948 resultados para Foreground Segmentation


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A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

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Colour image segmentation based on the hue component presents some problems due to the physical process of image formation. One of that problems is colour clipping, which appear when at least one of the sensor components is saturated. We have designed a system, that works for a trained set of colours, to recover the chromatic information of those pixels on which colour has been clipped. The chromatic correction method is based on the fact that hue and saturation are invariant to the uniform scaling of the three RGB components. The proposed method has been validated by means of a specific colour image processing board that has allowed its execution in real time. We show experimental results of the application of our method

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Els objectius del projecte es divideixen en tres blocs: Primerament, realitzar unasegmentació automàtica del contorn d'una imatge on hi ha una massa central. Tot seguit, a partir del contorn trobat, caracteritzar la massa. I finalment, utilitzant les característiques anteriors classificar la massa en benigne o maligne. En el projecte s'utilitza el Matlab com a eina de programació. Concretament les funcions enfocades al processat de imatges del toolbox de Image processing (propi de Matlab) i els classificadors de la PRTools de la Delft University of Technology

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Among various advantages, their small size makes model organisms preferred subjects of investigation. Yet, even in model systems detailed analysis of numerous developmental processes at cellular level is severely hampered by their scale. For instance, secondary growth of Arabidopsis hypocotyls creates a radial pattern of highly specialized tissues that comprises several thousand cells starting from a few dozen. This dynamic process is difficult to follow because of its scale and because it can only be investigated invasively, precluding comprehensive understanding of the cell proliferation, differentiation, and patterning events involved. To overcome such limitation, we established an automated quantitative histology approach. We acquired hypocotyl cross-sections from tiled high-resolution images and extracted their information content using custom high-throughput image processing and segmentation. Coupled with automated cell type recognition through machine learning, we could establish a cellular resolution atlas that reveals vascular morphodynamics during secondary growth, for example equidistant phloem pole formation. DOI: http://dx.doi.org/10.7554/eLife.01567.001.

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RESUME L'objectif de ce travail est de rappeler l'historique des hépatectomies majeures, les bases anatomiques et les techniques opératoires de la chirurgie du foie. Puis, à partir de 212 cas d'exérèses majeures recencées entre 1992 et 2001 dans le service de Chirurgie Viscérale du CHUV, les facteurs de risques, la mortalité et la morbidité des hépatectomies majeures seront étudiés et comparés avec les données récentes de la littérature. L'anatomie hépatique peut être considérée de plusieurs façons morphologiquement (poids, faces, lobe droit, gauche et caudé...), fonctionnellement (segmentation en fonction de la distribution des pédicules portes et de la localisation des veines hépatiques) et chirurgicalement. La terminologie est rappelée (hépatectomies «typique », «atypique », «réglée », «majeure »...). A partir de ces données, les exérèses du foie sont ensuite classées. Les techniques d'hépatectomies sont expliquées, en rappelant les principes généraux, et les voies d'abord. Puis, les techniques de contrôle de l'hémostase, la reconstruction des voies biliaires, les méthodes utilisées par rapport à la tranche de section hépatique et à la loge d'hépatectomie sont discutées, Les acquisitions récentes sont abordées. Sur les 494 hépatecomies réalisées entre janvier 1992 et août 2001, 212 sont majeures. Elles sont reparties en 7 groupes selon l'exérèse (lobectomie G ou D, hépatecomie G ou D etc...). `Sur ces 212 résections, 177 cas concernaient des lésions malignes et 35 cas des lésions bénignes. Les indications ont été classées en 4 groupes : cancer primitif du foie, métastases hépatiques, maladies bénignes (par exemple l'échinococcose alvéolaire), et 8 cas classés dans le groupe «autres ». Une intervention en urgence a été réalisée dans 7 cas. Le bilan préopératoire comprend un bilan biologique et morphologique. Une embolisation de l'artère hépatique a été réalisées dans 6 cas, alors qu'une embolisation dans la veine porte a été faite dans 17 cas. Les modalités chirurgicales (voies d'abord, contrôle vasculaire, drainage biliaire post- opératoire, transfusions per- opératoires, et interventions extra- hépatiques) sont expliqués. En fin, les méthodes statistiques utilisées sont rappelées. Il n'y a pas eu de décès per- opératoire. La mortalité post- opératoire dans les 30 jours a été de 3,3 % (7 cas) et la mortalité globale hospitalière de 5,2 %. Dans cette série, 132 patients n'ont eu aucune complication. La morbidité est de 17% si on considère les complications majeures, ayant concerné 36 patients, mais de 37,75 si l'on considère toutes les complications. Les complications chirurgicales sont le faite d'hémorragie, de fuite biliaire et d'infection du foyer opératoire. Dans notre étude, 33 facteurs de risque ont été analysés. L'analyse statistique uni- variée met den évidence les facteurs de risque suivants : Le nombre de culots de sang transfusés, la présence d'une hépatite, celle d'une cirrhose, le tabagisme, la lobectomie droite, et la présence d'une hypertension artérielle. L'analyse multi variée réalisée a permis de faire ressortir une combinaison de facteur de risque avec une valeur statistique significative et de réaliser une échelle et un score de gravité en fonction des facteurs de risques obtenus dans l'analyse uni variée. Le taux de mortalité globale hospitalière obtenu dans notre série (5,2%) est comparable aux résultats reportés dans d'autres séries.

