanne; Yeo, Audrey; Tan, Cher Heng

    2016-10-01

    To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy. The automatic detection of body composition is formulized as a three-class classification issue. Each image voxel in the training dataset is assigned with a correct label. A voxel classifier is trained and subsequently used to predict unseen data. Morphological operations are finally applied to generate volumetric segmented images for different structures. We applied this algorithm on datasets of (1) four contrast images, (2) water and fat images, and (3) unsuppressed images acquired from 190 subjects. The proposed method using four contrasts achieved most accurate and robust segmentation compared to the use of combined fat and water images and the use of unsuppressed image, average Dice coefficients of 0.94 ± 0.03, 0.96 ± 0.03, 0.80 ± 0.03, and 0.97 ± 0.01 has been achieved to bone region, subcutaneous adipose tissue (SAT), inter-muscular adipose tissue (IMAT), and muscle respectively. Our proposed method based on machine learning produces accurate tissue quantification and showed an effective use of large information provided by the four contrast images from Dixon MRI.

  11. Automation for a base station stability testing

    OpenAIRE

    Punnek, Elvis

    2016-01-01

    This Batchelor’s thesis was commissioned by Oy LM Ericsson Ab Oulu. The aim of it was to help to investigate and create a test automation solution for the stability testing of the LTE base station. The main objective was to create a test automation for a predefined test set. This test automation solution had to be created for specific environments and equipment. This work included creating the automation for the test cases and putting them to daily test automation jobs. The key factor...

  12. 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.

  13. Automated and unsupervised detection of malarial parasites in microscopic images

    Directory of Open Access Journals (Sweden)

    Purwar Yashasvi

    2011-12-01

    Full Text Available Abstract Background Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis and prone to human error (leading to erroneous diagnosis, even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria. Method A method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives. Results The image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites. Conclusion Image based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method

  14. Automated Image-Based Procedures for Adaptive Radiotherapy

    DEFF Research Database (Denmark)

    Bjerre, Troels

    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.......-based treatment replanning and real-time intrafraction guidance techniques. The selected contributions detail a number of findings and techniques, in particular: - For ten head & neck cancer patients, changes in tumour density were well described by linear functions with patient-specific slope and intercept...

  15. A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

    Directory of Open Access Journals (Sweden)

    Dimitris Stratoulias

    2017-10-01

    Full Text Available Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.

  16. A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head.

    Science.gov (United States)

    Delora, Adam; Gonzales, Aaron; Medina, Christopher S; Mitchell, Adam; Mohed, Abdul Faheem; Jacobs, Russell E; Bearer, Elaine L

    2016-01-15

    Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. This novel timesaving automation of template-based brain extraction ("skull-stripping") is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI. Copyright © 2015 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    1999-10-26

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

  18. Multispectral Image Road Extraction Based Upon Automated Map Conflation

    Science.gov (United States)

    Chen, Bin

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

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

    Science.gov (United States)

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

    2016-02-01

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

  20. 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.

  1. Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval

    International Nuclear Information System (INIS)

    Xu Jiajing; Napel, Sandy; Greenspan, Hayit; Beaulieu, Christopher F.; Agrawal, Neeraj; Rubin, Daniel

    2012-01-01

    . Equivalence across deformations was assessed using Schuirmann's paired two one-sided tests. Results: In simulated images, the concordance correlation between measured gradient and actual gradient was 0.994. The mean (s.d.) and standard deviation NDCG score for the retrieval of K images, K = 5, 10, and 15, were 84% (8%), 85% (7%), and 85% (7%) for CT images containing liver lesions, and 82% (7%), 84% (6%), and 85% (4%) for CT images containing lung nodules, respectively. The authors’ proposed method outperformed the two existing margin characterization methods in average NDCG scores over all K, by 1.5% and 3% in datasets containing liver lesion, and 4.5% and 5% in datasets containing lung nodules. Equivalence testing showed that the authors’ feature is more robust across all margin deformations (p < 0.05) than the two existing methods for margin sharpness characterization in both simulated and clinical datasets. Conclusions: The authors have described a new image feature to quantify the margin sharpness of lesions. It has strong correlation with known margin sharpness in simulated images and in clinical CT images containing liver lesions and lung nodules. This image feature has excellent performance for retrieving images with similar margin characteristics, suggesting potential utility, in conjunction with other lesion features, for content-based image retrieval applications.

  2. High-content live cell imaging with RNA probes: advancements in high-throughput antimalarial drug discovery

    Directory of Open Access Journals (Sweden)

    Cervantes Serena

    2009-06-01

    Full Text Available Abstract Background Malaria, a major public health issue in developing nations, is responsible for more than one million deaths a year. The most lethal species, Plasmodium falciparum, causes up to 90% of fatalities. Drug resistant strains to common therapies have emerged worldwide and recent artemisinin-based combination therapy failures hasten the need for new antimalarial drugs. Discovering novel compounds to be used as antimalarials is expedited by the use of a high-throughput screen (HTS to detect parasite growth and proliferation. Fluorescent dyes that bind to DNA have replaced expensive traditional radioisotope incorporation for HTS growth assays, but do not give additional information regarding the parasite stage affected by the drug and a better indication of the drug's mode of action. Live cell imaging with RNA dyes, which correlates with cell growth and proliferation, has been limited by the availability of successful commercial dyes. Results After screening a library of newly synthesized stryrl dyes, we discovered three RNA binding dyes that provide morphological details of live parasites. Utilizing an inverted confocal imaging platform, live cell imaging of parasites increases parasite detection, improves the spatial and temporal resolution of the parasite under drug treatments, and can resolve morphological changes in individual cells. Conclusion This simple one-step technique is suitable for automation in a microplate format for novel antimalarial compound HTS. We have developed a new P. falciparum RNA high-content imaging growth inhibition assay that is robust with time and energy efficiency.

  3. Anatomical based registration of multi-sector x-ray images for panorama reconstruction

    Science.gov (United States)

    Ben-Zikri, Yehuda Kfir; Mendez, Stacy; Linte, Cristian A.

    2017-03-01

    Accurate measurement of long limb alignment is an essential stage of the pre-operative planning of realignment surgery. This alignment is quantified according to the hip-knee-ankle (HKA) angle of the mechanical axis of the lower extremity and is measured based on a full-length weight-bearing X-ray or standard computed radiography (CR) image of the patient in standing position. Due to the limited field-of-view of the traditionally employed digital X-ray imaging systems, several sector images are required to capture the posture of a standing individual. These sector images need to then be "stitched" together to reconstruct the standing posture. To eliminate user-induced variability and time constraints associated with the traditional manual "stitching" protocol, we have created an image processing application to automate the stitching process, when there are no reliable external markers available in the images, by only relying on the most reliable anatomical content of the image. The application starts with a rough segmentation of the tibia and the sector images are then registered by evaluating the DICE coefficient between the edges of these corresponding bones along the medial edge. The identified translations are then used to register the original sector images into the standing panorama image. To test the robustness of our method, we randomly selected 40 datasets from a variant database consisting of nearly 100 patient X-ray images acquired for patient screening as part of a multi-site clinical trial. The resulting horizontal and vertical translation values from the automated registration were compared to the homologous translations recorded during the manual panorama generation conducted by a knowledgeable X-ray imaging technician. The mean and standard deviation of the differences for the horizontal translation parameters was -0:27+/-1:14 mm and 0:31+/-1:86 mm for the left and right tibia, respectively. The vertical translation differences for the left and

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  5. Automated Reduction of Data from Images and Holograms

    Science.gov (United States)

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

    1987-01-01

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

  6. Semi-automated analysis of three-dimensional track images

    International Nuclear Information System (INIS)

    Meesen, G.; Poffijn, A.

    2001-01-01

    In the past, three-dimensional (3-d) track images in solid state detectors were difficult to obtain. With the introduction of the confocal scanning laser microscope it is now possible to record 3-d track images in a non-destructive way. These 3-d track images can latter be used to measure typical track parameters. Preparing the detectors and recording the 3-d images however is only the first step. The second step in this process is enhancing the image quality by means of deconvolution techniques to obtain the maximum possible resolution. The third step is extracting the typical track parameters. This can be done on-screen by an experienced operator. For large sets of data however, this manual technique is not desirable. This paper will present some techniques to analyse 3-d track data in an automated way by means of image analysis routines. Advanced thresholding techniques guarantee stable results in different recording situations. By using pre-knowledge about the track shape, reliable object identification is obtained. In case of ambiguity, manual intervention is possible

  7. Choroidal vasculature characteristics based choroid segmentation for enhanced depth imaging optical coherence tomography images

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Qiang; Niu, Sijie [School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094 (China); Yuan, Songtao; Fan, Wen, E-mail: fanwen1029@163.com; Liu, Qinghuai [Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029 (China)

    2016-04-15

    Purpose: In clinical research, it is important to measure choroidal thickness when eyes are affected by various diseases. The main purpose is to automatically segment choroid for enhanced depth imaging optical coherence tomography (EDI-OCT) images with five B-scans averaging. Methods: The authors present an automated choroid segmentation method based on choroidal vasculature characteristics for EDI-OCT images with five B-scans averaging. By considering the large vascular of the Haller’s layer neighbor with the choroid-sclera junction (CSJ), the authors measured the intensity ascending distance and a maximum intensity image in the axial direction from a smoothed and normalized EDI-OCT image. Then, based on generated choroidal vessel image, the authors constructed the CSJ cost and constrain the CSJ search neighborhood. Finally, graph search with smooth constraints was utilized to obtain the CSJ boundary. Results: Experimental results with 49 images from 10 eyes in 8 normal persons and 270 images from 57 eyes in 44 patients with several stages of diabetic retinopathy and age-related macular degeneration demonstrate that the proposed method can accurately segment the choroid of EDI-OCT images with five B-scans averaging. The mean choroid thickness difference and overlap ratio between the authors’ proposed method and manual segmentation drawn by experts were −11.43 μm and 86.29%, respectively. Conclusions: Good performance was achieved for normal and pathologic eyes, which proves that the authors’ method is effective for the automated choroid segmentation of the EDI-OCT images with five B-scans averaging.

  8. Choroidal vasculature characteristics based choroid segmentation for enhanced depth imaging optical coherence tomography images

    International Nuclear Information System (INIS)

    Chen, Qiang; Niu, Sijie; Yuan, Songtao; Fan, Wen; Liu, Qinghuai

    2016-01-01

    Purpose: In clinical research, it is important to measure choroidal thickness when eyes are affected by various diseases. The main purpose is to automatically segment choroid for enhanced depth imaging optical coherence tomography (EDI-OCT) images with five B-scans averaging. Methods: The authors present an automated choroid segmentation method based on choroidal vasculature characteristics for EDI-OCT images with five B-scans averaging. By considering the large vascular of the Haller’s layer neighbor with the choroid-sclera junction (CSJ), the authors measured the intensity ascending distance and a maximum intensity image in the axial direction from a smoothed and normalized EDI-OCT image. Then, based on generated choroidal vessel image, the authors constructed the CSJ cost and constrain the CSJ search neighborhood. Finally, graph search with smooth constraints was utilized to obtain the CSJ boundary. Results: Experimental results with 49 images from 10 eyes in 8 normal persons and 270 images from 57 eyes in 44 patients with several stages of diabetic retinopathy and age-related macular degeneration demonstrate that the proposed method can accurately segment the choroid of EDI-OCT images with five B-scans averaging. The mean choroid thickness difference and overlap ratio between the authors’ proposed method and manual segmentation drawn by experts were −11.43 μm and 86.29%, respectively. Conclusions: Good performance was achieved for normal and pathologic eyes, which proves that the authors’ method is effective for the automated choroid segmentation of the EDI-OCT images with five B-scans averaging.

  9. 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.

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

    Science.gov (United States)

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

    2017-02-01

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

  11. Novel insights in agent-based complex automated negotiation

    CERN Document Server

    Lopez-Carmona, Miguel; Ito, Takayuki; Zhang, Minjie; Bai, Quan; Fujita, Katsuhide

    2014-01-01

    This book focuses on all aspects of complex automated negotiations, which are studied in the field of autonomous agents and multi-agent systems. This book consists of two parts. I: Agent-Based Complex Automated Negotiations, and II: Automated Negotiation Agents Competition. The chapters in Part I are extended versions of papers presented at the 2012 international workshop on Agent-Based Complex Automated Negotiation (ACAN), after peer reviews by three Program Committee members. Part II examines in detail ANAC 2012 (The Third Automated Negotiating Agents Competition), in which automated agents that have different negotiation strategies and are implemented by different developers are automatically negotiated in the several negotiation domains. ANAC is an international competition in which automated negotiation strategies, submitted by a number of universities and research institutes across the world, are evaluated in tournament style. The purpose of the competition is to steer the research in the area of bilate...

  12. Automated imaging dark adaptometer for investigating hereditary retinal degenerations

    Science.gov (United States)

    Azevedo, Dario F. G.; Cideciyan, Artur V.; Regunath, Gopalakrishnan; Jacobson, Samuel G.

    1995-05-01

    We designed and built an automated imaging dark adaptometer (AIDA) to increase accuracy, reliability, versatility and speed of dark adaptation testing in patients with hereditary retinal degenerations. AIDA increases test accuracy by imaging the ocular fundus for precise positioning of bleaching and stimulus lights. It improves test reliability by permitting continuous monitoring of patient fixation. Software control of stimulus presentation provides broad testing versatility without sacrificing speed. AIDA promises to facilitate the measurement of dark adaptation in studies of the pathophysiology of retinal degenerations and in future treatment trials of these diseases.

  13. 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

  14. Visual Servoing-Based Nanorobotic System for Automated Electrical Characterization of Nanotubes inside SEM.

    Science.gov (United States)

    Ding, Huiyang; Shi, Chaoyang; Ma, Li; Yang, Zhan; Wang, Mingyu; Wang, Yaqiong; Chen, Tao; Sun, Lining; Toshio, Fukuda

    2018-04-08

    The maneuvering and electrical characterization of nanotubes inside a scanning electron microscope (SEM) has historically been time-consuming and laborious for operators. Before the development of automated nanomanipulation-enabled techniques for the performance of pick-and-place and characterization of nanoobjects, these functions were still incomplete and largely operated manually. In this paper, a dual-probe nanomanipulation system vision-based feedback was demonstrated to automatically perform 3D nanomanipulation tasks, to investigate the electrical characterization of nanotubes. The XY-position of Atomic Force Microscope (AFM) cantilevers and individual carbon nanotubes (CNTs) were precisely recognized via a series of image processing operations. A coarse-to-fine positioning strategy in the Z-direction was applied through the combination of the sharpness-based depth estimation method and the contact-detection method. The use of nanorobotic magnification-regulated speed aided in improving working efficiency and reliability. Additionally, we proposed automated alignment of manipulator axes by visual tracking the movement trajectory of the end effector. The experimental results indicate the system's capability for automated measurement electrical characterization of CNTs. Furthermore, the automated nanomanipulation system has the potential to be extended to other nanomanipulation tasks.

  15. Automated processing of webcam images for phenological classification.

    Science.gov (United States)

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

    2017-01-01

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

  16. Automated processing of webcam images for phenological classification.

    Directory of Open Access Journals (Sweden)

    Ludwig Bothmann

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

  17. Automated Glacier Mapping using Object Based Image Analysis. Case Studies from Nepal, the European Alps and Norway

    Science.gov (United States)

    Vatle, S. S.

    2015-12-01

    Frequent and up-to-date glacier outlines are needed for many applications of glaciology, not only glacier area change analysis, but also for masks in volume or velocity analysis, for the estimation of water resources and as model input data. Remote sensing offers a good option for creating glacier outlines over large areas, but manual correction is frequently necessary, especially in areas containing supraglacial debris. We show three different workflows for mapping clean ice and debris-covered ice within Object Based Image Analysis (OBIA). By working at the object level as opposed to the pixel level, OBIA facilitates using contextual, spatial and hierarchical information when assigning classes, and additionally permits the handling of multiple data sources. Our first example shows mapping debris-covered ice in the Manaslu Himalaya, Nepal. SAR Coherence data is used in combination with optical and topographic data to classify debris-covered ice, obtaining an accuracy of 91%. Our second example shows using a high-resolution LiDAR derived DEM over the Hohe Tauern National Park in Austria. Breaks in surface morphology are used in creating image objects; debris-covered ice is then classified using a combination of spectral, thermal and topographic properties. Lastly, we show a completely automated workflow for mapping glacier ice in Norway. The NDSI and NIR/SWIR band ratio are used to map clean ice over the entire country but the thresholds are calculated automatically based on a histogram of each image subset. This means that in theory any Landsat scene can be inputted and the clean ice can be automatically extracted. Debris-covered ice can be included semi-automatically using contextual and morphological information.

