Relevant bibliographies by topics / Medical imaging

Academic literature on the topic 'Medical imaging'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 September 2024

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Journal articles on the topic "Medical imaging"

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Riederer, Stephen J., and Richard L. Ehman. "Medical Imaging." Science 270, no. 5239 (November 17, 1995): 1105. http://dx.doi.org/10.1126/science.270.5239.1105-a.

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Lederman, Lynne. "Medical Imaging." BioTechniques 41, no. 3 (September 2006): 243–47. http://dx.doi.org/10.2144/000112252.

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MINATO, Kotaro. "Medical Imaging." Journal of the Society of Mechanical Engineers 107, no. 1026 (2004): 353–56. http://dx.doi.org/10.1299/jsmemag.107.1026_353.

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Wells, P. N. T. "Medical imaging." IEE Proceedings A Physical Science, Measurement and Instrumentation, Management and Education, Reviews 134, no. 2 (1987): 97. http://dx.doi.org/10.1049/ip-a-1.1987.0014.

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Elliott, Alex. "Medical imaging." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 546, no. 1-2 (July 2005): 1–13. http://dx.doi.org/10.1016/j.nima.2005.03.127.

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Brody, Herb. "Medical imaging." Nature 502, no. 7473 (October 2013): S81. http://dx.doi.org/10.1038/502s81a.

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Barker, M. C. J. "Medical imaging." Physics Education 31, no. 2 (March 1996): 70–75. http://dx.doi.org/10.1088/0031-9120/31/2/013.

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Kreel, L. "Medical imaging." Postgraduate Medical Journal 67, no. 786 (April 1, 1991): 334–46. http://dx.doi.org/10.1136/pgmj.67.786.334.

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Illes, Judy. "Medical imaging." Academic Radiology 11, no. 7 (July 2004): 721–23. http://dx.doi.org/10.1016/j.acra.2004.05.009.

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Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING IN MEDICAL IMAGING." NATURE AND SCIENCE 03, no. 04 (October 27, 2020): 7–13. http://dx.doi.org/10.36719/2707-1146/04/7-13.

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Medical imaging technology plays an important role in the detection, diagnosis and treatment of diseases. Due to the instability of human expert experience, machine learning technology is expected to assist researchers and physicians to improve the accuracy of imaging diagnosis and reduce the imbalance of medical resources. This article systematically summarizes some methods of deep learning technology, introduces the application research of deep learning technology in medical imaging, and discusses the limitations of deep learning technology in medical imaging. Key words: Artificial Intelligence, Deep Learning, Medical Imaging, big data
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Dissertations / Theses on the topic "Medical imaging"

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Carlak, Hamza Feza. "Medical Electro-thermal Imaging." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614168/index.pdf.

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Breast cancer is the most crucial cancer type among all other cancer types. There are many imaging techniques used to screen breast carcinoma. These are mammography, ultrasound, computed tomography, magnetic resonance imaging, infrared imaging, positron emission tomography and electrical impedance tomography. However, there is no gold standard in breast carcinoma diagnosis. The object of this study is to create a hybrid system that uses thermal and electrical imaging methods together for breast cancer diagnosis. Body tissues have different electrical conductivity values depending on their state of health and types. Consequently, one can get information about the anatomy of the human body and tissue&rsquo
s health by imaging tissue conductivity distribution. Due to metabolic heat generation values and thermal characteristics that differ from tissue to tissue, thermal imaging has started to play an important role in medical diagnosis. To increase the temperature contrast in thermal images, the characteristics of the two imaging modalities can be combined. This is achieved by implementing thermal imaging applying electrical currents from the body surface within safety limits (i.e., thermal imaging in active mode). Electrical conductivity of tissues changes with frequency, so it is possible to obtain more than one thermal image for the same body. Combining these images, more detailed information about the tumor tissue can be acquired. This may increase the accuracy in diagnosis while tumor can be detected at deeper locations. Feasibility of the proposed technique is investigated with analytical and numerical simulations and experimental studies. 2-D and 3-D numerical models of the female breast are developed and feasibility work is implemented in the frequency range of 10 kHz and 800 MHz. Temporal and spatial temperature distributions are obtained at desired depths. Thermal body-phantoms are developed to simulate the healthy breast and tumor tissues in experimental studies. Thermograms of these phantoms are obtained using two different infrared cameras (microbolometer uncooled and cooled Quantum Well Infrared Photodetectors). Single and dual tumor tissues are determined using the ratio of uniform (healthy) and inhomogeneous (tumor) images. Single tumor (1 cm away from boundary) causes 55 °
mC temperature increase and dual tumor (2 cm away from boundary) leads to 50 °
mC temperature contrast. With multi-frequency current application (in the range of 10 kHz-800 MHz), the temperature contrast generated by 3.4 mm3 tumor at 9 mm depth can be detected with the state-of-the-art thermal imagers.
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Smith, Rhodri. "Motion correction in medical imaging." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/841883/.