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The Andalusian Public Health System Virtual Library (Biblioteca Virtual del Sistema Sanitario Público de Andalucía, BV-SSPA) provides access to health information resources and services to healthcare professionals through its Website. This virtual environment demands higher users’ knowledge in order to satisfy of the need of information of our users, as digital natives as digital immigrants, improving at the same time the communication with all of them. 1. To collect clients' views and expectations according to their nature of digital natives and immigrants. 2. To know our online reputation. A Collecting User Expectation Questionnaire will be built, taking into account the segmentation of the BV-SSPA users’ professional groups of the Andalusian Public Health System. A pilot test will be run to check the survey dimensions and items about practices, attitudes and knowledge of our users. Two Quality Function Deployment (QFD) matrices will enable the BV-SSPA services to be targeted to our digital natives or digital immigrants, according to their nature, finding the best way to satisfy their information needs. We provide feedback on BV-SSPA: users can have the opportunity to post feedback about the site via the 'Contact us' section and comment about their experience. And Web 2.0 is a shop window, providing the opportunity to show the comments; and through time, our online reputation will be built, but the BV-SSPA must manage its own personal branding. Web 2.0 tools are a driver of improvement, because they provide a key source of insight into people's attitudes. Besides, the BV-SSPA digital identity will be analyzed through indicators like major search engine referrals breakdown, top referring sites (non search engines), or top search engine referral phrases, among others. Definition of digital native and digital immigrant profiles of the BV-SSPA, and their difference, will be explained by their expectations. The design of the two QFD matrices will illustrate in just one graph the requirements of both groups for tackling digital abilities and inequalities. The BV-SSPA could deliver information and services through alternative channels. On the other hand, we are developing a strategy to identify, to measure and to manage a digital identity through communication with the user and to find out our online reputation. With the use of different tools from quantitative and qualitative methodology, and the opportunities offered by Web 2.0 tools, the BV-SSPA will know the expectations of their users as a first step to satisfy their necessities. Personalization is pivotal to the success of the Site, delivering tailored content to individuals based on their recorded preferences. The valuable user research can be used during new product development and redesign. Besides positive interaction let us build trust, show authenticity, and foster loyalty: we improve with effort, communication and show.

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In this paper, we explore the connection between labor market segmentation in two sectors, a modern protected formal sector and a traditional- unprotected-informal sector, and overeducation in a developing country. Informality is thought to have negative consequences, primarily through poorer working conditions, lack of social security, as well as low levels of productivity throughout the economy. This paper considers an aspect that has not been previously addressed, namely the fact that informality might also affect the way workers match their actual education with that required performing their job. We use micro-data from Colombia to test the relationship between overeducation and informality. Empirical results suggest that, once the endogeneity of employment choice has been accounted for, formal male workers are less likely to be overeducated. Interestingly, the propensity of being overeducated among women does not seem to be closely related to the employment choice.

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OBJECTIVES: The objective of this study is to describe the prenatal sonographic features and the results of DNA analysis on three fetuses with dyssegmental dysplasia, Silverman-Handmaker type (DD-SH). METHODS: A retrospective review of three fetuses with confirmed DD-SH was conducted. The fetal ultrasound findings, the radiological characteristics, and the results of the mutation analysis of the heparan sulphate perlecan gene 2 (HSPG2) were reviewed. RESULTS: There were three cases in two families with DD-SH diagnosed prenatally. The main prenatal ultrasound and the radiological features of DD-SH were severe limb shortening and vertebral segmentation and fusion defects (anisospondyly). The DNA analysis of the HSPG2 gene showed that the two affected fetuses in a nonconsanguineous family had a compound heterozygote for the c.646G > T transversion in exon 7 and a c.5788C > T transition in exon 46. The fetus born to the consanguineous couple had a homozygous mutation c.1356-27_1507 + 59del. CONCLUSION: DD-SH can be diagnosed prenatally using fetal ultrasound as early as 13 weeks. Xrays and DNA analysis of the HSPG2 gene are important for the confirmation of the diagnosis and for the preimplantation and prenatal diagnosis in pregnancies at risk. © 2013 John Wiley & Sons, Ltd.

