Relevant bibliographies by topics / Medical image visualisation systems

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Medical image visualisation systems'

Author: Grafiati
Published: 8 September 2021
Last updated: 29 July 2025

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Journal articles on the topic "Medical image visualisation systems"

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Niemitz, Lorenzo, Stefan D. van der Stel, Simon Sorensen, et al. "Microcamera Visualisation System to Overcome Specular Reflections for Tissue Imaging." Micromachines 14, no. 5 (2023): 1062. http://dx.doi.org/10.3390/mi14051062.

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In vivo tissue imaging is an essential tool for medical diagnosis, surgical guidance, and treatment. However, specular reflections caused by glossy tissue surfaces can significantly degrade image quality and hinder the accuracy of imaging systems. In this work, we further the miniaturisation of specular reflection reduction techniques using micro cameras, which have the potential to act as intra-operative supportive tools for clinicians. In order to remove these specular reflections, two small form factor camera probes, handheld at 10 mm footprint and miniaturisable to 2.3 mm, are developed us
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Samantaray, Minu, Millee Panigrahi, and Krishna Chandra Patra. "An improvised coral reef optimization-based image registration using modified mutual information method." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 1 (2023): 126. http://dx.doi.org/10.11591/ijeecs.v31.i1.pp126-133.

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Computer visualisation and medical applications require image registration. It includes transforming collective picture data into the familiar coordinate scheme. Metaheuristic-based methods were developed to explain the issue and improve the efficiency and accuracy of conventional image registration techniques due to their limitations. We describe a hybrid medical image registration technique using bio-inspired meta-heuristic algorithms: Betteroffspring and multi-crossover strategies increase convergent time and solution quality with an improvised coral reef optimization with modified mutual i
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Dzhioeva, O. "Mobile Ultrasound Systems as a Modern Tool for the Doctor." Medical University 2, no. 4 (2019): 134–38. http://dx.doi.org/10.2478/medu-2019-0018.

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Abstract Miniaturisation of ultrasound equipment in the form of a tablet- or smartphone-sized scanner is the result of rapid development of modern medical technology. Availability of mobile ultrasound devices has changed our approach to diagnostics of many cardiovascular diseases. Mobile visualisation can be performed at the patient’s bedside and is simple in use. The information obtained from mobile visualisation, despite being incomplete, is of undoubtable value for rapid diagnosis which leads to early treatment onset. These devices possess unique characteristics: low cost, wide availability
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von Pichler, C., S. Fischer, K. Radermacher, and G. Rau. "The Impact of 3-D Video Endoscopy on Binocular Perception and Visually Guided Manipulation." Perception 26, no. 1_suppl (1997): 36. http://dx.doi.org/10.1068/v970128.

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Monocular video endoscopic systems are established in the clinical routine of surgical endoscopy. The introduction of 3-D video systems could improve visualisation of the intracorporal operating site because of the stereoscopic depth information. The goal of our investigations has been to quantify the influence of this visualisation technology on visual perception, on visually controlled endoscopic manipulations, and on the intraoperative performance, including ergonomic and psychophysical aspects. These results are used to define guidelines for improvement and for the integration of such syst
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Palmer, R. L., P. Helmholz, and G. Baynam. "CLINIFACE: PHENOTYPIC VISUALISATION AND ANALYSIS USING NON-RIGID REGISTRATION OF 3D FACIAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 301–8. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-301-2020.

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Abstract. Facial appearance has long been understood to offer insight into a person’s health. To an experienced clinician, atypical facial features may signify the presence of an underlying rare or genetic disease. Clinicians use their knowledge of how disease affects facial appearance along with the patient’s physiological and behavioural traits, and their medical history, to determine a diagnosis. Specialist expertise and experience is needed to make a dysmorphological facial analysis. Key to this is accurately assessing how a face is significantly different in shape and/or growth compared t
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Palma, Belén, Pablo Casanova-Salas, Jesús Gimeno, Manuel Pérez-Aixendri, and José Vicente Riera. "Exploring Immersive Solutions for Surgery in the Virtuality Continuum: A Review." Surgeries 6, no. 2 (2025): 35. https://doi.org/10.3390/surgeries6020035.