  18. Semi-automated digital image analysis of patellofemoral joint space width from lateral knee radiographs

    Energy Technology Data Exchange (ETDEWEB)

    Grochowski, S.J. [Mayo Clinic, Department of Orthopedic Surgery, Rochester (United States); Amrami, K.K. [Mayo Clinic, Department of Radiology, Rochester (United States); Kaufman, K. [Mayo Clinic, Department of Orthopedic Surgery, Rochester (United States); Mayo Clinic/Foundation, Biomechanics Laboratory, Department of Orthopedic Surgery, Charlton North L-110L, Rochester (United States)

    2005-10-01

    To design a semi-automated program to measure minimum patellofemoral joint space width (JSW) using standing lateral view radiographs. Lateral patellofemoral knee radiographs were obtained from 35 asymptomatic subjects. The radiographs were analyzed to report both the repeatability of the image analysis program and the reproducibility of JSW measurements within a 2 week period. The results were also compared with manual measurements done by an experienced musculoskeletal radiologist. The image analysis program was shown to have an excellent coefficient of repeatability of 0.18 and 0.23 mm for intra- and inter-observer measurements respectively. The manual method measured a greater minimum JSW than the automated method. Reproducibility between days was comparable to other published results, but was less satisfactory for both manual and semi-automated measurements. The image analysis program had an inter-day coefficient of repeatability of 1.24 mm, which was lower than 1.66 mm for the manual method. A repeatable semi-automated method for measurement of the patellofemoral JSW from radiographs has been developed. The method is more accurate than manual measurements. However, the between-day reproducibility is higher than the intra-day reproducibility. Further investigation of the protocol for obtaining sequential lateral knee radiographs is needed in order to reduce the between-day variability. (orig.)

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

    Science.gov (United States)

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

    2013-01-01

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

  20. PyDBS: an automated image processing workflow for deep brain stimulation surgery.

    Science.gov (United States)

    D'Albis, Tiziano; Haegelen, Claire; Essert, Caroline; Fernández-Vidal, Sara; Lalys, Florent; Jannin, Pierre

    2015-02-01

    Deep brain stimulation (DBS) is a surgical procedure for treating motor-related neurological disorders. DBS clinical efficacy hinges on precise surgical planning and accurate electrode placement, which in turn call upon several image processing and visualization tasks, such as image registration, image segmentation, image fusion, and 3D visualization. These tasks are often performed by a heterogeneous set of software tools, which adopt differing formats and geometrical conventions and require patient-specific parameterization or interactive tuning. To overcome these issues, we introduce in this article PyDBS, a fully integrated and automated image processing workflow for DBS surgery. PyDBS consists of three image processing pipelines and three visualization modules assisting clinicians through the entire DBS surgical workflow, from the preoperative planning of electrode trajectories to the postoperative assessment of electrode placement. The system's robustness, speed, and accuracy were assessed by means of a retrospective validation, based on 92 clinical cases. The complete PyDBS workflow achieved satisfactory results in 92 % of tested cases, with a median processing time of 28 min per patient. The results obtained are compatible with the adoption of PyDBS in clinical practice.

  1. A picture tells a thousand words: A content analysis of concussion-related images online.

    Science.gov (United States)

    Ahmed, Osman H; Lee, Hopin; Struik, Laura L

    2016-09-01

    Recently image-sharing social media platforms have become a popular medium for sharing health-related images and associated information. However within the field of sports medicine, and more specifically sports related concussion, the content of images and meta-data shared through these popular platforms have not been investigated. The aim of this study was to analyse the content of concussion-related images and its accompanying meta-data on image-sharing social media platforms. We retrieved 300 images from Pinterest, Instagram and Flickr by using a standardised search strategy. All images were screened and duplicate images were removed. We excluded images if they were: non-static images; illustrations; animations; or screenshots. The content and characteristics of each image was evaluated using a customised coding scheme to determine major content themes, and images were referenced to the current international concussion management guidelines. From 300 potentially relevant images, 176 images were included for analysis; 70 from Pinterest, 63 from Flickr, and 43 from Instagram. Most images were of another person or a scene (64%), with the primary content depicting injured individuals (39%). The primary purposes of the images were to share a concussion-related incident (33%) and to dispense education (19%). For those images where it could be evaluated, the majority (91%) were found to reflect the Sports Concussion Assessment Tool 3 (SCAT3) guidelines. The ability to rapidly disseminate rich information though photos, images, and infographics to a wide-reaching audience suggests that image-sharing social media platforms could be used as an effective communication tool for sports concussion. Public health strategies could direct educative content to targeted populations via the use of image-sharing platforms. Further research is required to understand how image-sharing platforms can be used to effectively relay evidence-based information to patients and sports medicine

  2. 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.

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

    Science.gov (United States)

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

    2014-05-01

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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  5. Automation-aided Task Loads Index based on the Automation Rate Reflecting the Effects on Human Operators in NPPs

    International Nuclear Information System (INIS)

    Lee, Seungmin; Seong, Poonghyun; Kim, Jonghyun

    2013-01-01

    Many researchers have found that a high automation rate does not guarantee high performance. Therefore, to reflect the effects of automation on human performance, a new estimation method of the automation rate that considers the effects of automation on human operators in nuclear power plants (NPPs) was suggested. These suggested measures express how much automation support human operators but it cannot express the change of human operators' workload, whether the human operators' workload is increased or decreased. Before considering automation rates, whether the adopted automation is good or bad might be estimated in advance. In this study, to estimate the appropriateness of automation according to the change of the human operators' task loads, automation-aided task loads index is suggested based on the concept of the suggested automation rate. To insure plant safety and efficiency on behalf of human operators, various automation systems have been installed in NPPs, and many works which were previously conducted by human operators can now be supported by computer-based operator aids. According to the characteristics of the automation types, the estimation method of the system automation and the cognitive automation rate were suggested. The proposed estimation method concentrates on the effects of introducing automation, so it directly express how much the automated system support human operators. Based on the suggested automation rates, the way to estimate how much the automated system can affect the human operators' cognitive task load is suggested in this study. When there is no automation, the calculated index is 1, and it means there is no change of human operators' task load

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

    Science.gov (United States)

    Jiang, Luan; Ling, Shan; Li, Qiang

    2016-03-01

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

  7. Optimization of reference library used in content-based medical image retrieval scheme

    International Nuclear Information System (INIS)

    Park, Sang Cheol; Sukthankar, Rahul; Mummert, Lily; Satyanarayanan, Mahadev; Zheng Bin

    2007-01-01

    Building an optimal image reference library is a critical step in developing the interactive computer-aided detection and diagnosis (I-CAD) systems of medical images using content-based image retrieval (CBIR) schemes. In this study, the authors conducted two experiments to investigate (1) the relationship between I-CAD performance and size of reference library and (2) a new reference selection strategy to optimize the library and improve I-CAD performance. The authors assembled a reference library that includes 3153 regions of interest (ROI) depicting either malignant masses (1592) or CAD-cued false-positive regions (1561) and an independent testing data set including 200 masses and 200 false-positive regions. A CBIR scheme using a distance-weighted K-nearest neighbor algorithm is applied to retrieve references that are considered similar to the testing sample from the library. The area under receiver operating characteristic curve (A z ) is used as an index to evaluate the I-CAD performance. In the first experiment, the authors systematically increased reference library size and tested I-CAD performance. The result indicates that scheme performance improves initially from A z =0.715 to 0.874 and then plateaus when the library size reaches approximately half of its maximum capacity. In the second experiment, based on the hypothesis that a ROI should be removed if it performs poorly compared to a group of similar ROIs in a large and diverse reference library, the authors applied a new strategy to identify 'poorly effective' references. By removing 174 identified ROIs from the reference library, I-CAD performance significantly increases to A z =0.914 (p<0.01). The study demonstrates that increasing reference library size and removing poorly effective references can significantly improve I-CAD performance

  8. Automatic brain MR image denoising based on texture feature-based artificial neural networks.

    Science.gov (United States)

    Chang, Yu-Ning; Chang, Herng-Hua

    2015-01-01

    Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

  9. Automated dental implantation using image-guided robotics: registration results.

    Science.gov (United States)

    Sun, Xiaoyan; McKenzie, Frederic D; Bawab, Sebastian; Li, Jiang; Yoon, Yongki; Huang, Jen-K

    2011-09-01

    One of the most important factors affecting the outcome of dental implantation is the accurate insertion of the implant into the patient's jaw bone, which requires a high degree of anatomical accuracy. With the accuracy and stability of robots, image-guided robotics is expected to provide more reliable and successful outcomes for dental implantation. Here, we proposed the use of a robot for drilling the implant site in preparation for the insertion of the implant. An image-guided robotic system for automated dental implantation is described in this paper. Patient-specific 3D models are reconstructed from preoperative Cone-beam CT images, and implantation planning is performed with these virtual models. A two-step registration procedure is applied to transform the preoperative plan of the implant insertion into intra-operative operations of the robot with the help of a Coordinate Measurement Machine (CMM). Experiments are carried out with a phantom that is generated from the patient-specific 3D model. Fiducial Registration Error (FRE) and Target Registration Error (TRE) values are calculated to evaluate the accuracy of the registration procedure. FRE values are less than 0.30 mm. Final TRE values after the two-step registration are 1.42 ± 0.70 mm (N = 5). The registration results of an automated dental implantation system using image-guided robotics are reported in this paper. Phantom experiments show that the practice of robot in the dental implantation is feasible and the system accuracy is comparable to other similar systems for dental implantation.

  10. IMAGE CONSTRUCTION TO AUTOMATION OF PROJECTIVE TECHNIQUES FOR PSYCHOPHYSIOLOGICAL ANALYSIS

    Directory of Open Access Journals (Sweden)

    Natalia Pavlova

    2018-04-01

    Full Text Available The search for a solution of automation of the process of assessment of a psychological analysis of the person drawings created by it from an available set of some templates are presented at this article. It will allow to reveal more effectively infringements of persons mentality. In particular, such decision can be used for work with children who possess the developed figurative thinking, but are not yet capable of an accurate statement of the thoughts and experiences. For automation of testing by using a projective method, we construct interactive environment for visualization of compositions of the several images and then analyse

  11. Content-based multimedia retrieval: indexing and diversification

    NARCIS (Netherlands)

    van Leuken, R.H.

    2009-01-01

    The demand for efficient systems that facilitate searching in multimedia databases and collections is vastly increasing. Application domains include criminology, musicology, trademark registration, medicine and image or video retrieval on the web. This thesis discusses content-based retrieval

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

    International Nuclear Information System (INIS)

    Chow, Tiffany W; Verhoeff, Nicolaas PLG; Takeshita, Shinichiro; Honjo, Kie; Pataky, Christina E; St Jacques, Peggy L; Kusano, Maggie L; Caldwell, Curtis B; Ramirez, Joel; Black, Sandra

    2007-01-01

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

  13. Automation-aided Task Loads Index based on the Automation Rate Reflecting the Effects on Human Operators in NPPs

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Seungmin; Seong, Poonghyun [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of); Kim, Jonghyun [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

    2013-05-15

    Many researchers have found that a high automation rate does not guarantee high performance. Therefore, to reflect the effects of automation on human performance, a new estimation method of the automation rate that considers the effects of automation on human operators in nuclear power plants (NPPs) was suggested. These suggested measures express how much automation support human operators but it cannot express the change of human operators' workload, whether the human operators' workload is increased or decreased. Before considering automation rates, whether the adopted automation is good or bad might be estimated in advance. In this study, to estimate the appropriateness of automation according to the change of the human operators' task loads, automation-aided task loads index is suggested based on the concept of the suggested automation rate. To insure plant safety and efficiency on behalf of human operators, various automation systems have been installed in NPPs, and many works which were previously conducted by human operators can now be supported by computer-based operator aids. According to the characteristics of the automation types, the estimation method of the system automation and the cognitive automation rate were suggested. The proposed estimation method concentrates on the effects of introducing automation, so it directly express how much the automated system support human operators. Based on the suggested automation rates, the way to estimate how much the automated system can affect the human operators' cognitive task load is suggested in this study. When there is no automation, the calculated index is 1, and it means there is no change of human operators' task load.

  14. Context based mixture model for cell phase identification in automated fluorescence microscopy

    Directory of Open Access Journals (Sweden)

    Zhou Xiaobo

    2007-01-01

    Full Text Available Abstract Background Automated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task. Results The data is generated from Hela H2B GFP cells imaged during a 2-day period with images acquired 15 minutes apart using an automated time-lapse fluorescence microscopy. The patterns are described with four kinds of features, including twelve general features, Haralick texture features, Zernike moment features, and wavelet features. To generate a new set of features with more discriminate power, the commonly used feature reduction techniques are used, which include Principle Component Analysis (PCA, Linear Discriminant Analysis (LDA, Maximum Margin Criterion (MMC, Stepwise Discriminate Analysis based Feature Selection (SDAFS, and Genetic Algorithm based Feature Selection (GAFS. Then, we propose a Context Based Mixture Model (CBMM for dealing with the time-series cell sequence information and compare it to other traditional classifiers: Support Vector Machine (SVM, Neural Network (NN, and K-Nearest Neighbor (KNN. Being a standard practice in machine learning, we systematically compare the performance of a number of common feature reduction techniques and classifiers to select an optimal combination of a feature reduction technique and a classifier. A cellular database containing 100 manually labelled subsequence is built for evaluating the performance of the classifiers. The generalization error is estimated using the cross validation technique. The

  15. Ontology-Based Knowledge Organization for the Radiograph Images Segmentation

    Directory of Open Access Journals (Sweden)

    MATEI, O.

    2008-04-01

    Full Text Available The quantity of thoracic radiographies in the medical field is ever growing. An automated system for segmenting the images would help doctors enormously. Some approaches are knowledge-based; therefore we propose here an ontology for this purpose. Thus it is machine oriented, rather than human-oriented. That is all the structures visible on a thoracic image are described from a technical point of view.

  16. Using a patient image archive to diagnose retinopathy

    Energy Technology Data Exchange (ETDEWEB)

    Tobin Jr, Kenneth William [ORNL; Abramoff, M.D. [University of Iowa; Chaum, Edward [University of Tennessee, Knoxville (UTK); Giancardo, Luca [ORNL; Govindaswamy, Priya [Oak Ridge National Laboratory (ORNL); Karnowski, Thomas Paul [ORNL; Tennant, M [University of Alberta; Swainson, Stephen [University of Alberta

    2008-01-01

    Diabetes has become an epidemic that is expected to impact 365 million people worldwide by 2025. Consequently, diabetic retinopathy is the leading cause of blindness in the industrialized world today. If detected early, treatments can preserve vision and significantly reduce debilitating blindness. Through this research we are developing and testing a method for automating the diagnosis of retinopathy in a screening environment using a patient archive and digital fundus imagery. We present an overview of our content-based image retrieval (CBIR) approach and provide performance results for a dataset of 98 images from a study in Canada when compared to an archive of 1,355 patients from a study in the Netherlands. An aggregate performance of 89% correct diagnosis is achieved, demonstrating the potential of automated, web-based diagnosis for a broad range of imagery collected under different conditions and with different cameras.

  17. 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

    content M. The volume percentage of each component present is indicated by a subscript. For example, a lithologic unit was determined megascopically to have the composition (V)13(I)1(S)1(X1)83(X2)2. After microscopic analysis of the mixed phases, this composition was expressed as (V)13(I)1(S)1(V63E19I14M4)83(V67E11I13M9)2. Finally, these data were combined in a description of the bulk composition as V67E16I13M3S1. An AIAS can also analyze textural characteristics and can be used for quick and reliable determination of rank (reflectance). Our AIAS is completely software based and incorporates a television (TV) camera that has optimum response characteristics in the range of reflectance less than 5%, making it particularly suitable for coal studies. Analysis of the digitized signal from the TV camera is controlled by a microprocessor having a resolution of 64 gray levels between full illumination and dark current. The processed image is reconverted for display on a TV monitor screen, on which selection of phases or features to be analyzed is readily controlled and edited by the operator through use of a lightpen. We expect that automated image analysis, because it can rapidly provide a large amount of pertinent information, will play a major role in the advancement of coal petrography. ?? 1982.

  18. Semi-automated De-identification of German Content Sensitive Reports for Big Data Analytics.

    Science.gov (United States)

    Seuss, Hannes; Dankerl, Peter; Ihle, Matthias; Grandjean, Andrea; Hammon, Rebecca; Kaestle, Nicola; Fasching, Peter A; Maier, Christian; Christoph, Jan; Sedlmayr, Martin; Uder, Michael; Cavallaro, Alexander; Hammon, Matthias

    2017-07-01

    reports enables reliable detection and labeling of sensitive data in different types of medical reports. Key Points:   · Collaborations between different institutions require de-identification of patients' data. · Software-based de-identification of content-sensitive reports grows in importance as a result of 'Big data'. · A de-identification software was developed and tested natively and after training. · The proposed de-identification software worked quite reliably, following training with roughly 100 edited reports. · A final check of the texts by an authorized person remains necessary. Citation Format · Seuss H, Dankerl P, Ihle M et al. Semi-automated De-identification of German Content Sensitive Reports for Big Data Analytics. Fortschr Röntgenstr 2017; 189: 661 - 671. © Georg Thieme Verlag KG Stuttgart · New York.

  19. Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients.

    Science.gov (United States)

    Hosseini, Mohammad-Parsa; Nazem-Zadeh, Mohammad-Reza; Pompili, Dario; Jafari-Khouzani, Kourosh; Elisevich, Kost; Soltanian-Zadeh, Hamid

    2016-01-01

    Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus. A template database of 195 (81 males, 114 females; age range 32-67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark. Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, -4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than LocalInfo, FreeSurfer, and

  20. Classifying magnetic resonance image modalities with convolutional neural networks

    Science.gov (United States)

    Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis

    2018-02-01

    Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.

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

    Directory of Open Access Journals (Sweden)

    Manas M. Kawale

    2013-01-01

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

  2. Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

    Science.gov (United States)

    Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P

    2017-10-01

    In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.