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Our essay aims at studying suitable statistical methods for the clustering ofcompositional data in situations where observations are constituted by trajectories ofcompositional data, that is, by sequences of composition measurements along a domain.Observed trajectories are known as “functional data” and several methods have beenproposed for their analysis.In particular, methods for clustering functional data, known as Functional ClusterAnalysis (FCA), have been applied by practitioners and scientists in many fields. To ourknowledge, FCA techniques have not been extended to cope with the problem ofclustering compositional data trajectories. In order to extend FCA techniques to theanalysis of compositional data, FCA clustering techniques have to be adapted by using asuitable compositional algebra.The present work centres on the following question: given a sample of compositionaldata trajectories, how can we formulate a segmentation procedure giving homogeneousclasses? To address this problem we follow the steps described below.First of all we adapt the well-known spline smoothing techniques in order to cope withthe smoothing of compositional data trajectories. In fact, an observed curve can bethought of as the sum of a smooth part plus some noise due to measurement errors.Spline smoothing techniques are used to isolate the smooth part of the trajectory:clustering algorithms are then applied to these smooth curves.The second step consists in building suitable metrics for measuring the dissimilaritybetween trajectories: we propose a metric that accounts for difference in both shape andlevel, and a metric accounting for differences in shape only.A simulation study is performed in order to evaluate the proposed methodologies, usingboth hierarchical and partitional clustering algorithm. The quality of the obtained resultsis assessed by means of several indices

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We propose a segmentation method based on the geometric representation of images as 2-D manifolds embedded in a higher dimensional space. The segmentation is formulated as a minimization problem, where the contours are described by a level set function and the objective functional corresponds to the surface of the image manifold. In this geometric framework, both data-fidelity and regularity terms of the segmentation are represented by a single functional that intrinsically aligns the gradients of the level set function with the gradients of the image and results in a segmentation criterion that exploits the directional information of image gradients to overcome image inhomogeneities and fragmented contours. The proposed formulation combines this robust alignment of gradients with attractive properties of previous methods developed in the same geometric framework: 1) the natural coupling of image channels proposed for anisotropic diffusion and 2) the ability of subjective surfaces to detect weak edges and close fragmented boundaries. The potential of such a geometric approach lies in the general definition of Riemannian manifolds, which naturally generalizes existing segmentation methods (the geodesic active contours, the active contours without edges, and the robust edge integrator) to higher dimensional spaces, non-flat images, and feature spaces. Our experiments show that the proposed technique improves the segmentation of multi-channel images, images subject to inhomogeneities, and images characterized by geometric structures like ridges or valleys.

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In this paper, we propose a new paradigm to carry outthe registration task with a dense deformation fieldderived from the optical flow model and the activecontour method. The proposed framework merges differenttasks such as segmentation, regularization, incorporationof prior knowledge and registration into a singleframework. The active contour model is at the core of ourframework even if it is used in a different way than thestandard approaches. Indeed, active contours are awell-known technique for image segmentation. Thistechnique consists in finding the curve which minimizesan energy functional designed to be minimal when thecurve has reached the object contours. That way, we getaccurate and smooth segmentation results. So far, theactive contour model has been used to segment objectslying in images from boundary-based, region-based orshape-based information. Our registration technique willprofit of all these families of active contours todetermine a dense deformation field defined on the wholeimage. A well-suited application of our model is theatlas registration in medical imaging which consists inautomatically delineating anatomical structures. Wepresent results on 2D synthetic images to show theperformances of our non rigid deformation field based ona natural registration term. We also present registrationresults on real 3D medical data with a large spaceoccupying tumor substantially deforming surroundingstructures, which constitutes a high challenging problem.