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It is estimated that over half of current adults within Great Britain under the age of 65 will be diagnosed with cancer at some point in their lifetime. Medical Imaging forms an essential part of cancer clinical protocols and is able to furnish morphological, metabolic and functional information. The imaging of molecular interactions of biological processes in vivo with Positron Emission Tomography (PET) is informative not only for disease detection but also therapeutic response. The qualitative and quantitative accuracy of imaging is thus vital in the extraction of meaningful and reproducible information from the images, allowing increased sensitivity and specificity in the diagnosis and precision of image guided treatment. Furthermore the utilization of complementary information obtained via Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) in integrated PET-CT and PET-MR devices offers the potential for the synergistic effects of hybrid imaging to provide increased detection and precision of diagnosis with reduced radiation dose in a fully comprehensive single imaging examination. With the increasing sophistication in imaging technology respiratory organ motion during imaging has demonstrated itself to be a major degrading factor of PET image resolution. A modest estimate of respiratory motion amplitude of 5mm, results in PET system resolution degrading from ≈ 5mm to ≈8.5mm. This evidently has an impact on cancer lesion detectability. Therefore accurate and robust methods for respiratory motion correction are required for both clinical effectiveness and economic justification for purchasing state of the art hybrid PET scanners with high resolution capabilities. In addition the judicious use of imaging resources from hybrid imaging devices coupled with advanced image processing / acquisition protocols will allow optimization of data used for improving quantitative accuracy of PET images and those used for clinical interpretation. In essence it would prove impractical to use the MR scanner purely for monitoring respiratory motion. Numerous methods exist to attempt to correct PET imaging for respiratory motion. As presented in this thesis many methods demonstrate themselves to be ineffective in the clinical setting where the patients breathing patterns appear irregular in comparison to the idealized situation of regular periodic motion. Advanced respiratory motion correction techniques utilize hybrid PET/CT, PET/MR scanners coupled with an external source of information which serves as a surrogate to build a static correspondence to the estimated internal respiratory motion. Static models however are unable to adapt to their external environment and do not consider time dependent changes in the state of a system. A further confounding factor in the development and assessment of motion correction schemes for medical imaging data is the inability to acquire volumetric data with high contrast and high spatial and temporal resolution which serves as a ground truth for quantifying model accuracy and confidence. This thesis addresses both problems by analysing respiratory motion correspondence modelling under a manifold learning and alignment paradigm which may be used to consolidate many of the respiratory motion estimation models that exist today. A Bayesian approach is adopted in this work to incorporate a-priori information into the model building stage for a more robust, flexible adaptive respiratory motion estimation / correction framework. This thesis constructs and tests the first proposed adaptive motion model to correlate a surrogate signal with internal motion. This adaptive approach allows the relationship between external surrogate signal and internal motion to change dependent upon breathing pattern and system noise. The adaptive model was compared to a state-of the-art static model and allows more accurate motion estimates to be made when the patient is breathing with an irregular pattern. Testing performed on MRI data from 9 volunteers demonstrated the adaptive model was statistically more significant (p < 0.001) in the presence of irregular motion in comparison to a static model. The adaptive Kalman model on average reduced the error in motion by 30% in comparison to the static model. Utilizing the adaptive model during a typical PET study would theoretically result in ≈ 10% increase in PET resolution in comparison to relying on a static model alone for motion correction. The adaptive Kalman model has the capability to increase the performance of PET system resolution from ≈ 8.5mm to ≈ 5.8mm, ≈ 30%. A simulated PET study also demonstrated ≈ 30% increase in tumour uptake when using motion correction. Also demonstrated in the thesis is the first method to acquire volumetric imaging data from sparse MR samples during free breathing to allow the realization of high contrast, high resolution 4D models of respiratory motion using limited acquired data. The developed framework facilitates greater freedom in the acquisition of free breathing respiratory motion sequences which may be used to inform motion modelling methods in a range of imaging modalities as well as informing the development of generalizable models of human respiration. It is shown that the developed approach can provide equivalent motion vector fields in comparison to fully sampled 4D dynamic data. The incorporation of the manifold alignment step into the sparse motion model reduces the error in motion estimates by ≈ 16%. Example images of propagated motion are also presented as supplementary information. The thesis concludes by generalizing the concepts in this work and looking to utilize the developed methods to other problems in the medical imaging arena.
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Ye, Luming. "Perception Metrics in Medical Imaging." Thesis, KTH, Medicinsk teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102186.