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The evolution of technology and computer graphics algorithms has had a significant impact on the healthcare sector. It is possible to find proposals using virtual reality or augmented reality devices focused on multiple areas, such as education and skills acquisition, the visualisation of results, and disease detection or surgical planning. The use of these new technologies allows the generation of flexible spaces in which the spatial visualisation of clinical data, such as medical images, is enhanced. This also promotes collaboration between different members of the healthcare community. This
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Lysachenko, O. D., V. I. Shepitko, N. V. Boruta, L. B. Pelypenko, O. V. Vilkhova, and O. V. Voloshina. "VISUALISATION OF HISTOLOGICAL SPECIMENS AND TASKS AS A TECHNIQUE TO IMPROVE KNOWLEDGE, SKILLS AND ABILITIES OF MEDICAL STUDENTS." Актуальні проблеми сучасної медицини: Вісник Української медичної стоматологічної академії 23, no. 2.2 (2023): 85–87. http://dx.doi.org/10.31718/2077-1096.23.2.2.85.

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The purpose of this study is to explore the role of visualization of histological tasks in improving the quality of knowledge, abilities, and skills of medical students by summarizing the work experience of the Department of Histology, Cytology, and Embryology.
 In the field of higher medical education, the discipline of "Histology, Cytology, and Embryology" is crucial as it forms the foundation of students' understanding of the microscopic and functional morphology, structure, development, and regeneration of human cellular, tissue, and organ systems. However, first-year medical students
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Rahman, Md Masudur, Mahzabeen Islam, Mahbuba Nargis, et al. "Ultrasound elastography applications." Community Based Medical Journal 2, no. 1 (2013): 76–85. http://dx.doi.org/10.3329/cbmj.v2i1.14191.

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Ultrasound elastography (EUS) is a method to assess the mechanical properties of tissue, by applying stress and detecting tissue displacement using ultrasound. There are several EUS techniques used in clinical practice; strain (compression) EUS is the most common technique that allows real-time visualisation of the elastographic map on the screen. There is increasing evidence that EUS can be used to measure the mechanical properties of musculoskeletal tissue in clinical practice, with the future potential for early diagnosis to both guide and monitor therapy. This review describes the various
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Deng, Shujie, Gavin Wheeler, Nicolas Toussaint, et al. "A Virtual Reality System for Improved Image-Based Planning of Complex Cardiac Procedures." Journal of Imaging 7, no. 8 (2021): 151. http://dx.doi.org/10.3390/jimaging7080151.

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The intricate nature of congenital heart disease requires understanding of the complex, patient-specific three-dimensional dynamic anatomy of the heart, from imaging data such as three-dimensional echocardiography for successful outcomes from surgical and interventional procedures. Conventional clinical systems use flat screens, and therefore, display remains two-dimensional, which undermines the full understanding of the three-dimensional dynamic data. Additionally, the control of three-dimensional visualisation with two-dimensional tools is often difficult, so used only by imaging specialist
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Jung, Younhyun, Jinman Kim, Ashnil Kumar, David Dagan Feng, and Michael Fulham. "Efficient visibility-driven medical image visualisation via adaptive binned visibility histogram." Computerized Medical Imaging and Graphics 51 (July 2016): 40–49. http://dx.doi.org/10.1016/j.compmedimag.2016.04.003.

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Dissertations / Theses on the topic "Medical image visualisation systems"

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Agrafiotis, Dimitris. "Three dimensional coding and visualisation of volumetric medical images." Thesis, University of Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271864.

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Nguyen, Quang Vinh. "Space-efficient visualisation of large hierarchies /." Electronic version, 2005. http://adt.lib.uts.edu.au/public/adt-NTSM20051123.174122/index.html.

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Jung, Hoijoon. "Ubiquitous and Immersive Visualisation and Interaction of Volumetric Medical Images." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20703.

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Volumetric medical imaging is an indispensable data in patient management by non-invasively visualising human organ system. A wide range of research has been conducted to provide effective visualisation of medical imaging volumes, by introducing conventional 2D image slice viewing and recent 3D volume rendering techniques. They have been demonstrated in different medical imaging modalities and diverse clinical applications. The recent advances in software platforms / display technologies have been explored for their potentials to bring new capabilities and features to volume visualisation. We
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Kasrai, Reza. "On the perception of transparency : psychophysics and applications to medical image visualisation." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=38493.

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Transparency is used routinely as part of a host of visualisation functionalities in software applications for image-guided procedures, though little research is devoted to the rigorous validation of the use of transparency in clinical visualisation. This thesis presents three psychophysical studies aiming to understand how the human visual system interacts with transparent stimuli. The first sets out to measure the performance of users in a 3-D manual segmentation task. Visualising the stimuli in stereo improved performance, though no effect of transparent surface rendering was revealed. In a
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Luo, Hui. "Knowledge-based image understanding and classification systems for medical image databases." Buffalo, N.Y. : Dept. of Computer Science, State University of New York at Buffalo, 2001. http://www.cse.buffalo.edu/tech%2Dreports/2001%2D07.prn.Z.