  3. Automated quantitative cytological analysis using portable microfluidic microscopy.

    Science.gov (United States)

    Jagannadh, Veerendra Kalyan; Murthy, Rashmi Sreeramachandra; Srinivasan, Rajesh; Gorthi, Sai Siva

    2016-06-01

    In this article, a portable microfluidic microscopy based approach for automated cytological investigations is presented. Inexpensive optical and electronic components have been used to construct a simple microfluidic microscopy system. In contrast to the conventional slide-based methods, the presented method employs microfluidics to enable automated sample handling and image acquisition. The approach involves the use of simple in-suspension staining and automated image acquisition to enable quantitative cytological analysis of samples. The applicability of the presented approach to research in cellular biology is shown by performing an automated cell viability assessment on a given population of yeast cells. Further, the relevance of the presented approach to clinical diagnosis and prognosis has been demonstrated by performing detection and differential assessment of malaria infection in a given sample. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Automated audiometry using apple iOS-based application technology.

    Science.gov (United States)

    Foulad, Allen; Bui, Peggy; Djalilian, Hamid

    2013-11-01

    The aim of this study is to determine the feasibility of an Apple iOS-based automated hearing testing application and to compare its accuracy with conventional audiometry. Prospective diagnostic study. Setting Academic medical center. An iOS-based software application was developed to perform automated pure-tone hearing testing on the iPhone, iPod touch, and iPad. To assess for device variations and compatibility, preliminary work was performed to compare the standardized sound output (dB) of various Apple device and headset combinations. Forty-two subjects underwent automated iOS-based hearing testing in a sound booth, automated iOS-based hearing testing in a quiet room, and conventional manual audiometry. The maximum difference in sound intensity between various Apple device and headset combinations was 4 dB. On average, 96% (95% confidence interval [CI], 91%-100%) of the threshold values obtained using the automated test in a sound booth were within 10 dB of the corresponding threshold values obtained using conventional audiometry. When the automated test was performed in a quiet room, 94% (95% CI, 87%-100%) of the threshold values were within 10 dB of the threshold values obtained using conventional audiometry. Under standardized testing conditions, 90% of the subjects preferred iOS-based audiometry as opposed to conventional audiometry. Apple iOS-based devices provide a platform for automated air conduction audiometry without requiring extra equipment and yield hearing test results that approach those of conventional audiometry.

  5. Improved image retrieval based on fuzzy colour feature vector

    Science.gov (United States)

    Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

    2013-03-01

    One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

  6. Knowledge-based automated radiopharmaceutical manufacturing for Positron Emission Tomography

    International Nuclear Information System (INIS)

    Alexoff, D.L.

    1991-01-01

    This article describes the application of basic knowledge engineering principles to the design of automated synthesis equipment for radiopharmaceuticals used in Positron Emission Tomography (PET). Before discussing knowledge programming, an overview of the development of automated radiopharmaceutical synthesis systems for PET will be presented. Since knowledge systems will rely on information obtained from machine transducers, a discussion of the uses of sensory feedback in today's automated systems follows. Next, the operation of these automated systems is contrasted to radiotracer production carried out by chemists, and the rationale for and basic concepts of knowledge-based programming are explained. Finally, a prototype knowledge-based system supporting automated radiopharmaceutical manufacturing of 18FDG at Brookhaven National Laboratory (BNL) is described using 1stClass, a commercially available PC-based expert system shell

  7. A semi-automated algorithm for hypothalamus volumetry in 3 Tesla magnetic resonance images.

    Science.gov (United States)

    Wolff, Julia; Schindler, Stephanie; Lucas, Christian; Binninger, Anne-Sophie; Weinrich, Luise; Schreiber, Jan; Hegerl, Ulrich; Möller, Harald E; Leitzke, Marco; Geyer, Stefan; Schönknecht, Peter

    2018-07-30

    The hypothalamus, a small diencephalic gray matter structure, is part of the limbic system. Volumetric changes of this structure occur in psychiatric diseases, therefore there is increasing interest in precise volumetry. Based on our detailed volumetry algorithm for 7 Tesla magnetic resonance imaging (MRI), we developed a method for 3 Tesla MRI, adopting anatomical landmarks and work in triplanar view. We overlaid T1-weighted MR images with gray matter-tissue probability maps to combine anatomical information with tissue class segmentation. Then, we outlined regions of interest (ROIs) that covered potential hypothalamus voxels. Within these ROIs, seed growing technique helped define the hypothalamic volume using gray matter probabilities from the tissue probability maps. This yielded a semi-automated method with short processing times of 20-40 min per hypothalamus. In the MRIs of ten subjects, reliabilities were determined as intraclass correlations (ICC) and volume overlaps in percent. Three raters achieved very good intra-rater reliabilities (ICC 0.82-0.97) and good inter-rater reliabilities (ICC 0.78 and 0.82). Overlaps of intra- and inter-rater runs were very good (≥ 89.7%). We present a fast, semi-automated method for in vivo hypothalamus volumetry in 3 Tesla MRI. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. In-camera automation of photographic composition rules.

    Science.gov (United States)

    Banerjee, Serene; Evans, Brian L

    2007-07-01

    At the time of image acquisition, professional photographers apply many rules of thumb to improve the composition of their photographs. This paper develops a joint optical-digital processing framework for automating composition rules during image acquisition for photographs with one main subject. Within the framework, we automate three photographic composition rules: repositioning the main subject, making the main subject more prominent, and making objects that merge with the main subject less prominent. The idea is to provide to the user alternate pictures obtained by applying photographic composition rules in addition to the original picture taken by the user. The proposed algorithms do not depend on prior knowledge of the indoor/outdoor setting or scene content. The proposed algorithms are also designed to be amenable to software implementation on fixed-point programmable digital signal processors available in digital still cameras.

  9. Topogram-based automated selection of the tube potential and current in thoraco-abdominal trauma CT - a comparison to fixed kV with mAs modulation alone

    International Nuclear Information System (INIS)

    Frellesen, Claudia; Stock, Wenzel; Kerl, J.M.; Lehnert, Thomas; Wichmann, Julian L.; Beeres, Martin; Schulz, Boris; Bodelle, Boris; Vogl, Thomas J.; Nau, Christoph; Geiger, Emanuel; Wutzler, Sebastian; Ackermann, Hanns; Bauer, Ralf W.

    2014-01-01

    To investigate the impact of automated attenuation-based tube potential selection on image quality and exposure parameters in polytrauma patients undergoing contrast-enhanced thoraco-abdominal CT. One hundred patients were examined on a 16-slice device at 120 kV with 190 ref.mAs and automated mA modulation only. Another 100 patients underwent 128-slice CT with automated mA modulation and topogram-based automated tube potential selection (autokV) at 100, 120 or 140 kV. Volume CT dose index (CTDI vol ), dose-length product (DLP), body diameters, noise, signal-to-noise ratio (SNR) and subjective image quality were compared. In the autokV group, 100 kV was automatically selected in 82 patients, 120 kV in 12 patients and 140 kV in 6 patients. Patient diameters increased with higher kV settings. The median CTDI vol (8.3 vs. 12.4 mGy; -33 %) and DLP (594 vs. 909 mGy cm; -35 %) in the entire autokV group were significantly lower than in the group with fixed 120 kV (p < 0.05 for both). Image quality remained at a constantly high level at any selected kV level. Topogram-based automated selection of the tube potential allows for significant dose savings in thoraco-abdominal trauma CT while image quality remains at a constantly high level. (orig.)

  10. Topogram-based automated selection of the tube potential and current in thoraco-abdominal trauma CT - a comparison to fixed kV with mAs modulation alone

    Energy Technology Data Exchange (ETDEWEB)

    Frellesen, Claudia; Stock, Wenzel; Kerl, J.M.; Lehnert, Thomas; Wichmann, Julian L.; Beeres, Martin; Schulz, Boris; Bodelle, Boris; Vogl, Thomas J. [Clinic of the Goethe University, Department of Diagnostic and Interventional Radiology, Frankfurt (Germany); Nau, Christoph; Geiger, Emanuel; Wutzler, Sebastian [Clinic of the Goethe University, Department of Trauma, Hand and Reconstructive Surgery, Frankfurt (Germany); Ackermann, Hanns [Clinic of the Goethe University, Department of Biostatistics and Mathematical Modelling, Frankfurt (Germany); Bauer, Ralf W. [Clinic of the Goethe University, Department of Diagnostic and Interventional Radiology, Frankfurt (Germany); Klinikum der Goethe-Universitaet, Institut fuer Diagnostische und Interventionelle Radiologie, Frankfurt am Main (Germany)

    2014-07-15

    To investigate the impact of automated attenuation-based tube potential selection on image quality and exposure parameters in polytrauma patients undergoing contrast-enhanced thoraco-abdominal CT. One hundred patients were examined on a 16-slice device at 120 kV with 190 ref.mAs and automated mA modulation only. Another 100 patients underwent 128-slice CT with automated mA modulation and topogram-based automated tube potential selection (autokV) at 100, 120 or 140 kV. Volume CT dose index (CTDI{sub vol}), dose-length product (DLP), body diameters, noise, signal-to-noise ratio (SNR) and subjective image quality were compared. In the autokV group, 100 kV was automatically selected in 82 patients, 120 kV in 12 patients and 140 kV in 6 patients. Patient diameters increased with higher kV settings. The median CTDI{sub vol} (8.3 vs. 12.4 mGy; -33 %) and DLP (594 vs. 909 mGy cm; -35 %) in the entire autokV group were significantly lower than in the group with fixed 120 kV (p < 0.05 for both). Image quality remained at a constantly high level at any selected kV level. Topogram-based automated selection of the tube potential allows for significant dose savings in thoraco-abdominal trauma CT while image quality remains at a constantly high level. (orig.)

  11. Automated identification of intergranular corrosion in X-ray CT images

    International Nuclear Information System (INIS)

    Howell, Patricia A.; Winfree, William P.

    2003-01-01

    Characterization of a material or structure by computed tomography results in the acquisition of large quantities of data that need to be tediously examined to determine the location and size of damage. Since the computed tomography images are digital, there is significant potential for reducing the human effort evolved in this process by digital processing of this data to enhance the signatures of flaws and perform automated identification of suspected flaws. Techniques are presented that enhance the contrast between corroded and uncorroded regions to simplify the analysis and improve quality of flaw identification. Algorithms developed in part for computer vision, such as anisotropic diffusion and edge detection techniques, are applied to the data. Anisotropic diffusion techniques are shown to significantly reduce image noise while maintaining the contrast between intergranular corrosion and uncorroded regions and preserving the important features of the flaw. Edge detection techniques are shown to enable a rapid location of regions requiring further analysis. In regions identified by the edge detection technique, neural network techniques are applied to automate defect detection of the intergranular corrosion

  12. Fully automated atlas-based method for prescribing 3D PRESS MR spectroscopic imaging: Toward robust and reproducible metabolite measurements in human brain.

    Science.gov (United States)

    Bian, Wei; Li, Yan; Crane, Jason C; Nelson, Sarah J

    2018-02-01

    To implement a fully automated atlas-based method for prescribing 3D PRESS MR spectroscopic imaging (MRSI). The PRESS selected volume and outer-volume suppression bands were predefined on the MNI152 standard template image. The template image was aligned to the subject T 1 -weighted image during a scan, and the resulting transformation was then applied to the predefined prescription. To evaluate the method, H-1 MRSI data were obtained in repeat scan sessions from 20 healthy volunteers. In each session, datasets were acquired twice without repositioning. The overlap ratio of the prescribed volume in the two sessions was calculated and the reproducibility of inter- and intrasession metabolite peak height and area ratios was measured by the coefficient of variation (CoV). The CoVs from intra- and intersession were compared by a paired t-test. The average overlap ratio of the automatically prescribed selection volumes between two sessions was 97.8%. The average voxel-based intersession CoVs were less than 0.124 and 0.163 for peak height and area ratios, respectively. Paired t-test showed no significant difference between the intra- and intersession CoVs. The proposed method provides a time efficient method to prescribe 3D PRESS MRSI with reproducible imaging positioning and metabolite measurements. Magn Reson Med 79:636-642, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  13. Automated segmentation of dental CBCT image with prior-guided sequential random forests

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513 (United States); Chen, Ken-Chung; Tang, Zhen [Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030 (United States); Xia, James J., E-mail: dgshen@med.unc.edu, E-mail: JXia@HoustonMethodist.org [Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist 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, Shanghai Jiao Tong University School of Medicine, Shanghai Ninth People’s Hospital, Shanghai 200011 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu, E-mail: JXia@HoustonMethodist.org [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841 (Korea, Republic of)

    2016-01-15

    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 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors’ method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method

  14. Automated segmentation of dental CBCT image with prior-guided sequential random forests

    International Nuclear Information System (INIS)

    Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang; Chen, Ken-Chung; Tang, Zhen; Xia, James J.; Shen, Dinggang

    2016-01-01

    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 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors’ method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method

  15. Automation of Space Inventory Management

    Science.gov (United States)

    Fink, Patrick W.; Ngo, Phong; Wagner, Raymond; Barton, Richard; Gifford, Kevin

    2009-01-01

    This viewgraph presentation describes the utilization of automated space-based inventory management through handheld RFID readers and BioNet Middleware. The contents include: 1) Space-Based INventory Management; 2) Real-Time RFID Location and Tracking; 3) Surface Acoustic Wave (SAW) RFID; and 4) BioNet Middleware.

  16. Automated retinal vessel type classification in color fundus images

    Science.gov (United States)

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

    2013-02-01

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

  17. Network-based automation for SMEs

    DEFF Research Database (Denmark)

    Parizi, Mohammad Shahabeddini; Radziwon, Agnieszka

    2017-01-01

    The implementation of appropriate automation concepts which increase productivity in Small and Medium Sized Enterprises (SMEs) requires a lot of effort, due to their limited resources. Therefore, it is strongly recommended for small firms to open up for the external sources of knowledge, which...... could be obtained through network interaction. Based on two extreme cases of SMEs representing low-tech industry and an in-depth analysis of their manufacturing facilities this paper presents how collaboration between firms embedded in a regional ecosystem could result in implementation of new...... with other members of the same regional ecosystem. The findings highlight two main automation related areas where manufacturing SMEs could leverage on external sources on knowledge – these are assistance in defining automation problem as well as appropriate solution and provider selection. Consequently...

  18. Automated detection of exudates for diabetic retinopathy screening

    International Nuclear Information System (INIS)

    Fleming, Alan D; Philip, Sam; Goatman, Keith A; Williams, Graeme J; Olson, John A; Sharp, Peter F

    2007-01-01

    Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13 219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy

  19. Automated detection of exudates for diabetic retinopathy screening

    Energy Technology Data Exchange (ETDEWEB)

    Fleming, Alan D [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom); Philip, Sam [Diabetes Retinal Screening Service, David Anderson Building, Foresterhill Road, Aberdeen, AB25 2ZP (United Kingdom); Goatman, Keith A [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom); Williams, Graeme J [Diabetes Retinal Screening Service, David Anderson Building, Foresterhill Road, Aberdeen, AB25 2ZP (United Kingdom); Olson, John A [Diabetes Retinal Screening Service, David Anderson Building, Foresterhill Road, Aberdeen, AB25 2ZP (United Kingdom); Sharp, Peter F [Biomedical Physics, University of Aberdeen, Aberdeen, AB25 2ZD (United Kingdom)

    2007-12-21

    Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13 219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy.

  20. Automated detection of exudates for diabetic retinopathy screening

    Science.gov (United States)

    Fleming, Alan D.; Philip, Sam; Goatman, Keith A.; Williams, Graeme J.; Olson, John A.; Sharp, Peter F.

    2007-12-01

    Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13 219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy.

  1. Automated jitter correction for IR image processing to assess the quality of W7-X high heat flux components

    International Nuclear Information System (INIS)

    Greuner, H; De Marne, P; Herrmann, A; Boeswirth, B; Schindler, T; Smirnow, M

    2009-01-01

    An automated IR image processing method was developed to evaluate the surface temperature distribution of cyclically loaded high heat flux (HHF) plasma facing components. IPP Garching will perform the HHF testing of a high percentage of the series production of the WENDELSTEIN 7-X (W7-X) divertor targets to minimize the number of undiscovered uncertainties in the finally installed components. The HHF tests will be performed as quality assurance (QA) complementary to the non-destructive examination (NDE) methods used during the manufacturing. The IR analysis of an HHF-loaded component detects growing debonding of the plasma facing material, made of carbon fibre composite (CFC), after a few thermal cycles. In the case of the prototype testing, the IR data was processed manually. However, a QA method requires a reliable, reproducible and efficient automated procedure. Using the example of the HHF testing of W7-X pre-series target elements, the paper describes the developed automated IR image processing method. The algorithm is based on an iterative two-step correlation analysis with an individually defined reference pattern for the determination of the jitter.

  2. 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.

  3. Vision-based obstacle recognition system for automated lawn mower robot development

    Science.gov (United States)

    Mohd Zin, Zalhan; Ibrahim, Ratnawati

    2011-06-01

    Digital image processing techniques (DIP) have been widely used in various types of application recently. Classification and recognition of a specific object using vision system require some challenging tasks in the field of image processing and artificial intelligence. The ability and efficiency of vision system to capture and process the images is very important for any intelligent system such as autonomous robot. This paper gives attention to the development of a vision system that could contribute to the development of an automated vision based lawn mower robot. The works involve on the implementation of DIP techniques to detect and recognize three different types of obstacles that usually exist on a football field. The focus was given on the study on different types and sizes of obstacles, the development of vision based obstacle recognition system and the evaluation of the system's performance. Image processing techniques such as image filtering, segmentation, enhancement and edge detection have been applied in the system. The results have shown that the developed system is able to detect and recognize various types of obstacles on a football field with recognition rate of more 80%.