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Fonseca, Francisco Xavier dos Santos. "GPU power for medical imaging." Master's thesis, Universidade de Aveiro, 2011. http://hdl.handle.net/10773/7853.

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Mestrado em Engenharia de Computadores e Telemática
A aplicação CapView utiliza um algoritmo de classificação baseado em SVM (Support Vector Machines) para automatizar a segmentação topográfica de vídeos do trato intestinal obtidos por cápsula endoscópica. Este trabalho explora a aplicação de processadores gráficos (GPU) para execução paralela desse algoritmo. Após uma etapa de otimização da versão sequencial, comparou-se o desempenho obtido por duas abordagens: (1) desenvolvimento apenas do código do lado do host, com suporte em bibliotecas especializadas para a GPU, e (2) desenvolvimento de todo o código, incluindo o que é executado no GPU. Ambas permitiram ganhos (speedups) significativos, entre 1,4 e 7 em testes efetuados com GPUs individuais de vários modelos. Usando um cluster de 4 GPU do modelo de maior capacidade, conseguiu-se, em todos os casos testados, ganhos entre 26,2 e 27,2 em relação à versão sequencial otimizada. Os métodos desenvolvidos foram integrados na aplicação CapView, utilizada em rotina em ambientes hospitalares.
The CapView application uses a classification algorithm based on SVMs (Support Vector Machines) for automatic topographic segmentation of gastrointestinal tract videos obtained through capsule endoscopy. This work explores the use graphic processors (GPUs) to parallelize the segmentation algorithm. After an optimization phase of the sequential version, two new approaches were analyzed: (1) development of the host code only, with support of specialized libraries for the GPU, and (2) development of the host and the device’s code. The two approaches caused substantial gains, with speedups between 1.4 and 7 times in tests made with several different individual GPUs. In a cluster of 4 GPUs of the most capable model, speedups between 26.2 and 27.2 times were achieved, compared to the optimized sequential version. The methods developed were integrated in the CapView application, used in routine in medical environments.
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Zhang, Hongbin. "Signal detection in medical imaging." Diss., The University of Arizona, 2001. http://hdl.handle.net/10150/290512.