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Köpsén, Kristian. "Improving visualisation of bronchi in three-dimensional rendering of CT data." Thesis, Linköping University, Department of Biomedical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8375.

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<p>The medical imaging system Sectra PACS from Sectra Imtec contains a 3D mode that can be used for visualising image stacks from e.g. computed tomography. Various structures of human anatomy can be visualised in the 3D mode, but visualisations of the bronchial tree of the lungs rarely become good enough to be useful. The goal of this work was to investigate ways of improving such visualisations.</p><p>Various approaches were studied, evaluated and tested. The fact that most effort was needed for small structures with sizes similar to the resolution of the images made things slightly more comp
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Yu, Hongliang. "Automatic Rigid and Deformable Medical Image Registration." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-050905-100341/.

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Ghosh, Payel. "Medical Image Segmentation Using a Genetic Algorithm." PDXScholar, 2010. https://pdxscholar.library.pdx.edu/open_access_etds/25.

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Advances in medical imaging technology have led to the acquisition of large number of images in different modalities. On some of these images the boundaries of key organs need to be accurately identified for treatment planning and diagnosis. This is typically performed manually by a physician who uses prior knowledge of organ shapes and locations to demarcate the boundaries of organs. Such manual segmentation is subjective, time consuming and prone to inconsistency. Automating this task has been found to be very challenging due to poor tissue contrast and ill-defined organ/tissue boundaries. T
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Archwamety, Charnchai. "Design and simulation of a totally digital image system for medical image applications." Diss., The University of Arizona, 1987. http://hdl.handle.net/10150/184294.

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The Totally Digital Imaging System (TDIS) is based on system requirements information from the Radiology Department, University of Arizona Health Science Center. This dissertation presents the design of this complex system, the TDIS specification, the system performance requirements, and the evaluation of the system using the computer simulation programs. Discrete event simulation models were developed for the TDIS subsystems, including an image network, imaging equipment, storage migration algorithm, data base archive system, and a control and management network. The simulation system uses em
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Guo, Hongyu. "Diffeomorphic point matching with applications in medical image analysis." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011645.

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Books on the topic "Medical image visualisation systems"

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Wong, Stephen T. C. Medical Image Databases. Springer US, 1998.

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C, Wong Stephen T., ed. Medical image databases. Kluwer Academic, 1998.

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Great Britain. Medical Devices Directorate., ed. Philips Medical Systems BV 29 mobile image intensifier system. Department of Health, Medical Devices Directorate, 1994.

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Semmlow, John L. Biosignal and medical image processing. 2nd ed. Taylor & Francis, 2008.

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Dougherty, Geoff. Medical image processing: Techniques and applications. Springer, 2011.

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Pham, Binh. New approaches in medical image analysis: Binh Pham ... [et al.]. SPIE, International Society for Optical Engineering, 1999.

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Paul, Suetens, Young Ian T, Society of Photo-optical Instrumentation Engineers., and Association nationale de la recherche technique., eds. Medical image processing: 2-3 December 1985, Cannes, France. SPIE--the International Society for Optical Engineering, 1986.

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Berry, Elizabeth. A practical approach to medical image processing. Taylor & Francis, 2008.

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1963-, Singh Ajit, Goldgof Dmitry B, and Terzopoulos Demetri, eds. Deformable models in medical image analysis. IEEE Computer Society, 1998.

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M, Hanson Kenneth, Society of Photo-optical Instrumentation Engineers., and American Association of Physicists in Medicine., eds. Medical imaging 1998. SPIE, 1998.

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Book chapters on the topic "Medical image visualisation systems"

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Fröhlich, Magali, Christophe Bolinhas, Adrien Depeursinge, et al. "Holographic Visualisation and Interaction of Fused CT, PET and MRI Volumetric Medical Imaging Data Using Dedicated Remote GPGPU Ray Casting." In Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01045-4_12.

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Shekhar, Raj, Vivek Walimbe, and William Plishker. "Medical Image Processing." In Handbook of Signal Processing Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6859-2_12.

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Shekhar, Raj, Vivek Walimbe, and William Plishker. "Medical Image Processing." In Handbook of Signal Processing Systems. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6345-1_9.

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Bhalerao, Abhir, and Paul Summers. "Interactive Visualisation of MRI Vector and Tensor Fields." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45468-3_245.