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

    DEFF Research Database (Denmark)

    Lomsky, Milan; Richter, Jens; Johansson, Lena

    2005-01-01

    A new automated method for quantification of left ventricular function from gated-single photon emission computed tomography (SPECT) images has been developed. The method for quantification of cardiac function (CAFU) is based on a heart shaped model and the active shape algorithm. The model....... The maximal differences between the CAFU estimations and the true left ventricular volumes of the digital phantoms were 11 ml for the end-diastolic volume (EDV), 3 ml for the end-systolic volume (ESV) and 3% for the ejection fraction (EF). The largest differences were seen in the smallest heart....... In the patient group the EDV calculated using QGS and CAFU showed good agreement for large hearts and higher CAFU values compared with QGS for the smaller hearts. In the larger hearts, ESV was much larger for QGS than for CAFU both in the phantom and patient studies. In the smallest hearts there was good...

  5. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence.

    Science.gov (United States)

    Rajalakshmi, Ramachandran; Subashini, Radhakrishnan; Anjana, Ranjit Mohan; Mohan, Viswanathan

    2018-06-01

    To assess the role of artificial intelligence (AI)-based automated software for detection of diabetic retinopathy (DR) and sight-threatening DR (STDR) by fundus photography taken using a smartphone-based device and validate it against ophthalmologist's grading. Three hundred and one patients with type 2 diabetes underwent retinal photography with Remidio 'Fundus on phone' (FOP), a smartphone-based device, at a tertiary care diabetes centre in India. Grading of DR was performed by the ophthalmologists using International Clinical DR (ICDR) classification scale. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The retinal photographs were graded using a validated AI DR screening software (EyeArt TM ) designed to identify DR, referable DR (moderate non-proliferative DR or worse and/or DME) or STDR. The sensitivity and specificity of automated grading were assessed and validated against the ophthalmologists' grading. Retinal images of 296 patients were graded. DR was detected by the ophthalmologists in 191 (64.5%) and by the AI software in 203 (68.6%) patients while STDR was detected in 112 (37.8%) and 146 (49.3%) patients, respectively. The AI software showed 95.8% (95% CI 92.9-98.7) sensitivity and 80.2% (95% CI 72.6-87.8) specificity for detecting any DR and 99.1% (95% CI 95.1-99.9) sensitivity and 80.4% (95% CI 73.9-85.9) specificity in detecting STDR with a kappa agreement of k = 0.78 (p < 0.001) and k = 0.75 (p < 0.001), respectively. Automated AI analysis of FOP smartphone retinal imaging has very high sensitivity for detecting DR and STDR and thus can be an initial tool for mass retinal screening in people with diabetes.

  6. Information content of poisson images

    International Nuclear Information System (INIS)

    Cederlund, J.

    1979-04-01

    One major problem when producing images with the aid of Poisson distributed quanta is how best to compromise between spatial and contrast resolution. Increasing the number of image elements improves spatial resolution, but at the cost of fewer quanta per image element, which reduces contrast resolution. Information theory arguments are used to analyse this problem. It is argued that information capacity is a useful concept to describe an important property of the imaging device, but that in order to compute the information content of an image produced by this device some statistical properties (such as the a priori probability of the densities) of the object to be depicted must be taken into account. If these statistical properties are not known one cannot make a correct choice between spatial and contrast resolution. (author)

  7. Automated CT-based segmentation and quantification of total intracranial volume

    Energy Technology Data Exchange (ETDEWEB)

    Aguilar, Carlos; Wahlund, Lars-Olof; Westman, Eric [Karolinska Institute, Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Stockholm (Sweden); Edholm, Kaijsa; Cavallin, Lena; Muller, Susanne; Axelsson, Rimma [Karolinska Institute, Department of Clinical Science, Intervention and Technology, Division of Medical Imaging and Technology, Stockholm (Sweden); Karolinska University Hospital in Huddinge, Department of Radiology, Stockholm (Sweden); Simmons, Andrew [King' s College London, Institute of Psychiatry, London (United Kingdom); NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia, London (United Kingdom); Skoog, Ingmar [Gothenburg University, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, Gothenburg (Sweden); Larsson, Elna-Marie [Uppsala University, Department of Surgical Sciences, Radiology, Akademiska Sjukhuset, Uppsala (Sweden)

    2015-11-15

    To develop an algorithm to segment and obtain an estimate of total intracranial volume (tICV) from computed tomography (CT) images. Thirty-six CT examinations from 18 patients were included. Ten patients were examined twice the same day and eight patients twice six months apart (these patients also underwent MRI). The algorithm combines morphological operations, intensity thresholding and mixture modelling. The method was validated against manual delineation and its robustness assessed from repeated imaging examinations. Using automated MRI software, the comparability with MRI was investigated. Volumes were compared based on average relative volume differences and their magnitudes; agreement was shown by a Bland-Altman analysis graph. We observed good agreement between our algorithm and manual delineation of a trained radiologist: the Pearson's correlation coefficient was r = 0.94, tICVml[manual] = 1.05 x tICVml[automated] - 33.78 (R{sup 2} = 0.88). Bland-Altman analysis showed a bias of 31 mL and a standard deviation of 30 mL over a range of 1265 to 1526 mL. tICV measurements derived from CT using our proposed algorithm have shown to be reliable and consistent compared to manual delineation. However, it appears difficult to directly compare tICV measures between CT and MRI. (orig.)

  8. Automated CT-based segmentation and quantification of total intracranial volume

    International Nuclear Information System (INIS)

    Aguilar, Carlos; Wahlund, Lars-Olof; Westman, Eric; Edholm, Kaijsa; Cavallin, Lena; Muller, Susanne; Axelsson, Rimma; Simmons, Andrew; Skoog, Ingmar; Larsson, Elna-Marie

    2015-01-01

    To develop an algorithm to segment and obtain an estimate of total intracranial volume (tICV) from computed tomography (CT) images. Thirty-six CT examinations from 18 patients were included. Ten patients were examined twice the same day and eight patients twice six months apart (these patients also underwent MRI). The algorithm combines morphological operations, intensity thresholding and mixture modelling. The method was validated against manual delineation and its robustness assessed from repeated imaging examinations. Using automated MRI software, the comparability with MRI was investigated. Volumes were compared based on average relative volume differences and their magnitudes; agreement was shown by a Bland-Altman analysis graph. We observed good agreement between our algorithm and manual delineation of a trained radiologist: the Pearson's correlation coefficient was r = 0.94, tICVml[manual] = 1.05 x tICVml[automated] - 33.78 (R 2 = 0.88). Bland-Altman analysis showed a bias of 31 mL and a standard deviation of 30 mL over a range of 1265 to 1526 mL. tICV measurements derived from CT using our proposed algorithm have shown to be reliable and consistent compared to manual delineation. However, it appears difficult to directly compare tICV measures between CT and MRI. (orig.)

  9. 78 FR 44142 - Modification of Two National Customs Automation Program (NCAP) Tests Concerning Automated...

    Science.gov (United States)

    2013-07-23

    ... Customs Automation Program (NCAP) Tests Concerning Automated Commercial Environment (ACE) Document Image... (CBP's) plan to modify the National Customs Automation Program (NCAP) tests concerning document imaging... entry process by reducing the number of data elements required to obtain release for cargo transported...

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  11. 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.

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

    Science.gov (United States)

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

    2015-10-01

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

  13. Application of Bayesian Classification to Content-Based Data Management

    Science.gov (United States)

    Lynnes, Christopher; Berrick, S.; Gopalan, A.; Hua, X.; Shen, S.; Smith, P.; Yang, K-Y.; Wheeler, K.; Curry, C.

    2004-01-01

    The high volume of Earth Observing System data has proven to be challenging to manage for data centers and users alike. At the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC), about 1 TB of new data are archived each day. Distribution to users is also about 1 TB/day. A substantial portion of this distribution is MODIS calibrated radiance data, which has a wide variety of uses. However, much of the data is not useful for a particular user's needs: for example, ocean color users typically need oceanic pixels that are free of cloud and sun-glint. The GES DAAC is using a simple Bayesian classification scheme to rapidly classify each pixel in the scene in order to support several experimental content-based data services for near-real-time MODIS calibrated radiance products (from Direct Readout stations). Content-based subsetting would allow distribution of, say, only clear pixels to the user if desired. Content-based subscriptions would distribute data to users only when they fit the user's usability criteria in their area of interest within the scene. Content-based cache management would retain more useful data on disk for easy online access. The classification may even be exploited in an automated quality assessment of the geolocation product. Though initially to be demonstrated at the GES DAAC, these techniques have applicability in other resource-limited environments, such as spaceborne data systems.

  14. Pathfinder: multiresolution region-based searching of pathology images using IRM.

    OpenAIRE

    Wang, J. Z.

    2000-01-01

    The fast growth of digitized pathology slides has created great challenges in research on image database retrieval. The prevalent retrieval technique involves human-supplied text annotations to describe slide contents. These pathology images typically have very high resolution, making it difficult to search based on image content. In this paper, we present Pathfinder, an efficient multiresolution region-based searching system for high-resolution pathology image libraries. The system uses wave...

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

    Science.gov (United States)

    Ye, Hu; Wong, Koon-Pong; Wardak, Mirwais; Dahlbom, Magnus; Kepe, Vladimir; Barrio, Jorge R; Nelson, Linda D; Small, Gary W; Huang, Sung-Cheng

    2014-01-01

    Head movement during a dynamic brain PET/CT imaging results in mismatch between CT and dynamic PET images. It can cause artifacts in CT-based attenuation corrected PET images, thus affecting both the qualitative and quantitative aspects of the dynamic PET images and the derived parametric images. In this study, we developed an automated retrospective image-based movement correction (MC) procedure. The MC method first registered the CT image to each dynamic PET frames, then re-reconstructed the PET frames with CT-based attenuation correction, and finally re-aligned all the PET frames to the same position. We evaluated the MC method's performance on the Hoffman phantom and dynamic FDDNP and FDG PET/CT images of patients with neurodegenerative disease or with poor compliance. Dynamic FDDNP PET/CT images (65 min) were obtained from 12 patients and dynamic FDG PET/CT images (60 min) were obtained from 6 patients. Logan analysis with cerebellum as the reference region was used to generate regional distribution volume ratio (DVR) for FDDNP scan before and after MC. For FDG studies, the image derived input function was used to generate parametric image of FDG uptake constant (Ki) before and after MC. Phantom study showed high accuracy of registration between PET and CT and improved PET images after MC. In patient study, head movement was observed in all subjects, especially in late PET frames with an average displacement of 6.92 mm. The z-direction translation (average maximum = 5.32 mm) and x-axis rotation (average maximum = 5.19 degrees) occurred most frequently. Image artifacts were significantly diminished after MC. There were significant differences (Pdynamic brain FDDNP and FDG PET/CT scans could improve the qualitative and quantitative aspects of images of both tracers.

  16. An ImageJ-based algorithm for a semi-automated method for microscopic image enhancement and DNA repair foci counting

    International Nuclear Information System (INIS)

    Klokov, D.; Suppiah, R.

    2015-01-01

    Proper evaluation of the health risks of low-dose ionizing radiation exposure heavily relies on the ability to accurately measure very low levels of DNA damage in cells. One of the most sensitive methods for measuring DNA damage levels is the quantification of DNA repair foci that consist of macromolecular aggregates of DNA repair proteins, such as γH2AX and 53BP1, forming around individual DNA double-strand breaks. They can be quantified using immunofluorescence microscopy and are widely used as markers of DNA double-strand breaks. However this quantification, if performed manually, may be very tedious and prone to inter-individual bias. Low-dose radiation studies are especially sensitive to this potential bias due to a very low magnitude of the effects anticipated. Therefore, we designed and validated an algorithm for the semi-automated processing of microscopic images and quantification of DNA repair foci. The algorithm uses ImageJ, a freely available image analysis software that is customizable to individual cellular properties or experimental conditions. We validated the algorithm using immunolabeled 53BP1 and γH2AX in normal human fibroblast AG01522 cells under both normal and irradiated conditions. This method is easy to learn, can be used by nontrained personnel, and can help avoiding discrepancies in inter-laboratory comparison studies examining the effects of low-dose radiation. (author)

  17. An ImageJ-based algorithm for a semi-automated method for microscopic image enhancement and DNA repair foci counting

    Energy Technology Data Exchange (ETDEWEB)

    Klokov, D., E-mail: dmitry.klokov@cnl.ca [Canadian Nuclear Laboratories, Chalk River, Ontario (Canada); Suppiah, R. [Queen' s Univ., Dept. of Biomedical and Molecular Sciences, Kingston, Ontario (Canada)

    2015-06-15

    Proper evaluation of the health risks of low-dose ionizing radiation exposure heavily relies on the ability to accurately measure very low levels of DNA damage in cells. One of the most sensitive methods for measuring DNA damage levels is the quantification of DNA repair foci that consist of macromolecular aggregates of DNA repair proteins, such as γH2AX and 53BP1, forming around individual DNA double-strand breaks. They can be quantified using immunofluorescence microscopy and are widely used as markers of DNA double-strand breaks. However this quantification, if performed manually, may be very tedious and prone to inter-individual bias. Low-dose radiation studies are especially sensitive to this potential bias due to a very low magnitude of the effects anticipated. Therefore, we designed and validated an algorithm for the semi-automated processing of microscopic images and quantification of DNA repair foci. The algorithm uses ImageJ, a freely available image analysis software that is customizable to individual cellular properties or experimental conditions. We validated the algorithm using immunolabeled 53BP1 and γH2AX in normal human fibroblast AG01522 cells under both normal and irradiated conditions. This method is easy to learn, can be used by nontrained personnel, and can help avoiding discrepancies in inter-laboratory comparison studies examining the effects of low-dose radiation. (author)

  18. Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

    Directory of Open Access Journals (Sweden)

    Yansheng Li

    2016-08-01

    Full Text Available With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF. In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP, gray level co-occurrence (GLCM, maximal response 8 (MR8, and scale-invariant feature transform (SIFT. In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM dataset and the Wuhan University (WH dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.

  19. Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images

    International Nuclear Information System (INIS)

    Aoyama, Masahito; Li Qiang; Katsuragawa, Shigehiko; MacMahon, Heber; Doi, Kunio

    2002-01-01

    A novel automated computerized scheme has been developed to assist radiologists for their distinction between benign and malignant solitary pulmonary nodules on chest images. Our database consisted of 55 chest radiographs (33 primary lung cancers and 22 benign nodules). In this method, the location of a nodule was indicated first by a radiologist. The difference image with a nodule was produced by use of filters and then represented in a polar coordinate system. The nodule was segmented automatically by analysis of contour lines of the gray-level distribution based on the polar-coordinate representation. Two clinical parameters (age and sex) and 75 image features were determined from the outline, the image, and histogram analysis for inside and outside regions of the segmented nodule. Linear discriminant analysis (LDA) and knowledge about benign and malignant nodules were used to select initial feature combinations. Many combinations for subgroups of 77 features were evaluated as input to artificial neural networks (ANNs). The performance of ANNs with the selected 7 features by use of the round-robin test showed Az=0.872, which was greater than Az=0.854 obtained previously with the manual method (P=0.53). The performance of LDA (Az=0.886) was slightly improved compared to that of ANNs (P=0.59) and was greater than that of the manual method (Az=0.854) reported previously (P=0.40). The high level of its performance indicates the potential usefulness of this automated computerized scheme in assisting radiologists as a second opinion for distinction between benign and malignant solitary pulmonary nodules on chest images

  20. Platform for Automated Real-Time High Performance Analytics on Medical Image Data.

    Science.gov (United States)

    Allen, William J; Gabr, Refaat E; Tefera, Getaneh B; Pednekar, Amol S; Vaughn, Matthew W; Narayana, Ponnada A

    2018-03-01

    Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming interface to facilitate communication, data transfer, and job control between an MRI scanner and an off-site HPC resource. In this use case, Agave executed the graphical pipeline tool GRAphical Pipeline Environment (GRAPE) to perform automated, real-time, quantitative analysis of MRI scans. Same-session image processing will open the door for adaptive scanning and real-time quality control, potentially accelerating the discovery of pathologies and minimizing patient callbacks. We envision this platform can be adapted to other medical instruments, HPC resources, and analytics tools.