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The goal of this research is to develop computational methods for predicting how a given medical imaging system and reconstruction algorithm will perform when mathematical observers for tumor detection use the resulting images. Here the mathematical observer is the ideal observer, which sets an upper limit to the performance as measured by the Bayesian risk or receiver operating characteristic analysis. This dissertation concentrates on constructing the ideal observer in complex detection problems and estimating its performance. Thus the methods reported in this dissertation can be used to approximate the ideal observer in real medical images. We define our detection problem as a two-hypothesis detection task where a known signal is superimposed on a random background with complicated distributions and embedded in independent Poisson noise. The first challenge of this detection problem is that the distribution of the random background is usually unknown and difficult to estimate. The second challenge is that the calculation of the ideal observer is computationally intensive for non stylized problems. In order to solve these two problems, our work relies on multiresolution analysis of images. The multiresolution analysis is achieved by decomposing an image into a set of spatial frequency bandpass images so each bandpass image represents information about a particular fitness of detail or scale. Connected with this method, we will use three types of image representation by invertible linear transforms. They are the orthogonal wavelet transform, pyramid transform and independent component analysis. Based on the findings from human and mammalian vision, we can model textures by using marginal densities of a set of spatial frequency bandpass images. In order to estimate the distribution of an ensemble of images given the empirical marginal distributions of filter responses, we can use the maximum entropy principle and get a unique solution. We find that the ideal observer calculates a posterior mean of the ratio of conditional density functions, or the posterior mean of the ratio of two prior density functions, both of which are high dimensional integrals and have no analytic solution usually. But there are two ways to approximate the ideal observer. The first one is a classic decision process; that is, we construct a classifier following feature extraction steps. We use the integrand of the posterior mean as features, which are calculated at the estimated background close to the posterior mode. The classifier combines these features to approximate the integral (or the ideal observer). Finally, if we know both the conditional density function and the prior density function then we can also approximate the high dimensional integral by Monte Carlo integration methods. Since the calculation of the posterior mean is usually a very high dimensional integration problem, we must construct a Markov chain, which can explore the posterior distribution efficiently. We will give two proposal functions. The first proposal function is the likelihood function of random backgrounds. The second method makes use of the multiresolution representation of the image by decomposing the image into a set of spatial frequency bands. Sampling one pixel in each band equivalently updates a cluster of pixels in the neighborhood of the pixel location in the original image.
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Alomari, Zainab Rami Saleh. "Plane wave imaging beamforming techniques for medical ultrasound imaging." Thesis, University of Leeds, 2017. http://etheses.whiterose.ac.uk/18127/.

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In ultrasound array imaging, the beamforming operation is performed by aligning and processing the received echo signals from each individual array element to form a complete image. This operation can be performed in many different ways, where adaptive and non-adaptive beamformers are considered as the main categories. Adaptive beamformers exploit the statistical correlation between the received data to find a weighting value at the focal point, instead of using a fixed weighting window in non-adaptive beamforming. This results in a significant improvement in the image quality in terms of resolution and sidelobes reduction. This improvement is necessary for ultrafast imaging because of the lack of focusing in Plane Wave Imaging (PWI) that results in lowering the SNR, and thus the produced imaging quality is reduced. This thesis analyses different adaptive beamforming techniques for ultrafast imaging. For accurate medical diagnosis, the frame rate, the imaging resolution, contrast and speckle homogeneity are all considered as important parameters that contribute to the final imaging result. To be able to evaluate each technique by minimizing the effect of external parameters, two different analysis were performed. First an empirical expression for PWI lateral resolution is produced after studying the effect of the imaging parameters on this imaging method. Then a method for selecting the suitable steering angles in Compound Plane- Wave Imaging (CPWI) is introduced, with a detailed explanation for the effect of the compound angles on resolution and sidelobes level. In order to add the contrast improvement to the properties of adaptive beamformers, some techniques like the coherence-based factors and Eigenspace-Based Minimum Variance (ESBMV) are produced in the literature. After demonstrating the principle of Minimum Variance adaptive beamformer, a detailed comparison for the types of coherence-based factors is given. In addition, a new technique of Partial-ESBMV is introduced to modify reference ESBMV so that no Black Box Region artefacts nor dark spots appear when using this method in medical imaging. After explaining its background and properties using cystic and wire phantoms, the proposed method is applied to the real RF data of carotid artery, as an application to clarify the efficiency of this method in medical ultrasound imaging.
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Winder, Robert John. "Medical imaging : tissue volume measurement & medical rapid prototyping." Thesis, University of Ulster, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399689.

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Rajanayagam, Vasanthakumar. "Non-medical applications of imaging techniques : multi-dimensional NMR imaging." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/27513.

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The work described in this thesis concentrates on two aspects of Proton NMR imaging: development and evaluation of new/old experimental sequences and application of those techniques to study some non-medical systems that are of industrial importance. Two-dimensional Fourier transform spin warp imaging technique has been evaluated. Importantly, the adaptation of a conventional high resolution spectrometer to perform imaging has been demonstrated with means of "phantoms". This includes calibration of magnetic field gradients, mapping the static magnetic field and radiofrequency field distributions and intensity measurements related to proton spin densities. In addition, a preliminary study describes microscopic imaging of glass capillary tube phantoms containing water. Several different sequences related to Chemical Shift imaging including the one developed during the study have been described. A brief insight into chemical shift artifacts as well as some experimental methods of minimizing some of them have also been presented. The potential of NMR imaging to study non-medical systems has been explored in three different areas of interest: Chromatography columns. Porous rock samples and Wood samples. A variety of NMR imaging sequences have been used to study some interesting and challenging features of these systems which clearly extends the scope of NMR imaging science.
Science, Faculty of
Chemistry, Department of
Graduate
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Carr, Jonathan. "Surface reconstruction in 3D medical imaging." Thesis, University of Canterbury. Electrical Engineering, 1996. http://hdl.handle.net/10092/6533.