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Many Higher Education Institutions (HEIs) establish tuition below the equilibrium price to generate permanent demand excess. This paper first adapts Becker’s (1991) theory to understand why the HEIs price in this way. The fact that students are both consumers and inputs on the education production process gives rise to a market equilibrium where some firms have excess demand and charge high prices, and others charge low prices and have empty seats.Second, the paper analyzes this equilibrium empirically. We estimated the demand for undergraduate courses in Business Administration in the State of São Paulo. The results show that tuition, quality of incoming students and percentage of lecturers holding doctorates degrees are the determining factors of students’ choice. Since the student quality determines the demand for a HEI, it is calculated what the value is for a HEI to get better students; that is the total revenue that each HEI gives up to guarantee excess demand. Regarding the “investment” in selectivity, 39 HEIs in São Paulo give up a combined R$ 5 million (or US$ 3.14 million) in revenue per year per freshman class, which means 7.6% of the revenue coming from a freshman class.

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It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

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BACKGROUND: The yeast Schizosaccharomyces pombe is frequently used as a model for studying the cell cycle. The cells are rod-shaped and divide by medial fission. The process of cell division, or cytokinesis, is controlled by a network of signaling proteins called the Septation Initiation Network (SIN); SIN proteins associate with the SPBs during nuclear division (mitosis). Some SIN proteins associate with both SPBs early in mitosis, and then display strongly asymmetric signal intensity at the SPBs in late mitosis, just before cytokinesis. This asymmetry is thought to be important for correct regulation of SIN signaling, and coordination of cytokinesis and mitosis. In order to study the dynamics of organelles or large protein complexes such as the spindle pole body (SPB), which have been labeled with a fluorescent protein tag in living cells, a number of the image analysis problems must be solved; the cell outline must be detected automatically, and the position and signal intensity associated with the structures of interest within the cell must be determined. RESULTS: We present a new 2D and 3D image analysis system that permits versatile and robust analysis of motile, fluorescently labeled structures in rod-shaped cells. We have designed an image analysis system that we have implemented as a user-friendly software package allowing the fast and robust image-analysis of large numbers of rod-shaped cells. We have developed new robust algorithms, which we combined with existing methodologies to facilitate fast and accurate analysis. Our software permits the detection and segmentation of rod-shaped cells in either static or dynamic (i.e. time lapse) multi-channel images. It enables tracking of two structures (for example SPBs) in two different image channels. For 2D or 3D static images, the locations of the structures are identified, and then intensity values are extracted together with several quantitative parameters, such as length, width, cell orientation, background fluorescence and the distance between the structures of interest. Furthermore, two kinds of kymographs of the tracked structures can be established, one representing the migration with respect to their relative position, the other representing their individual trajectories inside the cell. This software package, called "RodCellJ", allowed us to analyze a large number of S. pombe cells to understand the rules that govern SIN protein asymmetry. CONCLUSIONS: "RodCell" is freely available to the community as a package of several ImageJ plugins to simultaneously analyze the behavior of a large number of rod-shaped cells in an extensive manner. The integration of different image-processing techniques in a single package, as well as the development of novel algorithms does not only allow to speed up the analysis with respect to the usage of existing tools, but also accounts for higher accuracy. Its utility was demonstrated on both 2D and 3D static and dynamic images to study the septation initiation network of the yeast Schizosaccharomyces pombe. More generally, it can be used in any kind of biological context where fluorescent-protein labeled structures need to be analyzed in rod-shaped cells. AVAILABILITY: RodCellJ is freely available under http://bigwww.epfl.ch/algorithms.html, (after acceptance of the publication).

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Three-dimensional segmented echo planar imaging (3D-EPI) is a promising approach for high-resolution functional magnetic resonance imaging, as it provides an increased signal-to-noise ratio (SNR) at similar temporal resolution to traditional multislice 2D-EPI readouts. Recently, the 3D-EPI technique has become more frequently used and it is important to better understand its implications for fMRI. In this study, the temporal SNR characteristics of 3D-EPI with varying numbers of segments are studied. It is shown that, in humans, the temporal variance increases with the number of segments used to form the EPI acquisition and that for segmented acquisitions, the maximum available temporal SNR is reduced compared to single shot acquisitions. This reduction with increased segmentation is not found in phantom data and thus likely due to physiological processes. When operating in the thermal noise dominated regime, fMRI experiments with a motor task revealed that the 3D variant outperforms the 2D-EPI in terms of temporal SNR and sensitivity to detect activated brain regions. Thus, the theoretical SNR advantage of a segmented 3D-EPI sequence for fMRI only exists in a low SNR situation. However, other advantages of 3D-EPI, such as the application of parallel imaging techniques in two dimensions and the low specific absorption rate requirements, may encourage the use of the 3D-EPI sequence for fMRI in situations with higher SNR.