  1. Automated Image Analysis of Offshore Infrastructure Marine Biofouling

    Directory of Open Access Journals (Sweden)

    Kate Gormley

    2018-01-01

    Full Text Available In the UK, some of the oldest oil and gas installations have been in the water for over 40 years and have considerable colonisation by marine organisms, which may lead to both industry challenges and/or potential biodiversity benefits (e.g., artificial reefs. The project objective was to test the use of an automated image analysis software (CoralNet on images of marine biofouling from offshore platforms on the UK continental shelf, with the aim of (i training the software to identify the main marine biofouling organisms on UK platforms; (ii testing the software performance on 3 platforms under 3 different analysis criteria (methods A–C; (iii calculating the percentage cover of marine biofouling organisms and (iv providing recommendations to industry. Following software training with 857 images, and testing of three platforms, results showed that diversity of the three platforms ranged from low (in the central North Sea to moderate (in the northern North Sea. The two central North Sea platforms were dominated by the plumose anemone Metridium dianthus; and the northern North Sea platform showed less obvious species domination. Three different analysis criteria were created, where the method of selection of points, number of points assessed and confidence level thresholds (CT varied: (method A random selection of 20 points with CT 80%, (method B stratified random of 50 points with CT of 90% and (method C a grid approach of 100 points with CT of 90%. Performed across the three platforms, the results showed that there were no significant differences across the majority of species and comparison pairs. No significant difference (across all species was noted between confirmed annotations methods (A, B and C. It was considered that the software performed well for the classification of the main fouling species in the North Sea. Overall, the study showed that the use of automated image analysis software may enable a more efficient and consistent

  2. Automated 3D closed surface segmentation: application to vertebral body segmentation in CT images.

    Science.gov (United States)

    Liu, Shuang; Xie, Yiting; Reeves, Anthony P

    2016-05-01

    A fully automated segmentation algorithm, progressive surface resolution (PSR), is presented in this paper to determine the closed surface of approximately convex blob-like structures that are common in biomedical imaging. The PSR algorithm was applied to the cortical surface segmentation of 460 vertebral bodies on 46 low-dose chest CT images, which can be potentially used for automated bone mineral density measurement and compression fracture detection. The target surface is realized by a closed triangular mesh, which thereby guarantees the enclosure. The surface vertices of the triangular mesh representation are constrained along radial trajectories that are uniformly distributed in 3D angle space. The segmentation is accomplished by determining for each radial trajectory the location of its intersection with the target surface. The surface is first initialized based on an input high confidence boundary image and then resolved progressively based on a dynamic attraction map in an order of decreasing degree of evidence regarding the target surface location. For the visual evaluation, the algorithm achieved acceptable segmentation for 99.35 % vertebral bodies. Quantitative evaluation was performed on 46 vertebral bodies and achieved overall mean Dice coefficient of 0.939 (with max [Formula: see text] 0.957, min [Formula: see text] 0.906 and standard deviation [Formula: see text] 0.011) using manual annotations as the ground truth. Both visual and quantitative evaluations demonstrate encouraging performance of the PSR algorithm. This novel surface resolution strategy provides uniform angular resolution for the segmented surface with computation complexity and runtime that are linearly constrained by the total number of vertices of the triangular mesh representation.

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

    International Nuclear Information System (INIS)

    Wang, Ta Wee

    2002-01-01

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

  4. Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey

    Science.gov (United States)

    Wang, Jason J.; Perrin, Marshall D.; Savransky, Dmitry; Arriaga, Pauline; Chilcote, Jeffrey K.; De Rosa, Robert J.; Millar-Blanchaer, Maxwell A.; Marois, Christian; Rameau, Julien; Wolff, Schuyler G.; Shapiro, Jacob; Ruffio, Jean-Baptiste; Maire, Jérôme; Marchis, Franck; Graham, James R.; Macintosh, Bruce; Ammons, S. Mark; Bailey, Vanessa P.; Barman, Travis S.; Bruzzone, Sebastian; Bulger, Joanna; Cotten, Tara; Doyon, René; Duchêne, Gaspard; Fitzgerald, Michael P.; Follette, Katherine B.; Goodsell, Stephen; Greenbaum, Alexandra Z.; Hibon, Pascale; Hung, Li-Wei; Ingraham, Patrick; Kalas, Paul; Konopacky, Quinn M.; Larkin, James E.; Marley, Mark S.; Metchev, Stanimir; Nielsen, Eric L.; Oppenheimer, Rebecca; Palmer, David W.; Patience, Jennifer; Poyneer, Lisa A.; Pueyo, Laurent; Rajan, Abhijith; Rantakyrö, Fredrik T.; Schneider, Adam C.; Sivaramakrishnan, Anand; Song, Inseok; Soummer, Remi; Thomas, Sandrine; Wallace, J. Kent; Ward-Duong, Kimberly; Wiktorowicz, Sloane J.

    2018-01-01

    The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.

  5. Optimization-based Method for Automated Road Network Extraction

    International Nuclear Information System (INIS)

    Xiong, D

    2001-01-01

    Automated road information extraction has significant applicability in transportation. It provides a means for creating, maintaining, and updating transportation network databases that are needed for purposes ranging from traffic management to automated vehicle navigation and guidance. This paper is to review literature on the subject of road extraction and to describe a study of an optimization-based method for automated road network extraction

  6. Dsm Based Orientation of Large Stereo Satellite Image Blocks

    Science.gov (United States)

    d'Angelo, P.; Reinartz, P.

    2012-07-01

    High resolution stereo satellite imagery is well suited for the creation of digital surface models (DSM). A system for highly automated and operational DSM and orthoimage generation based on CARTOSAT-1 imagery is presented, with emphasis on fully automated georeferencing. The proposed system processes level-1 stereo scenes using the rational polynomial coefficients (RPC) universal sensor model. The RPC are derived from orbit and attitude information and have a much lower accuracy than the ground resolution of approximately 2.5 m. In order to use the images for orthorectification or DSM generation, an affine RPC correction is required. In this paper, GCP are automatically derived from lower resolution reference datasets (Landsat ETM+ Geocover and SRTM DSM). The traditional method of collecting the lateral position from a reference image and interpolating the corresponding height from the DEM ignores the higher lateral accuracy of the SRTM dataset. Our method avoids this drawback by using a RPC correction based on DSM alignment, resulting in improved geolocation of both DSM and ortho images. Scene based method and a bundle block adjustment based correction are developed and evaluated for a test site covering the nothern part of Italy, for which 405 Cartosat-1 Stereopairs are available. Both methods are tested against independent ground truth. Checks against this ground truth indicate a lateral error of 10 meters.

  7. Automated baseline change detection phase I. Final report

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-12-01

    The Automated Baseline Change Detection (ABCD) project is supported by the DOE Morgantown Energy Technology Center (METC) as part of its ER&WM cross-cutting technology program in robotics. Phase 1 of the Automated Baseline Change Detection project is summarized in this topical report. The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. In support of this primary objective, there are secondary objectives to determine DOE operational inspection requirements and DOE system fielding requirements.

  8. Automated baseline change detection phase I. Final report

    International Nuclear Information System (INIS)

    1995-12-01

    The Automated Baseline Change Detection (ABCD) project is supported by the DOE Morgantown Energy Technology Center (METC) as part of its ER ampersand WM cross-cutting technology program in robotics. Phase 1 of the Automated Baseline Change Detection project is summarized in this topical report. The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. In support of this primary objective, there are secondary objectives to determine DOE operational inspection requirements and DOE system fielding requirements

  9. Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy.

    Science.gov (United States)

    Mudie, Lucy I; Wang, Xueyang; Friedman, David S; Brady, Christopher J

    2017-09-23

    As the number of people with diabetic retinopathy (DR) in the USA is expected to increase threefold by 2050, the need to reduce health care costs associated with screening for this treatable disease is ever present. Crowdsourcing and automated retinal image analysis (ARIA) are two areas where new technology has been applied to reduce costs in screening for DR. This paper reviews the current literature surrounding these new technologies. Crowdsourcing has high sensitivity for normal vs abnormal images; however, when multiple categories for severity of DR are added, specificity is reduced. ARIAs have higher sensitivity and specificity, and some commercial ARIA programs are already in use. Deep learning enhanced ARIAs appear to offer even more improvement in ARIA grading accuracy. The utilization of crowdsourcing and ARIAs may be a key to reducing the time and cost burden of processing images from DR screening.

  10. 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

  11. Recent advances in agent-based complex automated negotiation

    CERN Document Server

    Ito, Takayuki; Zhang, Minjie; Fujita, Katsuhide; Robu, Valentin

    2016-01-01

    This book covers recent advances in Complex Automated Negotiations as a widely studied emerging area in the field of Autonomous Agents and Multi-Agent Systems. The book includes selected revised and extended papers from the 7th International Workshop on Agent-Based Complex Automated Negotiation (ACAN2014), which was held in Paris, France, in May 2014. The book also includes brief introductions about Agent-based Complex Automated Negotiation which are based on tutorials provided in the workshop, and brief summaries and descriptions about the ANAC'14 (Automated Negotiating Agents Competition) competition, where authors of selected finalist agents explain the strategies and the ideas used by them. The book is targeted to academic and industrial researchers in various communities of autonomous agents and multi-agent systems, such as agreement technology, mechanism design, electronic commerce, related areas, as well as graduate, undergraduate, and PhD students working in those areas or having interest in them.

  12. Automated Scoring of Constructed-Response Science Items: Prospects and Obstacles

    Science.gov (United States)

    Liu, Ou Lydia; Brew, Chris; Blackmore, John; Gerard, Libby; Madhok, Jacquie; Linn, Marcia C.

    2014-01-01

    Content-based automated scoring has been applied in a variety of science domains. However, many prior applications involved simplified scoring rubrics without considering rubrics representing multiple levels of understanding. This study tested a concept-based scoring tool for content-based scoring, c-rater™, for four science items with rubrics…

  13. OSPACS: Ultrasound image management system

    Directory of Open Access Journals (Sweden)

    Bessant Conrad

    2008-06-01

    Full Text Available Abstract Background Ultrasound scanning uses the medical imaging format, DICOM, for electronically storing the images and data associated with a particular scan. Large health care facilities typically use a picture archiving and communication system (PACS for storing and retrieving such images. However, these systems are usually not suitable for managing large collections of anonymized ultrasound images gathered during a clinical screening trial. Results We have developed a system enabling the accurate archiving and management of ultrasound images gathered during a clinical screening trial. It is based upon a Windows application utilizing an open-source DICOM image viewer and a relational database. The system automates the bulk import of DICOM files from removable media by cross-validating the patient information against an external database, anonymizing the data as well as the image, and then storing the contents of the file as a field in a database record. These image records may then be retrieved from the database and presented in a tree-view control so that the user can select particular images for display in a DICOM viewer or export them to external media. Conclusion This system provides error-free automation of ultrasound image archiving and management, suitable for use in a clinical trial. An open-source project has been established to promote continued development of the system.

  14. Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

    Science.gov (United States)

    Kim, Deok-Hwan

    As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

  15. Development and validation of an automated, microscopy-based method for enumeration of groups of intestinal bacteria

    NARCIS (Netherlands)

    Jansen, GJ; Wildeboer-Veloo, ACM; Tonk, RHJ; Franks, AH; Welling, G

    An automated microscopy-based method using fluorescently labelled 16S rRNA-targeted oligonucleotide probes directed against the predominant groups of intestinal bacteria was developed and validated. The method makes use of the Leica 600HR. image analysis system, a Kodak MegaPlus camera model 1.4 and

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  17. "First generation" automated DNA sequencing technology.

    Science.gov (United States)

    Slatko, Barton E; Kieleczawa, Jan; Ju, Jingyue; Gardner, Andrew F; Hendrickson, Cynthia L; Ausubel, Frederick M

    2011-10-01

    Beginning in the 1980s, automation of DNA sequencing has greatly increased throughput, reduced costs, and enabled large projects to be completed more easily. The development of automation technology paralleled the development of other aspects of DNA sequencing: better enzymes and chemistry, separation and imaging technology, sequencing protocols, robotics, and computational advancements (including base-calling algorithms with quality scores, database developments, and sequence analysis programs). Despite the emergence of high-throughput sequencing platforms, automated Sanger sequencing technology remains useful for many applications. This unit provides background and a description of the "First-Generation" automated DNA sequencing technology. It also includes protocols for using the current Applied Biosystems (ABI) automated DNA sequencing machines. © 2011 by John Wiley & Sons, Inc.

  18. Automated high-content assay for compounds selectively toxic to Trypanosoma cruzi in a myoblastic cell line.

    Directory of Open Access Journals (Sweden)

    Julio Alonso-Padilla

    2015-01-01

    Full Text Available Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, represents a very important public health problem in Latin America where it is endemic. Although mostly asymptomatic at its initial stage, after the disease becomes chronic, about a third of the infected patients progress to a potentially fatal outcome due to severe damage of heart and gut tissues. There is an urgent need for new drugs against Chagas disease since there are only two drugs available, benznidazole and nifurtimox, and both show toxic side effects and variable efficacy against the chronic stage of the disease.Genetically engineered parasitic strains are used for high throughput screening (HTS of large chemical collections in the search for new anti-parasitic compounds. These assays, although successful, are limited to reporter transgenic parasites and do not cover the wide T. cruzi genetic background. With the aim to contribute to the early drug discovery process against Chagas disease we have developed an automated image-based 384-well plate HTS assay for T. cruzi amastigote replication in a rat myoblast host cell line. An image analysis script was designed to inform on three outputs: total number of host cells, ratio of T. cruzi amastigotes per cell and percentage of infected cells, which respectively provides one host cell toxicity and two T. cruzi toxicity readouts. The assay was statistically robust (Z´ values >0.6 and was validated against a series of known anti-trypanosomatid drugs.We have established a highly reproducible, high content HTS assay for screening of chemical compounds against T. cruzi infection of myoblasts that is amenable for use with any T. cruzi strain capable of in vitro infection. Our visual assay informs on both anti-parasitic and host cell toxicity readouts in a single experiment, allowing the direct identification of compounds selectively targeted to the parasite.

  19. 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

  20. Automated brain structure segmentation based on atlas registration and appearance models

    DEFF Research Database (Denmark)

    van der Lijn, Fedde; de Bruijne, Marleen; Klein, Stefan

    2012-01-01

    Accurate automated brain structure segmentation methods facilitate the analysis of large-scale neuroimaging studies. This work describes a novel method for brain structure segmentation in magnetic resonance images that combines information about a structure’s location and appearance. The spatial...... with different magnetic resonance sequences, in which the hippocampus and cerebellum were segmented by an expert. Furthermore, the method is compared to two other segmentation techniques that were applied to the same data. Results show that the atlas- and appearance-based method produces accurate results...

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

    International Nuclear Information System (INIS)

    Santana, Priscila do Carmo

    2010-01-01

    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)

  2. 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.

  3. Evaluation of training nurses to perform semi-automated three-dimensional left ventricular ejection fraction using a customised workstation-based training protocol.

    Science.gov (United States)

    Guppy-Coles, Kristyan B; Prasad, Sandhir B; Smith, Kym C; Hillier, Samuel; Lo, Ada; Atherton, John J

    2015-06-01

    We aimed to determine the feasibility of training cardiac nurses to evaluate left ventricular function utilising a semi-automated, workstation-based protocol on three dimensional echocardiography images. Assessment of left ventricular function by nurses is an attractive concept. Recent developments in three dimensional echocardiography coupled with border detection assistance have reduced inter- and intra-observer variability and analysis time. This could allow abbreviated training of nurses to assess cardiac function. A comparative, diagnostic accuracy study evaluating left ventricular ejection fraction assessment utilising a semi-automated, workstation-based protocol performed by echocardiography-naïve nurses on previously acquired three dimensional echocardiography images. Nine cardiac nurses underwent two brief lectures about cardiac anatomy, physiology and three dimensional left ventricular ejection fraction assessment, before a hands-on demonstration in 20 cases. We then selected 50 cases from our three dimensional echocardiography library based on optimal image quality with a broad range of left ventricular ejection fractions, which was quantified by two experienced sonographers and the average used as the comparator for the nurses. Nurses independently measured three dimensional left ventricular ejection fraction using the Auto lvq package with semi-automated border detection. The left ventricular ejection fraction range was 25-72% (70% with a left ventricular ejection fraction nurses showed excellent agreement with the sonographers. Minimal intra-observer variability was noted on both short-term (same day) and long-term (>2 weeks later) retest. It is feasible to train nurses to measure left ventricular ejection fraction utilising a semi-automated, workstation-based protocol on previously acquired three dimensional echocardiography images. Further study is needed to determine the feasibility of training nurses to acquire three dimensional echocardiography

  4. A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging.

    Science.gov (United States)

    Zhou, Ning; Cheung, William K; Qiu, Guoping; Xue, Xiangyang

    2011-07-01

    The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.

  5. An automated quantitative DNA image cytometry system detects abnormal cells in cervical cytology with high sensitivity.

    Science.gov (United States)

    Wong, O G; Ho, M W; Tsun, O K; Ng, A K; Tsui, E Y; Chow, J N; Ip, P P; Cheung, A N

    2018-03-26

    To evaluate the performance of an automated DNA-image-cytometry system as a tool to detect cervical carcinoma. Of 384 liquid-based cervical cytology samples with available biopsy follow-up were analyzed by both the Imager System and a high-risk HPV test (Cobas). The sensitivity and specificity of Imager System for detecting biopsy proven high-grade squamous intraepithelial lesion (HSIL, cervical intraepithelial neoplasia [CIN]2-3) and carcinoma were 89.58% and 56.25%, respectively, compared to 97.22% and 23.33% of HPV test but additional HPV 16/18 genotyping increased the specificity to 69.58%. The sensitivity and specificity of the Imager System for predicting HSIL+ (CIN2-3+) lesions among atypical squamous cells of undetermined significance samples were 80.00% and 70.53%, respectively, compared to 100% and 11.58% of HPV test whilst the HPV 16/18 genotyping increased the specificity to 77.89%. Among atypical squamous cells-cannot exclude HSIL, the sensitivity and specificity of Imager System for predicting HSIL+ (CIN2-3+) lesions upon follow up were 82.86% and 33.33%%, respectively, compared to 97.14% and 4.76% of HPV test and the HPV 16/18 genotyping increased the specificity to 19.05%. Among low-grade squamous intraepithelial lesion cases, the sensitivity and specificity of the Imager System for predicting HSIL+ (CIN2-3+) lesions were 66.67% and 35.71%%, respectively, compared to 66.67% and 29.76% of HPV test while HPV 16/18 genotyping increased the specificity to 79.76%. The overall results of imager and high-risk HPV test agreed in 69.43% (268) of all samples. The automated imager system and HPV 16/18 genotyping can enhance the specificity of detecting HSIL+ (CIN2-3+) lesions. © 2018 John Wiley & Sons Ltd.