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This thesis addresses two problems in medical imaging, the development of a system for 3D imaging with ultrasound and a system for making titanium prostheses for cranioplasty. Central to both problems is the construction and depiction of surfaces from volume data where the data is not acquired on a regular grid or is incomplete. A system for acquiring 3D pulse-echo ultrasound data using a conventional 2D ultrasound scanner equipped with an electro-magnetic spatial locator is described. The non-parallel nature of 2D B-scan slices acquired by the system requires the development of new visualisation algorithms to depict three dimensional structures. Two methods for visualising iso-valued surfaces from the ultrasound data are presented. One forms an intermediate volume reconstruction suitable for conventional ray-casting while the second method renders surfaces directly from the slice data. In vivo imaging of human anatomy is used to demonstrate reconstructions of tissue surfaces. Filtering and spatial compounding of scan data is used to reduce speckle. The manifestation of 2D artefacts in 3D surface reconstructions is also illustrated. Pulse-echo ultrasound primarily depicts tissue boundaries. These are characterised by incomplete acoustic interfaces contaminated by noise. The problem of reconstructing tissue interfaces from ultrasound data is viewed as an example of the general problem of reconstructing an object's shape from unorganised surface data. A novel method for reconstructing surfaces in the absence of a priori knowledge of the object's shape, is described and applied to 3D ultrasound data. The method uses projections through the surface data taken from many viewpoints to reconstruct surfaces. Aspects of the method are similar to work in computer vision concerning the determination of the shape of 3D objects from their silhouettes. This work is extended significantly in this thesis by considering the reconstruction of incomplete objects in the presence of noise and through the development of practical algorithms for pixel and voxel data. Furthermore, the reconstruction of realistic, non-convex objects is considered rather than simple geometric objects. 2D and 3D ultrasound data derived from phantoms, as well as artificial data, are used to demonstrate reconstructions. The second problem studied in this thesis concerns designing cranial implants to repair defects in the skull. Skull surfaces are extracted from X-ray CT data by ray-casting iso-valued surfaces. A tensor product B-spline interpolant is used in the ray-caster to reduce ripples in the surface data due to partial voluming and the large spacing between CT slices. The associated surface depth-maps are characterised by large irregular holes which correspond to the defect regions requiring repair. Defects are graphically identified by a user in surface-rendered images. Radial basis function approximation is introduced as a method of interpolating the surface of the skull across these defect regions. The fitted surface is used to produce CNC milling instructions to machine a mould in the shape of the surface from a block of hard plastic resin. A cranial implant is then formed by pressing flat titanium plate into the mould under high pressure in a hydraulic press. The system improves upon current treatment procedures by avoiding the manual aspects of fashioning an implant. It is also suitable when other techniques which use symmetry to reconstruct the skull are inadequate or not possible. The system has been successfully used to treat patients at Christchurch Hospital. Radial basis function (RBF) approximation has previously been restricted to problems where the number of interpolation centres is small. The use of newly developed fast methods for evaluating radial basis interpolants in the surface interpolation software results in a computationally efficient system for designing cranial implants and demonstrates that RBFs are potentially of wide interest in medical imaging and engineering problems where data does not lie on a regular grid.
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Silva, Luís António Bastião. "Medical imaging services supported on cloud." Master's thesis, Universidade de Aveiro, 2011. http://hdl.handle.net/10773/7245.