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Rajalingam, B., R. Santhoshkumar, P. Deepan, and P. Santosh Kumar Patra. "Medical Image Fusion." In Concepts of Artificial Intelligence and its Application in Modern Healthcare Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003333081-4.

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Granados, Alejandro, Niels Hald, Aimee Di Marco, et al. "Real-Time Visualisation and Analysis of Internal Examinations – Seeing the Unseen." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10404-1_77.

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Pathak, Rupali, Hemant Makwana, and Neha Sharma. "Exploring Image Segmentation Approaches for Medical Image Analysis." In Applied Data Science and Smart Systems. CRC Press, 2024. http://dx.doi.org/10.1201/9781003471059-42.

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Koussoube, Boureima, Moustapha Bikienga, and Telesphore Tiendrebeogo. "New Medical Image Hybrid Watermarking." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87873-2_3.

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Panda, Saswat, Sahul Kumar Parida, Ripusudan Khatri, and Rupinder Kaur. "Deep Learning for Medical Image Analysis." In Communication and Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2082-8_29.

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Berks, Georg, and Diedrich Graf v. Keyserlingk. "Fuzzy Sets in Medical Image Processing." In Fuzzy Systems in Medicine. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1859-8_13.

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Conference papers on the topic "Medical image visualisation systems"

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Jayabal, Rajamohan, S. Priya, Sahajanya S, and C. Manikandan. "Enhancing Secure Medical Image Transmission Using Visually Meaningful Medical Image Encryption." In 2024 International Conference on Computational Intelligence and Network Systems (CINS). IEEE, 2024. https://doi.org/10.1109/cins63881.2024.10862990.

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Boulehmi, Hela, and Rania Linda Filali. "Medical Image Segmentation Techniques: Advances and Challenges." In 2025 IEEE 6th International Conference on Image Processing, Applications and Systems (IPAS). IEEE, 2025. https://doi.org/10.1109/ipas63548.2025.10924519.

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Kapelner, Adam, Peter P. Lee, and Susan Holmes. "An Interactive Statistical Image Segmentation and Visualization System." In International Conference on Medical Information Visualisation - BioMedical Visualisation (MediVis 2007). IEEE, 2007. http://dx.doi.org/10.1109/medivis.2007.5.

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Prange, Alexander, Michael Barz, and Daniel Sonntag. "Speech-based Medical Decision Support in VR using a Deep Neural Network (Demonstration)." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/777.

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We present a speech dialogue system that facilitates medical decision support for doctors in a virtual reality (VR) application. The therapy prediction is based on a recurrent neural network model that incorporates the examination history of patients. A central supervised patient database provides input to our predictive model and allows us, first, to add new examination reports by a pen-based mobile application on-the-fly, and second, to get therapy prediction results in real-time. This demo includes a visualisation of patient records, radiology image data, and the therapy prediction results
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Schaefer, G., Shao Ying Zhu, and S. Ruszala. "Visualisation of medical infrared image databases." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616493.

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Zhang, Yan, Bogdan J. Matuszewski, and Lik-Kwan Shark. "A Novel Medical Image Segmentation Method using Dynamic Programming." In International Conference on Medical Information Visualisation - BioMedical Visualisation (MediVis 2007). IEEE, 2007. http://dx.doi.org/10.1109/medivis.2007.2.

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Ait-Aoudia, Samy, El-Hachemi Guerrout, and Ramdane Mahiou. "Medical Image Segmentation Using Particle Swarm Optimization." In 2014 18th International Conference on Information Visualisation (IV). IEEE, 2014. http://dx.doi.org/10.1109/iv.2014.68.

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Chen, Xin, Martin R. Varley, Lik-Kwan Shark, Glyn S. Shentall, and Mike C. Kirby. "Automatic 3D-2D image registration using partial digitally reconstructed radiographs along projected anatomic contours." In International Conference on Medical Information Visualisation - BioMedical Visualisation (MediVis 2007). IEEE, 2007. http://dx.doi.org/10.1109/medivis.2007.7.

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Zudilova-Seinstra, Elena, Peter Sloot, Avan Suinesiaputra, Rob van der Geest, and Johan Reiber. "Exploring 2D/3D Input Techniques for Medical Image Analysis." In 2009 Second International Conference in Visualisation (VIZ). IEEE, 2009. http://dx.doi.org/10.1109/viz.2009.18.

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Wesarg, Stefan, and Matthias Kirschner. "3D Visualization of Medical Image Data Employing 2D Histograms." In 2009 Second International Conference in Visualisation (VIZ). IEEE, 2009. http://dx.doi.org/10.1109/viz.2009.30.

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