  6. Voxel-based morphometry and automated lobar volumetry: The trade-off between spatial scale and statistical correction

    Science.gov (United States)

    Voormolen, Eduard H.J.; Wei, Corie; Chow, Eva W.C.; Bassett, Anne S.; Mikulis, David J.; Crawley, Adrian P.

    2011-01-01

    Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller subregion of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobar-scale ROI volumetry for assessing gray matter volume differences between groups. PMID:19619660

  7. Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

    Science.gov (United States)

    Liu, Min; Wang, Xueping; Zhang, Hongzhong

    2018-03-01

    In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks. A deep CNN based image fusion technique is used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given stack. Besides, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by canonical correlation analysis (CCA). Because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of objects, we embed the deep CNN based image fusion method within a multilinear framework to propose an image fusion based multilinear classifier. The experimental results on nematode multi-focal image stacks demonstrated that the deep CNN image fusion based multilinear classifier can reach a higher classification rate (95.7%) than that by the previous multilinear based approach (88.7%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work. The proposed deep CNN image fusion based multilinear approach shows great potential in building an automated nematode taxonomy system for nematologists. It is effective to classify multi-focal image stacks. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. Content-based analysis and indexing of sports video

    Science.gov (United States)

    Luo, Ming; Bai, Xuesheng; Xu, Guang-you

    2001-12-01

    An explosion of on-line image and video data in digital form is already well underway. With the exponential rise in interactive information exploration and dissemination through the World-Wide Web, the major inhibitors of rapid access to on-line video data are the management of capture and storage, and content-based intelligent search and indexing techniques. This paper proposes an approach for content-based analysis and event-based indexing of sports video. It includes a novel method to organize shots - classifying shots as close shots and far shots, an original idea of blur extent-based event detection, and an innovative local mutation-based algorithm for caption detection and retrieval. Results on extensive real TV programs demonstrate the applicability of our approach.

  9. SU-E-J-132: Automated Segmentation with Post-Registration Atlas Selection Based On Mutual Information

    International Nuclear Information System (INIS)

    Ren, X; Gao, H; Sharp, G

    2015-01-01

    Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to each chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

  10. SU-E-J-132: Automated Segmentation with Post-Registration Atlas Selection Based On Mutual Information

    Energy Technology Data Exchange (ETDEWEB)

    Ren, X; Gao, H [Shanghai Jiao Tong University, Shanghai, Shanghai (China); Sharp, G [Massachusetts General Hospital, Boston, MA (United States)

    2015-06-15

    Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to each chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

  11. A World Wide Web Region-Based Image Search Engine

    DEFF Research Database (Denmark)

    Kompatsiaris, Ioannis; Triantafyllou, Evangelia; Strintzis, Michael G.

    2001-01-01

    In this paper the development of an intelligent image content-based search engine for the World Wide Web is presented. This system will offer a new form of media representation and access of content available in WWW. Information Web Crawlers continuously traverse the Internet and collect images...

  12. Automated high resolution full-field spatial coherence tomography for quantitative phase imaging of human red blood cells

    Science.gov (United States)

    Singla, Neeru; Dubey, Kavita; Srivastava, Vishal; Ahmad, Azeem; Mehta, D. S.

    2018-02-01

    We developed an automated high-resolution full-field spatial coherence tomography (FF-SCT) microscope for quantitative phase imaging that is based on the spatial, rather than the temporal, coherence gating. The Red and Green color laser light was used for finding the quantitative phase images of unstained human red blood cells (RBCs). This study uses morphological parameters of unstained RBCs phase images to distinguish between normal and infected cells. We recorded the single interferogram by a FF-SCT microscope for red and green color wavelength and average the two phase images to further reduced the noise artifacts. In order to characterize anemia infected from normal cells different morphological features were extracted and these features were used to train machine learning ensemble model to classify RBCs with high accuracy.

  13. Automated Vehicle Monitoring System

    OpenAIRE

    Wibowo, Agustinus Deddy Arief; Heriansyah, Rudi

    2014-01-01

    An automated vehicle monitoring system is proposed in this paper. The surveillance system is based on image processing techniques such as background subtraction, colour balancing, chain code based shape detection, and blob. The proposed system will detect any human's head as appeared at the side mirrors. The detected head will be tracked and recorded for further action.

  14. Determination of fat and total protein content in milk using conventional digital imaging.

    Science.gov (United States)

    Kucheryavskiy, Sergey; Melenteva, Anastasiia; Bogomolov, Andrey

    2014-04-01

    The applicability of conventional digital imaging to quantitative determination of fat and total protein in cow's milk, based on the phenomenon of light scatter, has been proved. A new algorithm for extracting features from digital images of milk samples has been developed. The algorithm takes into account spatial distribution of light, diffusely transmitted through a sample. The proposed method has been tested on two sample sets prepared from industrial raw milk standards, with variable fat and protein content. Partial Least-Squares (PLS) regression on the features calculated from images of monochromatically illuminated milk samples resulted in models with high prediction performance when analysed the sets separately (best models with cross-validated R(2)=0.974 for protein and R(2)=0.973 for fat content). However when analysed the sets jointly with the obtained results were significantly worse (best models with cross-validated R(2)=0.890 for fat content and R(2)=0.720 for protein content). The results have been compared with previously published Vis/SW-NIR spectroscopic study of similar samples. Copyright © 2013 Elsevier B.V. All rights reserved.

  15. Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification

    Directory of Open Access Journals (Sweden)

    Stefan Dech

    2012-09-01

    Full Text Available We present a novel and innovative automated processing environment for the derivation of land cover (LC and land use (LU information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS, as introduced by the Open Geospatial Consortium (OGC, are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.

  16. Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation

    Science.gov (United States)

    Qin, Wenjian; Wu, Jia; Han, Fei; Yuan, Yixuan; Zhao, Wei; Ibragimov, Bulat; Gu, Jia; Xing, Lei

    2018-05-01

    Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31  ±  0.36% and average symmetric surface distance of 1.77  ±  0.49 mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

  17. Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment

    International Nuclear Information System (INIS)

    Wang Xiaohui; Park, Sang Cheol; Zheng Bin

    2009-01-01

    This study aims to assess three methods commonly used in content-based image retrieval (CBIR) schemes and investigate the approaches to improve scheme performance. A reference database involving 3000 regions of interest (ROIs) was established. Among them, 400 ROIs were randomly selected to form a testing dataset. Three methods, namely mutual information, Pearson's correlation and a multi-feature-based k-nearest neighbor (KNN) algorithm, were applied to search for the 15 'the most similar' reference ROIs to each testing ROI. The clinical relevance and visual similarity of searching results were evaluated using the areas under receiver operating characteristic (ROC) curves (A Z ) and average mean square difference (MSD) of the mass boundary spiculation level ratings between testing and selected ROIs, respectively. The results showed that the A Z values were 0.893 ± 0.009, 0.606 ± 0.021 and 0.699 ± 0.026 for the use of KNN, mutual information and Pearson's correlation, respectively. The A Z values increased to 0.724 ± 0.017 and 0.787 ± 0.016 for mutual information and Pearson's correlation when using ROIs with the size adaptively adjusted based on actual mass size. The corresponding MSD values were 2.107 ± 0.718, 2.301 ± 0.733 and 2.298 ± 0.743. The study demonstrates that due to the diversity of medical images, CBIR schemes using multiple image features and mass size-based ROIs can achieve significantly improved performance.

  18. Operational Based Vision Assessment Automated Vision Test Collection User Guide

    Science.gov (United States)

    2017-05-15

    AFRL-SA-WP-SR-2017-0012 Operational Based Vision Assessment Automated Vision Test Collection User Guide Elizabeth Shoda, Alex...June 2015 – May 2017 4. TITLE AND SUBTITLE Operational Based Vision Assessment Automated Vision Test Collection User Guide 5a. CONTRACT NUMBER... automated vision tests , or AVT. Development of the AVT was required to support threshold-level vision testing capability needed to investigate the

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

    Directory of Open Access Journals (Sweden)

    Mei Zhan

    2015-04-01

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

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

    Science.gov (United States)

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

    2015-04-01

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

  1. Automated processing of massive audio/video content using FFmpeg

    Directory of Open Access Journals (Sweden)

    Kia Siang Hock

    2014-01-01

    Full Text Available Audio and video content forms an integral, important and expanding part of the digital collections in libraries and archives world-wide. While these memory institutions are familiar and well-versed in the management of more conventional materials such as books, periodicals, ephemera and images, the handling of audio (e.g., oral history recordings and video content (e.g., audio-visual recordings, broadcast content requires additional toolkits. In particular, a robust and comprehensive tool that provides a programmable interface is indispensable when dealing with tens of thousands of hours of audio and video content. FFmpeg is comprehensive and well-established open source software that is capable of the full-range of audio/video processing tasks (such as encode, decode, transcode, mux, demux, stream and filter. It is also capable of handling a wide-range of audio and video formats, a unique challenge in memory institutions. It comes with a command line interface, as well as a set of developer libraries that can be incorporated into applications.

  2. Automated screening for retinopathy

    Directory of Open Access Journals (Sweden)

    A. S. Rodin

    2014-07-01

    Full Text Available Retinal pathology is a common cause of an irreversible decrease of central vision commonly found amongst senior population. Detection of the earliest signs of retinal diseases can be facilitated by viewing retinal images available from the telemedicine networks. To facilitate the process of retinal images, screening software applications based on image recognition technology are currently on the various stages of development.Purpose: To develop and implement computerized image recognition software that can be used as a decision support technologyfor retinal image screening for various types of retinopathies.Methods: The software application for the retina image recognition has been developed using C++ language. It was tested on dataset of 70 images with various types of pathological features (age related macular degeneration, chorioretinitis, central serous chorioretinopathy and diabetic retinopathy.Results: It was shown that the system can achieve a sensitivity of 73 % and specificity of 72 %.Conclusion: Automated detection of macular lesions using proposed software can significantly reduce manual grading workflow. In addition, automated detection of retinal lesions can be implemented as a clinical decision support system for telemedicine screening. It is anticipated that further development of this technology can become a part of diagnostic image analysis system for the electronic health records.

  3. The NIF DISCO Framework: facilitating automated integration of neuroscience content on the web.

    Science.gov (United States)

    Marenco, Luis; Wang, Rixin; Shepherd, Gordon M; Miller, Perry L

    2010-06-01

    This paper describes the capabilities of DISCO, an extensible approach that supports integrative Web-based information dissemination. DISCO is a component of the Neuroscience Information Framework (NIF), an NIH Neuroscience Blueprint initiative that facilitates integrated access to diverse neuroscience resources via the Internet. DISCO facilitates the automated maintenance of several distinct capabilities using a collection of files 1) that are maintained locally by the developers of participating neuroscience resources and 2) that are "harvested" on a regular basis by a central DISCO server. This approach allows central NIF capabilities to be updated as each resource's content changes over time. DISCO currently supports the following capabilities: 1) resource descriptions, 2) "LinkOut" to a resource's data items from NCBI Entrez resources such as PubMed, 3) Web-based interoperation with a resource, 4) sharing a resource's lexicon and ontology, 5) sharing a resource's database schema, and 6) participation by the resource in neuroscience-related RSS news dissemination. The developers of a resource are free to choose which DISCO capabilities their resource will participate in. Although DISCO is used by NIF to facilitate neuroscience data integration, its capabilities have general applicability to other areas of research.

  4. An automated algorithm for photoreceptors counting in adaptive optics retinal images

    Science.gov (United States)

    Liu, Xu; Zhang, Yudong; Yun, Dai

    2012-10-01

    Eyes are important organs of humans that detect light and form spatial and color vision. Knowing the exact number of cones in retinal image has great importance in helping us understand the mechanism of eyes' function and the pathology of some eye disease. In order to analyze data in real time and process large-scale data, an automated algorithm is designed to label cone photoreceptors in adaptive optics (AO) retinal images. Images acquired by the flood-illuminated AO system are taken to test the efficiency of this algorithm. We labeled these images both automatically and manually, and compared the results of the two methods. A 94.1% to 96.5% agreement rate between the two methods is achieved in this experiment, which demonstrated the reliability and efficiency of the algorithm.

  5. A Recommendation Algorithm for Automating Corollary Order Generation

    Science.gov (United States)

    Klann, Jeffrey; Schadow, Gunther; McCoy, JM

    2009-01-01

    Manual development and maintenance of decision support content is time-consuming and expensive. We explore recommendation algorithms, e-commerce data-mining tools that use collective order history to suggest purchases, to assist with this. In particular, previous work shows corollary order suggestions are amenable to automated data-mining techniques. Here, an item-based collaborative filtering algorithm augmented with association rule interestingness measures mined suggestions from 866,445 orders made in an inpatient hospital in 2007, generating 584 potential corollary orders. Our expert physician panel evaluated the top 92 and agreed 75.3% were clinically meaningful. Also, at least one felt 47.9% would be directly relevant in guideline development. This automated generation of a rough-cut of corollary orders confirms prior indications about automated tools in building decision support content. It is an important step toward computerized augmentation to decision support development, which could increase development efficiency and content quality while automatically capturing local standards. PMID:20351875

  6. Content-based retrieval in videos from laparoscopic surgery

    Science.gov (United States)

    Schoeffmann, Klaus; Beecks, Christian; Lux, Mathias; Uysal, Merih Seran; Seidl, Thomas

    2016-03-01

    In the field of medical endoscopy more and more surgeons are changing over to record and store videos of their endoscopic procedures for long-term archival. These endoscopic videos are a good source of information for explanations to patients and follow-up operations. As the endoscope is the "eye of the surgeon", the video shows the same information the surgeon has seen during the operation, and can describe the situation inside the patient much more precisely than an operation report would do. Recorded endoscopic videos can also be used for training young surgeons and in some countries the long-term archival of video recordings from endoscopic procedures is even enforced by law. A major challenge, however, is to efficiently access these very large video archives for later purposes. One problem, for example, is to locate specific images in the videos that show important situations, which are additionally captured as static images during the procedure. This work addresses this problem and focuses on contentbased video retrieval in data from laparoscopic surgery. We propose to use feature signatures, which can appropriately and concisely describe the content of laparoscopic images, and show that by using this content descriptor with an appropriate metric, we are able to efficiently perform content-based retrieval in laparoscopic videos. In a dataset with 600 captured static images from 33 hours recordings, we are able to find the correct video segment for more than 88% of these images.

  7. Lithography-based automation in the design of program defect masks

    Science.gov (United States)

    Vakanas, George P.; Munir, Saghir; Tejnil, Edita; Bald, Daniel J.; Nagpal, Rajesh

    2004-05-01

    In this work, we are reporting on a lithography-based methodology and automation in the design of Program Defect masks (PDM"s). Leading edge technology masks have ever-shrinking primary features and more pronounced model-based secondary features such as optical proximity corrections (OPC), sub-resolution assist features (SRAF"s) and phase-shifted mask (PSM) structures. In order to define defect disposition specifications for critical layers of a technology node, experience alone in deciding worst-case scenarios for the placement of program defects is necessary but may not be sufficient. MEEF calculations initiated from layout pattern data and their integration in a PDM layout flow provide a natural approach for improvements, relevance and accuracy in the placement of programmed defects. This methodology provides closed-loop feedback between layout and hard defect disposition specifications, thereby minimizing engineering test restarts, improving quality and reducing cost of high-end masks. Apart from SEMI and industry standards, best-known methods (BKM"s) in integrated lithographically-based layout methodologies and automation specific to PDM"s are scarce. The contribution of this paper lies in the implementation of Design-For-Test (DFT) principles to a synergistic interaction of CAD Layout and Aerial Image Simulator to drive layout improvements, highlight layout-to-fracture interactions and output accurate program defect placement coordinates to be used by tools in the mask shop.

  8. Experiments with a novel content-based image retrieval software: can we eliminate classification systems in adolescent idiopathic scoliosis?

    Science.gov (United States)

    Menon, K Venugopal; Kumar, Dinesh; Thomas, Tessamma

    2014-02-01

    Study Design Preliminary evaluation of new tool. Objective To ascertain whether the newly developed content-based image retrieval (CBIR) software can be used successfully to retrieve images of similar cases of adolescent idiopathic scoliosis (AIS) from a database to help plan treatment without adhering to a classification scheme. Methods Sixty-two operated cases of AIS were entered into the newly developed CBIR database. Five new cases of different curve patterns were used as query images. The images were fed into the CBIR database that retrieved similar images from the existing cases. These were analyzed by a senior surgeon for conformity to the query image. Results Within the limits of variability set for the query system, all the resultant images conformed to the query image. One case had no similar match in the series. The other four retrieved several images that were matching with the query. No matching case was left out in the series. The postoperative images were then analyzed to check for surgical strategies. Broad guidelines for treatment could be derived from the results. More precise query settings, inclusion of bending films, and a larger database will enhance accurate retrieval and better decision making. Conclusion The CBIR system is an effective tool for accurate documentation and retrieval of scoliosis images. Broad guidelines for surgical strategies can be made from the postoperative images of the existing cases without adhering to any classification scheme.