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Mestrado em Engenharia de Computadores e Telemática
Hoje em dia, as instituições de cuidados de saúde, utilizam a telemedicina para suportar ambientes colaborativos. Na área da imagem médica digital, a quantidade de dados tem crescido substancialmente nos últimos anos, requerendo mais infraestruturas para fornecer um serviço com a qualidade desejada. Os computadores e dispositivos com acesso à Internet estão acessíveis em qualquer altura e em qualquer lugar, criando oportunidades para partilhar e utilizar recursos online. Uma enorme quantidade de processamento computacional e armazenamento são utilizados como uma comodidade no quotidiano. Esta dissertação apresenta uma plataforma para suportar serviços de telemedicina sobre a cloud, permitindo que aplicações armazenem e comuniquem facilmente, utilizando qualquer fornecedor de cloud. Deste modo, os programadores não necessitam de se preocupar onde os recursos vão ser instalados a as suas aplicações não ficam limitadas a um único fornecedor. Foram desenvolvidas duas aplicações para tele-imagiologia com esta plataforma: repositório de imagens médicas e uma infraestrutura de comunicações entre centros hospitalares. Finalmente, a arquitetura desenvolvida é genérica e flexível permitindo facilmente a sua expansão para outras áreas aplicacionais e outros serviços de cloud.
Healthcare institutions resort largely, nowadays, to telemedicine in order to support collaborative environments. In the medical imaging area, the huge amount of medical volume data has increased over the past few years, requiring high-performance infrastructures to provide services with required quality. Computing devices and Internet access are now available anywhere and at anytime, creating new opportunities to share and use online resources. A tremendous amount of ubiquitous computational power and an unprecedented number of Internet resources and services are used every day as a normal commodity. This thesis presents a telemedicine service platform over the Cloud that allows applications to store information and to communicate easier, using any Internet cloud provider. With this platform, developers do not concern where the resources will be deployed and the applications will not be restricted to a specific cloud vendor. Two tele-imagiologic applications were developed along with this platform: a medical imaging repository and an interinstitutional communications infrastructure. Lastly, the architecture developed is generic and flexible to expand to other application areas and cloud services.
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Books on the topic "Medical imaging"

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Wolbarst, Anthony B., Patrizio Capasso, and Andrew R. Wyant. Medical Imaging. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118480267.

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Iniewski, Krzysztof, ed. Medical Imaging. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2009. http://dx.doi.org/10.1002/9780470451816.

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1949-, LeVine Harry, ed. Medical imaging. Santa Barbara, Calif: ABC-CLIO, 2010.

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Farris, Naff Clay, ed. Medical imaging. San Diego, Calif: Greenhaven Press, 2006.

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Erondu, Okechukwu Felix. Medical imaging. Rijeka: InTech, 2011.

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Zager, Masha. Medical imaging. Norwalk, CT: Business Communications Co., 2002.

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Shukla, Ashutosh Kumar. Medical Imaging Methods. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003112068.

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Bharath, A. A. Introductory Medical Imaging. Cham: Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-01631-8.

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Maier, Andreas, Stefan Steidl, Vincent Christlein, and Joachim Hornegger, eds. Medical Imaging Systems. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96520-8.

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Shukla, Ashutosh Kumar, ed. Medical Imaging Methods. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9121-7.

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Book chapters on the topic "Medical imaging"

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Krupinski, Elizabeth A. "Medical Imaging." In Handbook of Visual Display Technology, 545–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-14346-0_186.

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Krupinski, Elizabeth A. "Medical Imaging." In Handbook of Visual Display Technology, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-35947-7_186-1.

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Dallas, William J. "Medical Imaging." In ASST ’87 6. Aachener Symposium für Signaltheorie, 302–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-73015-3_57.

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Hoskins, Peter R., Stephen F. Keevil, and Saeed Mirsadraee. "Medical Imaging." In Cardiovascular Biomechanics, 163–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46407-7_9.

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Majumdar, Angshul. "Medical Imaging." In Compressed Sensing for Engineers, 151–99. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis, [2019] | Series: Devices, circuits, and systems: CRC Press, 2018. http://dx.doi.org/10.1201/9781351261364-10.

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Olson, Tim. "Medical Imaging." In Applied Fourier Analysis, 255–77. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7393-4_9.

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Jin, Miao, Xianfeng Gu, Ying He, and Yalin Wang. "Medical Imaging." In Conformal Geometry, 175–251. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75332-4_9.

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Sargsyan, Ashot E. "Medical Imaging." In Principles of Clinical Medicine for Space Flight, 181–207. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-68164-1_9.

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Gupta, Tapan K. "Medical Imaging." In Radiation, Ionization, and Detection in Nuclear Medicine, 187–250. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34076-5_4.