  9. Point Cloud Based Change Detection - an Automated Approach for Cloud-based Services

    Science.gov (United States)

    Collins, Patrick; Bahr, Thomas

    2016-04-01

    The fusion of stereo photogrammetric point clouds with LiDAR data or terrain information derived from SAR interferometry has a significant potential for 3D topographic change detection. In the present case study latest point cloud generation and analysis capabilities are used to examine a landslide that occurred in the village of Malin in Maharashtra, India, on 30 July 2014, and affected an area of ca. 44.000 m2. It focuses on Pléiades high resolution satellite imagery and the Airbus DS WorldDEMTM as a product of the TanDEM-X mission. 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. The pre-event topography is represented by the WorldDEMTM product, delivered with a raster of 12 m x 12 m and based on the EGM2008 geoid (called pre-DEM). For the post-event situation a Pléiades 1B stereo image pair of the AOI affected was obtained. The ENVITask "GeneratePointCloudsByDenseImageMatching" was implemented to extract passive point clouds in LAS format from the panchromatic stereo datasets: • A dense image-matching algorithm is used to identify corresponding points in the two images. • A block adjustment is applied to refine the 3D coordinates that describe the scene geometry. • Additionally, the WorldDEMTM was input to constrain the range of heights in the matching area, and subsequently the length of the epipolar line. The "PointCloudFeatureExtraction" task was executed to generate the post-event digital surface model from the photogrammetric point clouds (called post-DEM). Post-processing consisted of the following steps: • Adding the geoid component (EGM 2008) to the post-DEM. • Pre-DEM reprojection to the UTM Zone 43N (WGS-84) coordinate system and resizing. • Subtraction of the pre-DEM from the post-DEM. • Filtering and threshold based classification of

  10. Tag-Based Social Image Search: Toward Relevant and Diverse Results

    Science.gov (United States)

    Yang, Kuiyuan; Wang, Meng; Hua, Xian-Sheng; Zhang, Hong-Jiang

    Recent years have witnessed a great success of social media websites. Tag-based image search is an important approach to access the image content of interest on these websites. However, the existing ranking methods for tag-based image search frequently return results that are irrelevant or lack of diversity. This chapter presents a diverse relevance ranking scheme which simultaneously takes relevance and diversity into account by exploring the content of images and their associated tags. First, it estimates the relevance scores of images with respect to the query term based on both visual information of images and semantic information of associated tags. Then semantic similarities of social images are estimated based on their tags. Based on the relevance scores and the similarities, the ranking list is generated by a greedy ordering algorithm which optimizes Average Diverse Precision (ADP), a novel measure that is extended from the conventional Average Precision (AP). Comprehensive experiments and user studies demonstrate the effectiveness of the approach.

  11. Cost minimisation analysis: kilovoltage imaging with automated repositioning versus electronic portal imaging in image-guided radiotherapy for prostate cancer.

    Science.gov (United States)

    Gill, S; Younie, S; Rolfo, A; Thomas, J; Siva, S; Fox, C; Kron, T; Phillips, D; Tai, K H; Foroudi, F

    2012-10-01

    To compare the treatment time and cost of prostate cancer fiducial marker image-guided radiotherapy (IGRT) using orthogonal kilovoltage imaging (KVI) and automated couch shifts and orthogonal electronic portal imaging (EPI) and manual couch shifts. IGRT treatment delivery times were recorded automatically on either unit. Costing was calculated from real costs derived from the implementation of a new radiotherapy centre. To derive cost per minute for EPI and KVI units the total annual setting up and running costs were divided by the total annual working time. The cost per IGRT fraction was calculated by multiplying the cost per minute by the duration of treatment. A sensitivity analysis was conducted to test the robustness of our analysis. Treatment times without couch shift were compared. Time data were analysed for 8648 fractions, 6057 from KVI treatment and 2591 from EPI treatment from a total of 294 patients. The median time for KVI treatment was 6.0 min (interquartile range 5.1-7.4 min) and for EPI treatment it was 10.0 min (interquartile range 8.3-11.8 min) (P value time for EPI was 8.8 min and for KVI was 5.1 min. Treatment time is less on KVI units compared with EPI units. This is probably due to automation of couch shift and faster evaluation of imaging on KVI units. Annual running costs greatly outweigh initial setting up costs and therefore the cost per fraction was less with KVI, despite higher initial costs. The selection of appropriate IGRT equipment can make IGRT practical within radiotherapy departments. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

  12. Operator-based metric for nuclear operations automation assessment

    Energy Technology Data Exchange (ETDEWEB)

    Zacharias, G.L.; Miao, A.X.; Kalkan, A. [Charles River Analytics Inc., Cambridge, MA (United States)] [and others

    1995-04-01

    Continuing advances in real-time computational capabilities will support enhanced levels of smart automation and AI-based decision-aiding systems in the nuclear power plant (NPP) control room of the future. To support development of these aids, we describe in this paper a research tool, and more specifically, a quantitative metric, to assess the impact of proposed automation/aiding concepts in a manner that can account for a number of interlinked factors in the control room environment. In particular, we describe a cognitive operator/plant model that serves as a framework for integrating the operator`s information-processing capabilities with his procedural knowledge, to provide insight as to how situations are assessed by the operator, decisions made, procedures executed, and communications conducted. Our focus is on the situation assessment (SA) behavior of the operator, the development of a quantitative metric reflecting overall operator awareness, and the use of this metric in evaluating automation/aiding options. We describe the results of a model-based simulation of a selected emergency scenario, and metric-based evaluation of a range of contemplated NPP control room automation/aiding options. The results demonstrate the feasibility of model-based analysis of contemplated control room enhancements, and highlight the need for empirical validation.

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

    Directory of Open Access Journals (Sweden)

    G. Bohrmann

    2010-06-01

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

  14. Mobile object retrieval in server-based image databases

    Science.gov (United States)

    Manger, D.; Pagel, F.; Widak, H.

    2013-05-01

    The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

  15. Automated determination of size and morphology information from soot transmission electron microscope (TEM)-generated images

    International Nuclear Information System (INIS)

    Wang, Cheng; Chan, Qing N.; Zhang, Renlin; Kook, Sanghoon; Hawkes, Evatt R.; Yeoh, Guan H.; Medwell, Paul R.

    2016-01-01

    The thermophoretic sampling of particulates from hot media, coupled with transmission electron microscope (TEM) imaging, is a combined approach that is widely used to derive morphological information. The identification and the measurement of the particulates, however, can be complex when the TEM images are of low contrast, noisy, and have non-uniform background signal level. The image processing method can also be challenging and time consuming, when the samples collected have large variability in shape and size, or have some degree of overlapping. In this work, a three-stage image processing sequence is presented to facilitate time-efficient automated identification and measurement of particulates from the TEM grids. The proposed processing sequence is first applied to soot samples that were thermophoretically sampled from a laminar non-premixed ethylene-air flame. The parameter values that are required to be set to facilitate the automated process are identified, and sensitivity of the results to these parameters is assessed. The same analysis process is also applied to soot samples that were acquired from an externally irradiated laminar non-premixed ethylene-air flame, which have different geometrical characteristics, to assess the morphological dependence of the proposed image processing sequence. Using the optimized parameter values, statistical assessments of the automated results reveal that the largest discrepancies that are associated with the estimated values of primary particle diameter, fractal dimension, and prefactor values of the aggregates for the tested cases, are approximately 3, 1, and 10 %, respectively, when compared with the manual measurements.

  16. Automated determination of size and morphology information from soot transmission electron microscope (TEM)-generated images

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Cheng; Chan, Qing N., E-mail: qing.chan@unsw.edu.au; Zhang, Renlin; Kook, Sanghoon; Hawkes, Evatt R.; Yeoh, Guan H. [UNSW, School of Mechanical and Manufacturing Engineering (Australia); Medwell, Paul R. [The University of Adelaide, Centre for Energy Technology (Australia)

    2016-05-15

    The thermophoretic sampling of particulates from hot media, coupled with transmission electron microscope (TEM) imaging, is a combined approach that is widely used to derive morphological information. The identification and the measurement of the particulates, however, can be complex when the TEM images are of low contrast, noisy, and have non-uniform background signal level. The image processing method can also be challenging and time consuming, when the samples collected have large variability in shape and size, or have some degree of overlapping. In this work, a three-stage image processing sequence is presented to facilitate time-efficient automated identification and measurement of particulates from the TEM grids. The proposed processing sequence is first applied to soot samples that were thermophoretically sampled from a laminar non-premixed ethylene-air flame. The parameter values that are required to be set to facilitate the automated process are identified, and sensitivity of the results to these parameters is assessed. The same analysis process is also applied to soot samples that were acquired from an externally irradiated laminar non-premixed ethylene-air flame, which have different geometrical characteristics, to assess the morphological dependence of the proposed image processing sequence. Using the optimized parameter values, statistical assessments of the automated results reveal that the largest discrepancies that are associated with the estimated values of primary particle diameter, fractal dimension, and prefactor values of the aggregates for the tested cases, are approximately 3, 1, and 10 %, respectively, when compared with the manual measurements.

  17. Automated setpoint adjustment for biological contact mode atomic force microscopy imaging

    International Nuclear Information System (INIS)

    Casuso, Ignacio; Scheuring, Simon

    2010-01-01

    Contact mode atomic force microscopy (AFM) is the most frequently used AFM imaging mode in biology. It is about 5-10 times faster than oscillating mode imaging (in conventional AFM setups), and provides topographs of biological samples with sub-molecular resolution and at a high signal-to-noise ratio. Unfortunately, contact mode imaging is sensitive to the applied force and intrinsic force drift: inappropriate force applied by the AFM tip damages the soft biological samples. We present a methodology that automatically searches for and maintains high resolution imaging forces. We found that the vertical and lateral vibrations of the probe during scanning are valuable signals for the characterization of the actual applied force by the tip. This allows automated adjustment and correction of the setpoint force during an experiment. A system that permanently performs this methodology steered the AFM towards high resolution imaging forces and imaged purple membrane at molecular resolution and live cells at high signal-to-noise ratio for hours without an operator.

  18. Automated visual inspection system based on HAVNET architecture

    Science.gov (United States)

    Burkett, K.; Ozbayoglu, Murat A.; Dagli, Cihan H.

    1994-10-01

    In this study, the HAusdorff-Voronoi NETwork (HAVNET) developed at the UMR Smart Engineering Systems Lab is tested in the recognition of mounted circuit components commonly used in printed circuit board assembly systems. The automated visual inspection system used consists of a CCD camera, a neural network based image processing software and a data acquisition card connected to a PC. The experiments are run in the Smart Engineering Systems Lab in the Engineering Management Dept. of the University of Missouri-Rolla. The performance analysis shows that the vision system is capable of recognizing different components under uncontrolled lighting conditions without being effected by rotation or scale differences. The results obtained are promising and the system can be used in real manufacturing environments. Currently the system is being customized for a specific manufacturing application.

  19. Design of microcontroller based system for automation of streak camera

    International Nuclear Information System (INIS)

    Joshi, M. J.; Upadhyay, J.; Deshpande, P. P.; Sharma, M. L.; Navathe, C. P.

    2010-01-01

    A microcontroller based system has been developed for automation of the S-20 optical streak camera, which is used as a diagnostic tool to measure ultrafast light phenomenon. An 8 bit MCS family microcontroller is employed to generate all control signals for the streak camera. All biasing voltages required for various electrodes of the tubes are generated using dc-to-dc converters. A high voltage ramp signal is generated through a step generator unit followed by an integrator circuit and is applied to the camera's deflecting plates. The slope of the ramp can be changed by varying values of the capacitor and inductor. A programmable digital delay generator has been developed for synchronization of ramp signal with the optical signal. An independent hardwired interlock circuit has been developed for machine safety. A LABVIEW based graphical user interface has been developed which enables the user to program the settings of the camera and capture the image. The image is displayed with intensity profiles along horizontal and vertical axes. The streak camera was calibrated using nanosecond and femtosecond lasers.

  20. Design of microcontroller based system for automation of streak camera.

    Science.gov (United States)

    Joshi, M J; Upadhyay, J; Deshpande, P P; Sharma, M L; Navathe, C P

    2010-08-01

    A microcontroller based system has been developed for automation of the S-20 optical streak camera, which is used as a diagnostic tool to measure ultrafast light phenomenon. An 8 bit MCS family microcontroller is employed to generate all control signals for the streak camera. All biasing voltages required for various electrodes of the tubes are generated using dc-to-dc converters. A high voltage ramp signal is generated through a step generator unit followed by an integrator circuit and is applied to the camera's deflecting plates. The slope of the ramp can be changed by varying values of the capacitor and inductor. A programmable digital delay generator has been developed for synchronization of ramp signal with the optical signal. An independent hardwired interlock circuit has been developed for machine safety. A LABVIEW based graphical user interface has been developed which enables the user to program the settings of the camera and capture the image. The image is displayed with intensity profiles along horizontal and vertical axes. The streak camera was calibrated using nanosecond and femtosecond lasers.

  1. Design of microcontroller based system for automation of streak camera

    Energy Technology Data Exchange (ETDEWEB)

    Joshi, M. J.; Upadhyay, J.; Deshpande, P. P.; Sharma, M. L.; Navathe, C. P. [Laser Electronics Support Division, RRCAT, Indore 452013 (India)

    2010-08-15

    A microcontroller based system has been developed for automation of the S-20 optical streak camera, which is used as a diagnostic tool to measure ultrafast light phenomenon. An 8 bit MCS family microcontroller is employed to generate all control signals for the streak camera. All biasing voltages required for various electrodes of the tubes are generated using dc-to-dc converters. A high voltage ramp signal is generated through a step generator unit followed by an integrator circuit and is applied to the camera's deflecting plates. The slope of the ramp can be changed by varying values of the capacitor and inductor. A programmable digital delay generator has been developed for synchronization of ramp signal with the optical signal. An independent hardwired interlock circuit has been developed for machine safety. A LABVIEW based graphical user interface has been developed which enables the user to program the settings of the camera and capture the image. The image is displayed with intensity profiles along horizontal and vertical axes. The streak camera was calibrated using nanosecond and femtosecond lasers.

  2. Multimedia human brain database system for surgical candidacy determination in temporal lobe epilepsy with content-based image retrieval

    Science.gov (United States)

    Siadat, Mohammad-Reza; Soltanian-Zadeh, Hamid; Fotouhi, Farshad A.; Elisevich, Kost

    2003-01-01

    This paper presents the development of a human brain multimedia database for surgical candidacy determination in temporal lobe epilepsy. The focus of the paper is on content-based image management, navigation and retrieval. Several medical image-processing methods including our newly developed segmentation method are utilized for information extraction/correlation and indexing. The input data includes T1-, T2-Weighted MRI and FLAIR MRI and ictal and interictal SPECT modalities with associated clinical data and EEG data analysis. The database can answer queries regarding issues such as the correlation between the attribute X of the entity Y and the outcome of a temporal lobe epilepsy surgery. The entity Y can be a brain anatomical structure such as the hippocampus. The attribute X can be either a functionality feature of the anatomical structure Y, calculated with SPECT modalities, such as signal average, or a volumetric/morphological feature of the entity Y such as volume or average curvature. The outcome of the surgery can be any surgery assessment such as memory quotient. A determination is made regarding surgical candidacy by analysis of both textual and image data. The current database system suggests a surgical determination for the cases with relatively small hippocampus and high signal intensity average on FLAIR images within the hippocampus. This indication pretty much fits with the surgeons" expectations/observations. Moreover, as the database gets more populated with patient profiles and individual surgical outcomes, using data mining methods one may discover partially invisible correlations between the contents of different modalities of data and the outcome of the surgery.

  3. 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

  4. Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging.

    Science.gov (United States)

    Liu, Fang; Jang, Hyungseok; Kijowski, Richard; Bradshaw, Tyler; McMillan, Alan B

    2018-02-01

    Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging-based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging. Materials and Methods A PET/MR imaging AC pipeline was built by using a deep learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training. A set of 30 retrospective three-dimensional T1-weighted head images was used to train the model, which was then evaluated in 10 patients by comparing the generated pseudo CT scan to an acquired CT scan. A prospective study was carried out for utilizing simultaneous PET/MR imaging for five subjects by using the proposed approach. Analysis of covariance and paired-sample t tests were used for statistical analysis to compare PET reconstruction error with deep MRAC and two existing MR imaging-based AC approaches with CT-based AC. Results Deep MRAC provides an accurate pseudo CT scan with a mean Dice coefficient of 0.971 ± 0.005 for air, 0.936 ± 0.011 for soft tissue, and 0.803 ± 0.021 for bone. Furthermore, deep MRAC provides good PET results, with average errors of less than 1% in most brain regions. Significantly lower PET reconstruction errors were realized with deep MRAC (-0.7% ± 1.1) compared with Dixon-based soft-tissue and air segmentation (-5.8% ± 3.1) and anatomic CT-based template registration (-4.8% ± 2.2). Conclusion The authors developed an automated approach that allows generation of discrete-valued pseudo CT scans (soft tissue, bone, and air) from a single high-spatial-resolution diagnostic-quality three-dimensional MR image and evaluated it in brain PET/MR imaging. This deep learning approach for MR imaging-based AC provided reduced PET reconstruction error relative to a CT-based standard within the brain compared

  5. Automated profiling of individual cell-cell interactions from high-throughput time-lapse imaging microscopy in nanowell grids (TIMING).

    Science.gov (United States)

    Merouane, Amine; Rey-Villamizar, Nicolas; Lu, Yanbin; Liadi, Ivan; Romain, Gabrielle; Lu, Jennifer; Singh, Harjeet; Cooper, Laurence J N; Varadarajan, Navin; Roysam, Badrinath

    2015-10-01

    There is a need for effective automated methods for profiling dynamic cell-cell interactions with single-cell resolution from high-throughput time-lapse imaging data, especially, the interactions between immune effector cells and tumor cells in adoptive immunotherapy. Fluorescently labeled human T cells, natural killer cells (NK), and various target cells (NALM6, K562, EL4) were co-incubated on polydimethylsiloxane arrays of sub-nanoliter wells (nanowells), and imaged using multi-channel time-lapse microscopy. The proposed cell segmentation and tracking algorithms account for cell variability and exploit the nanowell confinement property to increase the yield of correctly analyzed nanowells from 45% (existing algorithms) to 98% for wells containing one effector and a single target, enabling automated quantification of cell locations, morphologies, movements, interactions, and deaths without the need for manual proofreading. Automated analysis of recordings from 12 different experiments demonstrated automated nanowell delineation accuracy >99%, automated cell segmentation accuracy >95%, and automated cell tracking accuracy of 90%, with default parameters, despite variations in illumination, staining, imaging noise, cell morphology, and cell clustering. An example analysis revealed that NK cells efficiently discriminate between live and dead targets by altering the duration of conjugation. The data also demonstrated that cytotoxic cells display higher motility than non-killers, both before and during contact. broysam@central.uh.edu or nvaradar@central.uh.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  6. Equivalences between refractive index and equilibrium water content of conventional and silicone hydrogel soft contact lenses from automated and manual refractometry.

    Science.gov (United States)

    González-Méijome, José M; López-Alemany, Antonio; Lira, Madalena; Almeida, José B; Oliveira, M Elisabete C D Real; Parafita, Manuel A

    2007-01-01

    The purpose of the present study was to develop mathematical relationships that allow obtaining equilibrium water content and refractive index of conventional and silicone hydrogel soft contact lenses from refractive index measures obtained with automated refractometry or equilibrium water content measures derived from manual refractometry, respectively. Twelve HEMA-based hydrogels of different hydration and four siloxane-based polymers were assayed. A manual refractometer and a digital refractometer were used. Polynomial models obtained from the sucrose curves of equilibrium water content against refractive index and vice-versa were used either considering the whole range of sucrose concentrations (16-100% equilibrium water content) or a range confined to the equilibrium water content of current soft contact lenses (approximately 20-80% equilibrium water content). Values of equilibrium water content measured with the Atago N-2E and those derived from the refractive index measurement with CLR 12-70 by the applications of sucrose-based models displayed a strong linear correlation (r2 = 0.978). The same correlations were obtained when the models are applied to obtain refractive index values from the Atago N-2E and compared with those (values) given by the CLR 12-70 (r2 = 0.978). No significantly different results are obtained between models derived from the whole range of the sucrose solution or the model limited to the normal range of soft contact lens hydration. Present results will have implications for future experimental and clinical research regarding normal hydration and dehydration experiments with hydrogel polymers, and particularly in the field of contact lenses. 2006 Wiley Periodicals, Inc.

  7. Automated LSA Assessment of Summaries in Distance Education: Some Variables to Be Considered

    Science.gov (United States)

    Jorge-Botana, Guillermo; Luzón, José M.; Gómez-Veiga, Isabel; Martín-Cordero, Jesús I.

    2015-01-01

    A latent semantic analysis-based automated summary assessment is described; this automated system is applied to a real learning from text task in a Distance Education context. We comment on the use of automated content, plagiarism, text coherence measures, and word weights average and their impact on predicting human judges summary scoring. A…

  8. Automated force volume image processing for biological samples.

    Directory of Open Access Journals (Sweden)

    Pavel Polyakov

    2011-04-01

    Full Text Available Atomic force microscopy (AFM has now become a powerful technique for investigating on a molecular level, surface forces, nanomechanical properties of deformable particles, biomolecular interactions, kinetics, and dynamic processes. This paper specifically focuses on the analysis of AFM force curves collected on biological systems, in particular, bacteria. The goal is to provide fully automated tools to achieve theoretical interpretation of force curves on the basis of adequate, available physical models. In this respect, we propose two algorithms, one for the processing of approach force curves and another for the quantitative analysis of retraction force curves. In the former, electrostatic interactions prior to contact between AFM probe and bacterium are accounted for and mechanical interactions operating after contact are described in terms of Hertz-Hooke formalism. Retraction force curves are analyzed on the basis of the Freely Jointed Chain model. For both algorithms, the quantitative reconstruction of force curves is based on the robust detection of critical points (jumps, changes of slope or changes of curvature which mark the transitions between the various relevant interactions taking place between the AFM tip and the studied sample during approach and retraction. Once the key regions of separation distance and indentation are detected, the physical parameters describing the relevant interactions operating in these regions are extracted making use of regression procedure for fitting experiments to theory. The flexibility, accuracy and strength of the algorithms are illustrated with the processing of two force-volume images, which collect a large set of approach and retraction curves measured on a single biological surface. For each force-volume image, several maps are generated, representing the spatial distribution of the searched physical parameters as estimated for each pixel of the force-volume image.

  9. PROTOTYPE CONTENT BASED IMAGE RETRIEVAL UNTUK DETEKSI PEN YAKIT KULIT DENGAN METODE EDGE DETECTION

    Directory of Open Access Journals (Sweden)

    Erick Fernando

    2016-05-01

    Full Text Available Dokter spesialis kulit melakukan pemeriksa secara visual objek mata, capture objek dengan kamera digital dan menanyakan riwayat perjalanan penyakit pasien, tanpa melakukan perbandingan terhadap gejala dan tanda yang ada sebelummnya. Sehingga pemeriksaan dan perkiraan jenis penyakit kulit. Pengolahan data citra dalam bentuk digital khususnya citra medis sudah sangat dibutuhkan dengan pra-processing. Banyak pasien yang dilayani di rumah sakit masih menggunakan data citra analog. Data analog ini membutuhkan ruangan khusus untuk menyimpan guna menghindarkan kerusakan mekanis. Uraian mengatasi permasalahan ini, citra medis dibuat dalam bentuk digital dan disimpan dalam sistem database dan dapat melihat kesamaan citra kulit yang baru. Citra akan dapat ditampilkan dengan pra- processing dengan identifikasi kesamaan dengan Content Based Image Retrieval (CBIR bekerja dengan cara mengukur kemiripan citra query dengan semua citra yang ada dalam database sehingga query cost berbanding lurus dengan jumlah citra dalam database.

  10. Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches.

    Science.gov (United States)

    Araki, Tadashi; Kumar, P Krishna; Suri, Harman S; Ikeda, Nobutaka; Gupta, Ajay; Saba, Luca; Rajan, Jeny; Lavra, Francesco; Sharma, Aditya M; Shafique, Shoaib; Nicolaides, Andrew; Laird, John R; Suri, Jasjit S

    2016-07-01

    The degree of stenosis in the carotid artery can be predicted using automated carotid lumen diameter (LD) measured from B-mode ultrasound images. Systolic velocity-based methods for measurement of LD are subjective. With the advancement of high resolution imaging, image-based methods have started to emerge. However, they require robust image analysis for accurate LD measurement. This paper presents two different algorithms for automated segmentation of the lumen borders in carotid ultrasound images. Both algorithms are modeled as a two stage process. Stage one consists of a global-based model using scale-space framework for the extraction of the region of interest. This stage is common to both algorithms. Stage two is modeled using a local-based strategy that extracts the lumen interfaces. At this stage, the algorithm-1 is modeled as a region-based strategy using a classification framework, whereas the algorithm-2 is modeled as a boundary-based approach that uses the level set framework. Two sets of databases (DB), Japan DB (JDB) (202 patients, 404 images) and Hong Kong DB (HKDB) (50 patients, 300 images) were used in this study. Two trained neuroradiologists performed manual LD tracings. The mean automated LD measured was 6.35 ± 0.95 mm for JDB and 6.20 ± 1.35 mm for HKDB. The precision-of-merit was: 97.4 % and 98.0 % w.r.t to two manual tracings for JDB and 99.7 % and 97.9 % w.r.t to two manual tracings for HKDB. Statistical tests such as ANOVA, Chi-Squared, T-test, and Mann-Whitney test were conducted to show the stability and reliability of the automated techniques.

  11. Some considerations on automated image processing of pathline photographs

    International Nuclear Information System (INIS)

    Kobayashi, T.; Saga, T.; Segawa, S.

    1987-01-01

    It is presently shown that flow visualization velocity vectors can be automatically obtained from tracer particle photographs by means of an image processing system. The system involves automated gray level threshold selection during the digitization process and separation or erasure of the intersecting path lines, followed by use of the pathline picture in the identification process and an adjustment of the averaging area in the rearrangement process. Attention is given to the results obtained for two-dimensional flows past an airfoil cascade and around a circular cylinder. 7 references

  12. NeuronMetrics: software for semi-automated processing of cultured neuron images.

    Science.gov (United States)

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

    2007-03-23

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

  13. Performance analysis of automated evaluation of Crithidia luciliae-based indirect immunofluorescence tests in a routine setting - strengths and weaknesses.

    Science.gov (United States)

    Hormann, Wymke; Hahn, Melanie; Gerlach, Stefan; Hochstrate, Nicola; Affeldt, Kai; Giesen, Joyce; Fechner, Kai; Damoiseaux, Jan G M C

    2017-11-27

    Antibodies directed against dsDNA are a highly specific diagnostic marker for the presence of systemic lupus erythematosus and of particular importance in its diagnosis. To assess anti-dsDNA antibodies, the Crithidia luciliae-based indirect immunofluorescence test (CLIFT) is one of the assays considered to be the best choice. To overcome the drawback of subjective result interpretation that inheres indirect immunofluorescence assays in general, automated systems have been introduced into the market during the last years. Among these systems is the EUROPattern Suite, an advanced automated fluorescence microscope equipped with different software packages, capable of automated pattern interpretation and result suggestion for ANA, ANCA and CLIFT analysis. We analyzed the performance of the EUROPattern Suite with its automated fluorescence interpretation for CLIFT in a routine setting, reflecting the everyday life of a diagnostic laboratory. Three hundred and twelve consecutive samples were collected, sent to the Central Diagnostic Laboratory of the Maastricht University Medical Centre with a request for anti-dsDNA analysis over a period of 7 months. Agreement between EUROPattern assay analysis and the visual read was 93.3%. Sensitivity and specificity were 94.1% and 93.2%, respectively. The EUROPattern Suite performed reliably and greatly supported result interpretation. Automated image acquisition is readily performed and automated image classification gives a reliable recommendation for assay evaluation to the operator. The EUROPattern Suite optimizes workflow and contributes to standardization between different operators or laboratories.

  14. [Time consumption and quality of an automated fusion tool for SPECT and MRI images of the brain].

    Science.gov (United States)

    Fiedler, E; Platsch, G; Schwarz, A; Schmiedehausen, K; Tomandl, B; Huk, W; Rupprecht, Th; Rahn, N; Kuwert, T

    2003-10-01

    Although the fusion of images from different modalities may improve diagnostic accuracy, it is rarely used in clinical routine work due to logistic problems. Therefore we evaluated performance and time needed for fusing MRI and SPECT images using a semiautomated dedicated software. PATIENTS, MATERIAL AND METHOD: In 32 patients regional cerebral blood flow was measured using (99m)Tc ethylcystein dimer (ECD) and the three-headed SPECT camera MultiSPECT 3. MRI scans of the brain were performed using either a 0,2 T Open or a 1,5 T Sonata. Twelve of the MRI data sets were acquired using a 3D-T1w MPRAGE sequence, 20 with a 2D acquisition technique and different echo sequences. Image fusion was performed on a Syngo workstation using an entropy minimizing algorithm by an experienced user of the software. The fusion results were classified. We measured the time needed for the automated fusion procedure and in case of need that for manual realignment after automated, but insufficient fusion. The mean time of the automated fusion procedure was 123 s. It was for the 2D significantly shorter than for the 3D MRI datasets. For four of the 2D data sets and two of the 3D data sets an optimal fit was reached using the automated approach. The remaining 26 data sets required manual correction. The sum of the time required for automated fusion and that needed for manual correction averaged 320 s (50-886 s). The fusion of 3D MRI data sets lasted significantly longer than that of the 2D MRI data. The automated fusion tool delivered in 20% an optimal fit, in 80% manual correction was necessary. Nevertheless, each of the 32 SPECT data sets could be merged in less than 15 min with the corresponding MRI data, which seems acceptable for clinical routine use.

  15. Content-addressable read/write memories for image analysis

    Science.gov (United States)

    Snyder, W. E.; Savage, C. D.

    1982-01-01

    The commonly encountered image analysis problems of region labeling and clustering are found to be cases of search-and-rename problem which can be solved in parallel by a system architecture that is inherently suitable for VLSI implementation. This architecture is a novel form of content-addressable memory (CAM) which provides parallel search and update functions, allowing speed reductions down to constant time per operation. It has been proposed in related investigations by Hall (1981) that, with VLSI, CAM-based structures with enhanced instruction sets for general purpose processing will be feasible.

  16. Content-based image retrieval using a signature graph and a self-organizing map

    Directory of Open Access Journals (Sweden)

    Van Thanh The

    2016-06-01

    Full Text Available In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL,Wang and MSRDI.

  17. Automated image analysis for quantitative fluorescence in situ hybridization with environmental samples.

    Science.gov (United States)

    Zhou, Zhi; Pons, Marie Noëlle; Raskin, Lutgarde; Zilles, Julie L

    2007-05-01

    When fluorescence in situ hybridization (FISH) analyses are performed with complex environmental samples, difficulties related to the presence of microbial cell aggregates and nonuniform background fluorescence are often encountered. The objective of this study was to develop a robust and automated quantitative FISH method for complex environmental samples, such as manure and soil. The method and duration of sample dispersion were optimized to reduce the interference of cell aggregates. An automated image analysis program that detects cells from 4',6'-diamidino-2-phenylindole (DAPI) micrographs and extracts the maximum and mean fluorescence intensities for each cell from corresponding FISH images was developed with the software Visilog. Intensity thresholds were not consistent even for duplicate analyses, so alternative ways of classifying signals were investigated. In the resulting method, the intensity data were divided into clusters using fuzzy c-means clustering, and the resulting clusters were classified as target (positive) or nontarget (negative). A manual quality control confirmed this classification. With this method, 50.4, 72.1, and 64.9% of the cells in two swine manure samples and one soil sample, respectively, were positive as determined with a 16S rRNA-targeted bacterial probe (S-D-Bact-0338-a-A-18). Manual counting resulted in corresponding values of 52.3, 70.6, and 61.5%, respectively. In two swine manure samples and one soil sample 21.6, 12.3, and 2.5% of the cells were positive with an archaeal probe (S-D-Arch-0915-a-A-20), respectively. Manual counting resulted in corresponding values of 22.4, 14.0, and 2.9%, respectively. This automated method should facilitate quantitative analysis of FISH images for a variety of complex environmental samples.

  18. AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data

    Directory of Open Access Journals (Sweden)

    Daniel Scheffler

    2017-07-01

    Full Text Available Geospatial co-registration is a mandatory prerequisite when dealing with remote sensing data. Inter- or intra-sensoral misregistration will negatively affect any subsequent image analysis, specifically when processing multi-sensoral or multi-temporal data. In recent decades, many algorithms have been developed to enable manual, semi- or fully automatic displacement correction. Especially in the context of big data processing and the development of automated processing chains that aim to be applicable to different remote sensing systems, there is a strong need for efficient, accurate and generally usable co-registration. Here, we present AROSICS (Automated and Robust Open-Source Image Co-Registration Software, a Python-based open-source software including an easy-to-use user interface for automatic detection and correction of sub-pixel misalignments between various remote sensing datasets. It is independent of spatial or spectral characteristics and robust against high degrees of cloud coverage and spectral and temporal land cover dynamics. The co-registration is based on phase correlation for sub-pixel shift estimation in the frequency domain utilizing the Fourier shift theorem in a moving-window manner. A dense grid of spatial shift vectors can be created and automatically filtered by combining various validation and quality estimation metrics. Additionally, the software supports the masking of, e.g., clouds and cloud shadows to exclude such areas from spatial shift detection. The software has been tested on more than 9000 satellite images acquired by different sensors. The results are evaluated exemplarily for two inter-sensoral and two intra-sensoral use cases and show registration results in the sub-pixel range with root mean square error fits around 0.3 pixels and better.

  19. A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques.

    Science.gov (United States)

    Tsipouras, Markos G; Giannakeas, Nikolaos; Tzallas, Alexandros T; Tsianou, Zoe E; Manousou, Pinelopi; Hall, Andrew; Tsoulos, Ioannis; Tsianos, Epameinondas

    2017-03-01

    Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  20. Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections.

    Science.gov (United States)

    Kuepper, Claus; Kallenbach-Thieltges, Angela; Juette, Hendrik; Tannapfel, Andrea; Großerueschkamp, Frederik; Gerwert, Klaus

    2018-05-16

    A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.