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Epstein, Charles L. "Medical Imaging." In Encyclopedia of Applied and Computational Mathematics, 881–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-540-70529-1_66.

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Conference papers on the topic "Medical imaging"

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Journeau, P. "Imaging medical imaging." In SPIE Medical Imaging, edited by Tessa S. Cook and Jianguo Zhang. SPIE, 2015. http://dx.doi.org/10.1117/12.2084490.

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Prinz, Michael, Manfred Gengler, and Ernst Schuster. "Medical imaging." In Sixth International Workshop on Digital Image Processing and Computer Graphics, edited by Emanuel Wenger and Leonid I. Dimitrov. SPIE, 1998. http://dx.doi.org/10.1117/12.301390.

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Donjon, J., T. Tsujiuchi, and L. Guyot. "Medical Imaging." In International Topical Meeting on Image Detection and Quality, edited by Lucien F. Guyot. SPIE, 1987. http://dx.doi.org/10.1117/12.966739.

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"Medical Imaging." In 2006 IEEE International Workshop on Medical Measurement and Applications. IEEE, 2006. http://dx.doi.org/10.1109/memea.2006.1644459.

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STANKOVIĆ, SLOBODANKA, and OLIVERA KLISURIĆ. "MEDICAL IMAGING — INDISPENSABLE MEDICAL TOOLS." In Proceedings of the 9th International Symposium on Interdisciplinary Regional Research. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812834409_0001.

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"Medical Imaging Conference." In 2008 IEEE Nuclear Science Symposium and Medical Imaging conference (2008 NSS/MIC). IEEE, 2008. http://dx.doi.org/10.1109/nssmic.2008.4774078.

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Venson, Jose E., Jean Berni, Carlos S. Maia, A. Marques da Silva, Marcos d'Ornelas, and Anderson Maciel. "Medical imaging VR." In VRST '16: 22th ACM Symposium on Virtual Reality Software and Technology. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2993369.2996333.

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Nevitt, Mark, David A. Belforte, and Morris R. Levitt. "Job Shop Market." In Medical Imaging. SPIE, 1989. http://dx.doi.org/10.1117/12.971035.

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Belforte, David A., David A. Belforte, and Morris R. Levitt. "Overview Of The Industry And Future Trends." In Medical Imaging. SPIE, 1989. http://dx.doi.org/10.1117/12.971024.

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Followwill, Dorman, David A. Belforte, and Morris R. Levitt. "Industrial Materials Processing Laser Markets." In Medical Imaging. SPIE, 1989. http://dx.doi.org/10.1117/12.971025.

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Reports on the topic "Medical imaging"

1

Chapman, Leroy. Application of Diffraction Enhanced Imaging to Medical Imaging. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada395133.

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Keto, E., and S. Libby. Medical imaging with coded apertures. Office of Scientific and Technical Information (OSTI), June 1995. http://dx.doi.org/10.2172/100008.

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Trenbath, Kim, Omkar Ghatpande, and Amy LeBar. Medical Imaging Equipment Energy Efficiency. Office of Scientific and Technical Information (OSTI), March 2023. http://dx.doi.org/10.2172/1968453.

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Barrett, Harrison H. Information Processing in Medical Imaging Meeting (IPMI). Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada278488.

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Heese, V., N. Gmuer, and W. Thomlinson. A survey of medical diagnostic imaging technologies. Office of Scientific and Technical Information (OSTI), October 1991. http://dx.doi.org/10.2172/5819036.

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Heese, V., N. Gmuer, and W. Thomlinson. A survey of medical diagnostic imaging technologies. Office of Scientific and Technical Information (OSTI), October 1991. http://dx.doi.org/10.2172/10121224.

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Chaple, Ivis. Production and Purification of Radiometals for Medical Imaging. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1843150.

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Jin, Zheming. Improving the performance of medical imaging applications using SYCL. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1630290.

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Lee, Hyoung-Koo. Application of a-Si:H radiation detectors in medical imaging. Office of Scientific and Technical Information (OSTI), June 1995. http://dx.doi.org/10.2172/100242.

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Jin, Zheming. Improving the Performance of Medical Imaging Applications using SYCL. Office of Scientific and Technical Information (OSTI), December 2019. http://dx.doi.org/10.2172/1577129.

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